Reviewerly/exHarmony huggingface dataset:
College Course Recommender System based on Sentiment Analysis

College plays a vital role in defining a student's future by providing relevant education, skills, and exposure. The choice of college courses heavily influences their career foundation and employment skill sets. However, the expanding number of college courses often leaves students struggling to make the best choice, leading to dropouts due to the lack of interest. Many systems rely on existing student reviews or the popularity of the course itself, which may not always result in relevant recommendations. Hence, some systems use sentiment analysis (SA) to evaluate students' opinions, considering qualitative and sentiment data to understand their interests better. However, current SA performance struggles to extract meaningful words due to dataset availability. Hence, a course recommendation system based on students' interests and competence would be valuable. This paper focuses on evaluating and understanding existing systems to provide students with an effective course recommendation system. It includes first gathering useful data that would improve the use of SA. Next, feature extraction techniques Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram were implemented and compared. SA will be performed to increase the relevance of the student's interests to recommend a course by implementing Fuzzy Logic and K-nearest neighbors. These algorithms will be evaluated by performance metrics such as accuracy to determine the most efficient way to recommend a course. The findings highlight the importance of considering students' subjective preferences and interests for better outcomes regarding student success and graduation rates.
An Experimental Based Study to Evaluate the Efficiency among Stream Processing Tools

With the advancement in internet technology, augmentation in regular data generation has been amplified at a drastic level. Several different industries, for instance hospitality, defense, railways, health care, social media, education, etc., are creating and crafting different and several types of raw and processed data at a significant level, whereas, each of them has their own unique reason to shelter and call their data imperative and crucial. Such large and huge amount of data needs some space to get saved and secured, this is what Big Data is. A Data Stream Processing Technology (DSPT) is the significant mechanism and the mainstay for compiling and computing the large amount of data as well as the way to collect and process the raw data to call it information. There are varieties of DSPT like Apache Spark, Flink, Kafka, Storm, Samza, Hadoop, Atlas.ti, Cassandra, etc. This paper aims at comparing the five well- known and widely used open source big data DSPT (i.e., Apache Spark, Flink, Kafka, Storm, and Samza). An extensive comparison will be performed based on 12 different yet interconnected standards. A matrix has been designed through which five different experiments were executed, based on which the juxtaposition will be prepared. This paper summarizes an extensive study of open source big data DPST with a practical experimental approach in a well-controlled and sophisticated environment
Optimizing K-Means Clustering: A Comparative Study of Optimization Algorithms For Convergence And Efficiency

The K-Means clustering algorithm is a widely used technique for grouping data into clusters, with applications spanning various domains. This study presents a comparative investigation into the optimization of K-Means clustering through the evaluation of different optimization algorithms. The primary focus is on enhancing the convergence speed and computational efficiency of the K-Means algorithm, with implications for diverse real-world scenarios. The research systematically examines a range of optimization techniques, including gradient descent, stochastic gradient descent, and metaheuristic algorithms such as genetic algorithms and simulated annealing. A comprehensive analysis of convergence speed, clustering quality, and computational efficiency is conducted across these algorithms. By assessing their performance on diverse datasets, the study aims to provide insights into the trade-offs between different optimization strategies and their implications for practical clustering tasks. The results reveal distinct convergence patterns, highlighting the advantages and limitations of each optimization algorithm. Gradient-based approaches demonstrate rapid convergence but susceptibility to local optima, while stochastic gradient descent and metaheuristic algorithms exhibit a balance between exploration and exploitation. The findings shed light on the interplay between optimization techniques, convergence speed, and clustering quality, offering valuable guidance for practitioners seeking to optimize K-Means clustering according to specific dataset characteristics and computational requirements. This comparative study contributes to the broader understanding of optimizing K-Means clustering algorithms and aids researchers and practitioners in selecting suitable optimization strategies for efficient and effective data clustering in real-world applications.
The Effect Of Implementing Ice Breaking Towards Students Motivation In Learning English At Second Grade In SMK Cendana Padang Panjang

