Reviewerly/exHarmony huggingface dataset:
Social Relation Atmosphere Recognition with Relevant Visual Concepts

Social relationships (e.g., couples, opponents) are the foundational part of society. Social relation atmosphere describes the overall interaction environment between social relationships. Discovering social relation atmosphere can help machines better comprehend human behaviors and improve the performance of social intelligent applications. Most existing research mainly focuses on investigating social relationships, while ignoring the social relation atmosphere. Due to the complexity of the expressions in video data and the uncertainty of the social relation atmosphere, it is even difficult to define and evaluate. In this paper, we innovatively analyze the social relation atmosphere in video data. We introduce a Relevant Visual Concept (RVC) from the social relationship recognition task to facilitate social relation atmosphere recognition, because social relationships contain useful information about human interactions and surrounding environments, which are crucial clues for social relation atmosphere recognition. Our approach consists of two main steps: (1) we first generate a group of visual concepts that preserve the inherent social relationship information by utilizing a 3D explanation module; (2) the extracted relevant visual concepts are used to supplement the social relation atmosphere recognition. In addition, we present a new dataset based on the existing Video Social Relation Dataset. Each video is annotated with four kinds of social relation atmosphere attributes and one social relationship. We evaluate the proposed method on our dataset. Experiments with various 3D ConvNets and fusion methods demonstrate that the proposed method can effectively improve recognition accuracy compared to end-to-end ConvNets. The visualization results also indicate that essential information in social relationships can be discovered and used to enhance social relation atmosphere recognition.
Regressive Gaussian Process Latent Variable Model for Few-Frame Human Motion Prediction

Human motion prediction has always been an interesting research topic in computer vision and robotics. It means forecasting human movements in the future conditioning on historical 3-dimensional human skeleton sequences. Existing predicting algorithms usually rely on extensive annotated or non-annotated motion capture data and are non-adaptive. This paper addresses the problem of few-frame human motion prediction, in the spirit of the recent progress on manifold learning. More precisely, our approach is based on the insight that achieving an accurate prediction relies on a sufficiently linear expression in the latent space from a few training data in observation space. To accomplish this, we propose Regressive Gaussian Process Latent Variable Model (RGPLVM) that introduces a novel regressive kernel function for the model training. By doing so, our model produces a linear mapping from the training data space to the latent space, while effectively transforming the prediction of human motion in physical space to the linear regression analysis in the latent space equivalent. The comparison with two learning motion prediction approaches (the state-of-the-art meta learning and the classical LSTM-3LR) demonstrate that our GPLVM significantly improves the prediction performance on various of actions in the small-sample size regime.
Neural Network-Based Post-Processing Filter on V-PCC Attribute Frames

Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and attribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video compression. This paper proposes an adaptive neural network-based post-processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.
PERANCANGAN SISTEM INFORMASI LAPORAN PERTANGGUNGJAWABAN BAGIAN PERENCANAAN DAN KEUANGAN DI SEKRETARIAT DAERAH KABUPATEN BANDUNG

The accountability report is one of the important indicators to know the performance of the APBD in a government agency. The process of preparing accountability reports in the planning and finance department still uses Microsoft Excel, which still brings many obstacles. Some of these obstacles show that the planning and finance departments have not optimized today's information technology, so the production of accountability reports has not been effective. Therefore, this study aims to design an information system for accountability reporting in the Planning and Finance Department of the Regional Secretariat of Bandung Regency. Data collection techniques using qualitative methods. The method for designing the accountability report system is uses the waterfall method, which consists of five phases. The design of this information system is web-based, using flowmap modeling and data flow diagrams, and implemented with the PHP programming language and MySQL database. It is hoped that this information system will facilitate budget entry and produce accurate and understandable accounting reports. This information system is expected to facilitate recording of budgets and produce accountability reports that are accurate and easy to understand.
Exploring advanced architectural variations of nnUNet

