Reviewerly/exHarmony huggingface dataset:
Searching Effective Transformer for Seq2Seq Keyphrase Generation

Keyphrase Generation (KG) aims to generate a set of keyphrases to represent the topic information of a given document, which is a worthy task of Natural Language Processing (NLP). Recently, the Transformer structure with fully-connected self-attention blocks has been widely used in many NLP tasks due to its advantage of parallelism and global context modeling. However, in KG tasks, Transformer-based models can hardly beat the recurrent-based models. Our observations also confirm this phenomenon. Based on our observations, we state the Information Sparsity Hypothesis to explain why Transformer-based models perform poorly in KG tasks. In this paper, we conducted exhaustive experiments to confirm our hypothesis, and search for an effective Transformer model for keyphrase generation. Comprehensive experiments on multiple KG benchmarks showed that: (1) In KG tasks, uninformative content abounds in documents while salient information is diluted globally. (2) The vanilla Transformer equipped with a fully-connected self-attention mechanism may overlook the local context, leading to performance degradation. (3) We add constraints to the self-attention mechanism and introduce direction information to improve the vanilla Transformer model, which achieves state-of-the-art performance on KG benchmarks.
Evaluation of eavesdropping error-rates in higher-dimensional QKD system implemented using dynamic spatial modes

Secure exchange of cryptographic keys is extremely important for any communication system where security and privacy of data is desirable. Although classical cryptographic algorithms provide computationally secure methods for secret key exchange, quantum key distribution (QKD) provides an extraordinary means to this end by guaranteeing unconditional security. Any malicious interception of communication by a man-in-the-middle on a QKD link immediately alerts sender and receiver by introducing an unavoidable error-rate. Higher-dimensional QKD protocols such as KMB09 exhibit higher eavesdropping error-rates with improved intrusion detection but their practical implementation is still awaited. In this paper, we present the design and implementation of KMB09 protocol using Laguerre–Gaussian orbital angular momentum to demonstrate and highlight the advantages of using dynamic spatial modes in QKD system. A complete error-rate analysis of KMB09 protocol implementation is presented with two different types of eavesdropping error-rates. Furthermore, we also demonstrate the decoy state method to show the robustness of the protocol against photon-number-splitting attack.
Parameterizable and Jerk-Limited Trajectories with Blending for Robot Motion Planning and Spherical Cartesian Waypoints

This paper presents two different approaches to generate a time local-optimal and jerk-limited trajectory with blends for a robot manipulator under consideration of kinematic constraints. The first approach generates a trajectory with blends based on the trapezoidal acceleration model by formulating the problem as a nonlinear constraint and a non-convex optimization problem. The resultant trajectory is locally optimal and approximates straight-line movement while satisfying the robot manipulator’s constraints. We apply the bridged optimization strategy to reduce the computational complexity, which borrows an idea from model predictive control by dividing all waypoints into consecutive batches with an overlap of multiple waypoints. We successively optimize each batch. The second approach is a combination of a trapezoidal acceleration model with a 7-degree polynomial to form a path with blends. It can be efficiently computed given the specified blending parameters. The same approach is extended to Cartesian space. Furthermore, a quaternion interpolation with a high degree polynomial under consideration of angular kinematics is introduced. Multiple practical scenarios and trajectories are tested and evaluated against other state-of-the-art approaches.
Flick Gesture Interaction in Augmented Reality: AR Carrom

Gestural input can make augmented reality applications feel natural and intuitive. While gestures used as simple input controls are common, gestures that interact directly with the virtual objects are less so. These interaction gestures pose additional challenges since they require the application to make many interpretations about the hand in the camera’s field of view such as depth, occlusion, size, and motion. In this work, we describe and propose a flick gesture control mechanic that estimates force and direction from a baseline pinch gesture. We demonstrate the gesture through an example implementation of an AR version of a game called Carrom in which we use our flick mechanic to dynamically interact with the virtual Carrom striker.
<i>Arden of Faversham</i>, the authorship problem: Shakespeare, Watson, or Kyd?