This study aims to find the effect of applying ice breaking on motivation to learn English. This research has been conducted at SMK Cendana Padang Panjang because there are still many students who feel learning English is difficult and boring and many students do not focus on the ongoing lesson. The type of research used is experimental research. The researcher used pre experimental research one group pretest and posttest. The population of this study were students of class XI at SMK Cendana Padang Panjang. In this study, the researcher used the total sample as a sampling technique. The data collection instrument used in this study was a closed questionnaire in the form of a linkert scale. To analyze the data from the experimental class and control class, the researcher conducted a normality test using the Lilifors test and homogeneity test using the F test and to test the hypothesis the researcher used the t test and was consulted into the t table with a significant level (α) = 0.05. Based on the results of the last study, the motivation score of students before who were taught by ice breaking was 74,1 and students who was taught by ice breaking was 86,95. There is significant effect of using Ice Breaking to improve students motivation and There is significant difference between students who are taught with the Ice Breaking strategy and those who are taught without Ice breaking.
Students’ Perceptions Towards The Implementation Of School Literacy Program At Eleventh Grade Of SMKN 1 Bukittinggi

The purpose of this research was to know the students’ perception of school literacy program. The design of this research was descriptive quantitative research by using Survey method. The instrument of this research was questionnaire to know the perception of students toward the school literacy program. The population of this research was all of the eleventh grade students of SMKN 1 Bukittinggi which consisted of 7 majors and 587 students. The sample was all of the population because this research was using total sampling technique. The research was carried out in December 2020. The instrument validity test was carried out by using expert validity to three experts as instrument validators and to analyze the data was done by identifying the responses, counting the percentages of each indicator and taking the conclusion. The results of the research showed that the average score of the habituation stage of school literacy program was 78,07% which was good perception. The average score of the development stage of school literacy program was 78,22% which was good perception. The last, the average score of the learning stage of school literacy program was 78,66% which was good perception. It can be concluded, the average mean of students’ perception toward the implementation of school literacy program was 78,30% which was in good category in the rating interpretation 60%-79,99%.
Analisis Perilaku Wisatawan Berdasarkan Data Ulasan di Website Tripadvisor Menggunakan CRISP-DM: Wisata Minat Khusus Pendakian Gunung Rinjani dan Gunung Bromo

Traveller behavior needs to be comprehensively identified and analyzed to determine changes in tourism preference in Indonesia. One relevant approach to identifying traveler behavior is sentiment analysis through review data on the Tripadvisor website by text mining approach. This study aims to recommend a sentiment analysis model that is useful for managers of particular interest climbing tourist destinations on Mount Rinjani and Mount Bromo based on the type of visit alone (solo), with couples (couple), with friends, and with family (family). The research method used is Cross Industry Standard Process for Data Mining (CRISP-DM), with an algorithm adapted to managing tourist destinations for Mount Rinjani and Mount Bromo, namely attractions, roads and modes of transportation (accessibility), and accommodation. To overcome the problem of data balance in datasets, the calculation process of the Decision Tree (DT) algorithm and the Support Vector Machine (SVM) is connected to the Synthetic Minority Over-sampling Technique (SMOTE) operator in the Rapidminer application. The results of this study showed that the SVM algorithm showed better performance with an accuracy value of 97.67%, precision of 100%, recall of 95.34, and f-measure of 97.61% in the classification of 1075 text data of Mount Bromo and 326 review data of Mount Rinjani. In addition, in the context of Mount Rinjani, the top five words that often appear in tourist review data on Mount Rinjani are as follows: summit (272), Rinjani (259), trek (201), hike (170), mountain (159). On the other hand, the top five words that often appear in tourist review data on Mount Bromo are as follows: Bromo (1864), sunrise (1124), view (854), crater (758), and mount (577). Thus, it can be seen that tourists with the type of visit alone (solo), with couples (couple), with friends (friends), and with family (family) have a preference for the types of attractions in the form of summits, craters, natural beauty of mountains, hiking trails, types of transportation modes as well as supporting accommodation that needs to be prepared to keep the sustainability of tourism.
Penerapan Metode Certainty Factor Dalam Diagnosis Gangguan Depresi

Globalization can cause several problems and pressures of mind for both individuals and groups. Various kinds of problems can certainly lead to psychological disorders, one of which is depression. Indonesia itself is one of the countries with a high number of people with depressive disorders. Depressive disorder itself can have many consequences ranging from lack of enthusiasm to even death. Facing these serious problems, the government should be able to address the mental disorder that is currently happening. However, the reality is still far from this. Inadequate infrastructure, equality problems for each region, and shortages of experts are the main problems at this time. The expert system is considered to be a solution in solving these problems. Web-based expert systems can replace the role of experts in the process of initial diagnosis of depressive disorders, patients can also access them easily. The calculation method implemented is the Certainty Factor method. This method is considered suitable in the diagnosis of depression. The implementation of the CF method in the diagnosis of depression can provide a confidence level of up to 94.9%. The expert system is expected to be able to eliminate human errors, speed up the diagnostic process, make it easier for health workers, and provide standards for related parties in handling mental disorders
A UAV Penetration Method Based on the Improved A* Algorithm

