Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN APLIKASI MOBILE SISTEM INFORMASI PRODUK PERTANIAN KABUPATEN BULELENG

The availability of accurate and up-to-date agricultural data is very much needed for agricultural development in Buleleng district. This data requirement can be facilitated by utilizing information and communication technology. The purpose of this research is to create a mobile-based Agricultural Product Information System (SIPROTANI) application. The SIPROTANI mobile application was developed following the agile software development model scrum. The data requirements for the system were obtained in collaboration with the Buleleng district agriculture office. Users of mobile-based SIPROTANI are Field Agricultural Extension (PPL) and administrators. Functions that have been developed in SIPROTANI are displaying and managing agricultural products in Buleleng Regency, managing farmer group data, managing farmer data, searching for agricultural product data, and displaying product reports. agriculture according to the criteria of product type, location, (range) harvest date. It is hoped that with this system, Buleleng Regency has accurate, up to date data related to agricultural products in Buleleng. In addition, this system can be used as a model for other districts in Bali and other regions in Indonesia. In the future, this system can be developed into e-commerce that connects farmers as producers and the community/stakeholders as consumers, so that the distribution chain of product marketing can be cut. Farmers will get a fair price for their products and consumers will get good quality products at a commensurate price.
20‐2: <i>Distinguished Paper:</i> Local Pupil Swim in VR/AR: Root Cause and Perception Model

The optics between display and human eye in a typical VR/AR HMD can introduce a common visual defect ‐ local pupil swim (also called local ripples or “orange peel” effect), where virtual content distorts locally with head movement. Compact optical design (such as pancake optics) is increasingly sensitive in design and manufacturing tolerance to this perceptual effect. This work provides a method to root cause and quantify the impact based on perceptual modeling, optics simulation and measurement.
Optimization of K-means Algorithm with Computer Programming for Feature Extraction

Feature extraction is a type of image algorithm. Its problems are prevalent today. Among them, feature selection is the most important problem. How to choose the best feature is a complicated problem. Different feature selection methods may select different feature sets, which may affect the accuracy of classification or regression. In this paper, k-means algorithm is used to select the best feature subset. It can cluster data points into different clusters, and then select the cluster with the highest degree of differentiation as the best feature subset. Finally, this paper solves the problem of error rate in the application of feature extraction and improves its accuracy and efficiency. In the future, this technology should be widely used so that it can contribute to human society.
Fine-to-Coarse Object Classification of Very Large Images

Even though deep neural networks have shown to be promising for object recognition tasks, they suffer from limitations for some real world applications: (i) their ability to recognize relatively small objects in megapixel images is limited; and (ii) they require large amounts of computations for processing high resolution data, especially in resource constraint edge devices. We develop a fine-grained attention-based neural network approach to address these shortcomings. Our model is able to capture the features for both small and large objects in high resolution image while discarding the less informative portions to reduce the computational burden. We use a likelihood-attention layer to aggregate contextually important information from various patches of the image. Experimental comparisons on four high resolution datasets demonstrate that our proposed method outperforms existing approaches in terms of accuracy and computation cost.
Document Information Extraction via Global Tagging

Document Information Extraction (DIE) is a crucial task for extracting key information from visually-rich documents. The typical pipeline approach for this task involves Optical Character Recognition (OCR), serializer, Semantic Entity Recognition (SER), and Relation Extraction (RE) modules. However, this pipeline presents significant challenges in real-world scenarios due to issues such as unnatural text order and error propagation between different modules. To address these challenges, we propose a novel tagging-based method – Global TaggeR (GTR), which converts the original sequence labeling task into a token relation classification task. This approach globally links discontinuous semantic entities in complex layouts, and jointly extracts entities and relations from documents. In addition, we design a joint training loss and a joint decoding strategy for SER and RE tasks based on GTR. Our experiments on multiple datasets demonstrate that GTR not only mitigates the issue of text in the wrong order but also improves RE performance.
Ensuring information security: issues of theory and practice

The authors examine current issues in the theory and practice of information security.
Exploring Generative AI as a Proxy User for Early Stage User Research - Preliminary Findings