Abstract Arden of Faversham, a play published anonymously in 1592, was for many years attributed to Thomas Kyd. In modern times, it has been claimed that Shakespeare contributed one or more scenes, and in a recent essay in this journal, Gary Taylor argued that Thomas Watson (1555–92) wrote at least one scene. There are several problems with this attribution. As noted elsewhere, Watson was an eminent neo-Latin scholar and no record exists of him ever writing English plays for the Elizabethan theatres. Taylor attributes one ‘theatrical work’ to him, but this was a welcoming oration that Watson wrote for Queen Elizabeth’s visit to the Earl of Hertford at Elvetham in 1591, a one-off event, which does not resemble a play. In this essay, I examine Taylor’s use of stylometric evidence. I show that his algorithm is designed to favor Watson, with his small canon, by calculating the number of words per hit. Despite Taylor’s repeated dismissal of Kyd’s authorship, I point out that his searches of other authors identified thirty-five matching passages with Kyd’s plays. Finally, I adduce recently available evidence that strongly confirms Kyd’s authorship of the whole play.
Comparison Between Three Registration Methods in the Case of Non-Georeferenced Close-Range Multispectral Images

This study evaluated three geometric transformations in an image registration method applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a Micasense <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> RedEdge camera. The detection of matching points was performed using SURF features, and outliers matching points were removed using the MSAC algorithm. For each geometric transformation (affine, similarity, and projective), we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each transformation. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best transformation was the affine transformation because it produces RMSEs of less than 1 pixel and having a Gaussian distribution.
Solving Classical Problem in New Context as Constructive Model of Training

research-article Share on Solving Classical Problem in New Context as Constructive Model of Training: Active Memory Array of Concurrent Processes Concept Author: Milen Loukantchevsky University of Ruse, Bulgaria University of Ruse, BulgariaView Profile Authors Info & Claims CompSysTech '21: International Conference on Computer Systems and Technologies '21June 2021 Pages 191–195https://doi.org/10.1145/3472410.3472430Online:07 October 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
MuCAI'21: 2nd ACM Multimedia Workshop on Multimodal Conversational AI

The second edition of the International Workshop on Multimodal Conversational AI puts forward a diverse set of contributions that aim to brainstorm this new field. Conversational agents are now becoming a commodity as this technology is being applied to a wide range of domains. Healthcare, assisting technologies, e-commerce, information seeking, are some of the domains where multimodal conversational AI is being explored. The wide use of multimodal conversational agents exposes the many challenges in achieving more natural, human-like, and engaging conversational agents. The research contributions of the Workshop actively address several of relevant challenges: How to include assistive-technologies in dialog systems? How can agents engage in negotiation in dialogs? How to handle the embodiment of conversational agents?
WLS Design of Arma Graph Filters Using Iterative Second-Order Cone Programming

We propose a weighted least-square (WLS) method to design autoregressive moving average (ARMA) graph filters. We first express the WLS design problem as a numerically-stable optimization problem using Chebyshev polynomial bases. We then formulate the optimization problem with a non-convex objective function and linear constraints for stability. We employ a relaxation technique and convert the non-convex optimization problem into an iterative second-order cone programming problem. Experimental results confirm that ARMA graph filters designed using the proposed WLS method have significantly improved frequency responses compared to those designed using previously proposed WLS design methods.
Improvement in the Level of Learning Achievement of University Students Associated with the Use of Concept Maps in Pre-COVID-19 Times

The research considered the experimental group and the control group, evaluated before and after the use of concept maps. The results demonstrate the effectiveness of the use of concept maps, favoring the level of achievement of learning in students. The experimental group, compared to the control group, raised the average level of learning achievement. The techniques used were observation and electronic processing and descriptive statistics were applied for data analysis; the teacher evaluation record instrument will check the level of student learning achievement. The results of the test applied to the students pre-test was 10% and post-test 89% very satisfactory, concluding the use of concept maps significantly favors the level of learning achievement of university students.
Employing LRCN Model for Application Classification in SDN

The rapid growth of network technologies has complicated networking operations and network resource optimization. Software-Defined Network (SDN) overcomes the limitations of traditional networks by separating the forwarding layer and the control layer of the network. Application classification in SDN manages network by categorizing traffic into various application classes. Efficient application classification in SDN facilitates network operators to allocate resources efficiently for different services. However, current machine learning classifiers do not give high classification accuracy on large-scale data. This article proposes a new hybrid deep learning-based network application classifier—Long-Term Recurrent Convolutional Network (LRCN), which is a combination of Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) models. The proposed LRCN model leverages SDN’s robust computing potential and logically centralized control. The performance of the proposed model has been compared with prominent machine learning and deep learning techniques. The Unicauca (version 2.0) dataset has been pre-processed and used for experimentation purposes. The results infer that the proposed model is highly accurate, having an accuracy of 99.43%.
Deterministic policy gradient algorithms for semi‐Markov decision processes

