Reviewerly/exHarmony huggingface dataset:
Three-dimensional scene encryption algorithm based on phase iteration algorithm of the angular-spectral domain

In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional &#x0028 3D &#x0029 scene encryption algorithm based on the phase iteration of the angular spectrum domain is proposed in this paper. The algorithm, which adopts the layer-oriented method, generates the computer generated hologram by encoding the three-dimensional scene. Then the computer generated hologram is encoded into three pure phase functions by adopting the phase iterative algorithm based on angular spectrum domain, and the encryption process is completed. The three-dimensional scene encryption can improve the capacity of the information, and the three-phase iterative algorithm can guarantee the security of the encryption information. The numerical simulation results show that the algorithm proposed in this paper realized the encryption and decryption of three-dimensional scenes. At the same time, it can ensure the safety of the encrypted information and increase the capacity of the encrypted information.
Linked Open Treebanks. Interlinking Syntactically Annotated Corpora in the LiLa Knowledge Base of Linguistic Resources for Latin

In spite of the current availability of large collections of treebanks that can be used and queried from one common place on the web, we are still far from achieving a real interconnection, both between treebanks themselves and with other (kinds of) linguistic resources.However, making resources interoperable is a crucial requirement to maximize the contribution of each single resource, as well as to account for the linguistic complexity of the texts provided by (annotated) corpora and particularly by treebanks.This paper describes how dependency treebanks are interlinked in a Knowledge Base of linguistic resources for Latin based on Linked Open Data practices and standards.The Knowledge base is built to make linguistic resources interact by integrating all types of annotation applied to a particular word/text into a common representation.1 http://universaldependencies.org/ 2 For instance, Ponti and Passarotti (2016) show the dramatic decrease of the accuracy rates of a dependency parsing pipeline trained on the ıt-tb when applied on texts of the Classical era taken from the ldt.
Directional Attention based Video Frame Prediction using Graph Convolutional Networks

This paper proposes a novel network architecture for video frame prediction based on Graph Convolutional Neural Networks (GCNN). Most recent methods often fail in situations where multiple close-by objects at different scales move in random directions with variable speeds. We overcome this by modeling the scene as a space-time graph with intermediate features from the pixels (or a local region) as vertices and the relationships among them as edges. Our main contribution lies within posing the frame generation problem with our proposed space-time graph, which enables the network to learn the spatial as well as temporal inter-pixel relationships independent of each other, thus making the system invariant to velocity differences among the moving objects present in the scene. Moreover, we also propose a novel directional attention mechanism for the graph based model to efficiently learn a significance score based on directional relationship between pixels in the original scene. We also show that the proposed model generalizes better on the much more challenging task of predicting semantic scene segmentation of future scenes, even without access to any raw RGB frames. We perform several proxy tasks such as comparison of the quality of the semantic segmentation produced on the generated frames and comparing the accuracies for the task of recognizing actions in case of the dataset consisting of human actions. We use the popular Cityscapes traffic scene segmentation dataset as well as UCF-101 and Penn Action containing human actions to quantitatively and qualitatively evaluate the proposed framework over the recent state-of-the-art.
A Novel Neural Approach for News Reprint Prediction

News media has become a prevalent information spreading platform, where news sites can reprint news from other sites. To better understand the mechanism of news propagation, it is necessary to model reprint behavior and predict whether a news site will reprint a piece of news. Most existing works in news reprint analysis focus on analyzing the semantic of news content, little work has been done on integrating reprint relationship among sites and news content for reprint prediction from the perspective of sites. The challenge of improving prediction performance lies in how to effectively incorporate these two kinds of information to learn a more comprehensive reprint behavior model. In this paper, we propose an Integrated Neural Reprint Prediction (INRP) model that considers both reprint relationship and news content. It models the reprint relationships as a directed weighted graph and maps it into a latent space to learn sites representations. During news content modeling process, sites representations are embedded as attention guidance to build up more site-specific content representations. Finally, sites and news representations are jointly modeled to predict whether a piece of news will be reprinted by a site. We empirically evaluate the performance of the proposed model on a real world dataset. Experimental results show that taking both the reprint relationship and news content information into consideration could allow us make more accurate analysis of reprint patterns. The mined patterns could serve as a feedback channel for both corporations and management departments.
Evaluation of Different Sarcasm Detection Models for Arabic News Headlines

