Reviewerly/exHarmony huggingface dataset:
An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greek

Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as "kosmos" (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. In this paper, we introduce EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. We show empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. We also discuss the challenges of fitting these models.
Structure Learning with Adaptive Random Neighborhood Informed MCMC

In this paper, we introduce a novel MCMC sampler, PARNI-DAG, for a fully-Bayesian approach to the problem of structure learning under observational data. Under the assumption of causal sufficiency, the algorithm allows for approximate sampling directly from the posterior distribution on Directed Acyclic Graphs (DAGs). PARNI-DAG performs efficient sampling of DAGs via locally informed, adaptive random neighborhood proposal that results in better mixing properties. In addition, to ensure better scalability with the number of nodes, we couple PARNI-DAG with a pre-tuning procedure of the sampler's parameters that exploits a skeleton graph derived through some constraint-based or scoring-based algorithms. Thanks to these novel features, PARNI-DAG quickly converges to high-probability regions and is less likely to get stuck in local modes in the presence of high correlation between nodes in high-dimensional settings. After introducing the technical novelties in PARNI-DAG, we empirically demonstrate its mixing efficiency and accuracy in learning DAG structures on a variety of experiments.
Online Signal Estimation on the Graph Edges via Line Graph Transformation

We propose the Line Graph Normalized Least Mean Square (LGNLMS) algorithm for online time-varying graph edge signals prediction. LGNLMS utilizes the Line Graph to transform graph edge signals into the node of its edge-to-vertex dual. This enables edge signals to be processed using established GSP concepts without redefining them on graph edges.
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs

In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as evaluators for texts produced by various generative models. In this study, we delve into the potential of LLMs as reliable assessors of factual consistency in summaries generated by text-generation models. Initially, we introduce an innovative approach for factuality assessment using LLMs. This entails employing a singular LLM for the entirety of the question-answering-based factuality scoring process. Following this, we examine the efficacy of various LLMs in direct factuality scoring, benchmarking them against traditional measures and human annotations. Contrary to initial expectations, our results indicate a lack of significant correlations between factuality metrics and human evaluations, specifically for GPT-4 and PaLM-2. Notable correlations were only observed with GPT-3.5 across two factuality subcategories. These consistent findings across various factual error categories suggest a fundamental limitation in the current LLMs' capability to accurately gauge factuality. This version presents the information more concisely while maintaining the main points and findings of the original text.
Collaboration in Immersive Environments: Challenges and Solutions

Virtual Reality (VR) and Augmented Reality (AR) tools have been applied in all engineering fields in order to avoid the use of physical prototypes, to train in high-risk situations, and to interpret real or simulated results. In order to complete a shared task or assign tasks to the agents in such immersive environments, collaboration or Shared Cooperative Activities are a necessity. Collaboration in immersive environments is an emerging field of research that aims to study and enhance the ways in which people interact and work together in Virtual and Augmented Reality settings. Collaboration in immersive environments is a complex process that involves different factors such as communication, coordination, and social presence. This paper provides an overview of the current state of research on collaboration in immersive environments. It discusses the different types of immersive environments, including VR and AR, and the different forms of collaboration that can occur in these environments. The paper also highlights the challenges and limitations of collaboration in immersive environments, such as the lack of physical cues, cost and usability and the need for further research in this area. Overall, collaboration in immersive environments is a promising field with a wide range of potential applications, from education to industry, and it can benefit both individuals and groups by enhancing their ability to work together effectively.
Improving Interpersonal Communication by Simulating Audiences with Language Models

How do we communicate with others to achieve our goals? We use our prior experience or advice from others, or construct a candidate utterance by predicting how it will be received. However, our experiences are limited and biased, and reasoning about potential outcomes can be difficult and cognitively challenging. In this paper, we explore how we can leverage Large Language Model (LLM) simulations to help us communicate better. We propose the Explore-Generate-Simulate (EGS) framework, which takes as input any scenario where an individual is communicating to an audience with a goal they want to achieve. EGS (1) explores the solution space by producing a diverse set of advice relevant to the scenario, (2) generates communication candidates conditioned on subsets of the advice, and (3) simulates the reactions from various audiences to determine both the best candidate and advice to use. We evaluate the framework on eight scenarios spanning the ten fundamental processes of interpersonal communication. For each scenario, we collect a dataset of human evaluations across candidates and baselines, and showcase that our framework's chosen candidate is preferred over popular generation mechanisms including Chain-of-Thought. We also find that audience simulations achieve reasonably high agreement with human raters across 5 of the 8 scenarios. Finally, we demonstrate the generality of our framework by applying it to real-world scenarios described by users on web forums. Through evaluations and demonstrations, we show that EGS enhances the effectiveness and outcomes of goal-oriented communication across a variety of situations, thus opening up new possibilities for the application of large language models in revolutionizing communication and decision-making processes.
Predicting Arbitrary State Properties from Single Hamiltonian Quench Dynamics

