Reviewerly/exHarmony huggingface dataset:
A comparative study of video annotation tools for scene understanding

Computers are powerful tools capable of solving a great variety of ever so complex problems, yet training them to interpret even the simplest video scenes can prove more challenging than one might imagine. Still being one of the major problems in computer vision, this issue recently is addressed by utilizing promising deep learning approaches in order to recognize objects and their semantics. For achieving this goal, huge artificial networks are fed with many human-created annotations using more or less sophisticated tools for speeding up the otherwise time-consuming task of manual annotation. Purposefully refraining from designing yet another of these annotation tools, in this work we strive for evaluating what makes existing ones great or not, i.e. we aim at determining effectiveness and efficiency of state-of-the-art object annotation tools when employed for annotating different kinds of video content. Our findings in a user study evaluating three comparable tools on three videos of distinct domains indicate a significant difference in annotation effort from a video perspective, yet no significance regarding utilized tools. Further, we determine a significant correlation between annotation time and accuracy.
Fuzzy k-NN classification with weights modified by most informative neighbors of nearest neighbors

In the conventional fuzzy k-NN classification rule, the vote cast by each nearest neighboring known (labelled) sample on the class membership grades of the unknown (unlabelled) sample is formed by weighting the nearest neighbor’s class membership grades by the inverse of the nearest neighbor’s dis tance to the unknown sample. This paper proposes a modification of the weight (distance) used for each nearest neighbor by employing the geometrical relation among the nearest neighbor, its most informative known neighbor of the same class and the unknown sample. It is also proposed that this modification be only (conditionally) applied when the feature vector of the unknown sample lies outside the convex hull of the feature vectors of the known samples of each class. Results on a large number of datasets from the UCI and KEEL repositories and synthetically generated datasets show that, in return for a modest increase in classification complexity over the original fuzzy k-NN rule, the proposed fuzzy k-NN rule offers a better classification accuracy than the accuracies of the original fuzzy kNN rule and most other nearest neighbor type algorithms.
Approaches for Using Machine Learning Algorithms with Large Label Sets for Rotorcraft Maintenance

The US Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC), in collaboration with the US Army Engineer Research and Development Center (ERDC), is using machine learning (ML) to transform the way rotorcraft maintenance logbook event data is scored for reporting purposes. Traditionally, human analysts manually inspect data fields completed by maintenance personnel and, using published guidelines in conjunction with their personal expertise, provide sets of labels or scores for each event. These labels are stored with the data and provide valuable insight into maintenance event histories. However, the inequity between the enormous volume of maintenance data generated daily and the ability of analysts to score the data results in only 10% of all data receiving scores; therefore, 90% of the recorded data does not contain this important value added feature. Classification algorithms for automating this scoring process trained on existing labeled data sets have been implemented with promising results. A particularly challenging element of this problem, however, involves the classification of the specific component on which maintenance was performed. Greater than 1200 unique labels exist that can be used to describe a rotorcraft component that is the subject of a maintenance action. Furthermore, the component labels are hierarchically structured, resulting in the occurrence of multiple levels of precision in identifying a component in the expert-labeled data used for training. Although computational efficiency of common classification algorithms has improved considerably, it is still quite challenging to harness these methods for problems that include large numbers of unique class labels. This paper describes several novel strategies for solving this problem, based on hierarchical ensemble models and strategic label set segmentation. Through these approaches, a best overall total component classification accuracy of 96% was achieved, in conjunction with a total per record accuracy for three different label categories of 93%. The approaches implemented to handle the large label set for classification of rotorcraft components, along with classification performance measures, are discussed.
Expertise-based decision support for managing food quality in agri-food companies

In many agri-food companies, food quality is often managed using expertise gained through experience. Overall quality enhancement may come from sharing collective expertise. In this paper, we describe the design and implementation of a complete methodology allowing an expert knowledge base to be created and used to recommend the technical action to take to maintain food quality. We present its functional specifications, defined in cooperation with several industrial partners and technical centres over the course of several projects carried out in recent years. We propose a systematic methodology for collecting the knowledge on a given food process, from the design of a questionnaire to the synthesis of the information from completed questionnaires using a mind map approach. We then propose an original core ontology for structuring knowledge as possible causal relationships between situations of interest. We describe how mind map files generated by mind map tools are automatically imported into a conceptual graph knowledge base, before being validated and finally automatically processed in a graph-based visual tool. A specific end-user interface has been designed to ensure that end-user experts in agri-food companies can use the tool in a convenient way. Finally, our approach is compared with current research.
ASCoT, the NASA Analogy Software Cost Tool Suite: Expanding Our Estimation Horizons Jet Propulsion Laboratory, California Institute of Technology Pasadena, CA 91109