Being sarcastic is to say something and to mean something else. Detecting sarcasm is key for social media analysis to differentiate between the two opposite polarities that an utterance may convey. Different techniques for detecting sarcasm are varying from rule-based models to Machine Learning and Deep Learning models. However, researchers tend to leverage Deep Learning in detecting sarcasm recently. On the other hand, the Arabic language has not witnessed much improvement in this research area. Bridging the gap in sarcasm detection of the Arabic language is the target behind this work. In this paper, efficient models in short text classification are tested for detecting sarcasm in the Arabic news headlines for the first time. The dataset used to train and test these different architectures was manually collected by scrapping two different websites, sarcastic and non-sarcastic. Detailed results for each model were also represented, based on different performance metrics, such as accuracy, precision, recall and F1 score.
Automatic Reuse of Prototypes in Software Engineering: A Survey of Available Tools

The use of prototypes as an excellent mechanism for communication between software users has been fullyaccepted in the literature. Both the academic and business worlds agree on its use as a software technique,used primarily to capture requirements and as a means of communication with the user. However, often,prototypes are developed very quickly or without the collaboration of the users. This is one of the mainreasons why the real power of the prototypes is not used in software development. The initial hypothesis ofthis research is that this problem occurs because prototypes are regarded as disposable milestones. This paperanalyzes whether there are adequate tools on the market to help development teams to reuse prototypes asan exceedingly mechanism for starting and expediting new software development projects. With the presentstudy we show different tools for prototypes that, even though they are a preliminary evaluation, show thedisadvantages to be solved in the future development and research of solutions.
An Application of Cognitive Diagnosis Modeling in TIMSS: A Comparison of Intuitive Definitions of Q-Matrices

Detection of students’ ability levels is one of the common aims in educational studies. Cognitive Diagnosis Modeling approach has been used recently for the purpose of ability level detection by defined Q-matrices. This paper aims to use Cognitive Diagnosis Modeling (CDM) in order to investigate the definition of a Q-matrix across the cognitive skills of different years and countries in TIMSS. There is a subjective way in defining Q-matrices; for this purpose, an application of building Q-matrices under specific CDMs, from a set of expert proposed attributes is examined. The proposed attributes are used to build Q-matrices for TIMSS mathematics questions across its cycles, and across different nations.
A Defeasible Logic-based Framework for Contextualizing Deployed Applications

In human to human communication, context increases the ability to convey ideas.However, in human to application and application to application communication, this property is difficult to attain.Context-awareness becomes an emergent need to achieve the goal of delivering more user-centric personalized services, especially in ubiquitous environments.However, there is no agreed-upon generic framework that can be reused by deployed applications to support context-awareness.In this paper, a defeasible logic-based framework for contextawareness is proposed that can enhance the functionality of any deployed application.The nonmonotonic nature of defeasible logic has the capability of attaining justifiable decisions in dynamic environments.Classical defeasible logic is extended by meta-rules to increase its expressiveness power, facilitate its representation of complex multi-context systems, and permit distributed reasoning.The framework is able to produce justified decisions depending on both the basic functionality of the system that is itself promoted by contextual knowledge and any crosscutting concerns that might be added by different authorities or due to further improvements to the system.Active concerns that are triggered at certain contexts are encapsulated in separate defeasible theories.A proof theory is defined along with a study of its formal properties.The framework is applied to a motivating scenario to approve its feasibility and the conclusions are analyzed using argumentation as an approach of reasoning.
QABR: A QoE-Based Approach to Adaptive Bitrate Selection in Video Streaming Services

HTTP Adaptive Streaming (HAS) has recently become the de facto choice of today's streaming providers to perform a smooth video content delivery to the end users.The key technology behind HAS is the adaptive bitrate selection (ABR) algorithm that adaptively selects the best suitable video bitrate based on either throughput or buffer monitoring techniques.In order to fulfill user's satisfaction, ABRs must be designed to accurately reflect the perceived quality of experience (QoE), which is influenced by the perceptual and technical factors.However, both throughput and buffer only account for the technical factors, leading to the insufficiency of today's ABRs in demonstrating human perception.Moreover, existing throughput and buffer-based algorithms are slow-responsive to significant network changes and unstable in terms of video quality, as found by recent research efforts.For those reasons, QABRa novel QoE-based bitrate selection algorithmis proposed in this paper that combines the underlying network parameters and user's instantaneous QoE (in accordance with perceptual factors).Experimental results demonstrate that QABR outperforms the referenced baseline algorithm in various evaluation criteria.
Crowd-Generated Data Mining for Continuous Requirements Elicitation

