Reviewerly/exHarmony huggingface dataset:
Metaheuristic Aided Improved LSTM for Multi-document Summarization: A Hybrid Optimization Model

Multi-document summarization (MDS) is an automated process designed to extract information from various texts that have been written regarding the same subject. Here, we present a generic, extractive, MDS approach that employs steps like preprocessing, feature extraction, score generation, and summarization. The input text goes preprocessing steps such as lemmatization, stemming, and tokenization in the first stage. After preprocessing, features are extracted, including improved semantic similarity-based features, term frequency-inverse document frequency (TF-IDF-based features), and thematic-based features. Finally, an improved LSTM model will be proposed to summarize the document based on the scores considered under the objectives such as content coverage and redundancy reduction. The Blue Monkey Integrated Coot Optimization (BMICO) algorithm is proposed in this paper for fine-tuning the optimal weight of the LSTM model that ensures precise summarization. Finally, the suggested BMICO’s effectiveness is evaluated, and the outcome is successfully verified.
'The eyes don't lie': How Eye-tracking Doubles as a Community Research and Pedagogical Tool

This experience report shares how eye tracking technology was incorporated into an introductory course on technical communication, offering students the opportunity to interface with a UX research tool that allows for direct feedback on multimodal projects. Discussing this project in light of research from the field focusing on community-based research and accessibility, the potentials and limitations of eye tracking are discussed, along with examples of how assignment framing and context serve essential functions in grounding the tool in human-centered design. Reflections on how eye tracking can be positioned as a technology that enhances student understanding of how their design work impacts communities are shared, including considerations for future projects and applications of the technology in both pedagogy and research.
A Real-Time Nut-Type Classifier Application Using Transfer Learning

Smart environments need artificial intelligence (AI) at the moment and will likely utilize AI in the foreseeable future. Shopping has recently been seen as an environment needing to be digitized, especially for payment processes of both packaged and unpackaged products. In particular, for unpackaged nuts, machine learning models are applied to newly collected dataset to identify the type. Furthermore, transfer learning (TL) has been identified as a promising method to diminish the time and effort for obtaining learning models for different classification problems. There are common TL architectures that can be used to transfer learned knowledge between different problem domains. In this study, TL architectures including ResNet, EfficientNet, Inception, and MobileNet were used to obtain a practical nut-type identifier application to satisfy the challenges of implementing a classifier for unpackaged products. In addition to the TL models, we trained a convolutional neural network (CNN) model on a dataset including 1250 images of 5 different nut types prepared from online-available and manually captured images. The models are evaluated according to a set of parameters including validation loss, validation accuracy, and F1-score. According to the evaluation results, TL models show a promising performance with 96% validation accuracy.
HATE SPEECH IN INDONESIA: A FORENSIC LINGUISTICS

This study intends to examine the scientific article about the phenomenon of hate speech in pandemic era in Indonesia from forensic linguistics view. By using a meta-analysis technique on Google Schoolar, an official website intended as a collection of scientific articles in Indonesia. A total of 150 articles were accessed from Google Scholar. To assure the review's focus, a selection process for inclusion and exclusion criteria was carried out before it began. There are 20 eligible articles to be reviewed. Findings showed that most of the researchers used social media as the source of data, most of the researchers used the theory of speech act to analyse the data.
Real-time path planning of multi-mobile storage robot based on improved sine-cosine algorithm

In a multi-mobile storage robot system, real-time path planning in a complex environment becomes the key to high efficiency and intelligence of the system. This paper proposes an improved adaptive sine-cosine algorithm (ASCA) based on the sine-cosine algorithm (SCA). In order to solve the problem of real-time path planning of multiple mobile storage robots in a complex environment with dynamic and static obstacles. The improved ASCA algorithm introduces an adaptive inertial weight factor. At the same time, a constrained variable update method is added. Finally, the evolution strategy in the swarm intelligence algorithm is combined with the differential algorithm. The experimental results show that the improved ASCA algorithm has faster convergence speed, stronger global search ability and better adaptability in complex environments than the SCA algorithm when dealing with the real-time path planning problem of multi-mobile storage robots. Situations that may produce invalid solutions or exceed the search space may occur.
GWO-KGC: A Novel Time Series Causal Analysis Scheme

