Reviewerly/exHarmony huggingface dataset:
DEVELOPING SPEAKING ENGLISH MODULE AT SURVIVAL CLASS OF LKP ALOHA EDU TEGALDLIMO IN ACADEMIC YEAR 2021/2022

The historical past of the improvement of this book (Speaking Module) media is via way of means of searching of studying media in general. In the Survival Class at LKP Aloha Edu, using media that discusses and learns talking know-how, mainly talking with local speaker. In fact, studying media may be very a good deal had to make it less complicated for instructors to switch know-how and make it less complicated for college students. Therefore, the researcher advanced the Speaking module within side the Survival elegance at LKP Aloha Edu. This examine pursuits to broaden studying media and enhance the talking exceptional of college students within side the LKP Aloha Edu, they continually have issues whilst talking in English, due to the fact they're afraid, language vocabs which might be pretty specific from English which has been standardized via way of means of the global community. This product, that's within side the shape of speaking module, has acknowledged the feasibility of the media in order that it has produced a legitimate and powerful media. The facts series method on this examine became to apply a studies and improvement questionnaire to provide suitable Speaking module primarily based totally on tests from media specialists cloth specialists, layout specialists, English instructors/ tutors and responses from college students.&#x0D; This research uses research and development (R&amp;D) methods. The development procedure follows the Borg and Gall procedure which generally can be carried out by involving 10 main steps, namely: 1) research and information collecting, 2) planning data, 3) developing preliminary from product, 4) preliminary field testing, 5) main product revision, 6) main field testing, 7) operational product revision, 8) operational field testing, 9) final product revision, 10) Dimensions and implementation. then for research conducted in the scope of undergraduate, the research is limited to five stages, namely 1) research and information collecting, 2) planning, 3) developing preliminary from product, 4) preliminary field testing, 5) main product revision.&#x0D; The results of this assessment indicate that the English vocabulary card media has a very good feasibility according to media experts getting a percentage of 96%, material experts 100%, , teacher assessments 97,75% and student responses 91,23% which means this Learning Media is "Very Appropriate" to use.
Analysis of the Effects of Environmental and Operational Factors on the Performance of Photovoltaic (PV) Systems

The rapid growth of global energy demand has increased the need for sustainable and renewable energy sources. Photovoltaic ( $$\mathrm{PV}$$ ) systems have emerged as a promising solution to this problem, offering clean and abundant energy that can be harnessed from the sun. As a result, the design and analysis of grid-connected $$\mathrm{PV}$$ systems have gained significant attention in recent years. This study aims to design and analyze a grid-connected $$\mathrm{PV}$$ system to meet the power demand requirements of a typical household in Oman. The study includes calculations to determine the system capacity, solar panel output power, and energy output, as well as diagrams of the system's operation and design. This study presents the design and analysis of a grid-connected photovoltaic ( $$\mathrm{PV}$$ ) system to meet power demand requirements. An area of $$200\,{\mathrm{ft}}^{2}$$ and solar panel wattage per square foot of $$9\,\mathrm{W}$$ were used to obtain a total power output of $$1.8\,\mathrm{kW}$$ . The system achieves this power output, corresponding to a daily energy output of $$9\,\mathrm{kWh}$$ and an annual output of $$\mathrm{3,285}\,\mathrm{kWh}$$ . The determination of the system's power requirement involved considering the total load to be produced, which amounted to $$\mathrm{6,967}\,\mathrm{kWh}$$ per month. To account for system losses, a safety margin of $$30\%$$ was incorporated, resulting in a total power requirement of $$13\,\mathrm{kW}$$ . Based on a solar panel rating of $$400\,\mathrm{W}$$ , the calculation yielded the selection of $$33$$ solar panels necessary to fulfill the power demand.
Limitations of the Improved CB (ICB) Measure

This paper discusses the limitations of the Improved Weighted Composite Complexity measure, a measure used to determine the cognitive complexity of software systems. This measure extends upon Chhillar and Bhasins’ weighted composite complexity measure by considering additional factors such as compound conditional statements, threads, and recursion. However, despite its ability to address some of the limitations of the weighted composite complexity measure, the Improved Weighted Composite Complexity measure has its own limitations. This paper explores these limitations and their implications for software engineering by examining the assumptions and theories underlying the Improved Weighted Composite Complexity measure, determining its strengths and weaknesses, comparing it with other cognitive-based complexity measure, and identifying specific contexts or situations where it may be unsuitable or inaccurate. The paper concludes with suggestions for future research to improve the accuracy and reliability of cognitive complexity measures.
Research on the Construction Strategy of Teaching Resource Library of the Course "Live Streaming E-commerce Operations" in Higher Vocational Colleges under the Background of Digitalization