The NASA Analogy Software Costing Tool Suite (ASCoT) consists of a cluster-based analogy estimator for estimating software development effort, a K-Nearest Neighbors (KNN) analogy estimator for estimating effort and delivered lines of code, a simple regression-based cost estimating relationship (CER) model that estimates cost in dollars, and a probabilistic version of COCOMO II. In this paper we document the analogy algorithms as well as summarize the results of the performance of the KNN and the principle components (PCA) cluster analogy models. KNN performance is assessed by varying the number of inputs and number of neighbors. Four different clustering methods: K-means, Spectral Clustering, Hierarchical Clustering, and Principle Components Analysis (PCA), and their respective evaluation criterion are described in detail. The comparative performance of all four estimation models is assessed using magnitude of relative error (MRE) measurements.
GPU-based digital image correlation system for real-time strain-controlled fatigue and strain field measurement

This article reports a novel GPU-based 2D digital image correlation system (2D-DIC) overcoming two major limitations of this technique: It measures marker-free, i.e. without sample preparation, and the sampling rate meets the recommendations of ASTM E606. The GPU implementation enables zero-normalized cross correlation (ZNCC) calculation rates of up to 25 kHz for 256 &times; 256 pixel ROIs. This high-speed image processing system is combined with a high-resolution telecentric lens observing a 10 mm field-of-view, coaxial LED illumination, and a camera acquiring 2040 &times; 256 pixel images with 1.2 kHz. The optics resolve the microstructure of the surface even of polished cylindrical steel specimen. The displacement uncertainty is below 0.5 &mu;m and the reproducibility in zero-strain tests approximately 10<sup>-5</sup> (1 &sigma;) of the field-of-view. For strain-controlled testing, a minimum of two displacement subsets per image are evaluated for average strain with a sampling rate of 1.2 kHz. Similar to mechanical extensometers, an analogue 0-10V displacement signal serves as a feedback for standard PID controllers. The average latency is below 2 ms allowing for cycle frequencies up to 10 Hz. For strain-field measurement, the number of ROIs limits the frame rate, e.g., the correlation rate of 25 kHz is sufficient to evaluate 10 images per second with 2500 ROIs each. This frame rate is still sufficient to compare the maximum and minimum strain fields within a cycle in real-time, e.g. for crack detection. The result is a marker-free and non-contact DIC sensor suitable for both strain-controlled fatigue testing and real-time full-field strain evaluation.
A line-of-sight rate estimation method for roll-pitch gimballed infrared seeker

Roll-pitch gimballed infrared seeker has wide field of view but suffers from the rapid roll motion of the zenith-pass problem. For estimating the line-of-sight (LOS) rate with the roll-pitch seeker, we propose a coordinate transformation method to compensate for the impact of rapid roll motion. A precise LOS rate dynamics model is derived on the transformed coordinate system. And based on the model, we design a novel LOS rate estimator with the Improved Iterated Cubature Kalman Filter algorithm (IICKF). The proposed IICKF is derived from reducing the dimensionality of the optimization problem of the ICKF, which inherits the estimation accuracy but decreases the computational complexity of the algorithm. For validating, Monte Carlo simulations are performed at last. The simulation results show that the proposed LOS rate estimator outperform the standard EKF, CKF, ICKF, and can well cope with the rapid roll motion of the zenith-pass problem in roll-pitch gimballed infrared seeker system.
A Variational Convex Hull Algorithm

Finding the convex hull of a given object or a point set is a very important problem. In this paper, we propose a variational convex hull model and numerical algorithms to solve it. Our model is based on level set representation. Efficient numerical algorithms and implementations based on splitting ideas are given. To test our proposed model, we conduct many experiments for objects represented by binary images, and the results suggest that our model can identify the convex hull accurately. Even more, our model can be easily modified to handle the outliers, and this is also demonstrated by numerical examples.
Computational Automated System for Red Blood Cell Detection and Segmentation

The implementation of digital image processing system of blood cell images has the potential to decrease the cost of making clinical decisions, while at the same time giving a more reliable result for red blood cells (RBCs) counting. In this chapter discuss the development of a novel system for the automatic detection and segmentation of RBCs in microscopic blood smear images which play an increasing important role in medical diagnosis. The aim of this study is creating a graphical user interface (GUI) to analyze and evaluate the concept of automated RBC segmentation based on the marker-controlled watershed algorithm for an identification and segmentation technique. The GUI was constructed and developed to allow user interaction with various applications through graphical icons and visual indicators, such as secondary notation instead of text. The user is given options to visually analyze the blood image, process it, and get an accurate count of the total RBC.
A Study on Self-attention Mechanism for AMR-to-text Generation

