Reviewerly/exHarmony huggingface dataset:
Quantum Key Distribution Shared Protocol Using Teleportation and Delayed Measurement

Authors:
raw_author_name: Cinthya Hernández
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Mexico.
raw_author_name: Francisco Delgado
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Mexico.
raw_author_name: Alan Anaya
raw_affiliation_string: Technical University of Denmark, Physics department, Denmark.

Abstract: Abstract With the rise of quantum computers, cryptographic protocols are being required to set secure communications. In the current study, a 3-party Quantum Key Distribution protocol is proposed to ensure safe communication considering ideas from BB84 and GR10 protocols against eavesdropping by using a parameterized partially entangled resource. The protocol is built so that the sender is the only one knowing the code key and the message encryption is made ongoing. Delayed measurements are applied just at the end of the protocol to decode the message effectively. The parameter of the entangled resource is used as a double control for the exchange rate of classical communications involved in the procedure. Finally, a security analysis is made, where multiple coordinated attacks could be a threat to this protocol.
KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Authors:
raw_author_name: Sardar Hasen Ali
raw_affiliation_string: Computer Science Department, University of Zakho, Duhok, Kurdistan Region, Iraq
raw_author_name: Maiwan Bahjat Abdulrazzaq
raw_affiliation_string: Computer Science Department, University of Zakho, Duhok, Kurdistan Region, Iraq

Abstract: Recognition of handwritten digits has garnered significant interest among researchers in the domain of recognizing pattern. This interest stems from the recognition's relevance in various real-life applications, including reading financial checks and official documents, which has remained a persistent obstacle. To address this challenge, researchers have developed numerous algorithms focusing on recognizing handwritten digits across different human languages. This paper presents a new Kurdish Handwritten dataset, consisting of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants, encompassing a broad and varied group. It serves as the primary dataset for training and evaluating algorithms in Kurdish digit recognition. We used Kurdish dataset named (KurdSet) and Arabic dataset for handwritten recognition, which holds 70,000 images of Arabic digits that were written by 700 various participants. Additionally, various models are utilized in the study, including ResNet50, DenseNet121, MobileNet, and a custom CNN (convolutional neural network). Additionally, the models' effectiveness was assessed through the examination of test accuracy, which measures the percentage of correctly classified digits in the evaluation phase. ResNet50 also performs exceptionally well that achieved test accuracy 99.67%, indicating its All models exhibit good performance, DenseNet121 and the Custom CNN Model demonstrate the highest test accuracy of 99.73%, highlighting their superior performance. capabilities in capturing relevant features. Despite its accuracy, MobileNet still exhibits good recognition capability with a test accuracy 99.54%.
Deep learning-based segmentation of breast masses using convolutional neural networks

Authors:
raw_author_name: S Moldovanu
raw_author_name: L Moraru

Abstract: Abstract Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.
Text Recognition for Library Collection in Different Light Conditions

Authors:
raw_author_name: Imam Yuadi
raw_affiliation_string: Department of Information and Library Science, Airlangga University, Surabaya, Indonesia
raw_author_name: Ullin Nihaya
raw_affiliation_string: Information Management, Tatung University, Taipei, Taiwan

Abstract: Book arrangement problems in Indonesia mainly utilize traditional systems. However, it contributes to user frustration and dissatisfaction, leading to difficulties in the retrieval process and hindering book monitoring. Therefore, an efficient arrangement is needed to make it easier for librarians to organize collections at the library. Hence, this study proposes and evaluates Optical Character Recognition (OCR) approaches such as Tesseract-OCR and Efficient and Accurate Scene Text (EAST) by considering various light factors and device applications. An evaluation was performed to determine the effectiveness of the methods for detecting classification numbers using the Character Error Rate (CER) and Word Error Rate (WER). The results obtained can be seen from the lighting and the use of image capture tools that can affect the text detection results. The higher the lighting, the better the text detection results obtained. However, in the extraction tool using a security camera yields a better output than the others. In conclusion, the modernization of book arrangement systems by implementing OCR through security cameras could enhance user experience in the retrieval process.
Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Authors:
raw_author_name: Tyler Parsons
raw_affiliation_string: Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
raw_author_name: Jaho Seo
raw_affiliation_string: Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
raw_author_name: Wongun Kim
raw_affiliation_string: Convergence Agricultural Machinery Group, Korea Institute of Industrial Technology, Gimje-si 54325, Republic of Korea

