Reviewerly/exHarmony huggingface dataset:
Cloud computing architecture for Tagging Arabic Text Using Hybrid Model

With the increasing role of technology in transferring information in our daily lives, the Arabic language has become the fourth language used on the Internet. Therefore, to develop different information systems in the Arabic language, we should determine the syntax and semantics of creating a text efficiently and accurately. Part of speech (POS) is one of the primary methods employed to develop any language corpus. Each language consists of several tags applied in different applications, such as natural language processing (NLP), speech synthesis, and information extraction. One of the main benefits of adopting cloud computing services is the offer a low cost and time to store your company data compared to traditional methods. This paper presents and deploys a cloud computing architecture for Tagging Arabic text using a hybrid model, which will help reduce the efforts and cost. The results show an excellent accuracy rate in tagging an Arabic text and quickly respond. Previous studies are compared based on relevant rating factors, which achieved high accuracy, procession, and recall rate of more than 95%. The cloud computing tagger attained an accuracy of 99.2%.
Efficient Distributed Coin-tossing Protocols

Ben-Or and Linial (1985) introduced the full information model for coin-tossing protocols involving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -processors with unbounded computational power using a common broadcast channel for all their communications. A bias- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> coin-tossing protocol outputs 1 with probability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> ; otherwise, it outputs 0 with probability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-X$</tex> ). A coin-tossing protocol's insecurity is the maximum change in the output distribution (in the statistical distance) that an adversary can cause. This work considers an adversary who monitors the protocol's communication and intervenes at most once by restarting the processor who just broadcast her message. For a given tolerance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> , our objective is to use the minimum number of processors, ensuring that this adversary can only change the output distribution by at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> . Historically, the “threshold coin-tossing protocols” have been optimal or asymptotically optimal against various adversary models. However, for our model, Khorasgani, Maji, and Mukherjee (2019) prove the existence of coin-tossing protocols that achieve the same tolerance as the threshold protocols using a smaller number of processors. Unfortunately, their protocol is not computationally efficient. Towards this objective, for any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x\in(0,1)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n\in \mathbb{N}$</tex> , this paper presents computationally efficient coin-tossing protocols approximating the new protocols of Khorasgani, Maji, and Mukherjee (2019). This protocol's running time is linear in the inverse of the accuracy parameter of this approximation, which can be set arbitrarily small.
Cultivation of College Students’ Low-Carbon Consumption Behaviours based on Mobile Education: A Behavioural View

With the increasing emphasis on sustainable development, low-carbon education has become an important work in colleges, emphasizing the practice of low-carbon life and letting students know that low-carbon is a good living habit. Under this guidance, the ideological and political workers in colleges need to combine education with low-carbon background, cultivate and develop their life planning, direction and values, which is of great and long-term significance to improve the living environment quality, moral quality and social responsibility of college students. We attempt to use the theory of behavioural science, starting from professor Fogg’s behaviour model, with the convenience of digital tools in the internet era, focusing on the motivation improvement, difficulty reduction and trigger factors of low-carbon consumption of college students, to explore the path of using mobile education to do a good job in low-carbon education, hoping to provide innovative education methods and useful suggestions for ideological and political education in colleges.
PrEmISES - optimizing the Semantically-Enabled data used by small and medium enterprises

This paper presents the architectural details for two main components used in the European project E8949 PrEmISES project: the “Analyzing and Indexing Component” plus the “Ontology generation Component”.The main use-cases for each component are given along with the implementation details. To reduce the complexity, each component was split in sub-components with a certain role. In this article, we detail each sub-components and the interaction among them.We based our testing primarily on a black-box methodology that checks the functionality of a system. The purpose of testing is to guarantee that PrEmISES satisfies all agreed requirements, including non-functional requirements. In this article, we present the methodology that checks the functionality of PrEmISES system.
Automatic Recognition of Identification Schemes for IoT Identifiers based on A Hybrid DNN Model

In Internet of Things (IoT), a unique identifier is essential for each object to serve as its digital identity and helps users to obtain the detailed information about this object. However, there are multiple encoding rules being used by different organizations to identify their objects, thus creating a great challenge for the applications that need to resolve multi-source heterogeneous identifiers. To address this issue, we propose an algorithm to automatically recognize the identification schemes of heterogeneous IoT identifiers, based on a hybrid deep neural network (DNN) model that combines a one-dimensional DNN model and a two-dimensional convolutional neural network (CNN) model. To satisfy the input criteria of the two-dimensional CNN, a novel identifier-to-polygon (I2P) strategy is presented to transform an IoT identifier to a unique polygon image. We evaluate our algorithm on the ID-20 dataset, showing that we can achieve an IoT identifier recognition accuracy of up to 94.77% and outperform other state-of-the-art methods.
Application of Matrix-NMS in Face Detection and Autonomous Driving

