Reviewerly/exHarmony huggingface dataset:
Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Authors:
raw_author_name: Mahsa Ebrahimian

Abstract: &lt;p&gt;Recommendation systems are widely used in various areas to personalize recommendations to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems. With the increasing complexity of attacks and changes in attackers' behaviour, more advanced approaches are required to find the hidden patterns in data. This thesis proposes a CNN-based hybrid model and architecture that integrates self-learning and flexible aspects of CNN with other supervised learning methods to enhance shillings attacks detection on collaborative filtering recommendation systems. We also introduce a new metric, the F-compatible score, to measure the compatibility ratio of merging the CNN with any other classifier in a given aggregate. This measure helps in monitoring the enhancement level of the detection results of shilling attacks. Two different approaches are proposed in this thesis, user-based, and item-based. The experimental analysis used three benchmark datasets: the Movie-Lens 100K, Netflix, and Movie-Lens 1M. A comprehensive comparative analysis is also completed to assess the performance of both individual-based and hybrid-based detection models. Experimental results show that the proposed hybrid models performed well on most attack profiles and reached an F1-score of up to 99%. We concluded that the superiority of hybrid models over individual models depends on the sparsity level of data and the divergence of results.&lt;/p&gt;
Scattering amplitude from quantum computing with reduction formula

Authors:
raw_author_name: Enke Wang
raw_affiliation_string: Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Guangdong Provincial Key Laboratory of Nuclear Science, Southern Nuclear Science Computing Center, South China Normal University, Guangzhou 510006, China; Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China
raw_author_name: Hongxi Xing
raw_affiliation_string: Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Guangdong Provincial Key Laboratory of Nuclear Science, Southern Nuclear Science Computing Center, South China Normal University, Guangzhou 510006, China; Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China; Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, China

Abstract: Utilizing the Lehmann-Symanzik-Zimmermann reduction formula, we present a new general framework for computing scattering amplitudes in quantum field theory with quantum computers in a fully nonperturbative way. In this framework, one only has to construct one-particle states of zero momentum, and no wave packets of incoming particles are needed. The framework is able to incorporate scatterings of bound states, and is ideal for scatterings involving a small number of particles. We expect this framework to have particular advantages when applied to exclusive hadron scatterings. As a proof of concept, by simulations on classical hardware, we demonstrate that in the one-flavor Gross-Neveu model, the fermion propagator, the connected fermion four-point function, and the propagator of a fermion-antifermion bound state obtained from our proposed quantum algorithm have the desired pole structure crucial to the implementation of the Lehmann-Symanzik-Zimmermann reduction formula.
Online Teaching Effect Evaluation and Analysis Using Combined Weighting Technique

Authors:
raw_author_name: Yonghui Shang

Abstract: An evaluation index system of online teaching satisfaction was established based on the data of a questionnaire survey. In addition, an evaluation index system of an online teaching effect with four first-level indexes and 14 second-level indexes was constructed from the aspects of pre-class preparation, video production, classroom teaching, and after-class guidance. Then, a combination weighting–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model was established using the analytic hierarchy process (AHP), Criteria Importance Through Inter-criteria Correlation method, and TOPSIS model. The objective is to evaluate the online teaching effect of 10 engineering technology universities in Henan Province. Results show that the online teaching effect of these universities is generally satisfactory. In addition, video production and classroom teaching are important factors affecting online teaching, which, however, is slightly influenced by pre-class preparation. On this basis, this study proposes to enhance online assessment, improve students’ attention in class, strengthen teacher–student communication, actively perfect teaching resources, and create a good learning environment.
Cyber Security: Study on Attack, Threat, Vulnerability

Authors:
raw_author_name: Dhyan Patel

Abstract: Abstract: The main aim of this research is to examine the various aspects of cyber infrastructure, encompassing both hardware and software systems, networks, enterprise networks, intranets, and the occurrence of cyber intrusions. The paper intends to elucidate the significance of network intrusions and cyber-theft, along with delving into the factors contributing to the proliferation of cybercrime. Furthermore, it provides a comprehensive definition of cyber security and its role in addressing network intrusion and cyber theft, while also exploring the drivers behind the surge in cybercrime and their repercussions. The authors conclude by suggesting preventive measures and potential solutions to mitigate the risks associated with cyber attacks. They emphasize that while technological advancements can aid in minimizing the impact of cyber attacks, human behavior and psychological tendencies remain significant vulnerabilities. Despite this, the authors express optimism in reducing cyber attacks through investment in organizational education campaigns that address psychological susceptibilities.
Optimalisasi Pemahaman Dasar-dasar Fotografi melalui Model Pembelajaran Berbasis Masalah (PBL) dengan Pendekatan Pembelajaran Berdiferensiasi dan Quizziz di SMKIT Robbani Singosari