Introduced by Vaswani et al., transformer architecture, with the effective use of self-attention mechanism, has shown outstanding performance in translating sequence of text from one language to another. In this paper, we conduct experiments using the self-attention in converting an abstract meaning representation (AMR) graph, a semantic representation, into a natural language sentence, also known as the task of AMR-to-text generation. On the benchmark dataset for this task, we obtain promising results comparing to existing deep learning methods in the literature.
Probably Secure Multi-user Multi-keyword Searchable Encryption Scheme in Cloud Storage

Searchable encryption server protects privacal data of data owner from leaks. This paper analyzes the security of a multi-user searchable encryption scheme and points out that this scheme does not satisfy the invisibility of trapdoors. In order to improve the security of the original scheme, this paper proposes a probably secure multi-user multi-keyword searchable encryption scheme. New secheme not only ensures the confidentiality of the cipher text keyword, but also does not increase the encryption workload of the data owner when the new data user joins. In the random oracle model, based on the hard problem of decisional Diffie-Hellman, it is proved that the scheme has trapdoor indistinguishability. In the end, obtained by the simulation program to achieve a new computationally efficient communication at low cost.
A Genetic Programming Approach for Searching on Nearest Neighbors Graphs

The use of nearest neighbors graphs has been leading to considerable gains over classic approaches (e.g., tree-indexing-based methods) in approximate nearest neighbor searches. Classical searches on these graphs are conduced in a greedy way by moving at each step to the neighbor (of current vertex) with the lowest distance to the query. In this work, we explore the combination of topological properties of graphs and the distance itself through a Genetic Programming framework to obtain a better indicator for selecting the next vertex in the search process. Our objective is to minimize the scan rate needed to reach the true nearest neighbors. Experimental results, conducted with three different graph-based methods over a large textual collection, show significant gains of the proposed approach over the classic search algorithm on graphs.
Comparative analysis of preprocessing tasks over social media texts in Spanish

One of the key aspects of the texts coming from social media is that they tend to be very noisy. This is mainly because of the usage of informal language and non-standard grammatical structures. Therefore, in order to use these contents as input for a text analysis process, it is highly recommended to previously clean and reduce the noise of the data. This work focuses on measuring the effectiveness that diverse cleaning and repairing tasks have on the data. The results obtained, indicate that the tasks of "tokens with no letters removal", and "stressed words processing" are the most effective. In addition, some tasks like hashtags or usernames processing, which behave very well in other datasets, are not that relevant in this one. This research is part of a more general one that pursues to build an automatic emotion classifier that makes use of the preprocessed comments as input.
WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms

Summary These years, researchers are interested in the web users perceived Quality of Experience called web QoE. Monitoring web browsing QoE can help service providers to determine if network conditions are contributing to user satisfaction. Making a relationship between web QoE and network parameters helps them to evaluate potential solutions in order to increase user satisfaction. Recent literature use metrics such as page load, wait time, or SpeedIndex to estimate the QoE of web users. However, QoE is a measure that remains subjective, and its evaluation is expensive and tedious because it requires a high human participation. Using Big Data Analytics, web service providers can anticipate user's behavior and take necessary actions which lead to better QoS/QoE. In this work, we have shown that we can make a relationship between QoS and QoE and predict QoE thanks to machine learning (ML) algorithms or deep learning approach. In fact, these new prediction solutions can help in prediction of the web QoE. Yet, this work focuses on some key performance indicators in the field of web browsing where Big Data Analytics are used. It gives a predictive solution based on Big Data tools to monitor and work toward enhancing QoS and QoE for the web users. Therefore, the design of a tool that can objectively measure this QoE with reasonable accuracy and in real time has become a primary need which is an interesting challenge to solve. As a first contribution, we analyzed the impact of network behavior on the quality of web services, then described some relevant research works done in ML field to predict QoE. We finally give the proposed tool based on prediction algorithms from ML and deep learning both used to monitor web QoE.
General Robotic System Software Design Methodology

Robotic systems are composed of at least one robot and zero or more auxiliary devices. They contain effectors, receptors and the control system. Currently control systems are computer based thus the software component is of paramount importance. Software engineering good practices require that first the designed software should be specified and then implemented. The paper focuses on the specification phase of robotic system software creation. The presented method decomposes a robotic system into embodied agents. Each embodied agent is composed of its control subsystem and zero or more real as well as virtual effectors and receptors. The description of activities of each of those subsystems uses the concepts of hierarchic finite state machines, behaviours, transition functions and supplementary predicates, i.e. terminal and error conditions as well as predicates defining inter subsystem communication mode. The ontological level of the specification, conforming to the executed class of tasks, is defined by the contents of the inter-subsystem communication buffers as well as their internal memory.
You are the Ocean

