Reviewerly/exHarmony huggingface dataset:
Analysis Sentiment On Social Media Instagram Towards Metaverse Games Saindbox Aplha 2 With Support Vector Machine Algorithm

Authors:
raw_author_name: I Gusti Prahmana
raw_affiliation_string: STMIK Kaputama
raw_author_name: Kristina Annatasia Br Sitepu
raw_affiliation_string: STMIK KAPUTAMA

Abstract: Metaverse is part from development technology in the metaverse SandBox Alpha 2 Game world taking place worldwide , games in the virtual world like real very possible thing done . metaverse now Already in progress for can be implemented most affected technology to opinion from particular society _ enthusiasts game metaverse saydbox alpha 2. where game can create her world alone and various game For look for missions and coins can make money to sell _ in metaverse sandbox alpha. since emergence exists game that has been appeared on facebook that has been replaced be meta, create attention world public increasingly highlight technology this , someone _ welcome game the with good and some have _ worries to development technology the . So study This will dig analysis sentiment public Indonesia against development and use metaverse technology uses method algorithm Algorithm Support Vector Machine. analysis sentiment that will done on social media Facebook. Programming language used _ is Language Jupyter Notebook Python. Study This get results opinion public Indonesia to metaverse technology that shows behave neutral , negative and positive .
Augmented Reality-Based 3D Technology Learning Media for Human Respiratory Organs

Authors:
raw_author_name: Novi Aryani Fitri
raw_affiliation_string: Politeknik Negeri Pontianak
raw_author_name: Yasir Arafat
raw_affiliation_string: Politeknik Negeri Pontianak
raw_author_name: None Suharsono
raw_affiliation_string: Politeknik Negeri Pontianak

Abstract: Augmented Reality (AR) technology is selected for seamlessly integrating virtual objects into the real world. Users can represent objects in three dimensions, offering an interactive, real-time experience. The educational sector widely embraces Augmented Reality due to its interactive and timely advantages. This study focuses on the human respiratory system, demonstrating Augmented Reality's efficacy as an interactive learning tool for understanding respiratory organs. The learning material utilizes three-dimensional visualizations with animations, sound, and vibrant colors, enhancing comprehension and preventing boredom. Employing the Multimedia Development Method and software tools like Unity 3D, Blender, CorelDraw, Vuforia, and Visual Studio Code, the research produces a learning media application facilitating the understanding of human respiratory organs. Evaluation through questionnaires, with a 93.16% approval rate from teachers and students, confirms the application's suitability as an effective learning tool.
The Alteration and Mineralization Characteristics of Miocene Porphyry Cu-Au Deposits of Chagai Magmatic Belt, District Chagai, Balochistan, Pakistan

Authors:
raw_author_name: Fida Murad
raw_affiliation_string: Centre of Excellence in Mineralogy, University of Balochistan, Quetta, Pakistan
raw_author_name: Abdul Ghaffar
raw_affiliation_string: Department of Earth Sciences, Kunming University of Science and Technology, Kunming, China
raw_author_name: Inayat Ullah
raw_affiliation_string: Centre of Excellence in Mineralogy, University of Balochistan, Quetta, Pakistan. 2Department of Earth Sciences, Kunming University of Science and Technology, Kunming, China
raw_author_name: Abdul Shakoor Mastoi
raw_affiliation_string: Centre for Pure and Applied Geology, University of Sindh, Jamshoro, Pakistan

Abstract: Subduction related Miocene porphyry type deposits are found in the east-west trending Chagai magmaticbelt (CMB) in Pakistan's western margin, Balochistan. This arc exists on the west segment of the Tethyan metallogenicbelt in the south-west of Pakistan. Tethyan metallogenic belt is widely spread over 12,000 km from east to westdirection from Indochina, Tibet, Pakistan, Iran, Turkey and Alpine mountain range in Europe. During the last thirty toforty years several porphyry deposits have been reported in the Chagai magmatic arc, including the very large RekoDiq H14-H15, large Saindak, Tanjeel, H35, H8 and medium Dasht-e-Kain porphyry deposits and many small porphyrycopper deposits. These porphyry deposits were developed within the phase of calc-alkaline type magmatism in theChagai arc. Tonalite, quartz diorite, and monzonite host the porphyry deposits within the adjacent sedimentary wallrock units of Sinjrani Volcanic Group, Juzzak, Saindak, and Amalaf Formations. The concentric zonal pattern ofhydrothermal alteration in these porphyry deposits of the Chagai magmatic arc follows the world's major porphyrydeposits' alteration pattern. Zones of hydrothermal alteration from distal to proximal part includes a potassic alteration,sericitic-clay-chlorite alteration, sericitic alteration, argillic alteration and propylitic alteration. Major ore mineralizationin these deposits is of copper, gold, silver, molybdenum, and minor constituents of other base metals that have beenreported to occur within hydrothermal alteration zones in the Miocene porphyry Chagai magmatic arc.
Regression Trees for Fast and Adaptive Prediction Intervals

Authors:
raw_author_name: Cabezas, Luben M. C.
raw_author_name: Izbicki, Rafael
raw_author_name: Stern, Rafael B.