Abstract: In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.
A Model to Assess the Impact of Computer Simulations and Photonics Demonstrations in Quantum Information Learning

Authors:
raw_author_name: Francisco Delgado
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Mexico.
raw_author_name: Alan Anaya
raw_affiliation_string: Technical University of Denmark, Physics department, Denmark.
raw_author_name: Alfonso Jaimes-Nájera
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Mexico.
raw_author_name: Marco Enríquez-Flores
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Mexico.

Abstract: Abstract Quantum information Education is a growing area where professionals and students of diverse disciplines converge to develop proper solutions. The interest of those students is materialized seeking introductory approaches to this knowledge. Still, learning design for diverse backgrounds requires multiple approximations for theoretical contents, applications, calculation scaffolding, etc. Innovation oriented towards this effort becomes valuable due to students’ limited time for such an experience. This work presents a proposed assessment for three technical innovation initiatives in Quantum information Education based on a mathematical model proposed to measure their impact on the research products through eight dimensions. Outcomes show an explicit sustained improvement in the quality of those products, while the model lets to identify the fingerprint of each innovation through a set of eight dimensions.
Perception Study on Kinetic Recycled Compact Disc Facade in Tourism Hall, Jarak - Wonosalam

Authors:
raw_author_name: E S Wijaya
raw_affiliation_string: Undergraduate Program of Architecture, Petra Christian University, Surabaya-60236

Abstract: Abstract Indonesia is a country, rich with culture and natural resources that brings high tourism potential. Jarak village in Wonosalam is one of the villages in East Java that has these tourism potential, besides the religious harmony between the dwellers that initiates the construction of a tourism hall with the concept of sustainability. The hall reflects the harmony between man and nature. The tourism hall uses the kinetic panels from recycled compact discs as the facade, which is rotatable when blown by the wind. This research is meant to study people’s perception of the installed kinetic facade panel. Here, we divide the respondents into two groups, professionals (academician and architects), and non professionals (Jarak dwellers, visitors and general). The aspects observed are psychology, aesthetics, flexibility, impact to the environment and economics. The result is that there is a difference in perception regarding the flexibility, and also, the colour aspect of the installed kinetic facade is the most memorable aspect of the installed kinetic facade.
Assessment of sand dunes in Missan Governorate, South-eastern Iraq: Implications for Extraction High Purity Silica

Authors:
raw_author_name: S J Al-Khafaji
raw_affiliation_string: Department of Geology, College of Science, University of Basrah, Basrah, Iraq
raw_author_name: H K Jasim
raw_affiliation_string: Department of Geology, College of Science, University of Baghdad, Baghdad, Iraq

Abstract: Abstract Sand dunes are widely spread in Missan Governorate, South-eastern Iraq in many areas including; Manziliyah, Khazina, Zubadiate, and Chailat. X-ray diffraction (XRD) results of sand samples of sizes 0.063-2 mm, 0.125-0.250mm, and 0.250-0.500mm, revealed that quartz is the main mineral phase, followed by calcite, feldspar and trace of gypsum. Chemically SiO 2 is the main oxide in sand of an average 61.36 % while the rest oxides; CaO (14.08%), Al 2 O 3 (6. 76%), Fe 2 O 3 (3.05%), K 2 O+Na 2 O (2.30 %) are considered as impurities that could affect the properties of sand. XRD tests indicated the formation of crystalline phase of synthetic silica. XRF showed an increases in SiO 2 to 85.37%, while all other oxides (impurities) were reduced to their minimum rate, particularly CaO (1.06 %). Field emission scanning electron microscope (FESEM) and Brunauer-Emmett-Teller (BET) tests indicated the existence of well compiled spherical particles of synthetic silica within the size of 8.55 nm and surface area of 66.51 m 2 \g. The medium sand size (0.250-0.500mm) is the appropriate size to produce high purity crystalline silica as it contains the greatest percentage of silica and the low percent of impurities compared to other sizes of sand.
Interferensi Bahasa Ibu Terhadap Penguasaan Bahasa Indonesia Menulis Cerita Rakyak