The nnUNet is a state-of-the-art deep learning based segmentation framework which automatically and systematically configures the entire network training pipeline. We extend the network architecture component of the nnUNet framework by newly integrating mechanisms from advanced U-Net variations including residual, dense, and inception blocks as well as three forms of the attention mechanism. We propose the following extensions to nnUNet, namely Residual-nnUNet, Dense-nnUNet, Inception-nnUNet, Spatial-Single-Attention-nnUNet, Spatial- Multi-Attention-nnUNet, and Channel-Spatial-Attention-nnUNet. Furthermore, within Channel-Spatial- Attention-nnUNet we integrate our newly proposed variation of the channel-attention mechanism. We demonstrate that use of the nnUNet allows for consistent and transparent comparison of U-Net architectural modifications, while maintaining network architecture as the sole independent variable across experiments with respect to a dataset. The proposed variants are evaluated on eight medical imaging datasets consisting of 20 anatomical regions which is the largest collection of datasets on which attention U-Net variants have been compared in a single work. Our results suggest that attention variants are effective at improving performance when applied to tumour segmentation tasks consisting of two or more target anatomical regions, and that segmentation performance is influenced by use of the deep supervision architectural feature.
Risk Analysis H2S And NH3 Exposure to Local Community Around Benowo Landfill Surabaya

Final waste processing in Indonesia adopts a landfill system. The Benowo landfill employs this system, which has a weakness in producing H2S and NH3 gases. The objective of this study was to assess the level of public health risk present in the Benowo landfill environment. The study design used was quantitative descriptive with a cross-sectional approach. The sample size consisted of 93 housewives selected using simple random sampling techniques, and gas measurements were conducted at two locations. The data analysis methods employed were univariate analysis and ARKL. The findings of this research demonstrated that the level of hydrogen sulfide (H2S) gas measured 0.02 ppm, while the concentration of ammonia (NH3) was 0.006 ppm. which were still below the quality standards set by East Java Governor Regulation No. 10 year 2009 regarding ambient air quality standards and stationary source emissions in East Java, where the limits for H2S were 0.03 ppm and NH3 were 2.00 ppm. The response dose value (Rfc) for H2S was 0.002 mg/m3, and for NH3 it was 0.5 mg/m3. The RQ value for H2S was greater than 1, indicating a potential risk, while the RQ value for NH3 was less than or equal to 1, indicating safety for the community. H2S gas poses a risk, whereas NH3 does not pose a risk to the communities in the Benowo landfill environment. It is recommended that individuals have a healthy lifestyle, and wear masks in the Benowo landfill environment and local government should be able to carry out the risk management through related agencies to the people lived near Benowo landfill area.&#x0D;
Navigating the Implementation of the Curriculum Digital Education in Austrian Secondary Schools: Challenges and Teacher Perspectives

Abstract For a considerable period, there was only a single year of compulsory Computer Science (CS) education in Austria that was required as part of a student’s academic career. The mandatory curriculum “Digital Education” (Digitale Grundbildung) was launched in September 2018 for all students in lower secondary education, formally integrating 21st-century skills into upper grades. The school’s management could choose to offer “Digital Education” as a stand-alone course or to include it in other classes. The 2022/2023 academic year saw the addition of an altered curriculum, where the subject was added to the normal timetable. Nonetheless, schools continue to deal with the problem of who is teaching what and how due to a staffing shortfall and a lack of instructional materials. This paper reports on experiences with the 2022 curriculum by evaluating a survey that examines the implementation of the mandatory curriculum “Digital Education” in Austrian secondary schools and the challenges that teachers face in navigating this new landscape. Aiming to do so, the qualitative data were interpreted, summarized, and discussed, whereas the quantitative data were analyzed in former papers. Despite the obstacles that emerged, teachers recognized the importance of digital literacy skills for their students and expressed a desire for more support in implementing the curriculum. The findings of this study have implications for policymakers, school administrators, and teacher training programs as they work to ensure the successful integration of “Digital Education” in secondary schools.
SIS-ASTROS: An Integrated Simulation Environment for the Artillery Saturation Rocket System (ASTROS)

Simulation is an important technique to support military training. There are different training goals depending on the different ranks of the military personnel being trained, which are addressed by different types of simulation (live, virtual, or constructive). However, a trend in current military training is the combination of different types of simulation in an integrated setup. Observing such kind of blended simulation, the Brazilian Army is making efforts to develop integrated simulation solutions. This work describes the conception of an integrated simulation environment for the Brazilian Army Artillery called SIS-ASTROS. Besides integrating different types of simulators, SIS-ASTROS presents a virtual tactical simulator to train ASTROS artillery batteries deployment activities of mid-rank officers. This simulator addresses aspects that can be classified as constructive, but also virtual ones. Due to this particularity in its design, the conception of this simulator represents an innovative contribution. Moreover, its development demanded solutions in the area of artificial intelligence, computer graphics, and distributed systems applied to simulation problems. This paper presents the key components of the integrated simulation system, highlighting the main contributions in the research and development of the virtual tactical simulator.
Flood Fill-Based Techniques for Large-Scale Changes in QuadTree Terrain Representations for Agent-Based Modelling and Simulation Systems