Abstract: Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid prediction regions around point predictions, but its naive application to regression problems yields non-adaptive regions. New conformal scores, often relying upon quantile regressors or conditional density estimators, aim to address this limitation. Although they are useful for creating prediction bands, these scores are detached from the original goal of quantifying the uncertainty around an arbitrary predictive model. This paper presents a new, model-agnostic family of methods to calibrate prediction intervals for regression problems with local coverage guarantees. Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage. We create this partition by training regression trees and Random Forests on conformity scores. Our proposal is versatile, as it applies to various conformity scores and prediction settings and demonstrates superior scalability and performance compared to established baselines in simulated and real-world datasets. We provide a Python package clover that implements our methods using the standard scikit-learn interface.
Quality Does Matter: A Detailed Look at the Quality and Utility of  Web-Mined Parallel Corpora

Authors:
raw_author_name: Ranathunga, Surangika
raw_author_name: de Silva, Nisansa
raw_author_name: Velayuthan, Menan

Abstract: We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied  Agents

Authors:
raw_author_name: Choi, Jae-Woo
raw_author_name: Yoon, Youngwoo
raw_author_name: Ong, Hyobin
raw_author_name: Kim, Jaehong
raw_author_name: Jang, Minsu

Abstract: Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners.
FLASH: Federated Learning Across Simultaneous Heterogeneities

Authors:
raw_author_name: Chang, Xiangyu
raw_author_name: Ahmed, Sk Miraj
raw_author_name: Krishnamurthy, Srikanth V.
raw_author_name: Guler, Basak
raw_author_name: Swami, Ananthram
raw_author_name: Oymak, Samet
raw_author_name: Roy-Chowdhury, Amit K.

Abstract: The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in data distribution, but also in data quality, as well as compute/communication latency. An integrated view of these diverse and concurrent sources of heterogeneity is critical; for instance, low-latency clients may have poor data quality, and vice versa. In this work, we propose FLASH(Federated Learning Across Simultaneous Heterogeneities), a lightweight and flexible client selection algorithm that outperforms state-of-the-art FL frameworks under extensive sources of heterogeneity, by trading-off the statistical information associated with the client's data quality, data distribution, and latency. FLASH is the first method, to our knowledge, for handling all these heterogeneities in a unified manner. To do so, FLASH models the learning dynamics through contextual multi-armed bandits (CMAB) and dynamically selects the most promising clients. Through extensive experiments, we demonstrate that FLASH achieves substantial and consistent improvements over state-of-the-art baselines -- as much as 10% in absolute accuracy -- thanks to its unified approach. Importantly, FLASH also outperforms federated aggregation methods that are designed to handle highly heterogeneous settings and even enjoys a performance boost when integrated with them.
Depth-aware Volume Attention for Texture-less Stereo Matching

Authors:
raw_author_name: Ding, Mingyu
raw_author_name: Zhan, Wei
raw_author_name: Tomizuka, Masayoshi

Abstract: Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tackle the texture deterioration in practical outdoor scenarios. Specifically, we introduce a depth volume supervised by the ground-truth depth map, capturing the relative hierarchy of image texture. Subsequently, the disparity discrepancy volume undergoes hierarchical filtering through the incorporation of depth-aware hierarchy attention and target-aware disparity attention modules. Local fine structure and context are emphasized to mitigate ambiguity and redundancy during volume aggregation. Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models. We extensively validate the superiority of our proposed methods on public datasets. Results demonstrate that our model achieves state-of-the-art performance, particularly excelling in scenarios with texture-less images. The code is available at https://github.com/ztsrxh/DVANet.
Learning Interpretable Concepts: Unifying Causal Representation Learning  and Foundation Models

Authors:
raw_author_name: Rajendran, Goutham
raw_author_name: Buchholz, Simon
raw_author_name: Aragam, Bryon
raw_author_name: Schölkopf, Bernhard
raw_author_name: Ravikumar, Pradeep

Abstract: To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and large language models show the utility of our unified approach.
International exchanges and cooperation in art education in Ukraine: challenges and opportunities

Authors:
raw_author_name: Tamara Skoryk
raw_affiliation_string: Institutional Arts Department, T.H. Shevchenko National University «Chernihiv Colehium», Chernihiv, Ukraine
raw_author_name: Kateryna Demchyk
raw_affiliation_string: Department of Theory and Methods of Preschool Education, Kamianets-Podilskyi Ivan Ohienko National University, Kamianets-Podilskyi, Ukraine
raw_author_name: Iryna Sidorova
raw_affiliation_string: Department of Vocal and Choral Training, Theory and Methodology of Music Education named after the People's, Faculty of Arts and Art-Educational Technologies, Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Vinnytsia, Ukraine

