Reviewerly/exHarmony huggingface dataset:
Developing the Wordtheme-Based French Digital Dictionary for Hotel and Restaurant Industry

This study aims to develop a digital dictionary in Wordtheme application package for hotel and restaurant industry. Developing such package requires ADDIE stage consisting of five phases, namely analysis, design, development, implementation, and evaluation. The data content of the dictionary was taken from textbooks and hospitality websites including French vocabulary, Indonesian translations, phonetics, genders, explanations, sample sentences, pictures, and vocabulary pronunciation. To know its feasibility before being tested on students, a validation test on the material and media aspects was held. The average scores of material validation and media value reached 88.6% and 86.1% respectively, which were categorized as "very good". Then, to know its effectiveness, pre- and post-tests were carried out and the students’ average scores in the pre- and post-tests were 63 and 86 for each. The result shows that the average score of N-Gain (g) was 0.73 which was in the “high” category, and this category indicates the effectiveness on the use of the Wordtheme digital dictionary in the learning process. Keywords: learning media, digital dictionary, French for hotel and restaurant industry, wordtheme
Does Trade Dependency Lead Social Enterprises to Achieve Better Performance?

The effects of current socio-economic and political challenges pursuing a social mission while relying on trade dependency strategy (i.e., depending more on market-based income than non-market-based income) have created tensions for social enterprises about which they should choose between trade vs. grant dependency strategy to achieve the dual objectives. Building on a sample of 164 UK social enterprises, this study demonstrates that relative to grant dependency, trade dependency has a more substantial positive effect, direct and indirect through learning orientation, on social performance, and only indirect through learning orientation on economic performance. However, this study also shows that as social enterprises strengthen the market share growth strategy relative to grant dependency, trade dependency has a stronger positive effect on economic performance but, surprisingly, no significant impact on social performance. These findings contribute to our understanding of the role of trade dependency in the context of social enterprises and provide guidance to social enterprise managers on whether they should focus on creating a learning environment in their enterprises, and growing market share while depending more on income obtained from the trade of goods and services than from grant, donations, and funding.
Implementasi Honeypot Sebagai Pendeteksi Serangan Pada Virtual Private Server (VPS)

VPS (Virtual Private Server) is a server virtualization technology that is currently widely used by users for a variety of purposes, including personal, work, office, and business. Virtual Private Server (VPS) is a technology that uses VPS. Since renting a virtual private server (VPS) is less expensive than purchasing or renting a dedicated server, VPS is widely used. However, VPS has some disadvantages as well, one of which concerns security. The Intrusion Detection System (IDS), the Virtual Private Network (VPN), and the Honeypot are just a few of the other options that can be utilized to secure servers. The Honeypot application is needed to protect Virtual Private Server (VPS), which requires a detection application that can reduce attacks on VPS. Cowrie is able to detect and record all attacks that occur on the server based on the results of attack testing that was carried out 25 times for each attack test, beginning with port scanning and progressing through DDoS attacks, brute force attacks, and individual, double, and multiple tests. considered to be very effective, but when multiple attacks were carried out simultaneously, the server experienced a downtime or lag
5 Machine learning-based quantum modeling to classify the traffic flow in smart cities

The intelligent transportation system (ITS) includes a number of highly effective research applications, one of which is the vehicle ad hoc network. It provides information that can be used to either prevent or lessen the occurrence of traffic bottlenecks. By using the V2V and V2I protocols, a vehicular ad hoc network is able to facilitate communication not just between the vehicles themselves but also between the vehicles and the infrastructure that connects them. In this chapter, machine learning-based quantum modeling to classify the traffic flow in smart cities is proposed and validated.
Removing the Blinkers to Heed Temporal Complexity: Rhythmic Interrelations and Temporal Work

Although interest in temporal complexity has grown recently, the concept has been rather narrowly conceived as scholars have focused on past, present, and future relations. We introduce the concept of temporal breadth as a necessary and complementary correction that better accounts for the diverse array of temporalities people and organizations encounter. Drawing together insights from rhythmanalysis and phenomenology, we argue that temporal complexity is best understood through a rhythm lens that involves bodily and placial engagement with time and temporality. We develop a framework that unpacks how actors can cultivate heedfulness to conduct temporal work and show how through transposing, syncopating, and harmonizing practices they can manipulate temporal complexity advantageously. Our framework thus provides new pathways to explain pathologies in organizations and society, including ecological challenges.
Contrastive-enhanced Domain Generalization with Federated Learning