Matrix-NMS is derived from the SOLO network, an instance segmentation network. Matrix-NMS was transplanted to the target detection network YOLO to improve the detection accuracy and recall rate of the actual application scenario of “similar objects closed to each other or have a certain occlusion relationship”. The model used in this article is based on YOLOv3.ResNet18 and ResNet50 were studied as pre-trained networks to compare the accuracy and recall rate of using Matrix-NMS or not. The results show that the model using Matrix-NMS not only has a certain degree of improvement in accuracy and recall rate, but also has the similar inference time. The accuracy and recall rate of the model have been improved to a certain extent. The study in this article could be applied to face detection, sign detection within complex road conditions for autonomous driving or for focus detection in medical imaging. In addition to Matrix-NMS, the convolution layer with DropBlock, the pre-trained networks ResNet50, and ResNet18 with Deformable Convolutional Networks have also been studied.
Educational Model of PAKEM in Compliance with Fe Tablet Consumption of Adolescent Girls

BACKGROUND: The low adherence to consumption of Fe tablets in adolescent girls illustrates that the iron supplementation program has not been maximized, so the proportion of anemia in the adolescent group is still high. School-based weekly Fe tablet intervention together with education is effective and ideal in preventing anemia. AIM: This study aims to determine the effect of the PAKEM education model on adherence to Fe tablet consumption. METHODS: This research was quasi-experimental with the randomized pre-test-post-test control group design. The population of this study was 249 young women in Class XI of several high schools, SMAN 12 Makassar and SMAN 1 Makassar, based on the results of the initial survey with low adherence to taking Fe tablets. The research sample was obtained by simple random sampling; 70 respondents were divided into intervention and control groups. Data were analyzed using paired t-test, Wilcoxon test, and Mann–Whitney U-test. RESULTS: There was an increase after the PAKEM educational intervention on knowledge (10.91–17.88), attitudes (56.11–73.60), motivation (38.05–53.49), and actions (0.83–7.34). There was a significant difference between the PAKEM intervention group and the leaflet and audiovisual controls on all study variables with p=0.000 (p&lt;0.05). CONCLUSION: The PAKEM education model was more influential than a leaflet and audiovisual media on the compliance of female adolescent Fe tablet consumption. It is recommended for health workers and educators to apply the PAKEM education model to prevent anemia in adolescent girls.
Webmetrical analysis of Ecology Section of RNPLS&amp;T website

The Ecology Section of RNPLS&amp;T website maintained since 2003 is targeted at both Library users and partners in ecological education and information. Access to bibliographic annotated database “Ecology: Science and technology” and to e-library in ecology is provided; new acquisition bibliography are published monthly; information on exhibitions, conference proceedings and webinar records are also available. These are the ways to inform Library users and partners on publications in ecology, sustainable development and RNPLS&amp;T’s efforts in ecology education and awareness building. The traffic to ecological pages evidences on the demand and usefulness of the provided information. The bibliometrical analysis of the Ecology Section was completed; the author provides its findings. The Section materials are characterized. The traffic statistics, i. e. number of views, visit depth, link sources, is provided for the period January 1, 2019 – December 31, 2019. AWStats vs. Yandex.Metrica data are compared; corresponding improvement lines are identified for RNPLS&amp;T website Ecology Section.
On Graph Matching Using Generalized Seed Side-Information

In this paper, matching pairs of stocahstically generated graphs in the presence of generalized seed side-information is considered. The graph matching problem emerges naturally in various applications such as social network de-anonymization, image processing, DNA sequencing, and natural language processing. A pair of randomly generated labeled Erdös-Rényi graphs with pairwise correlated edges are considered. It is assumed that the matching strategy has access to the labeling of the vertices in the first graph, as well as a collection of shortlists — called ambiguity sets — of possible labels for the vertices of the second graph. The objective is to leverage the correlation among the edges of the graphs along with the side-information provided in the form of ambiguity sets to recover the labels of the vertices in the second graph. This scenario can be viewed as a generalization of the seeded graph matching problem, where the ambiguity sets take a specific form such that the exact labels for a subset of vertices in the second graph are known prior to matching. A matching strategy is proposed which operates by evaluating the joint typicality of the adjacency matrices of the graphs. Sufficient conditions on the edge statistics as well as ambiguity set statistics are derived under which the proposed matching strategy successfully recovers the labels of the vertices in the second graph. Additionally, Fano-type arguments are used to derive necessary conditions for successful seeded graph matching.
An Interactive Machine Learning System for Image Advertisements