The potential of generative AI has exploded of late, largely due in part because of the improved accessibility that tools like ChatGPT afford for non-data-scientist / developer users. One potential area of application is for Generative AI models to serve as proxy users in early stage user research. User research is a crucial component of product development, helping to understand user needs, preferences, and behaviors. However, conducting user research can be time-consuming, resource-intensive, and may require access to a user population that is challenging to access (e.g., military users). Generative AI models have shown remarkable progress in generating human-like text and simulating user interactions based on a significant corpus of training materials that serves as the knowledge base for the AI’s reasoning. This paper provides preliminary findings from explorations on the feasibility of leveraging generative AI as a proxy user to inform early stage user research. Using the GPT-4.0 architecture and the Open-AI ChatGPT user interface (chat.openai.com), we conducted preliminary research for six different candidate end user populations. This was accomplished by generating generic product descriptions, notional user personas each respective product, contextualizing ChatGPT to act as the user persona, and then asking a series of generic user experience research (UXR) questions of the GPT model. Responses from ChatGPT were then scored by three UXR / Human Factors subject-matter experts to evaluate the perceived utility of ChatGPT’s responses in terms of supporting early stage product design as a proxy human user. By evaluating the effectiveness of generative AI as a proxy user, this research aims to shed light on its potential benefits and limitations in supporting early stage user research efforts. While additional research is still needed (e.g., comparing the results of ChatGPT to responses generated by actual end users, having SME’s evaluate the accuracy and completeness of ChatGPT’s responses), preliminary findings are promising for the potential that generative AI models hold to serve as early stage proxy users to inform research and product design efforts in domains where significant corpuses of data already exist for model training, and where access to human end users may be restricted our otherwise prohibited.
The Fictitious Writer F.V. Larrovitch

The paper presents the history of a literary hoax orchestrated by R. Wright and W. G. Jordan, members of the Authors Club in New York, in 1917-1918. They invented a Russian writer named F.V. Larrovitch, who never existed. They arranged “the author's” jubilee and published the book about him. The latter consisted of articles about his life and works, fragments of his novels and poems, bibliographical list of his works as well as the one of the critical studies on him, and even illustrations including his portrait and a photocopy of his manuscript. The book sparked a discussion in American periodicals; the hoax was suspected, but there were also papers reaffirming the existence of Larrovitch, which were contrived by the hoaxers. However, the hoax was exposed soon, as it might have been planned from the very beginning. The Larrovitch story deserves attention of literary historians as an example of stereotypes about Russia that were common in the 1910s America. The persona of Larrovitch was made of such stereotypes. At the same time the hoax owed its success to the keen interest which the Americans had in Russia at that time.
The Effect Of Photography Media On Students’ Writing Ability In Hortatory Exposition Text At Grade Eleven Of SMK GKPI 1 Pematang Siantar

The objective of this research was to find out the effect of photography media to writing ability on hortatory exposition text at eleven grade students of SMK GKPI 1 Pematang Siantar. This research focuses on the students’ writing ability in hortatory exposition text using photography media. This research used quantitative research with a quasi-experimental research design. The population of this study was the eleven grade students of SMK GKPI 1 Pematang Siantar with a total of 159 students. The sample of this study was divided into two classes, namely the experimental class (XI Kewirausahaan) consisting of 25 students who used photography media and the control class (XI TKJ) consisting of 25 students who used conventional media. Data collection instruments used writing tests for pre-test and post-test. Jacob et. al's writing assessment rubric was used in this research, which includes several aspects: content, organization, vocabulary, language use, and mechanics. The result of this research showed that at the 5% significance level (a=0.05), t-test was higher than t-table in which t-test= 3,514&gt;t-table= 1,677. As a result, HO is rejected while Ha is accepted. Therefore, it can be concluded that photography media significantly affects the ability of students at SMK GKPI 1 Pematang Siantar in writing hortatory exposition text.
Penerapan Algoritma Adaptive Response Rate Exponential Smoothing Terhadap Business Intelligence System

PT. XYZ is one of the companies in the field of furniture sales by offering its flagship product, namely spring bed. The company's business continues to grow every year, of course, the company must be able to complete its work quickly and precisely. One of the main problems of the company is that the increase in company sales is still not able to cover the company's expenses and sometimes the company still suffers losses. This happens because companies often make mistakes in purchasing product inventory stock. Not all types of spring beds sell well, so sometimes purchases are made of the type of spring bed that is not selling well, which results in stock accumulation and instability of the company's cash inflow and outflow. In this study, a Business Intelligence System was built, which is a form of information technology implementation to store, collect and analyze data into knowledge so that it can be used as prediction results. The prediction algorithm used in this research is the Adaptive Response Rate Exponential algorithm. The expected goal of this research is to build a Business Intelligence System that can calculate product sales predictions in the following month using the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm. Based on the results of the MAPE test, it can be concluded that the percentage of prediction accuracy from the ARRES algorithm on the sales transaction data of PT. XYZ is 53.33% which is categorized as quite accurate and the percentage of prediction error from the ARRES method is 46.67% which is categorized as reasonable
Action Prediction for Cooperative Exploration in Multi-agent Reinforcement Learning

