Reviewerly/exHarmony huggingface dataset:
Phase-Matching Quantum Key Distribution without Intensity Modulation

Quantum key distribution provides a promising solution for sharing secure keys between two distant parties with unconditional security. Nevertheless, quantum key distribution is still severely threatened by the imperfections of devices. In particular, the classical pulse correlation threatens security when sending decoy states. To address this problem and simplify experimental requirements, we propose a phase-matching quantum key distribution protocol without intensity modulation. Instead of using decoy states, we propose a novel method to estimate the theoretical upper bound on the phase error rate contributed by even-photon-number components. Simulation results show that the transmission distance of our protocol could reach 305 km in telecommunication fiber. Furthermore, we perform a proof-of-principle experiment to demonstrate the feasibility of our protocol, and the key rate reaches 22.5 bps under a 45 dB channel loss. Addressing the security loophole of pulse intensity correlation and replacing continuous random phase with 6 or 8 slices random phase, our protocol provides a promising solution for constructing quantum networks.
SIGNED: Smart cIty diGital twiN vErifiable Data Framework

Smart city digital twins can provide useful insights by making effective use of multi disciplinary urban data from diverse sources. Whilst these insights provide new information that helps cities in decision making, verifying the authenticity, integrity, traceability and data ownership across various functional units have become critical characteristics to ensure the data is from an authentic and trustworthy source. However, these characteristics are rarely considered in a digital twin ecosystem. In this research we introduce a novel framework, namely, ‘SIGNED: Smart cIty diGital twiN vErifiable Data framework’ that is designed on the basis of data ownership, selective disclosure and verifiability principles. Using Verifiable Credentials, SIGNED ensures digital twin data are verifiably authentic i.e., it covers provenance, transparency, and reliability through verifiable presentation. A proof of concept is designed and evaluated based on a smart water management use case to demonstrate the effectiveness of SIGNED in securing verifiable exchange of digital twin data across multiple functional units. The proof-of-concept demonstrates that SIGNED successfully allows the exchange of data in a trusted and verifiable manner at negilgible performance cost, thus enhancing security and alleviating privacy issues when sharing data between various functional units in a smart city.
Analisis Upaya Guru dalam Mengatasi Kesulitan Belajar Membaca Siswa

This research aims to find out how teachers try to overcome learning difficulties reading grade 1 students of SDN Sandana, Galang Tolitoli District. This research uses a qualitative approach with a descriptive method. The subjects of this study were first grade teachers and first graders. The data collection in this study is using interviews and documentation. The data analysis techniques used data collection, data reduction, data presentation and conclusion drawing. Based on the results of the study can be concluded that the teacher's efforts in overcoming the difficulty of learning to read grade 1 students are providing additional lessons, providing homework and providing motivation to learn to read.&#x0D; Keywords : Teacher, Learning Difficulties Reading, Students.
Penguasaan Morfologi dalam Menulis Artikel Populer

This study aims to describe the mastery of morphology in writing popular articles. This research uses a quantitative approach with the correlation method. The sample of this study was the students of the Indonesian Language and Literature Study Program STKIP Abdi Pendidikan Payakumbuh Foundation in 2016, amounting to 13 people. The results of the study, 1) mastery of student morphology in general is in the range of 86-95% with excellent qualifications, 2) popular article writing skills in general students are in the range of 76-85% with good qualifications, 3) the relationship of morphology mastery with writing skills popular student articles with r values ​​of 0.5738. After being analyzed with the t-test formula, the value of tcount&gt; ttable is 2.2155&gt; 1.812 with dk n-2 (12-2 = 10) and a significance level of 0.05 or 95%. Conclusion, there is a significant relationship between the mastery of morphology to the skills of writing popular articles&#x0D; Keywords: Morphology, Skills, Popular Article Writing.
Pengembangan Media Pembelajaran Lectora dalam Menelaah Struktur dan Kebahasaan Puisi Rakyat

