Reviewerly/exHarmony huggingface dataset:
Automatic essay scoring for discussion forum in online learning based on semantic and keyword similarities

Purpose The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy. Design/methodology/approach The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model. Findings The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2. Originality/value This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.
Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos

This research presents a novel method to assess inter-chain message relay time in the Cosmos blockchain network, which employs the Inter-Blockchain Communication (IBC) protocol for blockchain interoperability. While inter-chain transactions in Cosmos lag behind intra-chain transactions, this research conducts a thorough performance evaluation, emphasizing message relay time across diverse Cosmos chains. The results show a strong association between inter-chain transaction frequency and overall transaction processing speed (TPS), underscoring the inherent trade-off between scalability and compatibility in inter-chain communication protocols. A thorough understanding of inter-chain transaction performance is essential for advancing interoperability and improving cross-blockchain network designs. As a result, this research improves our understanding of Cosmos’ functionality and provides insightful recommendations for boosting the effectiveness and scalability of inter-chain communication. The study also sheds light on message delivery patterns in the Cosmos ecosystem, showing that IBC message relay time has an average duration of 55.448 s and is distributed lognormally. Notably, the time between the commitment of the IBC RecvPacket transaction and the commitment of the IBC Acknowledgement transaction (R to A time) considerably impacts the effectiveness of message transmission, which causes delays in the IBC message relay process.
Seismogram Rearrangement as a Perspective Basis for Defining New Attributes

Proper regulative in earthquake risk reduction, hazard assessment, earthquake resistant design and construction of structures, should be under perpetual improvement. They are mainly the result of earthquake study and analysis of post-seismic effects. In this context, a new frame for seismogram analysis is proposed, based on its decomposition into elementary and single (positive and negative) fluctuations (EF/SF). Decomposition enables rearrangement, both providing the frame for defining new attributes, where several main ones are proposed and elaborated in the paper: fan diagram of EFs structural axes; R- or RQ-envelope, formed over positive and over negative peaks of ordered SFs; two forms of difference functions of positive and negative R/RQ-envelopes, as a measure of seismogram’s (a)symmetry, in respect to t-axis; distributions of SFs durations. Several others are in perspective. Analogy between R-envelope and seismogram’s spectrum is considered from the aspects of arranged elements, and asymptotic behavior of these two functional attributes. More objective definition of seismogram duration is proposed, determined by linear regression across the scatter plot of SFs peaks. R-envelope showed itself as an invariant in the seismogram population, and the distribution of SFs durations as well. Results are presented through extensive graphics, backed with mathematical description. Thorough discussion of the concept and results is given.
Snakes and smartphones: exploring transdisciplinary design collaborations for the governance of snakebite

Snake Awareness Rescue Protection App (SARPA) is a digital platform used in Kerala, India, to prevent snakebites. SARPA connects users with local snake rescuers who are on hand to safely bag and translocate snakes that enter homes. This article reflects on the ‘doing’ of digital ecologies while researching SARPA, narrating my fieldwork and collaboration with SARPA’s developers to show how my research supported the platform’s design and implementation. Through this reflection, I demonstrate how collaboration with software designers and developers may provide a means for cultural geographers to put their scholarship into practice, enabling them to contribute to improving the usability, accessibility or environmental sustainability of digital technologies.
A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communication network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.
NL2SQL: Rule-Based Model for Natural Language to SQL

Every day, in today’s fast-paced, digital world, an enormous amount of data is generated. An intelligent and creative interface is required to organize and engage with massive amounts of new data. Traditional techniques rely on sequence-to-sequence models; however, such models are too generic to take advantage of the whole structure of a SQL query. Furthermore, the knowledge of SQL is not widespread among the common people. We investigate and build a rule-based approach for transforming natural language to SQL queries in this work, which also includes a voice input capability. Our approach involves parsing the natural language statement to extract the relevant keywords, mapping them to a semantic representation, and generating the corresponding SQL query using a set of pre-defined rules. The rules are designed to map natural language verbs, nouns, and phrases to SQL operators, tables, columns, and clauses. ...
Neural_BPR: Multi-processing popularity bias mitigating method in recommendation systems

