Reviewerly/exHarmony huggingface dataset:
My Husband! If you were the Good Husband, you would be able (曲) to give (扌) me the most Precious Thing, Even the Star in the Heaven (辰) (Tcheonzamun 641st-656th)

French Missionary Dallet (1874) wrote in his book that ‘the thousand character essay’ had been used as a textbook for Chinese character for the children in the ancient China. ‘The thousand character essay’ is in Korea called as ‘Tcheonzamun (千字文)’, and the Korean people deeply believe that Chinese people has created this famous book (Tcheonzamun) (Han, 1583). However, recently Park et al., (2021a) and Park et al., (2021b) suggested that Tcheonzamun possibly has been created by Korean-speaking people. There are two translating methods. The first method is through Korean pronunciation of Chinese character, and the second one is through the meaning of Chinese character in Tcheonzamun. Both the pronunciation of Chinese character in Korean language and the Chinese character itself is dependent on somewhat old book of Tcheonzamun (Han, 1583). Additionally, the method of deletion was used in this work. The same part of Chinese character was deleted in the range of same line. In this work, a line means the 4 Chinese characters. For example, (653-656 務(Mu) 玆(Za) 稼(Ga) 穡(Saeg)) is a line. And the translation through the meaning of Chinese character will be done with the remained part after the deletion. The thousand character essay is in Korea called as ‘Tcheonzamun’ (Han, 1583). This is the work of translation of Tcheonzamun poem. The title of this study is ‘My husband! If you were the good husband, you would be able (曲) to give (扌) me the most precious thing, even the star in the heaven (辰) (Tcheonzamun 641st-656th)’. This is the first method of this translating work. It is through Korean pronunciation of Chinese characters in this Tcheonzamun poem (Tcheonzamun 641st-656th). The theme of this Tcheonzamun poem is as follows. My husband! I(your wife) am smaller (幺) and smaller (幺) than you! Do you want me to work (務) well for you? My husband! Can you hear my hope? Here, this is my desire! I want to say (口) to you freely at our home (家), and I want to express to you my opinion (口) freely in our life (土)! And the miserable wife says her secret in her mind. “My husband! If you do not accept my sincere hope”, “My husband! From now on, I shall come (Ga_se!) to another man! (Mut_hyeo)”.
Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model

Pictorial visualization seamlessly integrates data and semantic context into visual representation, conveying complex information in a manner that is both engaging and informative. Extensive studies have been devoted to developing authoring tools to simplify the creation of pictorial visualizations. However, mainstream works mostly follow a retrieving-and-editing pipeline that heavily relies on retrieved visual elements from a dedicated corpus, which often compromise the data integrity. Text-guided generation methods are emerging, but may have limited applicability due to its predefined recognized entities. In this work, we propose ChartSpark, a novel system that embeds semantic context into chart based on text-to-image generative model. ChartSpark generates pictorial visualizations conditioned on both semantic context conveyed in textual inputs and data information embedded in plain charts. The method is generic for both foreground and background pictorial generation, satisfying the design practices identified from an empirical research into existing pictorial visualizations. We further develop an interactive visual interface that integrates a text analyzer, editing module, and evaluation module to enable users to generate, modify, and assess pictorial visualizations. We experimentally demonstrate the usability of our tool, and conclude with a discussion of the potential of using text-to-image generative model combined with interactive interface for visualization design.
A Brief Study of Privacy-Preserving Practices (PPP) in Data Mining

Data mining is the way toward mining fascinating patterns or information from an enormous level of the database. Data mining additionally opens another risk to privacy and data security.One of the maximum significant themes in the research fieldis privacy-preserving DM (PPDM). Along these lines, the investigation of ensuring delicate information and securing sensitive mined snippets of data without yielding the utility of the information in a dispersed domain.Extracted information from the analysis can be rules, clusters, meaningful patterns, trends or classification models. Privacy breach occur at some stage in the communication of data and aggregation of data. So far, many effective methods and techniques have been developed for privacy-preserving data mining, but yields into information loss and side effects on data utility and data mining effectiveness downgraded. In the f ocal point of consideration on the viability of Data Mining, Privacy and rightness should be improved and to lessen the expense.
CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction

Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray. We contend this duplication introduces noise in empty and occluded spaces, posing challenges for producing high-quality 3D geometry. Drawing inspiration from traditional multi-view stereo methods, we propose an end-to-end 3D neural reconstruction framework CVRecon, designed to exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning. Furthermore, we present Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature representation that encodes view-dependent information with improved integrity and robustness. Through comprehensive experiments, we demonstrate that our approach significantly improves the reconstruction quality in various metrics and recovers clear fine details of the 3D geometries. Our extensive ablation studies provide insights into the development of effective 3D geometric feature learning schemes. Project page: https://cvrecon.ziyue.cool/
musicAL.GO - Algorithmic Music Generation