Domain generalization (DG) aims to train a global model from different but related domains, which can be generalized to an unseen out-of-distribution domain. Most existing DG methods are based on the centralized learning paradigm, raising the privacy leakage concern. In this paper, we propose a contrastive-enhanced domain generalization framework in the federated learning paradigm (FedCDG), where there are a server and multiple clients. Each client owns data from one domain and builds a local model consisting of a domain-invariant feature extractor and a classifier head. The server generates a global model through aggregating and broadcasting local models' parameters, thus achieving knowledge sharing and keeping data confidential. To enhance the discrimination and generalization ability of the local model, we build an improved instance normalization module that focuses on task-relevant features with less domain-specific information. Moreover, for better class-wise alignment in the embedding space, we propose a prototype-based contrastive loss. Given the limited annotation budget in practice, we also extend the proposed framework into the semi-supervised DG setting (i.e., only 10 labelled samples per class). Experimental results on 3 benchmarks and different backbones show that the proposed framework yields promising performances for both DG and semi-supervised DG in the federated learning paradigm.
Integration of satellite imagery and meteorological data to estimate solar radiation using machine learning models

Knowing the behavior of solar energy is imperative for its use in photovoltaic systems; moreover, the number of weather stations is insufficient. This study presents a method for the integration of solar resource data: images and datasets. &amp;nbsp;For this purpose, variables are extracted from images obtained from the GOES-13 satellite and integrated with variables obtained from meteorological stations. Subsequently, this data integration was used to train solar radiation prediction models in three different scenarios with data from 2012 and 2017. The predictive ability of five regression methods was evaluated, of which, neural networks had the highest performance in the scenario that integrates the meteorological variables and features obtained from the images. The analysis was performed using four evaluation metrics in each year. In the 2012 dataset, an R 2&amp;nbsp; of 0.88 and an RMSE of 90.99 were obtained. On the other hand, in the 2017 dataset, an R 2&amp;nbsp; of 0.92 and an RMSE of 40.97 were achieved. The model integrating data improves performance by up to 4% in R 2 and up to 10 points less in the level of dispersion according to RMSE, with respect to models using separate data.
“Learning Functional Grammar is fun”: A snapshot of functional grammar unit at an English Education Department in Indonesia

This paper reports on a teaching program for the Functional Grammar (FG) unit at an English Education Study Program of a state university in West Java Indonesia in 2020-2022. Three cohorts of pre-service teachers in semester six were involved in the program (38 students in 2020, 26 in 2021, and 36 in 2022). The teaching program was conducted online due to the Covid 19 pandemic. The report focuses on exploring students’ understanding of Systemic Functional Linguistics (SFL) – FG in particular, and their ability to use SFL in text analysis. The study also addresses students’ opinions on learning SFL through FG unit. The study used a qualitative case study design and the data were obtained from participant observations, a questionnaire distributed at the end of the teaching program, and analysis of students’ essays on text analyses. In the interest of space, the paper will only present and discuss data from the questionnaire and three samples of student essays. The questionnaire data were analysed qualitatively using a thematic analysis of different aspects of the unit and SFL, and students’ essays were analysed using SFL, especially theme, transitivity, and mood systems in lexicogrammar stratum. The data from the questionnaire indicate that all the students in all cohorts responded positively to the teaching program. Many students in each cohort said that learning FG is fun, enjoyable, interesting, and useful for both daily and academic life. They displayed a good understanding of SFL, including its basic principles, notions, and values ­ – especially concerning their reading and writing improvement. The data from students’ essays also reveal their ability to show how and why the text means what it does and to evaluate why the text is or is not effective for its own purposes. On this basis, it is recommended that SFL be introduced to Indonesian university students to enhance their literacy capacity and to Indonesian teachers to improve their teaching skills and their ways to develop assessment that is linguistically informed.
Penggunaan Media Video Animasi Berbasis Powtoon Dalam Rangka Peningkatan Kemampuan Menulis Teks Berita Siswa Kelas VIII SMPN 2 Plantungan Kendal Tahun 2022