Existing methods cannot depict the intrinsic correlations of time series accurately. Aiming at the above problem, this paper proposes a novel time series causal analysis scheme called GWO-KGC, which incorporating grey wolf optimization algorithm into kernel-ganger causal analysis. Specifically, the model removes redundant and unrelated variables, and also recognizes the complex nonlinear relationships between variables, thus providing an optimal subset of input features. Meanwhile, in order to verify the correctness of the causal analysis results, the GWO is introduced to the echo state network, to validate the influencing factors. The simulation results show that GWO-KGC model can accurately identify the main influencing factors of the selected variables, which guarantees enhanced prediction accuracy.
A Medium and Long Term Load Forecasting Method Based on BP Neural Network and S-Shaped Curve Fitting

The purpose of this article is to propose a medium to long-term load forecasting method based on BP neural network and S-curve fitting. This article first uses a knowledge graph to determine the factors that affect power load forecasting, and selects the key factors that affect load forecasting. Secondly, in order to compensate for the shortcomings of the BP neural network method for forecasting, the paper proposes a load forecasting method based on BP neural network and S-curve fitting. This method compensates for the uncertainty of the neural network prediction results and the low accuracy of S-curve fitting in predicting load development trends. Finally, the accuracy of this method for load forecasting was verified through simulation examples.
A system and method for person identification and positioning incorporating object edge detection and scale-invariant feature transformation

During the overhead crane, due to the complex background and large image size, the direct processing algorithm usually leads to time-consuming operation and difficult to achieve accurate personnel positioning. To address these issues, we propose the feature points recognition and matching algorithm incorporating object edge detection and scale-invariant feature transform (SIFT), which is applied to the designed object detection and positioning algorithm for binocular stereo vision. The proposed method applies the Canny operator to extract contours from the locally cropped regions obtained using YOLOv5. The contour points are used as the key points of SIFT, and corresponding feature points are created descriptors, and feature points matching are performed. Experimental results indicate that the proposed method can filter feature points successfully. Moreover, the binocular stereo vision system with the binocular positioning algorithm designed has good performance, with the overall depth direction positioning error within 5% in a range of 12 m.
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review

Graph neural networks (GNNs) have been extensively used for many domains where data are represented as graphs, including social networks, recommender systems, biology, chemistry, etc. Despite promising empirical results achieved by GNNs for many applications, there are some limitations of GNNs that hinder their performance on some tasks. For example, since GNNs update node features mainly based on local information, they have limited expressive power for capturing long-range dependencies between nodes. To address some of these limitations, several recent works have started to explore augmenting GNNs with memory to improve their performance and expressivity. We provide a comprehensive review of the existing literature on memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has several established theories on how multiple memory systems and mechanisms operate in biological brains. We propose a taxonomy of memory-augmented GNNs and a set of criteria for comparing their memory mechanisms.We also provide critical discussions on the limitations of these works. Finally, we discuss the challenges and future directions for this area.
Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems

Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in sub-optimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user-server communication. Extensive evaluations with the real-world dataset corroborate LIBERATE’s capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy-preserving in federated recommender systems.
Efficient path planning for AUVs in unmapped marine environments using a hybrid local–global strategy

The ability of autonomous undersea vehicles (AUVs) to plan paths in unknown marine environments is the precondition for executing complicated missions. However, existing path planning algorithms based on underwater sensing equipment often struggle to achieve efficient exploration and generate high-quality trajectories. In this paper, we introduce a novel approach to efficiently handle the challenge of AUV navigation under limited information. Our solution combines global and local planning techniques to generate optimized paths that guarantee collision-free and efficient operations. In global path planning, we incrementally use the rolling windows to make decisions on high-level path branching while utilizing waypoints from selected branches to refine the calculation of local paths for enhanced accuracy. We employ an efficient small-scale path search strategy at the local path computation level by leveraging sensor-detected environments. In this stage, we propose an advanced rapidly exploring random tree (RRT) algorithm called circle-RRT. By combining adaptive circle sampling with dynamic step sizes, this algorithm can significantly reduce the generation of redundant sampling points and improve the efficiency of local path planning. We evaluated the efficiency of our algorithm in unknown environments through simulations and compared it with previous leading methods.
Co-occurrence of Epistemic Modality in Chinese