Analog quantum simulation is an essential routine for quantum computing and plays a crucial role in studying quantum many-body physics. Typically, the quantum evolution of an analog simulator is largely determined by its physical characteristics, lacking the precise control or versatility of quantum gates. This limitation poses challenges in extracting physical properties on analog quantum simulators, an essential step of quantum simulations. To address this issue, we introduce the Hamiltonian shadow protocol, which uses a single quench Hamiltonian for estimating arbitrary state properties, eliminating the need for ancillary systems and random unitaries. Additionally, we derive the sample complexity of this protocol and show that it performs comparably to the classical shadow protocol. The Hamiltonian shadow protocol does not require sophisticated control and can be applied to a wide range of analog quantum simulators. We demonstrate its utility through numerical demonstrations with Rydberg atom arrays under realistic parameter settings. The new protocol significantly broadens the application of randomized measurements for analog quantum simulators without precise control and ancillary systems.
Research on Target Detection Technology of Aircraft Satellite Images Based on Improved YOLOv5 Model

This study aims to improve the YOLOv5 model and design a multi-scale strategy FPN for the aircraft satellite image target detection task to improve the detection accuracy and coverage by detecting targets at different scales. In addition, the detection performance is further improved by introducing the MobileViTAttention attention mechanism to enhance the model's attention to important target regions. Meanwhile, we optimize the loss function to better train the model and improve the accuracy and stability of target detection. The experimental results show that the improved YOLOv5 model achieves significant improvements in target detection for aircraft satellite images. Compared with the traditional method and other target detection models, the improved YOLOv5 model improves 5.24% and 7.75% in terms of accuracy and recall, respectively. Finally, we discuss the potential value of the model for later practical applications such as military surveillance, urban planning and disaster response.
An Efficient Machine-Generated Data Modeling Approach Based on Domain-Aware Knowledge for Intelligent Consumer Electronics

Data modeling is a critical component in the development of next-generation consumer electronics (CE), as it provides massive data support to implement smart services related to CE products, such as complex correlation analysis of the CE market, the development of technology, and consumer behaviors. However, the existing works still face the following challenges: (1) the manual data processing is time-consuming and labor-intensive while analyzing the massive data with domain knowledge; (2) insufficient attention is given to the analysis of relationships among data collections; and (3) redundant data is not eliminated, leading to excess computational burden. To address these challenges, we propose an efficient automatic data modeling method that employs domain-aware knowledge, significantly reducing the cost of data modeling. Our approach starts by leveraging a knowledge-based classifier to extract domain-related resources from open common single-document summarization datasets (OC-SDS), thus reducing data acquisition expenses. Then after collecting related documents by original summaries, a two-stage filtering process is applied to eliminate redundant and non-related documents. Finally, the original summaries are iteratively updated until reaching the threshold, enhancing informativeness and introducing novel expressions without the need for manual marking. As a practical application, we take emergency news as an example and built a typical dataset based on our method. After an extensive analysis, the empirical results evidence that our method is superior in scale and quality compared to the existing methods, with over 13.7% more related data, providing a valuable contribution to the field of CE product development.
The development of syntactic complexity in university students’ essays