In software development projects, the process of requirements engineering (RE) is one in which requirements are elicited, analyzed, documented, and managed.Requirements are traditionally collected using manual approaches, including interviews, surveys, and workshops.Employing traditional RE methods to engage a large base of users has always been a challenge, especially when the process involves users beyond the organization's reach.Furthermore, emerging software paradigms, such as mobile computing, social networks, and cloud computing, require better automated or semi-automated approaches for requirements elicitation because of the growth in systems users, the accessibility to crowd-generated data, and the rapid change of users' requirements.This research proposes a methodology to capture and analyze crowd-generated data (e.g., user feedback and comments) to find potential requirements for a software system in use.It semi-automates some requirementselicitation tasks using data retrieval and natural language processing (NLP) techniques to extract potential requirements.It supports requirements engineers' efforts to gather potential requirements from crowd-generated data on social networks (e.g., Twitter).It is an assistive approach that taps into unused knowledge and experiences emphasizing continuous requirements elicitation during systems use.
Implementation of Eye Tracking in an IoT-Based Smart Home for Spinal Cord Injury Patients

Background: People with severe disabilities, especially spinal cord injury (SCI), encounter many difficulties in communicating with computer systems, because their problem is generally not addressed in computer programs. Objectives: With the advent of technology and the move toward Internet of Things (IoT), a new technique has been developed by combining an eye tracking system with IoT to create a platform for the use of SCI patients. Methods: Eye tracking methods include invasive, non-invasive and semi-invasive techniques. A non-invasive method in which the eye movement is traceable in visible or infrared light is used in this research. An algorithm is designed and developed that allows an IoT-based connection providing higher speed and precision compared with previous algorithms. Therefore, this technique is novel in application of eye tracking for issuing control commands in an IoT-based smart home by individuals with SCI or other physical disabilities. The total command receipt, confirmation, and execution time in this system is less than 10 seconds. The optical conditions in this research were 5 ~ 300 lux and 2 ~ 6 lux in invisible and infrared light processing, respectively. It produced 97% correct responses with the help of statistical weighted averaging and elimination of bad data. This system uses the Arduino Mega 2560 board in IoT part, and MATLAB software in feature-based face detection processing. Results: This methodology improved the quality and accuracy of gaze tracking due to the following reasons: enhanced image clarity, use of two-eye processing, and use of statistical weighted averaging, as well as the use of both visible/infrared lights to reduce the sensitivity to the ambient light. This system has the advantage of having no sensitivity to age, gender, hairstyle, beard, mustache, and veil, and the possibility of producing output for several devices. Conclusions: The proposed system is reliable for designing a smart home for SCI patients.
High Efficiency Video Coding Compliant Perceptual Video Coding Using Entropy Based Visual Saliency Model

In past years, several visual saliency algorithms have been proposed to extract salient regions from multimedia content in view of practical applications. Entropy is one of the important measures to extract salient regions, as these regions have high randomness and attract more visual attention. In the context of perceptual video coding (PVC), computational visual saliency models that utilize the charactertistics of the human visual system to improve the compression ratio are of paramount importance. To date, only a few PVC schemes have been reported that use the visual saliency model. In this paper, we conduct the first attempt to utilize entropy based visual saliency models within the high efficiency video coding (HEVC) framework. The visual saliency map generated for each input video frame is optimally thresholded to generate a binary saliency mask. The proposed HEVC compliant PVC scheme adjusts the quantization parameter according to visual saliency relevance at the coding tree unit (CTU) level. Efficient CTU level rate control is achieved by allocating bits to salient and non-salient CTUs by adjusting the quantization parameter values according to their perceptual weighted map. The attention based on information maximization has shown the best performance on newly created ground truth dataset, which is then incorporated in a HEVC framework. An average bitrate reduction of 6 . 57 % is achieved by the proposed HEVC compliant PVC scheme with the same perceptual quality and a nominal increase in coding complexity of 3 . 34 % when compared with HEVC reference software. Moreover, the proposed PVC scheme performs better than other HEVC based PVC schemes when encoded at low data rates.
PAPAN HURUF FLANEL: MEDIA PEMBELAJARAN KEAKSARAN AWAL UNTUK ANAK USIA DINI

