Reviewerly/exHarmony huggingface dataset:
Coreference Resolution Using Semantic Features and Fully Connected Neural Network in the Persian Language

Coreference resolution is one of the most critical issues in various applications of natural language processing, such as machine translation, sentiment analysis, summarization, etc.In the process of coreference resolution, in this paper, a fully connected neural network approach has been adopted to enhance the performance of feature extraction whilst also facilitating the mention pair classification process for coreference resolution in the Persian language.For this purpose, first, we focus on the feature extraction phase by fusing some handcrafted features, word embedding features and semantic features.Then, a fully connected deep neural network is utilized to determine the probability of the validity of the mention pairs.After that, the numeric output of the last layer of the utilized neural network is considered as the feature vector of the valid mention pairs.Finally, the coreference mention pairs are specified by utilizing a hierarchical accumulative clustering method.The proposed method's evaluation on the Uppsala dataset demonstrates a meaningful improvement, as indicated by the F-score 64.54%, in comparison to state-of-the-art methods.
Streaming Video Classification Using Machine Learning

This paper presents a method using neural networks and Markov Decision Process (MDP) to identify the source and class of video streaming services. The paper presents the design and implementation of an end-to-end pipeline for training and classifying a machine learning system that can take in packets collected over a network interface and classify the data stream as belonging to one of five streaming video services: You Tube, You Tube TV, Netflix, Amazon Prime, or HBO
Improving Arabic Instant Machine Translation: The Case of Arabic Triangle of Language

Recently, instant translator applications would be a very useful applications when traveling especially when one knows little about the language of the country she/he is traveling to.Arabic to English instant translation has not yet been made available by most applications.In this Article, we attempt to provide mimic way of an application for instant Arabic to English translation.The system provides translation for Arabic Triangle of Language (ālmṯlṯātāllġwyh) which includes Arabic three words that are homographs.The process starts by capturing an image for a homograph using a mobile phone camera, after that the captured word is recognized, taking the diacritic markers into consideration, using an Arabic Optical Character Recognition (OCR).Finally, the system provides an English translation to the homograph.The researchers made use of Histograms of Oriented Gradients (HOG) features and a set of structural and geometrical features of Arabic word segmented and the SVM (multi class) classifier for classification, then providing the English meaning.
Handling Minority Class Problem in Threats Detection Based on Heterogeneous Ensemble Learning Approach

Multiclass problems, such as detecting multi-steps behaviour of advanced persistent threats (APTs), have been a major global challenge due to their capability to navigates around defenses and to evade detection for a prolonged period. Targeted APT attacks present an increasing concern for both cyber security and business continuity. Detecting the rare attack is a classification problem with data imbalance. This paper explores the applications of data resampling techniques together with heterogeneous ensemble approach for dealing with data imbalance caused by unevenly distributed data elements among classes with the focus on capturing the rare attack. It has been shown that the suggested algorithms provide not only detection capability but can also classify malicious data traffic corresponding to rare APT attacks.
A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm

With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SURF feature descriptor and discrete-cosine transform domain digital image watermarking algorithm. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Correlation values are more than 90% in most of the attacks. It provides better results against different noise attacks and also better performance against rotation. Transparency and high computational efficiency, coupled with dual functions of copyright protection and content authentication, is the advantage of the proposed algorithm.
Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments

We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects. We explore the use of composite signed-distance fields in motion planning and detail how they can be used to generate signed-distance fields (SDFs) in real-time to incorporate predicted obstacle motions; to achieve this, we introduce the concept of predicted signed-distance fields. We benchmark our approach of using composite SDFs against performing exact SDF calculations on the workspace occupancy grid. Our proposed technique generates predictions substantially faster and typically exhibits an 81-97% reduction in time for subsequent predictions. We integrate our framework with GPMP2 to demonstrate a full implementation of our approach in real-time, enabling a 7-DoF Panda manipulator to smoothly avoid a moving obstacle in simulation and hardware experiments.
A quantum algorithm for simulating non-sparse Hamiltonians