The construction of digital teaching resources can better improve the teaching quality of live e-commerce operations courses, to cultivate e-commerce talents that meet the needs of the digital era. Taking live e-commerce operations courses as an example, this paper expounds the necessity and existing problems of the construction of digital teaching resource library, and discusses the construction strategy of digital teaching resource library.
Educational Reform Exploration of Engineering Mathematics from the Perspective of Artificial Intelligence

Based on the perspective of artificial intelligence technology, this paper explores the teaching reform of engineering mathematics courses. By analyzing the problems existing in engineering mathematics teaching and referring to the application of modern artificial intelligence technology in the field of education, an innovative teaching model is proposed. This study introduces technologies such as natural language processing and machine learning, and proposes an engineering mathematics teaching model based on personalized learning, aiming to improve students' learning effectiveness and interest. Through experimental verification, this teaching model has achieved significant results in improving students' learning motivation, cultivating innovative thinking abilities, and promoting peer cooperation. In addition, this paper discusses the role transformation of teachers in the implementation of this model and the corresponding training needs. Finally, this study prospects the promotion and future development of this teaching model.
Foreign Language Teaching Mode of Online Education under the Computer Multimedia Network Environment

The utilization of information technology has brought about significant changes in people's work and daily lives, and it has also played a crucial role in transforming the education sector and teaching methods. Currently, there is a growing trend of incorporating information technology into the field of education, where advanced methods and technologies are being employed for effective information dissemination. This paper aims to optimize the foreign language teaching mode in the computer network environment to promote the ecological development of online foreign language teaching. The article proposes that the use of educational ecology to guide the integration of computer networks and foreign language courses is helpful to analyze and solve the objective system imbalance and ecological imbalance in online foreign language teaching. Learning from the principles and laws of educational ecology to construct and optimize the ecological teaching mode, it can be realized that the ecologicalization of online foreign language teaching. In the context of students' online self-learning classrooms, a significant portion of students (38.2%) perceive their teachers' ability to guide and monitor their progress as average, while 16.5% feel that their teachers' skills in this area are lacking. Similarly, when it comes to evaluating the effectiveness of teachers utilizing computer multimedia networks to enhance teaching, 39.2% of students believe the impact is average, while 12.1% feel that there is minimal to no effect. Consequently, it becomes crucial to analyze and assess the foreign language teaching methods employed in online education within the computer multimedia network environment.
A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression

In recent years, genetic programming has achieved impressive results on evolutionary feature construction tasks. To increase search effectiveness, researchers have developed many semantic-based crossover and mutation operators to guide genetic programming searches toward the target semantics. However, semantics has not yet been explored for the hoist mutation operator, which is an operator designed for controlling the bloat effect. Although the hoist mutation operator can significantly reduce model sizes, the most informative subtree may be disrupted by the randomness in mutation. To address this issue, we develop a semantic-based hoist mutation operator in this paper to preserve the most informative subtree that has the largest cosine similarity between its semantics and the target semantics. Experimental results on 98 regression datasets from the Penn Machine Learning Benchmark show that using this operator not only significantly reduces model size, but also improves the test accuracy of features constructed by genetic programming. A comparison with seven bloat control methods shows that the proposed operator achieves the best trade-off between accuracy and model size. Moreover, an experiment on the state-of-the-art symbolic regression benchmark shows that genetic programming with the semantic-based hoist mutation operator achieves the best test accuracy and competitive model sizes compared with 22 symbolic regression and machine learning algorithms.
Strong coupling between in-fiber microcavity and single quantum emitter for long-distance quantum communication

High-performance single-photon sources (SPSs) are essential components for quantum information technology and have been realized by strong coupling between a single quantum emitter and an optical cavity. However, the configurations of conventional SPSs are not ideal for long-distance quantum communication as they are intrinsically incompatible with optical fibers. Here we propose and design a strong-coupling system based on a single quantum emitter directly coupled with an in-fiber microcavity. The in-fiber microcavity not only achieves a high coupling efficiency of 83% and a high Purcell factor of 360, but also pushes the coupling system to enter the strong-coupling region with a vacuum Rabi splitting up to 4.35 meV. This enables a high quantum efficiency of 99% for a SPS. Our work presents a promising platform for realizing high-performance SPSs for long-distance quantum communication.
ConGMC: Consistency-Guided Multimodal Clustering via Mutual Information Maximin