In Chinese, epistemic modality expressions include adverbs, auxiliary verbs, and sentence-final particles. This study focuses on the co-occurrence of various degrees of epistemic modality expressions and discusses the effect of their co-occurrence on the likelihood of sentences. First, we collected data on co-occurrence cases of various expressions from a large-scale corpus and analyzed the characteristics of the distribution of these co-occurrence patterns. Second, we examined how modal adverbs and sentence-final particles of different scales of modality affect the likelihood of sentences when they co-occur. We observed that lower certainty expressions precede higher ones, following a hierarchy of Modpossibility &gt; Modprobability &gt; Modcertainty, regardless of their categories. Moreover, the sentence-final particle 吧ba diminishes the likelihood of a proposition, while the sentence-final particle （是）…的 (shi)…de strengthens it. The findings in this study contribute to a deeper understanding of epistemic modality in Chinese and offer novel resources for the acquisition of the language.
Designing a Master of Accounting Study Program Profile Website Using the Wordpress Content Management System

This article discusses designing a study program profile website using the WordPress Content Management System (CMS). Higher education has a need to present relevant and up-to-date information to prospective students, students, lecturers and the general public. A study program profile website can be an effective tool to meet these needs. The design method used in this research involves the needs analysis stage, interface design, selecting an appropriate WordPress theme, developing special features relevant to the study program, and testing functionality. Using the WordPress CMS as a content management platform makes it easy to manage and update content independently without requiring advanced programming skills. The result of this research is a study program profile website that is responsive, attractive, and easy to navigate. The features implemented include information about study programs, curriculum, lecturers, facilities and the latest news. Apart from that, it is equipped with a search feature to make it easier for users to find the specific information they want. Implementing the WordPress CMS as a content management platform provides time and cost efficiency benefits in managing a website. In addition, the flexibility and ease of adding or changing content ensures that the information presented is always up to date and relevant. In conclusion, designing a study program profile website using CMS WordPress is an effective solution for presenting up-to-date and relevant information to stakeholders in the academic environment. This research makes a contribution to the field of information technology, especially in the use of CMS platforms for efficient content management in the context of higher education.
A machine translation model for abstractive text summarization based on natural language processing

“Knowledge is power and knowledge is liberating” conveys that there is a need for the capacity for creativity and that information is plentiful. The key application of natural language processing (NLP) is text summarization. It is a well-known technique for copying text, selecting accurate content, and get insight from the text. The purpose of this study is to propose for providing a summary of the text employing the seq2seq concept from the TensorFlow Python library. Through the use of deep learning-based data augmentation, the suggested method has the potential to increase the effectiveness of the text summary. Finally, the bilingual evaluation understudy (BLEU) criterion is used to judge the effectiveness of the suggested methodology
Analysis of Multicultural Education Policies Using Multi-Dimensional Policy Analysis Model

This study attempted to explore the direction in which policies should proceed by comprehensively analyzing multicultural education policies using a multi-dimensional policy analysis model. To this end, the multicultural education support plan announced by the Ministry of Education from 2006 to 2023 was analyzed and literature analysis was conducted.As a result of the study, it was found that at the normative level, it contained policy values that emphasized the understanding and harmony of diversity and guaranteeing educational opportunities for multicultural students. The structural level analyzed the organizational structure and support system of the government, related ministries, and local multicultural education support centers that plan, operate, and manage multicultural education policies. At the compositional level, we looked at the participation and resistance of teachers, students, and parents who are school members to multicultural education policies. The technical level analyzed the institutional basis such as curriculum, teacher training and education, and budget for operating multicultural education policies.
Unveiling deixis expressions in the Oxford junior secondary English webinar by Icy Lee