E-commerce recommendations address the problem of information overload, but recent research has identified the phenomenon of popularity bias in recommendation mechanisms. This phenomenon tends to homogenise recommendation results, i.e. popular products are widely recommended and non-popular products are under-exposed. The persistent presence of popularity bias in recommendation systems can lead to several negative consequences: (1) Consumers are unable to attain satisfactory personalized shopping experiences. (2) Small and medium enterprises are deprived of fair competitive opportunities, making survival challenging. (3) The user base of e-commerce platforms dwindles, leading to reduced transaction volumes. In this paper, we propose a multi-process fusion debiasing method. Convolutional neural networks are used to extract feature information to be embedded in the recommendation algorithm for pre-processing, in-processing and post-processing in turn to mitigate popularity bias and simultaneously enhance recommendation utility. The effectiveness of the proposed debiasing method is validated using the Bayesian Personalized Ranking (BPR) recommendation model as an example. Experimental results on two public datasets demonstrate that our model outperforms two baseline models and a state-of-the-art model. Specifically, compared to the traditional BPR model, the alleviation of popularity bias is around 70% to 80%, and the recommendation utility increases by approximately 30% to 50%.
Green Cloud Computing: Achieving Sustainability Through Energy-Efficient Techniques, Architectures, and Addressing Research Challenges

Green cloud computing aims to reduce the environmental impact of cloud computing. It contributes significantly to the world's energy use and carbon emissions. The cloud computing industry allows users from all over the world to access resources and processing power. Comparing it to specialist high-performance computing hardware results in cost savings and better performance. Large data centers are required for this service, which consumes a lot of energy and produces a lot of carbon dioxide. Utilizing energy-efficient procedures and sustainable infrastructures, data centers become more sustainable and reduce their carbon impact. Virtualization, energy-efficient hardware, energy-efficient cooling, and dynamic power management are some techniques that contribute to the "greening" of cloud computing. Architecture and various power consumption measurement parameters are surveyed in this paper. This computing requires significant amounts of power to run the data centers that support it. Data centers require a continuous and reliable power supply to ensure uninterrupted services to customers. Further, the research difficulties of green cloud computing are investigated.
Literary Psychological Analysis of the Novel entitled “Laut Bercerita” by Leila S. Chudori

Literary work contains meaning in it, not just a form of work that contains aesthetics. Poetry means imaginative literary works, the words used are arranged using aesthetic and simple words, readers who do not have the knowledge of analyzing poetry will have difficulty understanding the meaning of the poetry they read. The writer is interested in analyzing the poem "Rahasia Hujan" by Heri Isnaini, because this poem has lyrics formed from a unique and simple language structure but has a broad meaning and makes readers interested in his work. Keywords: poetry, mimetic, literacy psychological analysis. DOI: http://dx.doi.org/10.61436/pedagogy/v2i2.pp75-77
Comprehensive receptive field adaptive graph convolutional networks for action recognition

Action recognition has shown a broad prospect for application in human–computer interaction and autonomous vehicles. According to skeleton data being lightweight and robust to environmental disturbance, skeleton-based methods are increasingly used in action recognition. Existing Graph Convolutional Networks (GCNs) have limitations such as Global Average Pooling (GAP) causing information oversmoothing, attention mechanism increasing computational complexity, and fixed spatial configuration partition strategy limiting flexibility. Our model overcomes these limitations by incorporating Temporal Covariance Pooling (TCP) for better feature utilization, Sample-Shared attention for weight sharing, and a Comprehensive Receptive Field strategy for optimal joint connections. Experiments show that the recognition accuracy of our model outperforms baseline 2s-AGCN by 1.1% and 0.7% under X-Sub and X-View for NTU RGB+D dataset, 1.8% and 1.9% in X-Sub and X-Set for NTU RGB+D 120 dataset, and 1.6% on Kinetics-Skeleton dataset.
Research on the Realization Mechanism and Evaluation System of High-Quality Undergraduate Education in Private Universities Based on Deep Learning