This research aimed to describe quantitatively the development of students’ syntactical complexity during the Essay Writing Course measured with the L2SCA tool by Lu. The research basically implemented a descriptive-quantitative research design. The primary data were collected from 20 students majoring in Agribusiness and taking an essay writing course in Yogyakarta Muhammadiyah University (UMY) during one semester from February to July 2022 in a researcher class. They wrote three essays gradually from one session to the third session of the class. The essays were analyzed quantitatively using the L2SCA tool by following the indices of the tools as the reference to see the students’ syntactical complexity. In addition, the participants wrote three essays divided into three times writing tests. Overall, the analysis method was conducted by following a systematic procedure; classifying, scoring, displaying, describing, interpreting, discussing, and concluding. As a result, this study reveals that syntactical development of students’ essays during a semester session of writing course fluctuated from the first writing until the third writing. Although along the writing supervision, they had consultation sessions, the result did not show gradual and constant progress. It implied that the students’ acquisition of syntactical complexity represented in writing did not improve progressively in all syntactical indices. However, among the three essays the participants wrote, they excel most in second essay or argumentative essay. Further, it is necessary to relate the essay’s genre to the result of students’ writing for each of the essays has a special characteristic and level of difficulty.
Grammatical interference in Islamic school students’ English narrative writings

Employing a qualitative approach, this study aims to discover the types of grammatical interference that mostly occur in narrative texts written by Indonesian students at a senior high school level. The grammatical interference covered in this article is categorized into two, namely syntactical interference and morphological interference. Moreover, 20 eleventh-grader students of an Islamic school were chosen randomly to be the sample of this research. The students were asked to compose a narrative text with a topic that has been determined by the teacher. Their writings were then analyzed descriptively to find out the dominant types of grammatical interference made by the students. It was revealed that the students encountered morphological inferences related to tenses, articles, subject-plural forms, and pronouns. Besides, syntactical inferences regarding word order, noun phrase, and passive voice are found in the students’ writings. Thus, it implies that students often used the grammatical rules of their first language in composing their writings in English.
Thesis writing anxiety among Indonesian pre-service teachers of English: A survey study

For decades, language anxiety has been a popular research topic in the field of language learning and studies suggested that learners' second/foreign (L2) writing anxiety, one of the anxiety constructs, negatively affected learners' L2 writing achievement. This study aimed to investigate the thesis writing anxiety of Indonesian pre-service English teachers working on their theses in the final semesters of their study. The participants of this study were 105 pre-service teacher participants from ten universities across six different provinces in Indonesia. The study employed an online survey as the method of data collection. It found that generally, the participants had a moderate level of thesis writing anxiety. The detailed findings were further elaborated under four categories: “Difficulty in Grammar and Writing Anxiety”, “Negative Feelings when Writing a Thesis in English”, “Positive Feelings when Writing a Thesis in English”, and “Degree of Influence of Advisors and Peers to Writing Anxiety”. Based on the findings, possible contributions, implications, and limitations are suggested alongside suggested directions for future studies.
Error analysis on students’ essay writing: A case study

The objective of this research is to determine the types of errors committed by the students of the English Department at the Faculty of Language and Science, Wijaya Kusuma Surabaya University (FBS UWKS), as well as to describe the origins of these errors. The research conducted was descriptive in nature, utilizing qualitative methods. The participants involved in the study were fourth semester students of the English Department at FBS UWKS. Out of the 20 participants, a total of 80 data sources were obtained during the learning process. The researchers collected the data through the use of interview sheets and essays. Once collected, the data underwent a process of reduction, classification, display, and analysis. The procedures for data analysis were outlined as follows: 1) the collection of essays, 2) the classification, counting, and analysis of errors based on the error types proposed by Brown (2007), and 3) the analysis of errors to identify their sources using the concepts of interlingual transfer and intralingual transfer as proposed by Brown (2007) and Touchie (1986). From the analysis, it was discovered that the errors could be categorized into two main groups, namely essay organization errors and grammatical errors. The essay organization errors consisted of seven classifications, including issues with the thesis statement, topic sentences, unity, coherence and cohesion, as well as supporting sentences. On the other hand, the grammatical errors encompassed omission, addition, and punctuation mistakes. The sources of these errors were found to be inadequate learning, false conceptualizations, overgeneralization, and ineffective teaching practices.
Exploring challenges and strategies in English speaking among Indonesian university students: A case study of AKM university