We present a quantum algorithm for simulating the dynamics of Hamiltonians that are not necessarily sparse. Our algorithm is based on the input model where the entries of the Hamiltonian are stored in a data structure in a quantum random access memory (qRAM) which allows for the efficient preparation of states that encode the rows of the Hamiltonian. We use a linear combination of quantum walks to achieve poly-logarithmic dependence on precision. The time complexity of our algorithm, measured in terms of the circuit depth, is O(t\sqrt{N}\norm{H}\,\polylog(N, t\norm{H}, 1/\epsilon)), where t is the evolution time, $N$ is the dimension of the system, and $\epsilon$ is the error in the final state, which we call precision. Our algorithm can be directly applied as a subroutine for unitary implementation and quantum linear systems solvers, achieving \widetilde{O}(\sqrt{N}) dependence for both applications.
Labanotation Generation From Motion Capture Data for Protection of Folk Dance

Labanotation is one of the well-known notation systems for the documenting and archiving of human motion. It plays a powerful role in dance protection, choreography analysis, and so on. Recently, researchers are committed to using computer technology to automatically generate Labanotation rather than manually drawing. However, the existing generation methods cannot deal with the various changes in motion data, such as different scales, angles, motion modes and limbs. In this paper, we aim to generate Labanotation from motion capture data acquired through real folk dance performances. The main steps include feature extraction, motion segmentation and unit movement analysis. Firstly, a normalized feature named Lie group feature is extracted, which can cope with the challenges of different scales and angles in motion data. Secondly, in order to divide motion with different modes into unit fragments for further recognizing, we propose a segmentation method that combines the speed threshold and the region partition. Thirdly, to generate Laban symbols of unit movements for different limbs, two kinds of neural networks are used for the analysis. On the one hand, LieNet, a powerful network for analyzing time series data based on Lie group structure, is utilized to recognize the lower limb movements. On the other hand, extreme-learning machine, a single hidden layer feedforward neural network, is used to identify the upper limb postures. Experimental results demonstrate that our method of feature extraction, motion segmentation and unit movement analysis achieves better results than the previous works, which makes the generated Labanotation score more reliable.
Improved Canny Edge Detector Using Principal Curvatures

Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms.It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect "true" edges, while suppressing "false" non-edge filter responses.Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values.In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex.This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification.Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny's algorithm.
Range of applicability of the Hu-Paz-Zhang master equation

We investigate a case of the Hu-Paz-Zhang master equation of the Caldeira-Leggett model without Lindblad form obtained in the weak-coupling limit up to the second-order perturbation. In our study, we use Gaussian initial states to be able to employ a sufficient and necessary condition, which can expose positivity violations of the density operator during the time evolution. We demonstrate that the evolution of the non-Markovian master equation has problems when the stationary solution is not a positive operator, i.e., does not have physical interpretation. We also show that solutions always remain physical for small times of evolution. Moreover, we identify a strong anomalous behavior, when the trace of the solution is diverging. We also provide results for the corresponding Markovian master equation and show that positivity violations occur for various types of initial conditions even when the stationary solution is a positive operator. Based on our numerical results, we conclude that this non-Markovian master equation is superior to the corresponding Markovian one.
Blind Robust and Self-Embedding Fragile Image Watermarking for Image Authentication and Copyright Protection with Recovery Capability

This paper proposes a dual watermarking scheme that can integrate the functions of authentication, copyright protection, and image recovery in the same cover image.The robust watermarking utilizes a single watermark using the discrete cosine transform (DCT) domain for copyright protection, while the fragile scheme utilizes two self-embedding watermarks in a spatial domain for authenticating and restoring digital image content.The mechanism of the two schemes is carried out sequentially and uses a block-based approach.A new blind robust watermarking is proposed using a quantitative property of intermediate frequency coefficient for embedding and adaptive embedding strength selection to balance transparency and robustness.Fragile watermarks are inserted into a robust watermarked image based on the improved replacement approach of the least significant bits.Experimental results show that the proposed method can withstand various processing attacks; enhance the accuracy of deceptive localization with good visual quality of image recovery.
Comprehensive assessment of image compression algorithms