This paper presents an algorithm for addressing the penetration problem between coverage regions. The algorithm combines the subregion coverage sequence determination and the A* algorithm-based drone obstacle avoidance methods to achieve path planning from the drone’s starting point to the destination. The boustrophedon algorithm is employed to calculate optimal paths for independent subregions, while a genetic algorithm is utilized to determine an optimized coverage sequence, thereby minimizing the overall path length between regions. Additionally, the Laguerre graph construction algorithm is introduced, leveraging the Laguerre graph derived from the Voronoi graph to effectively describe the positional relationships of a given set of disjoint circles. To ensure obstacle avoidance, the A* algorithm is then applied to search for the shortest path within the Laguerre graph.
Impact Of The Development Of The Internet On Students' Learning Achievement (Case Study At SMA Muhammadiyah Sawangan)

The author's purpose in this research is to determine the influence of the internet, the benefits and impacts of using the internet on student achievement and to find out the problems, bad effects of the internet and find solutions to the bad effects caused by the internet for students. The research approach used in this study is a qualitative approach, namely a research approach that presents data in the form of a narrative/description. The type of research we use is descriptive research. Descriptive research aims at solving problems that exist today because there are so many kinds of research that tell, analyze, and apply research with survey results, questionnaires, observations, or with text techniques, case studies, studies. time and motion, qualitative analysis, cooperative studies, or cooperative studies, or operational studies. The results of the research are the internet is very helpful in learning activities, because wherever someone can be free to find learning information.
Cryptanalysis and Improvement of a Blockchain Based Lightweight Authentication and Key Agreement Scheme for Internet of Vehicles

The Internet of Vehicles (IoV) is a fast developing field that seeks to use cutting-edge technologies to enhance the sustainability, efficiency, and safety of transportation networks. The IoV can gain greatly from integrating blockchain in terms of security, trust, decentralization, smart contracts, and data management. This paper presents the weakness of a blockchain based lightweight authentication and key agreement scheme for internet of vehicles(BLAIV) proposed by Zheng et al. [1]. By performing thorough security analysis we have observed that the BLAIV scheme is vulnerable to various security attacks. The main cause for the security attacks is the BLAIV scheme stores the security parameters in the Onboard unit(OBU) of vehicle, due to which the BLAIV scheme is vulnerable to various attacks like vehicle or OBU capture attacks, session key computation attacks, and impersonation attacks. The paper also suggests possible changes to improve the BLAIV scheme.
Research on optimization model of container live migration

Container virtualization technology is emerging in cloud computing due to its lightweight nature. Container live migration is the basis of many cloud management capabilities, which allow a running container to be completely moved to another physical node to continue running with minimal downtime. The performance of container live migration is the focus of the research. However, through the detailed analysis of the existing container live migration system, this paper finds that there are still some problems affecting the performance, including low parallelism of dump, non-convergence of pre-copy policy and low parallelism of root file system and running state migration. In order to solve these problems, this paper proposes and designs optimization strategies and algorithms, such as resource-aware parallel dumping mechanism, post-copy policy-based running state migration and multi-priority transfer scheduling parallel algorithm, and implements a high-performance container hot migration system Dmigrate based on Docker.
Nonclassicality of entangled Schrödinger cat states associated to generalized displaced Fock states

Abstract In this paper, we introduce some distinct classes of entangled cat states associated to generalized displaced Fock states. For this purpose, we use the formalism of nonlinear coherent states corresponding to nonlinear oscillator algebra which yields various kinds of f -deformed entangled states. We also take a particular class of Gilmore-Perelomov-type of SU (1, 1) and a class of SU (2) coherent states. We then obtain the amount of entanglement between subsystems of the quantum states of interest by applying the measure of concurrence. Furthermore, examining some of the most important criteria, such as Mandels Q parameter, quadrature squeezing and Vogels characteristic function, we study the nonclassicality of the introduced quantum states. The numerical results show remarkable values of entanglement, sub-Poissonian statistics of the field, and squeezing indicating that the introduced states can be regarded as possible candidates for nonclassical entangled states. Afterwards, we see that the Vogel function for quantum states of interests goes beyond the value of characteristic function of the ground state, which shows the nonclassicality of the introduced states.
Introduction