Aligning multiple heterogeneous modalities in a parameter-sharing encoder to mine consistent information is a core idea of multimodal learning. However, two drawbacks hinder the development of such methods for clustering tasks: (1) each modality contains a considerable amount of superfluous information that cannot be aligned, impeding the mining of consistent information; (2) one-to-one alignment is contradictory to the clustering principle of minimum intra-cluster distance, leading to suboptimal clustering results. In this paper, we propose a novel Consistency-Guided Multimodal Clustering method (ConGMC) to remove superfluous information within the modalities unsupervised through information theory while improving one-to-one alignment for the clustering task. ConGMC contains multiple unimodal encoders and a multimodal shared encoder, where the former learns unimodal representation while the latter aligns multiple modalities to learn the cluster partition. Specifically, we first construct a mutual information maximin function to distinguish consistent information from superfluous information, in which the consistent and superfluous information are maximally retained and removed, respectively. Then a Clustering-Friendly Alignment strategy (CF-Align) is designed to address the contradiction between the alignment and clustering tasks. CF-Align dynamically adjusts the set of negative samples according to the learned cluster partition to avoid increasing the intra-cluster distance. Finally, we consider the cluster partition as a consistent constraint to optimize the multimodal shared encoder, enabling consistent information to guide the training process iteratively. Moreover, a variational optimization algorithm is proposed to ensure that ConGMC converges to a local optimum. Numerous experimental results on twelve real-world datasets validate that the proposed ConGMC method outperforms the state-of-the-art multimodal clustering methods.
A User Allocation Method for DASH Multi-Servers Considering Coalition Structure Generation in Cooperative Game

Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).
An Approach to Prevent Neighborhood Attack over Social Media

Social media sites contain the personal information of the users, which entice the attackers The attacker uses several types of attacks on the social networking site to obtain sensitive information from the users. As different types of passive and active attacks are carried out on social media sites, user privacy may be compromised; to avoid this, the network operator releases data anonymously. Data from social media users is gathered and stored by social media operators for distribution to various third-party consumers. Because the fetched data frequently contains sensitive information, the network operator makes the entire graph available in anonymized and sanitized forms. It does not, however, provide a complete guarantee of user privacy. This research provided a way for anonymizing social network graphs using a neighborhood adjacency matrix-based anonymization process. This anonymization procedure could be utilized to defend against the social network graph's neighborhood attack. By adding fake edges to the social network graph, the suggested anonymization procedure increases the number of isomorphic neighborhood networks. As a result, a user's unique neighborhood network cannot be used to re-identify them in a social network graph.
Deformation Analysis in Aluminium Magnesium Alloy Using Digital Image Correlation

Image processing is extensively used to examine surface deformations. Digital Image Correlation (DIC) detects two-dimensional subpixel displacements in two images to analyze deformations in geomechanical structures. In this study, the analysis is done on an aluminium-magnesium alloy. An optical microscope is used to capture the image, which is then processed and utilized to anticipate material deformation. To acquire findings of strain points on the material, the Ncorr application in the MATLAB environment is employed. . Strain graphs on the Exx, Exy, and Eyy axes, as well as U-displacement and V- displacement graphs, are generated to detect the deformation.
Manajerial Dakwah Islam Melalui Perayaan Sekaten Sebagai Representasi Kekayaan Kebudayaan Islam Indonesia

This research aims to find out the strategies and managerial processes of da'wah contained in the sekaten tradition in responding to the richness of religion, culture, and the background of the Indonesian people. The researcher's approach in this study was to use a qualitative research methodology using library research methods. In this case, the author tries to collect material using various bibliographical data from various sources, including books, notes, and previous research articles. The Sekaten tradition is a celebration held in Yogyakarta, Indonesia, which has a close relationship with Islamic religious values. In this celebration, there are various elements related to Islamic da'wah. Overall, the Sekaten tradition has become an important vehicle for spreading Islamic da'wah and strengthening the community's religious identity. Through the transmission of Islamic teachings, moral and ethical development, as well as motivation and inspiration, the Sekaten celebration makes a positive contribution in the formation of individuals who are faithful, pious, and have concern for religious values ​​in everyday life.
Face Recognition untuk Smart Door Lock menggunakan Metode Haar-Cascades Classifier dan LBPH