Agent-based modeling and simulation (ABMS) is a predominant paradigm for developing simulation systems in real-world applications. Despite this, the development of agent-based simulations with increasing value for users ought to rely on terrain representation and computation services distributed over the web. This distributed architecture is required to improve simulation systems’ implementation and maintenance capabilities and enhance the realism of agent-based simulations that can only be developed by exploring non-static, large-scale, and real-world terrain maps. To approach these challenges, this paper details the key components of a distributed service-oriented multi-agent simulation architecture. With that, the modeling and implementation of terrain simulation services are based on a hierarchical QuadTree representation approach. The paper also details how Flood Fill-based computational techniques can be implemented to promptly execute large-scale terrain changes due to the effects of floods in river banks and wildfires in forest areas. This solution also explores a dictionary-based memory to maintain the integrity of the terrain model, permitting to save and restore the terrain structure changes computed in the various levels of the QuadTree branches and nodes. Experiments aiming to assess the computational performance of the implemented terrain representation structures and terrain modification algorithms involved three real-world terrain maps. Tests were developed with increasing terrain structure changes due to river floods and wildfires to evaluate the computing time results of the implemented techniques.
Comparison of Smart Contract Platforms from the Perspective of Developers

Blockchain technologies promise to transform businesses by offering benefits like efficiency, transparency, fewer intermediaries, streamlined processes, and cost savings. With several public and private blockchain options available, companies and developers face the challenge of choosing a suitable platform for their needs. Ethereum has emerged as the most popular platform for the development of blockchain programs, but alternatives such as Algorand, Cardano, EOS, and Neo have also gained traction. We assess these five platforms from a developer’s perspective. Based on a catalog of criteria and metrics, we compare the platforms regarding their key features, including practical aspects like availability of documentation and ease of installation. We specify three use cases that are characteristic of blockchain applications: fungible tokens, non-fungible tokens, and a basic supply chain. By implementing these use cases on all platforms and logging the effort as well as any incidents, we identify their strengths and weaknesses in a way that supports developers in choosing an appropriate platform.
Machine Learning and Blockchain Intersection in Cryptocurrency Price Prediction

Since the introduction of Bitcoin in 2008 as the first practical decentralized cryptocurrency, the interest in cryptocurrencies and their underlying technology, Blockchain, has skyrocketed. Security, anonymity, and lack of a central controlling authority make them ideal for users who are privacy-minded. Academic research on machine learning, Blockchain, and their intersection in cryptocurrency price prediction has increased significantly in recent years. On that account, we present the review of published research that involve applications of machine learning techniques in the prediction of cryptocurrency prices. Given the novelty of Blockchain technology, the number of published research in this field is currently limited, but it is increasing rapidly. Our search resulted in a final sample of 18 papers categorized and reviewed thoroughly. Given the rising trend of research in these fields, we hope to provide a strong starting point for interested researchers.
Bringing Web 3.0 and DAO into Democratic Class: A study of Pedagogy in Higher Education

Blockchain technology introduces a paradigm shift into higher education, presenting opportunities for innovative transformations in human organization and democratic operations. Education-based Decentralized Autonomous Organizations (DAOs) on Web 3.0 are anticipated to instigate change across the spectrum, from classroom pedagogy to broader educational systems. Our research concentrates on the micro-level exploration of the merger between the decentralization principles of educational DAOs and traditional democratic classrooms. In the course of our experimental study, we analyze an experimental postgraduate course that uses a governing token to stimulate decision-making processes, empowering students with a participatory role that mirrors DAO operations. The implementation of popular DAO tools is discussed with the goal of crafting a classroom environment reflecting the decentralized decision-making structures intrinsic to DAOs. While we underscore the potential of decentralized teaching methodologies to enhance democratic education, we particularly focus on students as the primary facilitators of collective decision-making. According to our investigation, students showed a greater understanding and positivity towards decentralization than expected, suggesting that a suitable environment could boost positive behavior in class. Furthermore, in an architecturally decentralized class, a properly operational tokenomics could enhance classroom liberty, particularly for elite students, who might exert significant influence due to tokenomics and reward mechanisms.
Framework for Design and Development of Blockchain Applications Using Smart Contracts