In this chapter, we will introduce the background knowledge of audio signal processing, voice processing systems, and attacks on speaker verification and speech recognition systems. Finally, we present the organization of this book.
TeleNG-CNOS: A Service-Based System that Combines Self-Adaptive and Multi-center Disaster Tolerance

For SBS systems, adaptive ability is becoming increasingly important. In order to better study and evaluate adaptive strategies, predecessors have proposed some prototype systems, but none of them are suitable for SBS systems deployed in multiple centers. We propose TeleNG-CNOS based on the background of the operator's business support system, which considers both adaptive capabilities and deployment of disaster recovery strategies. Pave the way for future research on adaptive strategies, and also hope to provide a solution for similar SBS adaptive systems.
Research on User Information Security based on Cloud Computing

As an important support for social information, cloud computing technology not only brings convenience to people's lives, but also brings challenges. In the cloud computing service mode, users can transmit and obtain information at anytime and anywhere through different terminals, can store information by purchasing and leasing servers. However, in the process of cloud computing service mode, there are risks that threaten user information security. Through the analysis of the cloud computing service model, this paper analyzes the two main problems of user information security more deeply and puts forward four feasible suggestions which based on cloud computing technology.
Automatic Semantic Typing of Pet E-commerce Products Using Crowdsourced Reviews: An Experimental Study

This paper considers the problem of semantically typing pet products using only independent and crowdsourced reviews provided for them on e-commerce websites by customers purchasing the product, rather than detailed product descriptions. Instead of proposing new methods, we consider the feasibility of established text classification algorithms in support of this goal. We conduct a detailed series of experiments, using three different methodologies and a two-level pet product taxonomy. Our results show that classic methods can serve as robust solutions to this problem, and that, while promising when more data is available, language models and word embeddings tend both to be more computationally intensive, as well as being susceptible to degraded performance in the long tail.
Exploring the User's Perception of Updates in Intelligent Systems

With the rapid advancements in artificial intelligence and connected systems, understanding users' perceptions becomes crucial. This study delved into the "Perception of Updates" in intelligent systems, combining expert and user interviews to elucidate the various facets of perceived system upgrades.For a deeper dive, semi-structured interviews were carried out with experts and users alike. The expert group consisted of professionals from diverse fields, including user experience, product design, intelligent vehicle design, voice system specialists, and academics, all of whom have substantial experience in the domain. Key insights from these sessions ranged from the evaluation of intelligent product upgrades to discerning design recommendations under perceived enhancements.On the other hand, user interviews engaged 20 users with experience in using intelligent products. The focal point here was their experiences and recall of moments when they distinctly perceived an update or progression in the intelligence of the systems they used.Emerging from these interviews were structural analyses of the perception of updates. Three primary dimensions were discerned: enhancement of perceived functions, addition of new functions, and perceived evolutionary capabilities of the system. Notably, there were also implications on the effects of this perception. Positive outcomes included increased trust in the product, heightened purchase intentions, brand loyalty, a sense of security, and a propensity to recommend the product to peers. Interestingly, among user interviewees, the perception most significantly influenced product recommendations and brand loyalty. Meanwhile, experts highlighted the implications on brand loyalty and trust, reinforcing the intrinsic link between perceived updates and continued user engagement. However, there were also potential drawbacks to consider, such as concerns over privacy and perceived threats, though these were less frequently mentioned.This study highlights the importance of understanding user perceptions of system updates, offering valuable insights for improving user experience in modern technology.
Measurement device hacking-free mutual quantum identity authentication over a deployed optical fiber

Quantum identity authentication serves as a crucial technology for secure quantum communication, but its security often faces challenges due to quantum hacking of measurement devices. This study introduces a measurement-device-independent mutual quantum identity authentication (MDI MQIA) scheme capable of ensuring secure user authentication, despite the use of measurement devices vulnerable to quantum hacking. To realize the MDI MQIA scheme, we proposed and applied a modified Bell state measurement based on linear optics, enabling the probabilistic measurement of all Bell states. Furthermore, the proposed experimental setup adopted a plug-and-play architecture, thus efficiently establishing the indistinguishability of two photons prepared by the communication members. Finally, we successfully performed a proof-of-principle experimental demonstration of the proposed scheme using a field-deployed fiber, achieving quantum bit error rates of less than 3%.