Abstract: Composing music is a very interesting challenge that tests the composer’s creative capacity, whether it is a human or a computer. Although there have been many arguments on the matter, almost all of music is some regurgitation or alteration of a sonic idea created before. Thus, with enough data and the correct algorithm, machine learning and deep learning algorithms and techniques should be able to make music that would sound human. This paper outlines approaches to music composition through Neural Network models, specifically char-RNN and although there were some mixed results by the model, it is evident that musical ideas can be gleaned from these algorithms in hopes of making a new piece of music. Based on this principle of algorithmically generating new music, this project aims at creating a web application for music lovers providing a repository of copyright free musical pieces generated artificially, enabled with numerous other features and functionalities.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction

Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. The federated learning technique is well-suited to tackle these challenges. However, federated learning comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. We show how a subset of clients can be recruited without sacrificing model performance whilst, at the same time, significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 Intensive Care Units, we show how the models trained in federations made up from recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time.
The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.
Development of Web-based Tuition Payment Information System Supported by Whatsapp Notifications

The use of the internet at the Cersa Pasaman Vocational High School (SMK) is still not optimal, the internet is only used for school information and there is no school financial information system. Based on observations, tuition payments at schools are still manual and make the work of the finance department slower. This type of research is research and development data retrieval is carried out to users of the web-based SPP payment information system. The research sample consisted of 30 people consisting of 5 people for a limited trial and 25 people for a wider trial taken from the principal, Administration Officer, teachers and students of SMK Cersa Pasaman, the instrument of this research was a questionnaire to determine the feasibility of the SPP payment information system. The results obtained from this research development is a web-based tuition payment information system. Based on the results of this study, it can be concluded that the developed information system is declared valid with an average value of 85.67%. The information system developed was very practical from the results of the limited trial to teachers and students with an average value of 92.60%, while the results of a wider trial obtained an average value of 92.68%. Based on the findings of this study, it was concluded that the web-based SPP payment information system at SMK Cersa Pasaman was valid, practical and feasible to use for student tuition payments.
The Implementation of Picture Series as Learning Media to Improve Students' Writing Ability Related to Historical Events in Recount Text

The objectives of this research are: (1) to find out the class situation during the implementation of picture series in teaching writing, and (2) to determine the improvement of students' ability to write recount text using picture series. The research used a qualitative. The classroom action research design used in this research consisted of two cycles. The data were collected from observation, field notes, and interviews. The research participants are 34 students in the tenth grade of SMA YKM Tanjungsari. The results showed that picture series could improve students’ ability to write recount text, create a fun learning atmosphere, and increase students’ writing interest. In the first cycle, the percentage of learning activities was 18%; in the second cycle, 80% of students got excellent categories. The two cycles could effectively improve students' writing ability in recounting text. The students could develop their ideas to make a story and were active and enthusiastic in the teaching-learning process. It demonstrates that writing a recount text using picture series as learning media for X MIPA 1 student at SMA YKM Tanjungsari has been improved.
Zero Trust Chain A Design Pattern for Improved Interoperability and Security in Polkadot

This research article presents various design patterns for improving interoperability in Polkadot, a blockchain platform. These patterns include chain bridges, interoperability standards, common asset identifiers, governance agreements, oracle chains, and a hypothetical design pattern called Zero Trust Chain. Implementation of these design patterns can help improve security and confidence in transactions between different chains on the Polkadot network, allowing for faster and more efficient communication. The article also emphasizes the importance of interoperability in blockchain technology and highlights Polkadot's flexibility in creating customized specialized chains that can further improve interoperability on the network. Overall, this article highlights how design patterns can improve interoperability in Polkadot, which could lead to greater adoption of blockchain technology in various industries.
Training and Evaluation of a Multilingual Tokenizer for GPT-SW3

This paper provides a detailed discussion of the multilingual tokenizer used for GPT-SW3. It was trained on the Nordic Pile using the SentencePiece library and the BPE algorithm. We outline the tokenizer's most important features and share details on its learned vocabulary. In addition, we systematically analyze the properties and evaluate the performance of the tokenizer with regard to the different languages present in the data.
Comparison of SAT-based and ASP-based Algorithms for Inconsistency Measurement

We present algorithms based on satisfiability problem (SAT) solving, as well as answer set programming (ASP), for solving the problem of determining inconsistency degrees in propositional knowledge bases. We consider six different inconsistency measures whose respective decision problems lie on the first level of the polynomial hierarchy. Namely, these are the contension inconsistency measure, the forgetting-based inconsistency measure, the hitting set inconsistency measure, the max-distance inconsistency measure, the sum-distance inconsistency measure, and the hit-distance inconsistency measure. In an extensive experimental analysis, we compare the SAT-based and ASP-based approaches with each other, as well as with a set of naive baseline algorithms. Our results demonstrate that overall, both the SAT-based and the ASP-based approaches clearly outperform the naive baseline methods in terms of runtime. The results further show that the proposed ASP-based approaches perform superior to the SAT-based ones with regard to all six inconsistency measures considered in this work. Moreover, we conduct additional experiments to explain the aforementioned results in greater detail.
Bose Einstein condensate as nonlinear block of a Machine Learning pipeline