The significance of deixis expression within the English language context, particularly within the spoken form, is crucial for the further examination of deixis as a specific area of pragmatics in order to enhance the theory and practice of verbal communication development. Thus, this recent investigation aimed to assess the presence of deixis in the Oxford junior secondary English webinar conducted by Icy Lee from a pragmatics and discourse analysis perspective. The researchers employed a qualitative research approach and adopted a descriptive analysis method. The data were acquired from a webinar video available on the YouTube channel and website of the Oxford junior secondary English webinar. The transcripts were meticulously analyzed and evaluated based on Levinson’s (1983) theory. In summary, the findings uncovered that Icy Lee’s webinar exhibited five classifications of deixis: person, time, social, discourse, and place deixis. Notably, person deixis was the most frequently utilized category, accounting for 223 utterances (48%). Each deictic expression possesses a diverse significance contingent upon the speech context. However, certain implications were still brought to light in this current investigation, and it is anticipated that subsequent researchers will explore the webinar in alternative contexts.
Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism

This research addresses the pressing need to optimize Python-based social science applications for high-performance computing (HPC)systems, emphasizing the combined use of task and data parallelism techniques. The paper delves into a substantial body of research,recognizing Python’s interpreted nature as a challenge for efficient social science data processing. The paper introduces a Pythonprogram that exemplifies the proposed methodology. This program uses task parallelism with multi-processing and data parallelismwith dask to optimize data processing workflows. It showcases how researchers can effectively manage large datasets and intricatecomputations on HPC systems. The research offers a comprehensive framework for optimizing Python-based social science applicationson HPC systems. It addresses the challenges of Python’s performance limitations, data-intensive processing, and memory efficiency.Incorporating insights from a rich literature survey, it equips researchers with valuable tools and strategies for enhancing the efficiencyof their social science applications in HPC environments.
A cross-modal conditional mechanism based on attention for text-video retrieval

&lt;abstract&gt;&lt;p&gt;Current research in cross-modal retrieval has primarily focused on aligning the global features of videos and sentences. However, video conveys a much more comprehensive range of information than text. Thus, text-video matching should focus on the similarities between frames containing critical information and text semantics. This paper proposes a cross-modal conditional feature aggregation model based on the attention mechanism. It includes two innovative modules: (1) A cross-modal attentional feature aggregation module, which uses the semantic text features as conditional projections to extract the most relevant features from the video frames. It aggregates these frame features to form global video features. (2) A global-local similarity calculation module calculates similarities at two granularities (video-sentence and frame-word features) to consider both the topic and detail features in the text-video matching process. Our experiments on the four widely used MSR-VTT, LSMDC, MSVD and DiDeMo datasets demonstrate the effectiveness of our model and its superiority over state-of-the-art methods. The results show that the cross-modal attention aggregation approach can effectively capture the primary semantic information of the video. At the same time, the global-local similarity calculation model can accurately match text and video based on topic and detail features.&lt;/p&gt;&lt;/abstract&gt;
Advancing document-level event extraction: Integration across texts and reciprocal feedback

The primary objective of document-level event extraction is to extract relevant event information from lengthy texts. However, many existing methods for document-level event extraction fail to fully incorporate the contextual information that spans across sentences. To overcome this limitation, the present study proposes a document-level event extraction model called Integration Across Texts and Reciprocal Feedback (IATRF). The proposed model constructs a heterogeneous graph and employs a graph convolutional network to enhance the connection between document and entity information. This approach facilitates the acquisition of semantic information enriched with document-level context. Additionally, a Transformer classifier is introduced to transform multiple event types into a multi-label classification task. To tackle the challenge of event argument recognition, this paper introduces the Reciprocal Feedback Argument Extraction strategy. Experimental results conducted on both our COSM dataset and the publicly available ChFinAnn dataset demonstrate that the proposed model outperforms previous methods in terms of F1 value, thus confirming its effectiveness. The IATRF model effectively solves the problems of long-distance document context-aware representation and cross-sentence argument dispersion.
STUDI LITERATUR: PENERAPAN METODE ANITA CASSIDY DALAM PERENCANAAN STRATEGIS SISTEM INFOMASI

Advances in information technology make information systems / information technology have an important role for the running of an organization today causing changes in the role of the role of efficiency and effectiveness to a strategic role. Information system is an important tool to achieve organizational goals effectively. Each organization must identify information needs systematically and carry out an analysis of the mission and functions performed, who performs them, the data and supporting information needed to perform the various functions and processes needed to structure the information most usefully. With Information Systems Planning and Information Technology (IS/IT) it is hoped that it can be a guide in developing IS/IT in the future to achieve business goals