Authors:
raw_author_name: Bachtiar Adi Saputra

Abstract: This research investigates the effectiveness of implementing the Differentiated Learning Approach and the Quizziz platform to enhance the understanding of the fundamentals of photography at the Robbani Singosari Integrated Islamic Elementary School (SDIT) level. Challenges in photography learning, such as passive student engagement, an inadequately conducive learning environment, difficulty comprehending the material, monotonous teaching methods, and the incongruity of media with student characteristics, serve as the backdrop for this study. The incorporation of the Differentiated Learning Approach facilitates the adjustment of material to accommodate diverse learning styles, addressing the challenges encountered in the learning process. Additionally, the study centers on the utilization of Quizziz as an evaluative tool, revealing students' positive responses to the engaging and interactive learning experience facilitated by the platform. The research findings underscore the substantial role of the Differentiated Learning Approach in augmenting learning efficacy, particularly in surmounting obstacles to material comprehension and establishing an inclusive learning milieu. The employment of Quizziz as an assessment tool not only validates its effectiveness in gauging student comprehension but also introduces an entertaining and interactive facet that enriches the overall learning experience. This study aligns with prior research affirming the constructive influence of the Differentiated Learning Approach and the amalgamation of online platforms in fostering heightened student interest and engagement. Implications of these findings encompass recommendations for refining learning strategies at the elementary school level, especially within the context of photography education. The integration of the Differentiated Learning Approach and Quizziz is anticipated to make a significant contribution to enhancing student comprehension and cultivating a more gratifying learning experience in the current digital era.
Cryptanalysis of efficient semi-quantum secret sharing protocol using single particles

Authors:
raw_author_name: Gan Gao
raw_affiliation_string: Department of Electrical Engineering, Tongling University, Tongling, 244061, CHINA

Abstract: Abstract In the paper[Chin. Phys. B 32 (2023) 070308], Xing et al . proposed a semi-quantum secret sharing protocol by using single particles. We study the security of the proposed protocol and find that it is not secure, that is, the three dishonest agents, Bob, Charlie and Emily can collude to obtain Alice’s secret without the help of David.
Assistance in Preparing Final Reports for Educational Internship Students at SMA Negeri 01 Buay Madang

Authors:
raw_author_name: None Sugiarti

Abstract: Assistance in making final reports for educational internship students at SMA Negeri 01 Buay Madang includes several key points. Students who undertake educational internships at these schools have valuable experience in observing, interacting, and contributing to the educational environment. The final report reflects students' in-depth understanding of the learning process, school management, and the dynamics of teacher-student relationships at the high school. In the report, students present an evaluation of learning methods, challenges faced, and recommendations for improvement. Apart from that, student assistance also includes understanding the school curriculum and student involvement in extracurricular activities. In conclusion, the intern students succeeded in integrating educational theory with practice in the field, proving their professional growth and contribution to educational development at SMA Negeri 01 Buay Madang.
Impact of Motivation on Employee Performance of Selected Real Estate Firms in Abuja

Authors:
raw_author_name: Esther Yimi Bagobiri

Abstract: This study examined how motivation affected worker performance in selected Abuja real estate companies. The research design employed in the study is a survey and data were collected through the primary source where 66 structured questionnaires were distributed among the staff of five real estate companies in Abuja which include Osakwe &amp; Associates and Wale Adewusi Associate. Data collected were analyzed using multiple regression analysis. The findings showed that employee motivation has a favorable and significant impact on performance in terms of job efficiency in Abuja real estate. Other findings included the fact that job security has a favorable yet negligible effect on the performance in terms of work efficiency in Abuja real estate. Salary and wages also have a positive and large impact on performance in terms of work efficiency in Abuja real estate, as well as rewards, which have a favorable and big impact on both. According to the report, real estate companies in Abuja should keep up their efforts to motivate staff members by guaranteeing their jobs, paying them a living wage, and rewarding them because doing so improves productivity.
Skeleton-based Human Activity Classification in Sparse Image Sequences

Authors:
raw_author_name: Wlodzimierz Kasprzak
raw_affiliation_string: Warsaw University of Technology
raw_author_name: Pawel Piwowarski
raw_affiliation_string: Warsaw University of Technology