Physical systems can be used as an information processing substrate and with that extend traditional computing architectures. For such an application the experimental platform must guarantee pristine control of the initial state, the temporal evolution and readout. All these ingredients are provided by modern experimental realizations of atomic Bose Einstein condensates. By embedding the nonlinear evolution of a quantum gas in a Machine Learning pipeline, one can represent nonlinear functions while only linear operations on classical computing of the pipeline are necessary. We demonstrate successful regression and interpolation of a nonlinear function using a quasi one-dimensional cloud of potassium atoms and characterize the performance of our system.
An Empirical Study of Multimodal Model Merging

Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the models are trained on similar tasks and with the same initialization. In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities. Furthermore, we conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient modality-agnostic architecture. Through comprehensive experiments, we systematically investigate the key factors impacting model performance after merging, including initialization, merging mechanisms, and model architectures. We also propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes. Our analysis leads to an effective training recipe for matching the performance of the modality-agnostic baseline (i.e., pre-trained from scratch) via model merging. Our method also outperforms naive merging significantly on various tasks, with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% on ADE20k. Our code is available at https://github.com/ylsung/vl-merging
Interpreting Vision and Language Generative Models with Semantic Visual Priors

When applied to Image-to-text models, interpretability methods often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. Those explanations are expensive to compute and unable to comprehensively explain the model's output. Therefore, these models often require some sort of approximation that eventually leads to misleading explanations. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized over other explainability methods.
A Robotic Arm-based Telepresence for Mixed-Reality Telecollaboration System

In mixed-reality telecollaboration, it is often difficult for remote users to actively and naturally control the viewpoint. We propose a viewpoint controllable telepresence with a robotic arm that carries a stereo camera in the local environment so that the remote user can control the robotic arm by moving his or her head to actively and flexibly observe the local environment. Additionally, we built a mixed-reality telecollaboration prototype that adds avatar and nonverbal cues for the remote user to the local user side, enabling the remote user to better guide the local user. In this paper, we discuss our prototype, make design recommendations, and discuss configuration and implementation.
Ten years of Immersive VR Installations - Past, Present, and Future

Virtual Reality (VR) has found application in many fields including art history, education, research, and smart industry.Immersive 3D screens, large-scale displays, and CAVE systems are time-tested VR installations in research and scientific visualization.In this paper, we present learnings and insights from ten years of operating and maintaining a visualization center with large-scale immersive displays and installations.Our report focuses on the installations themselves as well as the various developments of the center over time.In addition, we discuss the advantages, challenges, and future development of a location-based VR center.
Establishing Design Computing and Extended Reality Facilities for Remote Virtual Reality Training

We discuss the existing facilities at the Design Computing and Extended Reality (DCXR) Lab at George Mason University, which comprise mostly commercial-off-the-shelf computing and extended reality devices, for conducting research on virtual reality-based training. We also share thoughts on extending the facilities for conducting more sophisticated virtual reality (VR) training research in the future, which features more advanced functionalities such as remote VR training, adaptive training, and co-training in VR. In particular, we discuss a remote VR training platform to be established between George Mason University and Purdue University.
A Piecewise Approach to Mapping Interactions between Room-scale Environments in Remote Mixed/Augmented Reality

Naturalistic interactions from remote settings require the user to have the ability to move and interact with their environment. However, a direct mapping of the interactions onto their photorealistic avatars will cause errors, primarily due to the different spatial configurations between the environments. Hence, the interactions need to be redirected, naturalistically. This redirection is a computationally intensive process and current techniques can only be used for localized spaces. We propose a piecewise approach, where the interaction mapping is split into a 2D locomotion and a 3D gesture redirection mechanism, reducing the computational requirement and making it applicable to room-scale environments.
An Exploratory Study on Learning in 3D Multi-user Virtual Environments

In this study, we propose and evaluate a desktop 3D multi-user virtual learning system for the study of butterflies. Multiple users can use avatars to learn and communicate through voice. Results indicate that the system received positive ratings in terms of perception, system effectiveness, learning experience, enjoyment, and attractiveness. However, areas for improvement were identified. The findings have practical implications for the design of educational multi-user virtual reality.
F2RPC: Fake to Real Portrait Control from a Virtual Character

This study explores a new method for generating virtual characters in video for use in virtual and augmented reality services. We propose F2RPC as a unified framework for improving both the appearance and motion of the characters, unlike previous method focusing only on one or the other. Specifically, our F2RPC consists of two modules for image destylization (AdaGPEN) and face reenactment (PCGPEN), respectively. Experimental results show that ours successfully solves the task compared to the combination of state-of-the-art destylization and reenactment method.