Advertising is omnipresent in all countries around the world and has a strong influence on consumer behavior. Given that advertisements aim to be memorable, attract attention and convey the intended information in a limited space, it seems striking that previous research in economics and management has mostly neglected the content and style of actual advertisements and their evolution over time. With this in mind, we collected more than one million print advertisements from the English-language weekly news magazine “The Economist” from 1843 to 2014. However, there is a lack of interactive intelligent systems capable of processing such a vast amount of image data and allowing users to automatically and manually add metadata, explore images, find and test assertions, and use machine learning techniques they did not have access to before. Inspired by the research field of interactive machine learning, we propose such a system that enables domain experts like marketing scholars to process and analyze this huge collection of image advertisements.
Local Perception based Auxiliary Neural Network for Object Tracking

In recent years, tracking algorithms based on deep learning frameworks in the field of object tracking have attracted much attention. The focus of their research is to improve the performance of algorithms by designing deep network models and constructing training data sets. However, this type of algorithm usually uses an end-to-end method to output the predicted target position, and the predicted result always has a certain deviation from the real target position. This paper proposes a object tracking algorithm based on local perception-assisted neural network. The algorithm expands the training dataset. The expanded dataset adds training samples with partial target content and enhances the training label. Train an auxiliary neural network model with local perception capabilities, when the predicted result of the tracking algorithm deviates from the real position, the auxiliary neural network model is used to offset the tracking result to promote the accuracy of the tracking result. Experiments on the VOT2018 and VOT2016 benchmark datasets show that compared with the 6 mainstream algorithms in the object tracking field, the algorithm in this paper has better tracking performance under the three indicators of EAO, accuracy, robustness.
Cross Sample Similarity for Stable Training of GAN

Recently attention network finding similarity in non-local area within a 2D image has shown outstanding improvement in multi-class generation task in GAN. However it frequently shows unstable training state sometimes falling in mode collapse. We propose cross sample similarity loss to penalize similar features of fake samples that are rarely observed in reals. Proposed method shows improved FID score compared to baseline methods on CelebA, LSUN, and decreased mode collapse on Cifar10[Krizhevsky 2009].
Analysis of the Photovoltaic Systems Construction Capacities and Conditions on the Preschool Buildings Roofs in Serbia

Preschool buildings represent a relevant potential for construction of photovoltaic panels, to be integrated into roofs of these buildings. Given that roof structures are passive, construction of photovoltaic systems does not interfere with the building functionality, does not it in any way adversely affect the environment. Installation of photovoltaic systems on building roofs brings the production and consumption of electricity closest possible, therefore such systems ensure significant reduction in power losses in transmission and distribution grids. In addition to locally produced electricity, construction of photovoltaic systems on the preschool buildings’ roofs produces an educational effect as well. This paper presents the results of evaluations of the available roof surfaces of preschool buildings in Serbia for the photovoltaic panels installation. For each category of preschool building, an estimate of possible annual production per unit of average roof area was made. Summarizing all the results, estimates of possible installed capacity and annual electricity production for different scenarios are given.
CATALIST: CAmera TrAnsformations for Multi-LIngual Scene Text Recognition

We present a CATALIST model that ‘tames’ the attention (heads) of an attention-based scene text recognition model. We provide supervision to the attention masks at multiple levels, i.e., line, word, and character levels while training the multi-head attention model. We demonstrate that such supervision improves training performance and testing accuracy. To train CATALIST and its attention masks, we also present a synthetic data generator ALCHEMIST that enables the synthetic creation of large scene-text video datasets, along with mask information at character, word, and line levels. We release a real scene-text dataset of 2k videos, \(\text {CATALIST}_\text {d}\) with videos of real scenes that potentially contain scene-text in a combination of three different languages, namely, English, Hindi, and Marathi. We record these videos using 5 types of camera transformations - (i) translation, (ii) roll, (iii) tilt, (iv) pan, and (v) zoom to create transformed videos. The dataset and other useful resources are available as a documented public repository for use by the community.
Downscaling of Spatial Irradiance Based on Cloud Advection using Transfer Functions