This research is motivated by the lack of ability of class VIII students of SMPN 2 Plantungan Kendal regency in writing news text. This is due to the low interest in learning and understanding of students in writing, especially writing news text. The learning used by the teacher is considered less interesting and less innovative so that learning is considered boring. The teacher’s lack of understanding in utilizing learning media is one of the factors causing teachers difficulties in using various online based learning media, one of which is the Powtoon Media. The purpose of this study was to determine the role of the use of Powtoon media in order to improve students’ learning to write news text for grade VIII SMPN 2 Plantungan Kabupaten Kendal. This study used descriptive qualitative methoed. Data collection techniques used in this study are observation, documentation, angket and assigment or tes. This test is used to obtain data regarding the level of mastery of the material on the final result value. Based on the results of the research, it shows that there is an increase in the ability through the results of tes that have benn done by studentds. This is evident from the results of the average value of writing the previous news text, that is 70,73. Wrile the results of the average value after using learning with the Powtoon Media that is 81,4.
Longitudinal Analysis of Wildcard Certificates in the WebPKI

The use of wildcard certificates and multi-domain certificates can impact how sensitive a certificate is to attacks and how many (sub)domains and machines may be impacted if a private key is compromised. Unfortunately, there are no globally agreed-upon best practices for these certificate types and the recommendations have changed many times over the years. In this paper, we present a 10-year longitudinal analysis of the usage of wildcard certificates and multi-domain certificates on the internet. Our analysis captures and highlights substantial differences in the heterogenous wildcard and multi-domain certificate practices. The results also show that there are several ways that CAs and domain owners have chosen to improve their practices, with many appearing to reduce the number of domains (and subdomains) for which each certificate is responsible.
Blockchain and Data Privacy in Human Resource Management

Blockchain technology can be used to enhance data privacy in human resource management by providing a secure, decentralized platform for storing and sharing employee data. This allows for secure access to sensitive information, such as personal identification and employment history, while maintaining the privacy of that information by preventing unauthorized access. Additionally, blockchain can be used to track and verify the accuracy of employee data, reducing the risk of errors or fraud. However, it is important to note that while blockchain can provide enhanced security and privacy, it still relies on proper implementation and maintenance to ensure that data remains protected.
RANCANG BANGUN SISTEM MONITORING PENJUALAN BARANG DI TOKO MAINAN LOVELLA TOYS

Companies that master technology and information will be able to master and become leaders in their field of business. By utilizing information technology, companies can run business processes more quickly and can process data more accurately. Lovella Toys Toy Store is a company engaged in selling toys for hobbies. Recording of sales that still uses Manual Notes for proof of transactions every day, and then at the end of the sale will be input into a Microsoft Excel file. It can make the data inaccurate because can be an input error that causes the transaction not input correctly in the Sales Data Format. A web-based computerized system is needed so that every piece of information provides added value to the process of quality management, decision-making, and problem-solving along with a competitive advantage that is useful for sales activities. This study uses qualitative research methods using field research approaches and library research in its data collection methods. The results are in the form of a web-based monitoring data processing system prototype which is expected to minimize and even solve current problems.
A Relative Approach to Comparative Performance Analysis for Quantum Optimization

We discuss a small study on how to compare the performance of various solving techniques for quadratic unconstrained binary optimization (QUBO). Since well-known metrics are seldomly applicable, we suggest comparing the relative performance, i.e., how much the quality of solution (compared to other solutions of the same solver) for a QUBO shifts between different solving techniques. We propose looking for big shifts systematically for an empirical complexity analysis.
CoolMomentum mitigating local minima in variational quantum eigensolvers

Abstract We compare the use of adaptive moment estimation (ADAM), simultaneous perturbation stochastic approximation (SPSA), Nakanishi–Fujii–Todo method (NFT), and CoolMomentum in a variational quantum eigensolver. Using a random weighted max-cut problem, we numerically analyze these methods and confirm that CoolMomentum performs better than the other methods. ADAM and SPSA tend to get trapped in local minima or exhibit infeasible optimization durations. Although NFT exhibits fast convergence, it tends to suffer from local minima similar to ADAM and SPSA. Contrarily, CoolMomentum shows a higher accuracy under noiseless and realistic hardware noise conditions.
Genetic Algorithm-Based Data Allocation in Multi Media Using Cloud Computing

The recent growth of Internet-of-Things (IoT) applications using cloud computing has been amazing. One of the advancements is heterogeneous cloud computing, which has made it possible to use the cloud for a range of infrastructure solutions, including multimedia big data. The optimizations of on-premise heterogeneous memory have been the subject of several past studies. However, the performance and financial limits brought on by hardware distributions and manipulative techniques are placing restrictions on the heterogeneous cloud memory. It is an NP-hard combinatorial issue to distribute data jobs across dispersed memory with different capacities. In order to provide high performance cloud-based heterogeneous memory service offerings, this study focuses on this problem and suggests a unique solution called Cost-Aware Heterogeneous Cloud Memory Model. It allocates data to the cloud-based memory via genetic programming. In our suggested method, we take into account a number of important elements that have a significant influence on how well Communication expenses, data transfer operating costs, energy performance, and time constraints all play a role in how cloud memories operate. Finally, we put our suggested paradigm to the test via experimental assessments. The trial findings have demonstrated the viability and scalability of our technique as a cost-conscious cloud-based solution.
Analysing Software