Abstract: The purpose of the article is to study international exchanges and cooperation in art education in Ukraine, to identify challenges and opportunities in the development of these areas in the future. to achieve this goal, the article uses general scientific methods of analysis and synthesis. The methods of abstraction, comparison, content analysis, and the prognostic method were also used, which made it possible to formulate some comments for the future development of international cooperation in art education in Ukraine. The results of the study highlight the importance of international cooperation, which is becoming an increasingly important aspect of modern art education. The analysis of the legislative framework has shown that Ukraine has created favourable conditions for academic exchange, and joining the Bologna system contributes to the internationalisation of higher education in the arts. These trends are also reflected in the development strategies of art universities in different regions of Ukraine, such as Kharkiv, Lviv and Kyiv, where establishing international exchanges is considered an important priority for educational institutions. In addition, the Kyiv National University of Culture and Arts actively uses digital channels to transmit information, especially in remote locations. Although distance learning has its limitations in art education, the conditions of modern reality, such as Russian aggression, create an opportunity to organise international cooperation with foreign partners without physical risk. It is important to note that there are common challenges to the successful implementation of international exchanges and cooperation in art education, such as cultural and language barriers, financial support, visa and administrative restrictions, as well as academic requirements and potential global threats. However, a possible way to overcome these difficulties is to use the international experience of involving NGOs or special state structures that would coordinate the provision of grants for academic mobility of arts students. The conclusions emphasise that it is also worth considering the possibility of special digital platforms that provide opportunities for internships and distance learning, which can be an effective solution for organising international cooperation in the current situation.
Sequential composition of propositional logic programs

Authors:
raw_author_name: Christian Antić
raw_affiliation_string: Vienna University of Technology, Vienna, Austria

Abstract: Abstract This paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any explicit reference to operators. This bridges the conceptual gap between the syntax and semantics of a propositional logic program in a mathematically satisfactory way.
Language Teaching Materials for Writing Advertising Texts

Authors:
raw_author_name: Gusti Yarmi
raw_affiliation_string: Postgraduate Indonesian language and literature education, Jakarta State University, Indonesia

Abstract: This study is based on extensive library research, utilizing a wide range of information and resources from various sources. Having the right instructional materials is crucial for the successful completion of any academic program, including Indonesian language classes. Junior high school Indonesian language classes in Class VIII explore a range of subjects, one of which is the art of crafting advertisement copy. To achieve optimal outcomes, it is crucial to utilize appropriate instructional resources while acquiring proficiency in the Indonesian language for advertising purposes. This research aimed to provide a comprehensive description of the different types of advertisements that can be utilized as teaching materials in Indonesian language courses, along with an exploration of the diverse concepts associated with these materials.
Scaling adaptive quantum simulation algorithms via operator pool tiling

Authors:
raw_author_name: John S. Van Dyke
raw_affiliation_string: Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, Virginia 24061, USA
raw_author_name: George S. Barron
raw_affiliation_string: Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, Virginia 24061, USA
raw_author_name: Nicholas J. Mayhall
raw_affiliation_string: Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, Virginia 24061, USA
raw_author_name: Edwin Barnes
raw_affiliation_string: Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, Virginia 24061, USA
raw_author_name: Sophia E. Economou
raw_affiliation_string: Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, USA; Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, Virginia 24061, USA

Abstract: A systematic and easily generalizable method to perform adaptive variational quantum simulations on lattice models, termed operator tiling, is proposed, in which a small problem instance is first studied to learn the most relevant operators. State preparation circuits using operators identified from solving the small problem instance are then iteratively tiled to larger problem instances. The method is demonstrated on strongly correlated spin models in one and two dimensions and shown to yield effective ans\"atze.
A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Authors:
raw_author_name: P. R. Joe Dhanith
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, Chennai, 600127, India
raw_author_name: B. Surendiran
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, 609609, India
raw_author_name: G. Rohith
raw_affiliation_string: School of Electronics and Communication Engineering, Vellore Institute of Technology Chennai Campus, Chennai, 600127, India
raw_author_name: K. Valli Devi
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, Chennai, 600127, India

Abstract: Abstract Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an $$aspect$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>aspect</mml:mi> </mml:mrow> </mml:math> and $$context$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>context</mml:mi> </mml:mrow> </mml:math> words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.
A cross-talk robust multichannel VAD model for multiparty agent  interactions trained using synthetic re-recordings

Authors:
raw_author_name: Han, Hyewon
raw_author_name: Kumar, Naveen

Abstract: In this work, we propose a novel cross-talk rejection framework for a multi-channel multi-talker setup for a live multiparty interactive show. Our far-field audio setup is required to be hands-free during live interaction and comprises four adjacent talkers with directional microphones in the same space. Such setups often introduce heavy cross-talk between channels, resulting in reduced automatic speech recognition (ASR) and natural language understanding (NLU) performance. To address this problem, we propose voice activity detection (VAD) model for all talkers using multichannel information, which is then used to filter audio for downstream tasks. We adopt a synthetic training data generation approach through playback and re-recording for such scenarios, simulating challenging speech overlap conditions. We train our models on this synthetic data and demonstrate that our approach outperforms single-channel VAD models and energy-based multi-channel VAD algorithm in various acoustic environments. In addition to VAD results, we also present multiparty ASR evaluation results to highlight the impact of using our VAD model for filtering audio in downstream tasks by significantly reducing the insertion error.