Authors:
raw_author_name: Anastasia Baan

Abstract: The aim of this research is to describe the form of mother tongue that often interferes with mastery of Indonesian, as well as to find out the factors that cause mother tongue interference with mastery of Indonesian in writing the Mamasa folklore by class IX students of SMPN 2 Sesenapadang Mamasa. The type of research used is qualitative research. The research method used is direct observation (observation). The analysis techniques used are data collection, data redaction, data presentation and conclusions and withdrawal/verification. The results of the research show that it is true that there is interference in Mamasa's mother tongue with the mastery of Indonesian in writing Mamasa folk tales by class IX students of SMPN 2 Sesenapadang Mamasa, due to the students/speakers' daily habit of using their mother tongue.
Dampak Penggunaan Disfemisme Para Gamers Pada Media Sosial Youtube

Authors:
raw_author_name: Rina .
raw_author_name: Dina Gasong

Abstract: The aim of this research is to explain the form and meaning of dysphemism expressions in content created by gamers on YouTube social media channels and to explain the impact of the use of dysphemism on the implications of character education. The type of research used is a qualitative descriptive method. Data collection procedures were carried out using tapping, note-taking and documentation techniques. The data analysis method used in this research uses descriptive analysis techniques with the triangulation method. The research results found in this study concluded that the use of dysphemism by game content creators on their YouTube channels contained five types of dysphemism expressions including; (a) the use of taboo terms, such as words that cannot be said, taboos, or prohibitions; (b) obscene cursing and swearing; (c) comparison of humans with animals; (d) nicknames or greetings related to human physical characteristics; and (e) terms of ridicule and disrespect towards someone. Meanwhile, the impact of dysphemism on character education in this research is in the form of; (1) Violation of religious values; (2) violation of the value of love of peace; (3) violation of the value of responsibility.
Pengaruh Penggunaan Metode Diskusi Terhadap Peningkatan Hasil Belajar Bahasa Indonesia

Authors:
raw_author_name: Anastasia Baan

Abstract: The aim of this research is to find out how students' communication skills during discussions can have an influence on student learning outcomes, as well as knowing student learning outcomes, and to find out the significant influence between teacher communication skills on the learning outcomes of class XI students at SMKN 1 Tana Toraja. This research is a quantitative descriptive research with the type of research using a field survey. Data collection techniques in this research used observation, questionnaires, interviews and documentation. The data analysis technique in this research is Product moment correlation. The results of this research can be concluded that students' communication skills in the discussion method have a good influence with a percentage of 65% and student learning outcomes are sufficient with a percentage of 35%. There is a significant influence between students' communication skills on learning outcomes in the Indonesian language subject class XI SMKN 1 Tana Toraja. Hypothesis testing is as follows: where ro = 0.005 is greater than r table at a significance level of 5% = 0.396, thus Ha is accepted and HO is rejected. This means that the better the students' ability to communicate, the better the grades obtained by students at SMKN 1 Tana Toraja.
Clay minerals of Shiranish Formation in, Erbil, Duhok northern Iraq

Authors:
raw_author_name: Mohanad Hamid Al-jaberi
raw_affiliation_string: University of Basrah. Iraq

Abstract: Abstract In the Upper Cretaceous of northern Iraq (upper Campanian-Maastrichtian), the Shiranish Formation is examined for clay minerals using X-ray diffraction in the Duhok (Sandor section) and Erbil cities (Lailuk section). In the marl section of the Shiranish Formation, the layers of chlorite, illite, kaolinite, palygorskite, and a mixed layer of montmorillonite-chlorite strata were discovered to have formed antigenically. The variation in minerals indicates the Cretaceous’ changing climate. Palygorskite is a result of leaching processes that are more successful due to variations in the parent rocks. The transition of palygorskite from montmorillonite and illite can occasionally be impacted by post-depositional diagenetic processes. Because of the warm conditions that supply the eroded clastic materials from earlier successions of Cretaceous in the area, chlorite, mixed-layers, and illite may have predominated over kaolinite. The variation in source rocks and drainage conditions is reflected in the variation of clay minerals in the investigated sequence, which could improve leaching rate processes.
Cavity-Enhanced Single-Photon Emission from a Single Impurity-Bound Exciton in ZnSe