This study aims to develop lectora learning media in studying the structure and language of folk poetry in seventh grade junior high school students. This study uses the Goll, Gall, and Borg R&amp;D methods. The research data was collected through a questionnaire, unstructured interviews, documentation, and observation. This research data analysis utilizes Likert scale calculation. There are three validators in this research, namely media expert, graphic expert, and education practitioner. In addition, students also responded. A small scale trial was conducted on 7F grade Alizzah Batu junior high school students totaling 25 students. Then the usage test was carried out on 7E and 7G grade students totaling 53 students. From the results of the assessment from the instructional media material experts scored 3.01 or could be categorized as "feasible". Meanwhile, from the learning media graphic expert, the score was 3.53 or categorized as "very feasible". Next assessment given by education practitioners is 3.83 or categorized as "very feasible". Then the product was tested on 25 students. Of the ten questions given about the appearance and content of the media the average value obtained was 3.608 or categorized as "very feasible". After a little improvement was made again the trial use of class VII E and G as many as 53 students obtained an average value of 3.49 or categorized as "very feasible". Conclusion, this learning media is considered appropriate to be used, therefore, this learning media is classified according to the conditions in the classroom.&#x0D; Keywords: Learning Media, Lectora, People's Poetry
Penggunaan Video Sebagai Media Tutorial Pembelajaran terhadap Kemampuan Berceramah Siswa

The purpose of this study was to determine the use of video as a learning activity tutorial media on students' speaking abilities. This research uses a quasi experiment method, the technique used in sampling is total sampling technique, the data analysis technique used in this study begins with the analysis prerequisite test that is the distribution of normalitis test, variant homogeneity test and linearity relationship test. The results of the study, obtained Fcount = 19.747 and p = 0,000, average learning outcomes 20.78, the average questionnaire was 60.09. Conclusion, there is an influence of the use of instructional video media on the results of students' lecturing abilities&#x0D; Keywords: Video, Media, Learning, Lecture
A Wavelet-Based Approach to FRF Identification From Incomplete Data

Frequency Response Function (FRF) estimation from measured data is an essential step in the design, the control, and the analysis of complex dynamical systems, including thermal and motion systems.Especially for systems that require long measurement time, missing samples in the data record, e.g., due to measurement interruptions, often occur.The aim of this paper is to achieve accurate identification of non-parametric FRF models of periodically excited systems from noisy output measurements with missing samples.An identification framework is established that exploits a wavelet-based transform to separate the effect of the missing samples in the time domain from the system characteristics in the frequency domain.The framework encompasses both a time-invariant and a time-varying wavelet-based estimator, which provide different mechanisms to address the missing samples.Experimental results from a thermo-dynamical system confirm that the estimators enable accurate identification.
A NOVEL ARABIC CORPUS FOR TEXT CLASSIFICATION USING DEEP LEARNING AND WORD EMBEDDING

Over the last years, Natural Language Processing (NLP) for Arabic language has obtained increasing importance due to the massive textual information available online in an unstructured text format, and its capability in facilitating and making information retrieval easier. One of the widely used NLP task is “Text Classification”. Its goal is to employ machine learning technics to automatically classify the text documents into one or more predefined categories. An important step in machine learning is to find suitable and large data for training and testing an algorithm. Moreover, Deep Learning (DL), the trending machine learning research, requires a lot of data and needs to be trained with several different and challenging datasets to perform to its best. Currently, there are few available corpora used in Arabic text categorization research. These corpora are small and some of them are unbalanced or contains redundant data. In this paper, a new voluminous Arabic corpus is proposed. This corpus is collected from 16 Arabic online news portals using an automated web crawling process. Two versions are available: the first is imbalanced and contains 3252934 articles distributed into 8 predefined categories. This version can be used to generate Arabic word embedding; the second is balanced and contains 720000 articles also distributed into 8 predefined categories with 90000 each. It can be used in Arabic text classification research. The corpus can be made available for research purpose upon request. Two experiments were conducted to show the impact of dataset size and the use of word2vec pre-trained word embedding on the performance of Arabic text classification using deep learning model.
CATANIC: Automatic generation model of image captions based on multiple attention mechanism

Abstract As a cross-field of computer vision and natural language processing, image caption generation has been a widely concerned researched topic. However, it is challenging because the automatically generated captioning needs to conform to the image content and language logic. In this paper, we propose a novel image caption generation model based on the DenseNet169 pre-training network and the modified Transformer model, which follows an encoding-decoding architecture. First, we use the DenseNet169 network as the encoder to extract the initial features of the image, and then introduce the “Attention on Attention” module between the encoder and the decoder to correct the initial features of the picture and filter out the attention results that do not match the picture content. At the same time, we introduce the “Attention on Attention” module into the internal decoder of the Transformer model, which strengthens the correlation between the image feature and textual descriptions, and applies the modified Transformer model as a decoder to transform the modified image feature vectors into textual descriptions. Experimental results show that our model is not only able to generate captions that match the content of the pictures as well as the linguistic logic, but also has fewer training parameters and requires less training time cost than a number of different image captioning models with pre-trained convolutional networks as encoders.
Development of the Perception Scale for Flipped Learning Model