A home security system is one of the features that every homeowner must own and contemplate if they want their residence to be secure from theft and other unwanted security disturbances. Therefore, we require a support system that can enhance domestic security. In this research, the constructed system uses faces as security information. This system captures facial images using an ESP-CAM 32 board integrated with an Arduino UNO. As the system's output, this system will employ the Selenoid DoorLock and Relay features. This system detects faces using the Haar-Cascade Classifier and recognizes faces using the Local Binary Pattern Histogram (LBPH). Implementation of the method for obtaining results, namely Smart Door Lock, can autonomously unlock the door with a presentation of greater than 85%. However, if the face detected to open the door is not the same as the registrant's face and has less than 85% of the required data, the door will not open.
Hierarchical Clustering-Based Synthetic Minority Data Generation for Handling Imbalanced Dataset

Predictive modeling is a new area of data science and machine learning that is gaining popularity. It provides sustained business growth, accurate future predictions, and trend estimations. Predictive modeling is the process of creating, processing, and validating a model that may be used to make future predictions using known results. Predictive modeling depends on the complete and precise datasets, however some of the datasets are imbalanced in nature that leads to data misclassification. Models trained on an imbalanced dataset with a small number of minority class instances, despite their high accuracy, would perform poorly during training. In this paper, an approach for synthetic minority class data generation using agglomerative hierarchical clustering and ward’s linkage criteria is proposed. Experimentation is carried out using five real-world datasets, namely Abalone, Page Blocks, Pima, Vehicle, and Yeast available at KEEL Data Repository. Testing of the experimentation is done using the SVM classifier with radial-bias kernel function. Data visualization is performed for understanding statistical properties, correlations, and distribution of class instances in the feature space. The classifier model is evaluated for before and after synthetic data generation using f-measure, recall, precision, and accuracy.
Navigation of a Compartmentalized Robot Fixed in Globally Rigid Formation

The subject of this research is navigation of a compartmentalized robot made up of several structures which are car-like with a global rigid formation. The system must move safely to a predetermined target in an already-known workspace that is filled with obstacles. A target convergence and obstacle avoidance method is put forth that is effective for any quantity of obstacles. The smallest distance between an obstacle and the point closest to it on each line segment that forms the rectangular protected zone of the car-like units can be calculated analytically using the minimum distance technique, as proposed in this paper. The Lyapunov-based control scheme (LbCS) is used to construct continuous acceleration-based controls. The computer simulations are presented to validate the proposed controllers, and the system demonstrates the rigorous upkeep of a rigid formation. The successful presentation of controllers opens further research in developing and applying controllers by considering various compartmentalized robots in swarming models, splitting and rejoining units, and applying rotational leadership concepts.
Exploring Sentiment in Tweets: An Ordinal Regression Analysis

The fundamental goal of sentiment analysis is to find and categorize any views or feelings that are communicated in a text. Nowadays, discussing thoughts and expressing feelings through social networking sites is widespread. Consequently, a vast amount of data is generated every day, which can be mined successfully to extract valuable information. Performing sentiment analysis on such data can be useful for producing an aggregated view of particular products. Due to the prevalence of slang and misspellings, sentiment analysis on Twitter is frequently a challenging undertaking. Additionally, we are constantly exposed to new terms, which makes it more difficult to assess and compute the sentiment compared to traditional sentiment analysis. Twitter limits a tweet's length to 140 characters. Consequently, obtaining important information from brief messages is another obstacle. Knowledge-based approaches and machine learning can significantly contribute to the sentiment analysis of tweets. The amount of data produced by people, i.e., users of a certain social site, is growing exponentially as a result of changing behavior of various types of networking sites like Snapchat, Instagram, Twitter, etc. The purpose of this paper is to determine the emotions underlying these posts. We have decided to use Twitter as our platform for this. In this study, we investigate the views expressed by Twitter users concerning certain companies. By computing a basic sentiment score and then categorizing them as positive or negative, the corporation would be provided with critical feedback about its products from individuals around the world. The proposed LSTM model has proved to be 93% efficient in comparison with previous models which were accurate up to 86%.