A large class of sequential decision-making problems under uncertainty, with broad applications from preventive maintenance to event-triggered control can be modeled in the framework of semi-Markov decision processes (SMDPs). Unlike Markov decision processes (MDPs), SMDPs are underexplored in the online and reinforcement learning (RL) settings. In this paper, we extend the well-known deterministic policy gradient (DPG) theorem in MDPs to SMDPs under average-reward criterion. The existing stochastic policy gradient methods not only require, in general, a large number of samples for training, but they also suffer from high variance in the gradient estimation when applied to problems with deterministic optimal policy. Our DPG method can potentially remedy these issues. On the basis of this method and depending on the choice of a critic, different actor–critic algorithms can easily be developed in the RL setup. We present two example actor–critic algorithms. Both algorithms employ our developed policy gradient theorem for their actors, but use two different critics; one uses a simple SARSA update while the other one uses the same on-policy update but with compatible function approximators. We demonstrate the efficacy of our method both mathematically and via simulations.
Anomaly Detection in Drone-Captured Images Using Machine Learning Techniques and Deep Learning Architectures

Drones have found application in many fields ranging from agriculture to defense. With their on-board sensors, they provide useful insights in all the domains. It becomes important to study the abnormal behaviors observed due to the various sensors which hamper the drones’ smooth functioning. From temporally coherent streams of footage obtained, we aim to analyze the image frames, both quantitatively and qualitatively. The pixel level intensity variation was studied to find the patterns in them. Based on the intensity fluctuation distribution, the frames were classified into normal or anomalous. The images were evaluated using machine learning techniques. The best result was yielded by naïve Bayes classification with an accuracy of 92.079%. From the images, semantic inference was established through deep learning architectures through the application of a convolutional neural network. The best result was obtained when using a one-block VGG, yielding a validation accuracy of 92.17% and a test accuracy of 90.435%.
How Video Super-Resolution and Frame Interpolation Mutually Benefit

Video super-resolution (VSR) and video frame interpolation (VFI) are inter-dependent for enhancing videos of low resolution and low frame rate. However, most studies treat VSR and temporal VFI as independent tasks. In this work, we design a spatial-temporal super-resolution network based on exploring the interaction between VSR and VFI. The main idea is to improve the middle frame of VFI by the super-resolution (SR) frames and feature maps from VSR. In the meantime, VFI also provides extra information for VSR and thus, through interacting, the SR of consecutive frames of the original video can also be improved by the feedback from the generated middle frame. Drawing on this, our approach leverages a simple interaction of VSR and VFI and achieves state-of-the-art performance on various datasets. Due to such a simple strategy, our approach is universally applicable to any existing VSR or VFI networks for effectively improving their video enhancement performance.
The Autonomous Path Planning Algorithm for the USV Under Complex Battlefield Situations

The autonomous path planning problem of the USV in the complex battlefield situation environment is studied, an autonomous path planning algorithm for the USV based on polar coordinate modeling and crowd search algorithm is proposed. First, the basic principles of the crowd search algorithm are studied, and the self-interest behavior, altruistic behavior, pre-action behavior and uncertainty reasoning behavior of the crowd search algorithm are analyzed in detail. Then the search step, search direction and individual position of the crowd search algorithm update the rules and algorithm flow. Next, the algorithm in this paper, the traditional differential evolution algorithm, and the artificial bee colony optimization algorithm are used for typical function optimization problems. Simulation experiments show that the algorithm proposed in this paper is significantly better than other algorithms. Finally, based on polar coordinate modeling, the USV path code is placed in two-dimensional polar coordinates, and then the distance relationship between the current path point, the next path point and the obstacle point of the USV are used to determine whether to plan the path. When obstacles are encountered, the shortest and absolutely safe navigation route is obtained by calling the crowd search algorithm for path planning. Simulation experiments verify the effectiveness of the algorithm proposed in this paper.
Hybrid NR Video Quality Metric with Decodable Payload