The main purpose of this research is to develop a measure to determine university students' perceptions of the flipped learning model.Exploratory and confirmatory factor analysis were conducted within the scope of the validity study of the scale, and its internal consistency was analyzed for its reliability.The data for exploratory factor analysis was collected from 360 preservice teachers who experienced the flipped learning model in the 2019-2020 academic year.As a result of the exploratory factor analysis, a three-dimensional structure (learning readiness, learning support and motivational interaction) consisting of 22 items was obtained, and factor loadings were between .82 and .50.The total variance ratio explained by the factors of the scale was determined as 57.59.Moreover, the confirmatory factor analysis was carried out on the data obtained from 354 pre-service teachers who did not participate in the exploratory factor analysis study.The most important values for CFA were χ² =719.34;df=201; RMSEA = 0.08; SRMR =0.06; CFI = 0.97 and NNFI =0.97, which showed an acceptable level of fit confirming the three-factor structure.The findings show that the developed scale is a valid and reliable measure to determine the perceptions of university students regarding the flipped learning model.
Informatics and ICT as Learning Subjects in Primary and Secondary Education in Greece

Informatics and Information & Communication Technology (ICT) have been closely intertwined with education for about 50 years worldwide and about 40 years in Greece.This article focuses on the content of the learning subjects of Informatics and ICT in Greek schools, which are included as independent subjects in all primary and secondary school grades, aiming at welleducated graduates to be able to use ICT in an effective, creative, and ethically correct way, and to be prepared to participate actively in the digitally enhanced society.To this end, a review was conducted to all current curricula of the primary and secondary education in Greece for the learnign subjects of Informatics and ICT to present the basic principles and content of the instruction of these two subjects, as well as the student's skills that the teaching of these subjects aims to develop.Informatics and ICT as learning subjects in Greek are being taught in all grades of primary and secondary education by specialized Informatics teachers.For the instruction of them, constructivist learning theories are proposed, and collaborative, project-based and gamebased approaches are promoted.given the rapid advances in computer and communication science, it is imperative that ICT curricula are updated or reformed to meet the needs of the everchanging landscape.
The Effects of Flipped Classroom Strategy Based on “Addie Model” for Algebraic Skill Development

The study aimed to investigate the effect of the flipped classroom strategy based on ADDIE Model on Prince Faisal College students' development of algebraic thinking skills.To achieve the goal of the study, the researchers developed a measure of development of algebraic thinking skills.The study used the quasi-experimental approach with a pre-post design, and the study was applied to (53) students, who were divided into two groups: an experimental group of ( 27) students, and a control group of (26) students.The results showed there is an effect of the strategy on the dimensional measurement of the test of developing all algebraic thinking skill.Flipped Classroom helped develop algebraic thinking skills.The study recommended using the stages of ADDIE model in applying the flipped classroom strategy, and flipped classroom based on ADDIE model enhances student learning, because the strategy integrates student learning at home and in the classroom.
Towards Programmable Networking With CoopNet: A Horizontal Parallel Multipath Approach

The advent of the Internet of Things (IoT) and machine-to-machine (M2M) communication provide a system for collecting and manipulating big data and a platform for sensing, actuating, and automating the environment. IoT, M2M communication, social networking, and mass multimedia severely strain the communication infrastructure. Thus, the archaic communication frameworks require necessary improvements. One such improvement is the simultaneous usage of parallel communication links of differing radio access networks. This paper presents a Machine Learning (ML) optimization for link selection and use in CoopNet, a horizontal programmable communication architecture. Programmable networking paves the way for advancing communication to improve performance, reliability, security, and policy-based applications, including network decoupling. The ML implementation in CoopNet improves throughput by over 17%, delay by 10%, and reduces individual link utilization.
A Review of Upgradeable Smart Contract Patterns based on OpenZeppelin Technique

The Ethereum blockchain is one of the main public platforms to run smart contracts and enable decentralised applications.Since data stored in a blockchain is considered immutable, smart contracts deployed in Ethereum are regarded as tamper-proof and therefore offer strong protection against attacks aiming at tinkering with the execution flow of an application.Yet, like any other software, a smart contract needs to be maintained over time to fix bugs or add new features.Deploying every updated version as a brand-new smart contract in Ethereum leads to problems such as migrating the contract state from the old version and enabling clients to point to the new version in a timely fashion.The OpenZeppelin framework addresses this limitation by providing libraries that enable the deployment of upgradeable smart contracts.This is achieved by relying on proxies that act as intermediaries between clients and smart contracts, allowing the latter to be updated transparently.In this paper, we present the upgradeable smart contract patterns supported by OpenZeppelin and compare them in terms of security, cost, and performance.To show this paradigm's prevalence in Ethereum, we also analyse the usage of OpenZeppelin Upgradeable smart contracts over the last four years.
Mobilenetv2_CA Lightweight Object Detection Network in Autonomous Driving