Due to the new development stage, it is especially important to improve the education quality of private undergraduate universities. As a result, it is a new hot issue for the construction of a mechanism and assessment system for the quality improvement of private undergraduate education. In this paper, after analyzing and researching the quality of undergraduate education in present-day universities, the mechanism of deep learning is applied to the establishment of the assessment system. Finally, 1082 samples collected from the data center platform of a private university are analyzed as the research object. From the results, the final size of the combined weights of the seven evaluation items constituting the assessment system differed basically little. They were 12.81%, 15.78%, 15.28%, 14.38%, 12.83%, 12.81%, 15.01%, and 13.27%, respectively. In the comparison of this paper's method with FAHP+TOPSIS combined evaluation, euclidean map method, and genetic algorithm assignment, the difference between the seven weight values of the euclidean map method is larger, 5.56%. The evaluation times of the four methods were 41 s, 38 s, 47 s, and 118 s. Compared with the other three methods, the genetic algorithm assignment took the most time.
A high-performance voting-based ensemble model of graph convolutional extreme learning machines for identifying geochemical anomalies related to mineralization

How to effectively identify mineralization anomalies from geochemical survey data is one of the key problems that must be properly solved in geochemical exploration. Although supervised machine learning and deep learning techniques have been adopted to solve this problem, the performance of the established mineralization anomaly identification models is not satisfactory because the mutual relationship between neighborhood data points is not taken into account in the modeling of geochemical exploration data. The graph convolutional extreme learning machines (GCELMs) provide a new technique for modeling the graph data transformed from geochemical exploration data. To improve the performance of mineralization anomaly identification algorithms, the GCELM techniques were adopted to establish the supervised classification models for identifying mineralization anomalies from geochemical exploration data. To improve the robustness of the GCELM models in mineralization anomaly identification, an ensemble classification model called voting-based GCELM (V-GCELM) model was established by voting the outputs of multiple GCELM models. The Baishan area of Jilin Province (China) was taken as the study area for carrying out an experiment of mineralization anomaly identification from geochemical exploration data. The synthetic minority over-sampling technique (SMOTE) was used to alleviate the class-imbalance of the training data. The GCELM model, V-GCELM model and extreme learning machine (ELM) model were established for identifying mineralization anomalies, and their performances were compared in terms of the receiver operating characteristic (ROC) curves, area under the curves (AUCs) and lift indices. The results show that the V-GCELM model has the best performance, and its AUC and lift index are 0.9989 and 28.6, respectively. The GCELM model has the second-best performance, and its AUC and lift index are 0.9977 and 20.1, respectively. The ELM model has the worst but still high performance, its AUC and lift index are 0.9402 and 9.2, respectively. The mineralization anomalies identified by the three models have close spatial correlation with the polymetallic deposits found in the study area, and spatially controlled by the main controlling factors in the study area. Therefore, GCELM and V-GCELM are promising techniques for building the high-performance classification models for identifying mineralization anomalies from geochemical exploration data.
The Impact of the Application of Neuro Marketing in Influencing Tourists' Decisions in Determining a Place to Tour: Literature Review

In the current digital era, the application of neuromarketing in the tourism industry can influence tourists in various ways, such as through the use of attractive visual designs, the use of words or narratives that trigger positive emotions, as well as sensory experiences that give satisfaction, for example one of which is the results posts by influencers on Instagram social media. By understanding how tourists' brains or thoughts respond to marketing stimuli, tourist destinations can optimize their marketing strategies to achieve better results. Therefore, the aim of this research is to see the impact of applying neuromarketing in influencing tourists' decisions regarding tourist attractions, especially in Bali Province. The design of this research is a literature review. Search for articles on Google Scholar and ProQuest with keywords based on Perception and Visit Decision. 776 articles were found in the 2013-2023 period, then screening was carried out and 38 relevant articles were found to be discussed and analyzed. This research found positive results that there is an impact of neuromarketing on consumer decisions, including tourists' decisions in choosing tourist destinations, namely the branding and advertising that is carried out will provide stimulation to the tourist's brain to respond. So it is hoped that this research will become a strategy that can be implemented by the tourism industry in Indonesia in making the marketing system in the tourism industry more effective. Keywords:Neuro Marketing, Branding, Advertising, Visit Intention, Visit Decision.
Efficient real-time detection of electrical equipment images using a lightweight detector model