Attractive learning media become an attraction in early childhood learning. The use of media can provides convenience for teachers of Early childhood education (PAUD) in delivering material to be given to children. This study aims to conduct development research in making learning media. The media developed is alphabet flannel board (letter board). This media development was carried out for the introduction of early literacy, using the Research and Development (R &amp; D) research model. The stages of R &amp; D research include potential problems, information gathering, product design, design validation, design revisions, products, small-scale product trials, large-scale trials and mass production. In this study not all stages are carried out considering the limited time and cost so that the research only reaches the stage of feasibility validation and design revision to be the final product. Feasibility of the Alphabet Flannel Board is validated by the expert and the assessment of the user in this case PAUD teacher. Based on data analysis, the results of this study indicate that the Alphabet Flannel Board is quite feasible to be used as media. The mean value of the validation shows 3.25 of a scale of 5 which means it's quite decent.
Image Semantic Segmentation Using Deep Convolutional Nets, Fully Connected Conditional Random Fields, and Dilated Convolution

Deep convolutional neural networks (DCNNs) have recently demonstrated state-of-the-art performance in advanced vision tasks, such as image classification and object detection. This work focuses on solving image semantic segmentation tasks. First, we combine a new feature extraction network with a dilated convolution layer to improve the accuracy of the model's mission. Second, we introduce multi-scale feature fusion technology to improve the performance of DCNN. Third, we combine the DCNN with fully connected conditional random field to overcome the inaccurate positioning of DCNN and optimize their output. Our approach is demonstrated on the PASCAL VOC-2012 Image Semantic Segmentation dataset, where 78.1% IOU accuracy is achieved in the test set. Our approach can compute neural network responses intensively at 9 frames per second on modern GPUs.
Prediction of Reviews Rating: A Survey of Methods, Techniques and Hybrid Architectures

In the last recent years, the customer reviews has attracted an increasing interest by researchers and companies thanks to the advancement in Ecommerce and business applications.The ability to successfully decide whether a review will be helpful to other customers and thus give the product more exposure is vital to companies that support these reviews, companies like Google, Amazon and Yelp!The problem is that some customers are giving rating in conflict with their comments obliging other reviewers to read the comments to understand the reason of this contradiction.Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects.The accuracy of sentiment analysis has a direct effect on decision making in both business and government.Also, recommender system is one of the hot topics in the field of big data, especially with the development and growth of the World Wide Web.This paper presents a survey covering the methods in the field of reviews rating prediction.We also propose a hybrid architecture that combines techniques from sentiment analysis and recommender systems fields, to develop a solution that has the ability to predict rating of products while taking into consideration the analysis of sentiments included in the products reviews.
Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity

Euclidean distance between instances is widely used to capture the manifold structure of data and for graph-based dimensionality reduction. However, in some circumstances, the basic Euclidean distance cannot accurately capture the similarity between instances; some instances from different classes but close to the decision boundary may be close to each other, which may mislead the graph-based dimensionality reduction and compromise the performance. To mitigate this issue, in this paper, we proposed an approach called Laplacian Eigenmaps based on Clustering-Adjusted Similarity (LE-CAS). LE-CAS first performs clustering on all instances to explore the global structure and discrimination of instances, and quantifies the similarity between cluster centers. Then, it adjusts the similarity between pairwise instances by multiplying the similarity between centers of clusters, which these two instances respectively belong to. In this way, if two instances are from different clusters, the similarity between them is reduced; otherwise, it is unchanged. Finally, LE-CAS performs graph-based dimensionality reduction (via Laplacian Eigenmaps) based on the adjusted similarity. We conducted comprehensive empirical studies on UCI datasets and show that LE-CAS not only has a better performance than other relevant comparing methods, but also is more robust to input parameters.
Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements.It becomes possible with the maturity and commercialization of 5G technology.However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions.For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility.The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions.In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA).Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission.Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model.Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored.To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform.The results show that the performance of NAVSA is about 5∼8% better than some state-of-the-art algorithms in wireless networks.