In Chaps. 3–5, we consider systems modelled at an abstract level in the form of one or more FSMs. However, if a model has been derived from an actual piece of software, or once software has been developed based on a given model, then what does the functional correctness of the model say about the software?
Facial Landmarks Detection System with OpenCV Mediapipe and Python using Optical Flow (Active) Approach

To achieve positive detection results, a variety of face landmark methods supported by the convolutional neural network have been developed. The instability landmarks thus emerge in video frames as a result of CNNs, on the other hand, are extremely sensitive to input picture noise. This paper provides a light and effective face landmark identification technology based on a lightweight U-Net model based on semantic segmentation and an Optical Flow (Active) Approach (OFA) for solving the problem of landmark shaking. The OFA employs a quick optical flow approach to determine the motion path of the landmark, as well as a route to increasing landmark maintenance. A lightweight U-Net model is used to predict face landmarks with a reduced size of the model and lower computational. To subsume unstable shaking, the predictable face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process. A lightweight U-Net model is used to model face landmarks in reduced model size and lower computational. To subsume the unstable shaking, the predicted face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process.
The Portrayal of American Indian Identity in The Lone Ranger and Tonto Fistfight in Heaven

This study delved into the cultural identity of American Indians residing in the Spokane reservation area, with a focus on Sherman Alexie's collection of stories, "The Lone Ranger and Tonto Fistfight in Heaven." The stories portrayed various aspects of the divide between American Indians and white people, as well as the distinctions between reservation-based Indians, urban Indians, modern Indians, and traditional Indians. The main objective of the research was to explore how the identity of American Indians is depicted in Alexie's book using Homi K Bhabha's cultural identity theories of hybridity and mimicry. This study employed the literary criticism method, particularly postcolonial studies, to analyze the representation of hybridity and mimicry. The primary data source for this investigation was the collection of short stories, "The Lone Ranger and Tonto Fistfight in Heaven," by Sherman Alexie. The data was gathered from the text, identifying words or sentences that illustrate the representation of hybridity and mimicry in the stories. The data collection techniques include reading and note-taking strategies. This study revealed two main aspects of Indian cultural identity: Hybridity, characterized by a blend of Indian and white culture, evident in language, behavior, ways of thinking, and lifestyles. Then Mimicry, seen in Indian behavior, lifestyle, and ways of thinking that resemble those of white people. In conclusion, the research highlighted how Indian cultural identity in Sherman Alexie's work reflects both hybridity and mimicry, shedding light on the complexities of cultural assimilation and adaptation in American Indian communities.
Dampak Penerapan Elfan Bookless Library System di Dinas Perpustakaan dan Kearsipan Daerah Kabupaten Sinjai

Digital libraries are one of the government's innovative products in an effort to increase interest in literacy and provide a place of reference for the community which is expected to function better. This digital library has been implemented in several regions in Indonesia, one of which is in Sinjai Regency. the policy of implementing digital libraries in Sinjai Regency began to be implemented in 2019. This article uses qualitative research with a descriptive approach. The data obtained is then selected, presented and analyzed using the content analysis method. The results of the study show thatElfan Bookless Library System contains approximately 5000 books that can be accessed by users. The Sinjai Regional Library and Archives Service provides services to visitors by using a QR Code that can be scanned at the Sinjai Regional Library and Archives Service. In addition, the free internet network also makes it easy for visitors to access these services. However, the majority of visitors are not aware of any information on these digital library services which is presented side by side with POCADI (Digital Reading Corner).
Notes on Operations: Collaborative Learning on Linked Data through a Virtual Study Group

This paper examines the challenges and successes of a virtual Linked Data Study Group that began at a multi-campus academic institution in 2018, and later grew to include a total of seven librarians from multiple institutions across the country. It describes the group’s planning for their monthly meetings and the discussions at the meetings which covered such topics as Linked Data basic concepts, BIBFRAME Editor, Sinopia Editor, and Wikidata. It also presents a collaborative project the group undertook after two years’ learning. The paper concludes with a summary of what the Linked Data Study Group has achieved thus far, the challenges they faced, and their future plans.