JPEG image coding standard has been a dominant format in a wide range of applications in soon three decades since it has been released as an international standard. The landscape of applications and services based on pictures has evolved since the original JPEG image coding algorithm was designed. As a result of progress in the state of the art image coding, several alternative image coding approaches have been proposed to replace the legacy JPEG with mixed results. The majority among them have not been able to displace the unique position that the legacy JPEG currently occupies as the ubiquitous image coding format of choice. Those that have been successful, have been so in specific niche applications. In this paper, we analyze why all attempts to replace JPEG have been limited so far, and discuss additional features other than compression efficiency that need to be present in any modern image coding algorithm to increase its chances of success. Doing so, we compare rate distortion performance of state of the art conventional and learning based image coding, in addition to other desired features in advanced multimedia applications such as transcoding. We conclude the paper by highlighting the remaining challenges that need to be overcome in order to design a successful future generation image coding algorithm.
Single Fundus Image Super-Resolution Via Cascaded Channel-Wise Attention Network

Fundus image is commonly used in aiding the diagnosis of ophthalmic diseases. A high-resolution (HR) image is valuable to provide the anatomic information on the eye conditions. Recently, image super-resolution (SR) though learning model has been shown to be an economic yet effective way to satisfy the high demands in the clinical practice. However, the reported methods ignore the mutual dependencies of low-and high-resolution images and did not fully exploit the dependencies between channels. To tackle with the drawbacks, we propose a novel network for fundus image SR, named by Fundus Cascaded Channel-wise Attention Network (FC-CAN). The proposed FCCAN cascades channel attention module and dense module jointly to exploit the semantic interdependencies both frequency and domain information across channels. The channel attention module rescales channel maps in spatial domain, while the dense module preserves the HR components by up- and down-sampling operation. Experimental results demonstrate the superiority of our net-work in comparison with the six methods.
Deep Fusion Local-Content and Global-Semantic for Image Inpainting

The upsampling layers are adopted in almost all the existing encoder-decoder based generative adversarial networks (GANs), which have shown promising results in the image inpainting field. However, existing upsampling layers (e.g. deconvolution and bilinear interpolation) suffer from two limitations: (1) they obtain few semantic information from the global structure. (2) upsampling layer could hardly capture the local content details. To eliminate the above issues, we propose a deep Fusion local-content and global-semantic (DFLG) model that is both effective and general. The DFLG model mainly consists of four components: the Local Content-Response (LCR) module, the pixel-shuffle operator, the Global Semantic-Aware (GSA) module and the reassembly module. Firstly, the LCR module divides the channel into several groups, then utilizes the squeeze-and-excitation mechanism within each group to capture the correlation between channels. Secondly, the pixel shuffle operator reshapes depth on the channel space into width and height on the spatial space, which transforms the correlation within groups on the channel space into correlation within patches on the spatial space. Next, the GSA module employs a patch-based spatial attention mechanism to calculate the correlation between different patches. Finally, the reassembly module refines the feature map. Furthermore, we propose a novel loss function called Attention Loss (ATLoss), which guides the network to concentrate on regions with obvious artifacts. The experiments on CelebA-HQ, Places2, and Paris StreetView datasets demonstrate the effectiveness of our proposed methods in image inpainting tasks and the capability of obtaining images with higher quality.
A nonlinear service composition method based on the Skyline operator

The concept of service composition can provide the complex functionality for users. As the widespread application of cloud computing, the number of services grows exponentially. It becomes more difficult to find out the optimal service composition solution quickly. This paper proposes a nonlinear service composition method based on the Skyline operator. The Skyline operator is to find a collection of data that cannot be dominated by others, which is used to prune the redundant services to reduce the search space. Then the service composition problem is formulated as a nonlinear integer programming model by a mathematical programming language (AMPL), and solved by the existing nonlinear solvers Bonmin. The experiments show that the proposed method can effectively improve the efficiency of service composition, while ensuring the quality of solution.
Coplanar Antenna Design for Microwave Entangled Signals Propagating in Open Air