A lightweight network target detection algorithm was proposed, based on MobileNetv2_CA, focusing on the problem of high complexity, a large number of parameters, and the missed detection of small targets in the target detection network based on candidate regions and regression methods in autonomous driving scenarios. First, Mosaic image enhancement technology is used in the data pre-processing stage to enhance the feature extraction of small target scenes and complex scenes; second, the Coordinate Attention (CA) mechanism is embedded into the Mobilenetv2 backbone feature extraction network, combined with the PANet and Yolo detection heads for multi-scale feature fusion; finally, a Lightweight Object Detection Network is built. The experimental test results show that the designed network obtained the highest average detection accuracy of 81.43% on the Voc2007 + 2012 dataset, and obtained the highest average detection accuracy of 85.07% and a detection speed of 31.84 FPS on the KITTI dataset. The total amount of network parameters is only 39.5 M. This is beneficial to the engineering application of MobileNetv2 network in automatic driving.
Aspheric surface measurement by absolute wavelength scanning interferometry with model-based retrace error correction

This paper presents a non-nulling absolute interferometric method for fast and full-area measurement of aspheric surfaces without the necessity of any mechanical movement. Several single frequency laser diodes with some degree of laser tunability are used to achieve an absolute interferometric measurement. The virtual interconnection of three different wavelengths makes it possible to accurately measure the geometrical path difference between the measured aspheric surface and the reference Fizeau surface independently for each pixel of the camera sensor. It is thus possible to measure even in undersampled areas of the high fringe density interferogram. After measuring the geometrical path difference, the retrace error associated with the non-nulling mode of the interferometer is compensated for using a calibrated numerical model (numerical twin) of the interferometer. A height map representing the normal deviation of the aspheric surface from its nominal shape is obtained. The principle of absolute interferometric measurement and numerical error compensation are described in this paper. The method was experimentally verified by measuring an aspheric surface with a measurement uncertainty of λ /20, and the results were in good agreement with the results of a single-point scanning interferometer.
A cloud-based router management system for enterprise network management

Every operation of the IT industry has been moved to the cloud and evolved to a point where minimal or no human intervention is needed. The Networking domain is the last to catch up to this trend. Most networking Devices like routers, switches, and firewalls require administrators to log in to them manually to maintain network policies defined by the business processes. For businesses with large-scale operations, it's impossible to maintain these network devices without a centralized management system. This system can be pictured as software deployed on the cloud providing a centralized control panel for administrators to monitor and maintain their network devices, it even provides capabilities to automate certain network operations. Costs of operation & maintenance of large-scale networks are high as it requires personnel specializing in highly isolated domains, platforms, and vendors. Due to reliance on network device command-line interfaces and vendor-specific practices. Teams from multiple departments cannot collaborate effectively. Timely backups and storage of device configurations are critical, manually doing this is cumbersome and may introduce security risks. Fault reporting and identification can eat up valuable time and resources of a business. Provision of new policies across the network is critical and has to be done swiftly to evade newly discovered security risks but due to the above-mentioned overheads, it takes time.
AAEBERT: Debiasing BERT-based Hate Speech Detection Models via Adversarial Learning

Hate speech datasets contain bias which machine learning models propagate. When these models classify tweets written in African American English (AAE), they predict AAE tweets as hate/abusive at a higher rate than tweets written in Standard American English (SAE). This paper assesses bias in language models fine-tuned for hate speech detection and the effectiveness of adversarial learning in reducing such bias. We introduce AAEBERT, a pre-trained language model for African American English obtained by re-training BERT-base on AAE tweets. AAEBERT is used to extract the representation of each tweet in the various hate speech datasets and to classify tweets into two classes - AAE dialect and non-AAE dialect. A three-layer feedforward neural network that takes the representation from AAEBERT and a dialect label as input is used as the adversarial network for debiasing. We evaluate bias in language models fine-tuned for hate speech detection. Then assess the effectiveness of adversarial debiasing in these models by comparing results before and after adversarial debiasing is applied. Analysis reveals that the fine-tuned models are biased towards AAE, and adversarial debiasing is effective in reducing bias.