Blockchain technology is generating a lot of buzz due to its potential to disrupt many existing industries and business processes. Blockchain technology, which was first developed as part of Bitcoin’s underlying infrastructure, provides a framework for decentralized and transparent transaction management amongst untrustworthy parties. Many people feel that this blockchain component would alter existing supply chain processes, which often include many untrustworthy organizations, from raw material extraction to the ultimate consumption of a finished product by the end customer. While numerous claims have been made about its apparent benefits in supply chain management, there have only been a few technological applications established that are beneficial in real-world circumstances. A smart contract is a computer protocol that digitally enables, validates, and enforces contract fulfillment utilizing Blockchain technology. Because smart contracts are trackable and irreversible and enable the performance of credible transactions without the involvement of third parties, they can be used to effectively replace existing supply chain mechanisms. In this paper, we present a system for facilitating the sale of products between untrustworthy organizations using smart contract technology to replace the “letter of credit” payment mechanism effectively. An innovative dispute resolution method is created, and decentralized software is built and deployed on the Ethereum blockchain using Solidity smart contracts. We’ve demonstrated how to create a revolutionary public decentralized application that may take the place of well-established conventional instruments like letters of credit using just tools that are open source. We also create a dispute resolution algorithm that, in theory, outperforms other accessible online conflict settlement tools.
A Blockchain-Based Micro-services Architecture for Distributed Business

With the growth of modern businesses in scale and complexity, digital applications that span service boundaries, connect multiple organizations, and manage user identities require careful consideration. Existing enterprise architectures are monolithic, creating obstacles for digital transformation to distributed multi-enterprise ecosystems. We propose a blockchain-based micro-services architecture that enables the self-driven construction of distributed businesses with capabilities for adaptation, innovation, and robustness. By extending the concepts of Service Oriented Architecture (SOA) and Enterprise Service Bus (ESB), we developed the Blockchain Service Bus (BSB) model across enterprise scenarios. Different entities can register micro-services on the blockchain to form a unified, standardized platform that accelerates a new stage of the distributed ecosystem. Under this architecture, end-users can own a unified, decentralized identity. It allows the seamless integration of services across organizations in a federated economy, motivating scalable collaborative business models.
Summary, Conclusions, and Outlook

The final chapter summarizes the entire presentation beginning with the basic problem of political gridlock, tracing its sources to ways for unblocking it. The theme in part emphasizes respect from better understanding of history and commonalities. Even so, all current ideologies face a credibility crisis caused by massive migrations and the coding revolution. Reconciliation and collaboration require finding new philosophical terrain that embraces a multi-vision view of the future that tolerates wide differences in imagery and image filtering. Religious humanism has this capability because it is a broad, flexible framework for American and world culture. Together with innovative analytical and narrative tools drawn from the coding revolution itself, religious humanism can reinvigorate America’s core values and mythological images: Lady Liberty, Uncle Sam, Ground Zero, nation of nations, and city on the hill – all useful fictions can take on new tones and features. As Joseph Campbell said, unity is more likely when a nation has commonly accepted myths that embody a realistic and uplifting mission.
Detection and Visualization of User Facial Expressions

The work covers topics in face detection, prediction of the position of the face landmarks as well as control of the graphical model. The purpose of the work is to create a vision system that detects the user’s facial expressions and visualizes them on the created computer model. The scope of work includes detection of the face and face landmarks, creation of a graphic model of a character whose facial expressions can be modified, and control of the created model using data obtained from the camera image. The main objectives of the project are easy accessibility, simplicity of use and low cost of the tools used.
An Approach to Predicting Social Events via Dailies Tracking

The study is devoted to a novel method for a real-time prediction of significant discontinuities in social states using the automatic analyses of Arabic dailies’ overall logical structures. The paper introduces the novel, named the super-frequent N-gram approach and the Regression Mean Rank Dependency characteristic and presents their arrangement with a new model. It makes it possible to reliably forecast social changes based on the dailies’ semantic content’s high-level repercussions. An evaluation of the approach in a prominent Arabic daily demonstrates its ability to reflect changes in the social state and expose significant events of the “Arab Spring”. The study shows that the resulting N-gram models corresponding to different sizes of N can provide a more vital prediction tool so that the methodology can consistently predict substantial changes in the social state in an online simulation fashion.