English language education in Indonesia spans from primary school to university levels. However, achieving mastery in English, particularly in speaking, remains a significant challenge for Indonesian university students. This study aimed to investigate the problems faced by these students in speaking English and describe their efforts to overcome this deficiency. The research utilized a descriptive qualitative method, employing questionnaires and interviews as instruments. The study participants consisted of 45 second-semester students from the English Education Department at AKM University (pseudonym). The findings revealed that students encountered both linguistic and psychological challenges when speaking English. Linguistic problems included confusion in tense usage, a lack of grammar understanding, and difficulty in selecting appropriate vocabulary. Psychological issues encompassed feelings of nervousness, lack of confidence, fear of making mistakes, and the fear of being laughed at. Furthermore, insufficient practice emerged as an environmental factor contributing to speaking difficulties. To address these challenges, students adopted various learning strategies proposed by Oxford (1990), including memory strategies, cognitive strategies, metacognitive strategies, affective strategies, and social strategies.
STRATEGI SEKOLAH MENENGAH KEJURUAN SWASTA BINA INFORMATIKA TANGERANG SELATAN MENANGANI KRISIS AGAR BERTAHAN SELAMA PANDEMI COVID-19

To avoid the transmission of covid-19 through physical or face-to-face meetings, the government had implemented policies ranging from Large-Scale Social Restrictions (PSBB) to the Implementation of Community Activity Restrictions (PPKM) Level 1 to Level 4. Private Vocational High Schools (SMKS) Bina Informatika South Tangerang, including private educational institutions affected by the implementation of PSBB and PPKM. Most educational activities are implemented online through the Zoom Meeting application or Google Meet. The new student acceptance rate (PPDB) decreased by 10% during the two years of the covid-19 pandemic because many parents lost their income and chose to send their children to public schools so that costs were cheaper. In addition, almost half of the students from several classes were delayed in paying their monthly school fees because their parents' income was decreasing. Not to mention that online learning is considered less effective due to several obstacles. Through this research, the author wants to know the strategy of SMKS Bina Informatika in dealing with the crisis to survive during the covid-19 pandemic. This study uses a qualitative method with a descriptive approach and SWOT analysis through library support to strengthen the results of the study. While the data collection techniques are through interviews, observation, documentation and triangulation or a combination. Based on the results of the study, when covid-19 cases increased at the beginning of the April 2020 pandemic, SMKS Bina Informatika implemented full online learning. Meanwhile, when the covid-19 pandemic slowed from August 2021 to March 2022, Bina Informatics implemented hybrid or 50% face-to-face learning. 100% face-to-face learning since April 2022 when the South Tangerang area was declared covid-19 free. To facilitate access to subject matter, student and teacher attendance, and assessment, the BI-SMART application is used which can be accessed via mobile phones and computers. For students whose parents are financially constrained, SMKS Bina Informatika provides a flexible payment deadline, and all students receive a 50% discount. For new students there is dispensation for tuition fees or school buildings can be repaid three to four times.
Fault tolerance systems in open source cloud computing environments–A systematic review

Considering that there are several distinct cloud computing environments and several suggested approaches for the treatment of fault tolerance for such environments, the objective of the study presented here is a systematization of fault tolerance proposals that results in a survey and the generation of a guided consultation environment for reading the relevant techniques for each case. With the systematization of proposed solutions, it is intended to obtain a document that administrators of cloud computing systems can use. This work points out which techniques apply to which problems, including the advantages and disadvantages of each technique, and facilitates the support process for these administrators in handling the failures. Finally, with the information obtained, a website will be generated to store some of this information. This virtual environment is a prototype of a recommendation environment for cloud fault tolerance. At first, the recommendation will occur through guided search so that administrators of cloud computing systems can have better conditions to handle failures in their environments
PENGEMBANGAN MEDIA PEMBELAJARAN E-LEARNING BERBASIS EDMODO PADA MATA PELAJARAN PRAKARYA KEWIRAUSAHAAN KELAS X SMA

The purpose of this study was to develop Edmodo-based learning media in SMA Negeri 17, Bekasi City. The method used in this research is the research and development (R&amp;D) method using the Borg and Gall model. The research was carried out by starting the research stage and information and planning, preliminary development of the product, preliminary field testing, revision of the main product, objectives and implementation. The development stage is carried out in three stages, namely product manufacturing, internal product testing, and product revision. Testing was carried out by 3 experts, namely 2 instructional media experts, and 1 material expert. The results of the study were carried out with the CVR results, stating that learning was very feasible to use, with a percentage of 93% and 100%, and material experts related to learning materials were very feasible to use with a percentage of 90.91%. It was conveyed that the development of Edmodo-based e-learning learning media in PKWU subjects, batik material is suitable to be applied in high school through distance learning, so that it is more supportive of teachers to deliver learning materials well.&#x0D; Keywords: E-Learning, Edmodo, PKWU, Batik