Abstract: Research results on human activity classification in video are described, based on initial human skeleton estimation in video frames. Both single person actions and two-person interactions are considered. The initial skeleton data is estimated in selected video frames by OpenPose, HRNet or other dedicated library. Important contributions of presented work are computational steps of skeleton tracking and -refinement, and relational feature extraction from pairs of skeleton joints. It is shown, that this feature engineering significantly increases the classification accuracy. Regarding the final neural network encoder-classifier, two different architectures are designed and tested. The first solution is a lightweight MLP network, implementing the idea of a "mixture of pose experts". Several pose classifiers (experts) are trained independently on different time periods (snapshots) of single-person visual actions (or 2-person interactions), while the final classification is a time-related pooling of weighted expert classifications. All pose experts use the same deep encoding network. The second (middle weight) solution is based on a LSTM network.Both solutions are trained and tested on the action set of the well-known NTU RGB+D dataset, although only 2D data are used.Our results show comparable performance with some of the best reported STM- and CNN-based classifiers for this dataset. We conclude that by reducing the noise of skeleton data, highly successful lightweight- and midweight-approaches to visual activity recognition in image sequences can be achieved.
Multimodal Robot Programming Interface Based On RGB-D Perception and Neural Scene Understanding Modules

Authors:
raw_author_name: Bartłomiej Kulecki
raw_affiliation_string: Poznan University of Technology

Abstract: In this paper, we propose a system for natural and intuitive interaction with the robot. Its purpose is to allow a person with no specialized knowledge and no training in robot programming to program a robotic arm.We utilize data from the RGB-D camera to segment the scene and detect objects. We also estimate the configuration of the operator's hand and the position of the visual marker to determine the intentions of the operator and the actions of the robot. To this end, we utilize trained neural networks and operations on the input point clouds. Also, voice commands are used to define or trigger the execution of the motion. Finally, we performed a set of experiments to show the properties of the proposed system.
COVID-19 Collectivism

Authors:
raw_author_name: Michael D. Stein

Abstract: Abstract This chapter explores how efforts to address COVID-19 in the years to come will require concern for the public good and a willingness to engage in collective activities toward better therapeutics and social supports for those affected. As an example, the chapter cites a slogan used by the Long COVID Alliance, “Nothing about us, without us.”
Can Contact Tracing Work Here?

Authors:
raw_author_name: Michael D. Stein

Abstract: Abstract The chapter discusses the shortcomings of contact tracing in the United States during the pandemic. The chapter identifies how lack of testing capacity, workforce limitations, and lack of trust in public health services all contributed to these shortcomings. It suggests the importance of addressing these issues to shore up testing infrastructure in the event of future pandemics.
Exploration of factors predicting the accuracy rate of the English reading section of the College Scholastic Ability Test using the decision tree model.

Authors:
raw_author_name: Kyungmin Kim

Abstract: The study aimed to identify factors that could affect the accuracy rate of the English section of the College Scholastic Ability Test (CSAT). A total of 45 variables related to the passages and questions that influence the rate of accuracy were included. The decision tree regression model was used to find out the possible variables and analyze the impact of each factor. The decision tree model revealed that the question type had the greatest impact on the accuracy rate of the English reading comprehension test, followed by the points of the question, adjective variation (ADJV), the number of dependent clauses per T-unit (DC_T), the number of clauses per T-unit (C_T), word count (nwords), and topic. In conclusion, this study shows that question-related factors have a greater impact on the accuracy rate than passage-related factors. Furthermore, this study provides implications for teaching and learning related to English reading in the College Scholastic Ability Test.
A case study of teaching English poetry through EdTech tools with learner-centered approach: Focusing on fostering pre-service English teachers’ collaborative communication, creative thinking and digital literacy