In this paper, we propose a hybrid NR video quality metric with decodable payload. Although NR video quality metric has a strong need from the industry, developing these metrics has been a challenging task. Recently, hybrid models that use both processed video sequences and bit-stream data have shown promising results. In particular, from bitstream data, useful information can be extracted for video quality estimation, which includes codec information and impairment due to transmission errors. In the proposed method, we used packet information from the bit-stream data and estimated transmission impairment by decoding the payload to estimate the video quality. The proposed method was tested for the H.264 standard and it showed promising results. Also, the proposed method was independently evaluated using large datasets and it produced good performance.
Improved Software Implementation for Montgomery Elliptic Curve Cryptosystem

The last decade witnessed rapid increase in multimedia and other applications that require transmitting and protecting huge amount of data streams simultaneously. For such applications, a high-performance cryptosystem is compulsory to provide necessary security services. Elliptic curve cryptosystem (ECC) has been introduced as a considerable option. However, the usual sequential implementation of ECC and the standard elliptic curve (EC) form cannot achieve required performance level. Moreover, the widely used Hardware implementation of ECC is costly option and may be not affordable. This research aims to develop a high-performance parallel software implementation for ECC. To achieve this, many experiments were performed to examine several factors affecting ECC performance including the projective coordinates, the scalar multiplication algorithm, the elliptic curve (EC) form, and the parallel implementation. The ECC performance was analyzed using the different factors to tune-up them and select the best choices to increase the speed of the cryptosystem. Experimental results illustrated that parallel Montgomery ECC implementation using homogenous projection achieves the highest performance level, since it scored the shortest time delay for ECC computations. In addition, results showed that NAF algorithm consumes less time to perform encryption and scalar multiplication operations in comparison with Montgomery ladder and binary methods. Java multi-threading technique was adopted to implement ECC computations in parallel. The proposed multithreaded Montgomery ECC implementation significantly improves the performance level compared to previously presented parallel and sequential implementations.
A Sure-Based Unsupervised Deep Learning Method for Sar Despeckling

Synthetic Aperture Radar (SAR) images most often get corrupted by multiplicative (granular) signal variations known as ‘speckle’ during the remote acquisition. In this paper, we propose an unsupervised deep learning approach for SAR despeckling without the need for clean SAR references. To this end, we employ Stein's unbiased risk estimator (SURE) that facilitates estimation of the true mean-squared error between the output of the de-speckling network and the ground truth, however, using only available SAR images. We utilize thus unsupervised trained U-Net architecture, coupled with the proposed SURE-based loss function. Experiments are conducted on synthetic as well as real Sentinel-l SAR images. The results are compared both qualitatively and quantitatively with popular methods in SAR despeckling.
Data Incubation — Synthesizing Missing Data for Handwriting Recognition

In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training samples. By training our controllable handwriting synthesizer on the same data, we can synthesize handwriting with previously underrepresented content (e.g., URLs and email addresses) and style (e.g., cursive and slanted). Moreover, we propose a framework to analyze a recognizer that is trained with a mixture of real and synthetic training data. We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only. Overall, we achieve a 66% reduction in Character Error Rate.
Toward the Predictability of Dynamic Real-Time DNN Inference

Deep neural networks (DNNs) have been widely used in many cyber–physical systems (CPSs). However, it is still a challenging work to deploy DNNs in real-time systems. In particular, the execution time of DNN inference must be predictable, s.t. it could be known whether the runtime inference can complete within a required timing constraint. Moreover, the timing constraints may change dynamically with the runtime environment in many embedded applications, such as autonomous cars. A possible way to meet such dynamic real-time requirements is to execute different subnetworks of a DNN at runtime. However, improper construction of subnetworks may not only introduce unpredictable inference time, s.t. the real-timing constraints could be violated unexpectedly, but also has poor compatibility with the well-optimized machine learning framework (e.g., TensorFlow). In this article, we study the predictability when executing different subnetworks of a DNN. In particular, we present a featurewise runtime adaptation framework for DNN inference, which is implemented and validated on NVIDIA Jetson TX2 and Nano with TensorFlow. The experimental results show that our method can achieve predictable inference time in comparison with the state-of-the-art methods.
Opinion of students on Mobile learning as a tool for classroom teaching and learning

Mobile learning, also known as m-Learning, is defined as the need and ability to learn through virtual media, such as personal electronic gadgets, social interactions, and content. A mixed method research was conducted using Concurrent mixed method research design among PG students to know their opinion on m-learning. Google forms were used for collecting data. Themes identified were self learning, convenient/practical, new experience and stress free. The study concludes that m-learning can be used as teaching learning tool and students have positive opinion on its use even though the role of teacher is inevitable.Mobile technology is the future for higher education.