Authors:
raw_author_name: Robert M. Pettit
raw_affiliation_string: Institute for Research in Electronics and Applied Physics and Joint Quantum Institute, University of Maryland, College Park, Maryland 20742, United States
raw_author_name: Edo Waks
raw_affiliation_string: Institute for Research in Electronics and Applied Physics and Joint Quantum Institute, University of Maryland, College Park, Maryland 20742, United States

Abstract: Impurity-bound excitons in ZnSe quantum wells are bright single-photon emitters─a crucial element in photonics-based quantum technology. However, to achieve the efficiencies required for practical applications, these emitters must be integrated into optical cavities that enhance their radiative properties and far-field emission patterns. In this work, we demonstrate cavity-enhanced emission from a single impurity-bound exciton in a ZnSe quantum well. We utilize a bullseye cavity structure optimized to feature a small mode volume and a nearly Gaussian far-field transverse mode that can efficiently couple to an optical fiber. The fabricated device displays emission that is more than an order of magnitude brighter than bulk impurity-bound exciton emitters in the ZnSe quantum well, as well as clear antibunching, which verifies the single-photon emission from the source. Time-resolved photoluminescence spectroscopy reveals a Purcell-enhanced radiative decay process with a Purcell factor of 1.43. This work paves the way toward high-efficiency spin-photon interfaces using an impurity-doped II–VI semiconductor coupled to nanophotonics.
Experimental Evaluation: Can Humans Recognise Social Media Bots?

Authors:
raw_author_name: Maxim Kolomeets
raw_affiliation_string: School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK; St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line of V.O. 39, St. Petersburg 199178, Russia
raw_author_name: Olga Tushkanova
raw_affiliation_string: St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line of V.O. 39, St. Petersburg 199178, Russia
raw_author_name: Vasily Desnitsky
raw_affiliation_string: St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line of V.O. 39, St. Petersburg 199178, Russia
raw_author_name: Lidia Vitkova
raw_affiliation_string: St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line of V.O. 39, St. Petersburg 199178, Russia
raw_author_name: Andrey Chechulin
raw_affiliation_string: St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line of V.O. 39, St. Petersburg 199178, Russia

Abstract: This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the VKontakte social network. We then compared the “human” answers with the “ground-truth” bot labels (‘a bot’/‘not a bot’). Based on the experiment, we evaluated the bot detection efficiency of annotators in three scenarios typical for cybersecurity but differing in their detection difficulty as follows: (1) detection among random accounts, (2) detection among accounts of a social network ‘community’, and (3) detection among verified accounts. The study showed that humans could only detect simple bots in all three scenarios but could not detect more sophisticated ones (p-value = 0.05). The study also evaluates the limits of hypothetical and existing bot detection systems that leverage non-expert-labelled datasets as follows: the balanced accuracy of such systems can drop to 0.5 and lower, depending on bot complexity and detection scenario. The paper also describes the experiment design, collected datasets, statistical evaluation, and machine learning accuracy measures applied to support the results. In the discussion, we raise the question of using human labelling in bot detection systems and its potential cybersecurity issues. We also provide open access to the datasets used, experiment results, and software code for evaluating statistical and machine learning accuracy metrics used in this paper on GitHub.
Vehicle Detection and Distance Estimation Using Improved YOLOv7 Model

Authors:
raw_author_name: Xiaoxu Liu
raw_affiliation_string: Auckland University of Technology, New Zealand
raw_author_name: Wei Qi Yan
raw_affiliation_string: Auckland University of Technology, New Zealand

Abstract: In this book chapter, the authors propose a low-cost distance estimation approach to develop more accurate predictions from a 3D perspective for vehicle detection and ranging by using inexpensive monocular cameras. This distance estimation model integrates YOLOv7 model with an attention module (CBAM) and transformer, as well as extend the prediction vector as the fundamental architecture to improved high-level semantic understanding and enhanced feature extraction ability. This integration significantly improved detection and ranging performance, offering a more suitable and cost-effective solution for distance estimation.