Infrared technology holds significant importance in the detection of electrical equipment, as it has the capability to swiftly and securely identify electrical apparatus. To simplify the implementation of proficient detection frameworks for electrical equipment within constrained settings (like embedded apparatus), this study presents an enhanced, lightweight model of the single-shot multibox detector (SSD). This model specifically addresses the detection of multiple equipment objects within infrared imagery. The model realized the lightweight of the model by using the network structure characteristics of squeezenet to modify the backbone network of SSD, and compensated for the impact of the lightweight model on the detection accuracy by adding multiple convolutional layers and connecting branches to enhance the propagation ability and extraction ability of features. To ensure a comprehensive evaluation of the model’s detection capabilities, all the models discussed in this study employed the technique of random weight initialization. This approach was utilized to validate the optimal structure of the model and its performance. The experimentation was conducted on both the PASCAL VOC 2007 benchmark dataset and an infrared image dataset encompassing five distinct categories of electrical equipment found within substations. The experimental outcomes indicate that this model offers an efficient approach for achieving lightweight, real-time detection of electrical apparatus.
Legal Tech, the Law Firm and the Imagination of the Right Legal Answer

Abstract Legal tech is growing, and its growth provokes anxieties about the future of the legal profession as such. In this article, we examine the impact of legal tech on the central role of lawyers at law firms in crafting an imagined ‘right legal answer’ by drawing on Duncan Kennedy’s suggestion that a claim to the rightness of one’s legal propositions is a central characteristic of the legal profession. We first ask how changes in the organisation of legal services affect the ability of lawyers at law firms to produce that ‘right legal answer’. While legal tech only exacerbates already ongoing processes of eradication of routine tasks, we find that it continues to mask the role of ideology in arriving at a right legal answer under a new layer of technological projection. Second, we ask how lawyers’ ability to produce ‘the right legal answer’ is affected by, first, expert systems and, second, a legal tech application named Bryter, representing a no-code system. We find that expert systems do not permit to uphold the unity of the lawyer required for Kennedy’s model of the right legal answer, but that no-code systems as Bryter do so. No-code systems can be reduced to a slogan: Have the lawyer, but evict her ideological temptations more efficiently than before!
A Framework for a Mobile Knowledge Management Application for Crisis and Emergency Management

Abstract The widespread availability of mobile devices offers enormous opportunities for first responders to have instant access to sources of information, and collaborate with one another when responding to an emergency. Although various knowledge management systems have been developed in different domains, no mobile knowledge management application so far has been developed in the area of emergency or crisis management. This study addresses various technical requirements for a mobile knowledge management application specifically designed for first responders to an emergency or crisis.
Implementation of Literacy Culture in Forming Students' Integrity Character at Muhammadiyah 1 Middle School, Central Parigi

As a process, education requires a programmed and stable system, as well as clear goals so that the intended direction is easily achieved. The formulation of the problem in this research is: What is the literacy culture at SMP Muhammadiyah 1 Central Parigi, How is the implementation of literacy culture in the formation of students' integrity character at SMP Muhammadiyah 1 Central Parigi, What are the supporting and inhibiting factors for the implementation of literacy culture in the formation of students' integrity character at SMP Muhammadiyah 1 Parigi Middle. The aim of the research is the supporting and inhibiting factors for the implementation of literacy culture in the formation of students' integrity character at SMP Muhammadiyah 1 Central Parigi. Literature study research method. The type of research used is literature study. The literature study method is a series of activities related to methods of collecting library data, reading and taking notes, and managing research materials . The results of this research are aimed at collecting and reviewing various studies which conclude by looking at the results of student character and applying literacy culture in schools.