This paper explores "You Are the Ocean," an interactive art installation where a participant's brainwaves control a projected ocean simulation. Using an EEG headset, the approximate attention and meditation levels of the participant are measured. As the participant increases her attention and focus, the ocean and sky become stormier. When the participant calms her mind, the ocean too becomes calm. This paper describes the concept, implementation, and participant interaction surrounding the installation. While many existing artworks have used EEG headsets, "You Are the Ocean" is novel in its use of interactive, photorealistic real-time rendering to communicate the intrinsic connection between humans and the planet.
An Unsupervised Genetic Algorithm Framework for Rank Selection and Fusion on Image Retrieval

Despite the major advances on feature development for low and mid-level representations, a single visual feature is often insufficient to achieve effective retrieval results in different scenarios. Since diverse visual properties provide distinct and often complementary information for a same query, the combination of different features, including handcrafted and learned features, has been establishing as a relevant trend in image retrieval. An intrinsic difficulty task consists in selecting and combining features that provide a high-effective result, which is often supported by supervised learning methods. However, in the absence of labeled data, selecting and fusing features in a completely unsupervised fashion becomes an essential, although very challenging task. The proposed genetic algorithm employs effectiveness estimation measures as fitness functions, making the evolutionary process fully unsupervised. Our approach was evaluated considering 3 public datasets and 35 different descriptors achieving relative gains up to +53.96% in scenarios with more than 8 billion possible combinations of rankers. The framework was also compared to different baselines, including state-of-the-art methods.
Computerized Adaptive Testing Method Using Integer Programming to Minimize Item Exposure

This is an extension from a selected paper from JSAI2019. Computerized adaptive testing (CAT) estimates an examinee’s ability sequentially and selects test items that have the highest accuracy for estimating the ability. However, conventional CAT selects the same items for examinees who have equivalent ability. As described herein, we propose CAT that minimizes item exposure and which adaptively selects different items for examinees of equal ability, while retaining accuracy. This paper presents the method’s effectiveness using simulation data and actual test data.
Application of Artificial Intelligence in the National Airspace System – A Primer

The National Airspace System (NAS), as a portion of the US' transportation system, has not yet begun to model or adopt integration of Artificial Intelligence (AI) technology. However, users of the NAS, i.e., Air transport operators, UAS operators, etc. are beginning to use this technology throughout their operations. At issue within the broader aviation marketplace, is the continued search for a solution set to the persistent daily delays and schedule perturbations that occur within the NAS. Despite billions invested through the NAS Modernization Program, the delays persist in the face of reduced demand for commercial routings. Every delay represents an economic loss to commercial transport operators, passengers, freighters, and any business depending on the transportation performance. Therefore, the FAA needs to begin to address from an advanced concepts perspective, what this wave of new technology will affect as it is brought to bear on various operations performance parameters, including safety, security, efficiency, and resiliency solution sets. This paper is the first in a series of papers we are developing to explore the application of AI in the National Airspace System (NAS). This first paper is meant to get everyone in the aviation community on the same page, a primer if you will, to start the technical discussions. This paper will define AI; the capabilities associated with AI; current use cases within the aviation ecosystem; and how to prepare for insertion of AI in the NAS. The next series of papers will look at NAS Operations Theory utilizing AI capabilities and eventually leading to a future intelligent NAS (iNAS) environment.
An SDN-based device-aware live video service for inter-domain adaptive bitrate streaming

The emerging popularity of live streaming services poses a great challenge for the rigid and static traditional Internet architecture. The rise in adaptation of Software Defined Networking (SDN) by Internet Service Providers (ISP) and Content Delivery Networks (CDN) presents an opportunity to dynamically adapt and respond in real-time to high definition (HD) mega events or dynamic short-lived broadcast events. In this paper, we present an SDN-based system design that utilizes a communication framework between ISPs and CDNs to interact and thus enable a reliable and resource efficient live streaming service. We build and deploy an optimization model that can maximize the video quality for users while minimizing the resource utilization for both ISPs and CDNs. The model considers device capabilities, network constraints and the subscription level of users with the ISP/CDN. Our system is a network-assisted, cross-layer, approach that implements multicast at the network layer and can dynamically adapt the video bitrates that are served to each client at the application layer. We build a prototype of our proposed design and evaluate real-world scenarios with up to 500 users streaming multiple videos at different bitrates. Results show that our approach can increase average user goodput by up to 70% while almost eliminating frame drops by handling network congestion.