Authors:
raw_author_name: Sihyun Kim

Abstract: This study explores the integration of Educational Technology (EdTech) in a learner-centered approach to teaching English poetry, focusing on collaborative communication, creative thinking, and digital literacy, which are the competencies that students need to thrive, outlined by the Ministry of Education of the Republic of Korea. The research involved 17 participants from a “Teaching English Poetry” class. Prior to the midterm, the researcher demonstrated the use of various EdTech tools for teaching English poetry. After the midterm, pre-service English teachers were required to conduct micro-teaching using at least three different EdTech tools. Surveys based on the Technological Pedagogical Content Knowledge (TPACK) framework were used to measure how teacher’s TPACK capabilities affect students’ collaborative communication, creative thinking, and digital literacy. The results indicate a significant improvement in students’ engagement and comprehension of poetry, facilitated by the diverse capabilities of EdTech tools. These tools also promoted effective teamwork and problem-solving, enhancing students’ collaboration competencies. In conclusion, this study underscores the importance of EdTech tools in enriching the teaching and learning of English poetry, particularly in cultivating creativity, collaboration, and digital literacy. By fostering an environment conducive to collaboration and interactive learning, EdTech not only boosts student motivation but also enhances their comprehension and appreciation of English poetry.
Machine learning-based item difficulty prediction model using item text features

Authors:
raw_author_name: Yunha Choi

Abstract: This study investigates the difficulty of English reading passages in the College Scholastic Ability Test (CSAT) in South Korea, analyzing corpus linguistic features from 2015 to 2023 using Coh-Metrix software. A notable innovation of this research is the application of the Random Forest algorithm, which overcomes the limitations of traditional regression analysis, such as multicollinearity and overfitting. Focusing on textual complexity, the analysis utilizes 108 explanatory variables to predict the difficulty of CSAT English items. Rigorous methods, including grid search and cross-validation, were employed to ensure the reliability of the results. The Random Forest regression model demonstrated high predictive accuracy. Crucial variables influencing the difficulty of items were identified, providing valuable insights for educators and policymakers to improve CSAT preparation strategies. This research marks a significant advancement in language assessment by combining corpus analysis with machine learning algorithms. It not only enhances the prediction of test difficulty but also supports students and teachers with effective, tailored preparation strategies to address the varied challenges encountered in English public education.
Exploring the chronostratigraphy of a Bronze Age settlement through core drilling

Authors:
raw_author_name: Mirijam Zickel
raw_affiliation_string: Universität zu Köln Geographisches Institut Zülpicher Str. 45 Köln Deutschland

Abstract: Abstract Core drilling has been used successfully in archaeology for a variety of applications, including mapping stratigraphy across large areas, estimating feature depth and content, understanding site formation processes, as well as complementing information obtained through surface collection and geophysical survey. In this paper, we provide an overview of the first core drilling campaign carried out in 2021 on the outer settlement of the Bronze Age tell from Toboliu in western Romania. The overall aim of this coring program was to gather information regarding the nature of various features within the settlement, with a focus on the anomalies visible in the geomagnetic site plan. Based on 165 drilled cores, a reconstruction of the site’s stratigraphy is presented in this paper. This reconstruction was controlled and confirmed by excavations in one of the coring transects where the stratigraphic interpretation of the cores could be linked with the excavated stratigraphy. Additionally, samples for radiocarbon dating were collected from several cores. Based on this data, we discuss preliminary insights into possible house locations and preservation issues, as well as the chronological development and occupation sequence of this complex site.
A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

Authors:
raw_author_name: Zhi Yang
raw_affiliation_string: MOE Key Laboratory of Deep Underground Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, China

Abstract: To estimate earthquake losses for regional buildings, it is important to consider the varying seismic performances of different types of buildings. To achieve this, a dynamic elastic–plastic analysis based on detailed structural modeling is more accurate than the capacity spectrum method based on a single degree of freedom to obtain the response and damage of various structures under seismic activity and provide a more precise estimate of earthquake losses for buildings. Detailed information about the building facilities is necessary to create a fine structural model. However, obtaining precise actual structural details can be challenging with existing methods, especially when there are a large number of buildings to consider. This paper proposes a new method called story-based random structure (SBRS) modeling to address this issue, which is based on the common and reasonable layout of the structure. The process involves choosing design parameters that can represent the structural arrangement of a building as variables. These parameters include the materials’ type and strength, proportions, the components’ size, and other relevant factors. The values for these variables are determined based on engineering experience and design specifications. The range of values for these variables is also determined to ensure that all design requirements are met. Finally, the Latin Hypercubic Sampling (LHS) method is used to randomly sample and combine the variables, establishing a detailed structural model of the building with structural uncertainty. According to the results of the analysis, this method can simulate the structure and component information of a building by using a probabilistic approach, even without knowing the specific structural design. This method can be based on a small amount of readily available building information and solves the problem of the rapid and refined modeling of a large number of buildings at the regional scale. It also makes it possible to estimate the earthquake loss of regional buildings based on their seismic capacity.