Open-air microwave quantum communication and metrology protocols must be able to transfer quantum resources from a cryostat, where they are created, to an environment dominated by thermal noise. Indeed, the states carrying such quantum resources are generated in a cryostat characterized by a temperature <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mtext>in</mml:mtext></mml:msub><mml:mo>&amp;#x2243;</mml:mo><mml:mn>50</mml:mn></mml:math> mK and an intrinsic impedance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>Z</mml:mi><mml:mtext>in</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn>50</mml:mn><mml:mspace width="thinmathspace" /><mml:mi mathvariant="normal">&amp;#x03A9;</mml:mi></mml:math>. Then, an antenna-like device is required to transfer them with minimal losses into open air, characterized by an intrinsic impedance of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>Z</mml:mi><mml:mtext>out</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn>377</mml:mn><mml:mspace width="thinmathspace" /><mml:mi mathvariant="normal">&amp;#x03A9;</mml:mi></mml:math> and a temperature <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mtext>out</mml:mtext></mml:msub><mml:mo>&amp;#x2243;</mml:mo><mml:mn>300</mml:mn></mml:math> K. This device accomplishes a smooth impedance matching between the cryostat and the open air. Here, we study the transmission of two-mode squeezed thermal states, developing a technique to design the optimal shape of a coplanar antenna to preserve the entanglement. Based on a numerical optimization procedure, we find the optimal shape of the impedance, and we propose a functional ansatz to qualitatively describe this shape. Additionally, this study reveals that the reflectivity of the antenna is very sensitive to this shape, so that small changes dramatically affect the outcoming entanglement, which could have been a limitation in previous experiments employing commercial antennae. This work is relevant in the fields of microwave quantum sensing and quantum metrology with special application to the development of the quantum radar, as well as any open-air microwave quantum communication protocol.
63‐2: Continuous Observation of Neutral‐Plane Splitting throughout the Folding Process of Foldable Displays Using Optical Microscopy and Digital Image Correlation Method

Strain control with neutral‐plane splitting is an essential technology in designing foldable displays. We established a method for analyzing continuous changes in cross‐sectional strain distribution throughout the folding process. Digital image correlation (DIC) method with an optical microscope successfully revealed that the neutral‐plane position changes with the folding radius.
Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts

A multi-agent path finding (MAPF) problem is concerned with finding paths for multiple agents such that certain objectives, such as minimizing makespan, are reached in a conflict-free manner. In this paper, we solve a practical MAPF problem with automated guided vehicles (AGVs) for the conveying of luggage in segment-based layouts (MAPF-SL). Most existing algorithms for MAPF are mainly focused on grid environments. However, the conflict prevention problem is more challenging with segment-based layouts in which software is constrained to oversee that vehicles remain on predefined travel paths. Hence, the existing multi-agent path finding algorithms cannot be applied directly to solve MAPF-SL. In this paper, we propose an algorithm, called WHCA*S-RL, that combines Deep Reinforcement Learning (DRL) with a heuristic approach for solving MAPF-SL. DRL is used for determining travel directions while the heuristic approach oversees the planning in a segment-based layout. Our experiment results show that the proposed WHCA*S-RL approach can be successfully used for making path plans in which traffic congestion is both avoided and relieved. In this way, individual vehicles are found to reach goal destinations faster than the approach with search only.
Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes.

Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references—recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines’ texts and publication dates.
COVID-19 and Higher Education in Latin America: Challenges and possibilities in the transition to online education

The COVID-19 pandemic challenges higher-education institutions in Latin America. Switching from face-to-face to online instruction has profound implications regarding access in a strikingly unequal geographical region. The article highlights that the pandemic may have lasting consequences in the ways we understand the provision of higher education in Latin America. The increasing volatile and uncertain scenario requires institutions, educators, and students to adapt to the new normal shortly.