Spatiotemporal aggregation of solar irradiance occurs when a spatially distributed receiver (e.g. a PV generation facility) collects variable, geographically distributed irradiance and reduces it to a single electrical generation output. Models of this phenomenon exist and are designed to take variability from a single point irradiance monitor and predict how that variability will be reduced by aggregation. We have applied these models in reverse to assess whether the same models can be used to predict the variability of a single point measurement given an aggregate irradiance time series as an input. Results for an advection-based model show that this approach leads to overprediction of the high frequency variability due to overprediction of the site-to-site correlation, even during highly correlated advection conditions. While some modifications to the cloud advection model improve its performance, the wavelet variability model better represents the site pair decorrelation and produces superior results in representing the disaggregated time series and variability metrics. Further work may be warranted to further improve upon these efforts and enable reliable, transfer function-based downscaling of irradiance data.
Legal and Regulatory Landscape of Blockchain Technology in Various Countries

Blockchain technology can be leveraged to record information securely, ranging from public sector data to private records. It has the potential of being ubiquitous due to its far-reaching use cases and revolutionary features. The deployment of blockchain technology can radically transform corporate and government operations and services. The blockchain legislative landscape is rapidly evolving, and an in-depth analysis is provided to offer a legal and contextual perspective of the regulatory trends across the globe. Part I explores the widespread use of blockchain technology for various industries and business applications. It also outlines two types of legislation that can be enacted, namely enabling and prohibitive legislation, to advance the policy objectives of a country. Part II examines the regulatory responses of various countries relating to blockchain use cases and applications.
Smart Integration of Blockchain in Air Cargo Handling for Profit Maximization

Transportation and logistics management are critical to the economic growth of a country. Smart transportation is becoming possible thanks to the advancement of digital technology implementation. To decrease the vulnerability of digitized systems from cyber-attacks, Blockchain has recently become one of the most commonly adopted technologies for free, decentralized, and trustworthy intelligent transportation networks. By analyzing the potential application of Blockchain technologies in the smart transportation of cargo by air, this study hopes to contribute to the area of logistics management. For efficient and safe air cargo movement, we propose a distributed blockchain layered framework that helps in transforming air logistics operations in national and international trades and we validated our framework by mathematically comparing the cost with and without the implementation of Blockchain.
Simulations of Realistic Trombone Notes in the Time-Domain

Time pressure waveforms associated with four musical notes produced at different volumes on a trombone were measured and then reproduced using a computational model. Special care was taken to accurately model the change in the trombone’s cross-sectional area. An axisymmetric formulation of the compressible Euler equations was used and then numerically solved using the discontinuous Galerkin method. To evaluate the chosen model, the numerical solutions were compared against the measured data collected outside the bell. We found that accounting for the nonlinear behaviour for both high and low sound volumes yielded a good quantitative match between the computed and measured tones. For all four notes, once the sound pressure level drops 30 dB below the main peak, the computed pressure overestimated the measured spectral components.
Extraction of Pig Outline and Estimation of Body Weight by Detection of Multiple Lines Structured Light Using Discrete Fourier Transform

Because pig farms are labor intensive, computer vision-based pig weight estimation system that involve robust maintenance-free parts have been developed; with this system, information on the shape of the pig is used to estimate the pig’s weight. While 2D shape information can be useful, 3D information is preferable to ensure more accurate weight estimates. The approach described here uses multiple lines structured light laser projection to extract such 3D shape information. This method allows for the extraction of a clear pig image regardless of the environment and offers more accurate pig weight estimates. This paper introduces the robust image processing system and describes its effectiveness for practical use on pig farms.
Driver’s Illegal Driving Behavior Detection with SSD Approach

In this paper, an advanced detection approach of illegal driving behavior is proposed using Single Shot MultiBox Detector (SSD) based on deep learning. The detection of driver’s illegal driving behavior includes cellphone usage, cigarette smoke and no fastening seat belt. Doing this can greatly reduce the occurrence of traffic accidents. In order to validate the detection effect using SSD on small target objects, such as cigarette in complex environment, we use not only three online databases, i.e. HMDB human motion database, WIDER FACE Database, Hollywood-2 Database, but also a real database collected by ourselves. The experimental results show that the SSD approach has a better performance than the Faster Regions with Convolutional Neural Network (Faster R-CNN) for detecting driver’s illegal driving behavior.