Multi-agent reinforcement learning methods have shown significant progress, however, they continue to exhibit exploration problems in complex and challenging environments. To address the above issue, current research has introduced several exploration-enhanced methods for multi-agent reinforcement learning, they are still faced with the issues of inefficient exploration and low performance in challenging tasks that necessitate complex cooperation among agents. This paper proposes the prediction-action Qmix (PQmix) method, an action prediction-based multi-agent intrinsic reward construction approach. The PQmix method employs the joint local observation of agents and the next joint local observation after executing actions to predict the real joint action of agents. The method calculates the action prediction error as the intrinsic reward to measure the novel of the joint state and encourages agents to actively explore the action and state spaces in the environment. We compare PQmix with strong baselines on the MARL benchmark to validate it. The result of experiments demonstrates that PQmix outperforms the state-of-the-art algorithms on the StarCraft Multi-Agent Challenge (SMAC). In the end, the stability of the method is verified by experiments.
Educational Values in the Vita Constantini Cantemyrii by Dimitrie Cantemir

The capitalization and editing of D. Cantemir’s scientific heritage began almost three centuries ago, the process though is far from over. While some of the works of the Moldavian prince have been substantially edited and studied, some others remain in the shadows without due attention. Such is the case of Vita Constantini Cantemyrii… of which yet another copy was discovered in the Archive of Old Documents in Moscow, which reproduces quite truthfully the realities of the XVIIth century under the form of a freely narrated story. Presenting his father as a brave, courageous man, determined, skillful, wise, and caring parent, Dimitrie Cantemir observed one of the commandments of the decalogue – “Honor your father and mother”. As he took refuge to Russia after 1711 he was able to introduce his work to the scientific community in that country and cause his works to be printed. Some did get printed during his lifetime while some others only had that chance after his death.
Robust multi-scale weighting-based edge-smoothing filter for single image dehazing

The guided image filter (GIF) and weighted guided image filter (WGIF) are local linear model-based good edge-preserving filters. However, due to fixed regularization parameter, they suffer from halo artifacts (morphological artifacts) in the sharp regions. To overcome this issue, a robust multi-scale weighting-based edge-smoothing filter (RMWEF) for single image dehazing is proposed in this paper. It removes morphological artifacts and over-smoothness strongly and preserves edge information precisely in both flat and sharp regions. The proposed dehaze method has four-steps. First, initial transmission map and atmospheric map are estimated by using a novel dark channel prior (DCP) method. Then, the morphological artifacts of initial transmission map are reduced by using non-local haze line averaging (NL-HLA) method. In the third step, transmission map is refined by using the proposed RMWEF. Finally, the haze free image is restored. Theoretical and experimental analysis proves that the proposed algorithm produce effective dehaze results quicker than the existing methods.
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent  Ecosystem

This paper envisions a revolutionary AIOS-Agent ecosystem, where Large Language Model (LLM) serves as the (Artificial) Intelligent Operating System (IOS, or AIOS)--an operating system "with soul". Upon this foundation, a diverse range of LLM-based AI Agent Applications (Agents, or AAPs) are developed, enriching the AIOS-Agent ecosystem and signaling a paradigm shift from the traditional OS-APP ecosystem. We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts: LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level). We begin by introducing the architecture of traditional OS. Then we formalize a conceptual framework for AIOS through "LLM as OS (LLMOS)", drawing analogies between AIOS and traditional OS: LLM is likened to OS kernel, context window to memory, external storage to file system, hardware tools to peripheral devices, software tools to programming libraries, and user prompts to user commands. Subsequently, we introduce the new AIOS-Agent Ecosystem, where users can easily program Agent Applications (AAPs) using natural language, democratizing the development of software, which is different from the traditional OS-APP ecosystem. Following this, we explore the diverse scope of Agent Applications. We delve into both single-agent and multi-agent systems, as well as human-agent interaction. Lastly, drawing on the insights from traditional OS-APP ecosystem, we propose a roadmap for the evolution of the AIOS-Agent ecosystem. This roadmap is designed to guide the future research and development, suggesting systematic progresses of AIOS and its Agent applications.
THE ROLE OF LABOR IN RELATIONS TO PSYCHE AND PHYSIOLOGY

The solution of the psychophysiological problem to this day causes a number of difficulties, which are associated with the underlying paradox of the discrepancy between the substrate and attributes. The presence of this paradox is a consequence of the recog-nition of the direct connection of the psyche and the brain. At the same time, the psyche (including consciousness) is not a property of the brain, but of a person – a social material substrate, a being capable of producing its own material life (labor). Mental and physio-logical processes are directly related to labor, and not to each other, are its sides. Labor affects human biology through transformed natural objects, to which it is forced to adapt, and generates ideal forms (consciousness, the psyche as a whole), transforming the human substrate and the natural environment associated with it.
METAPHYSICAL BASES OF PHYSICAL REALITY

Our representations about space and time, even considering their «a priori» status according to Kant, change over time. Their evolution necessarily accompa-nies the development of our knowledge of physical reality. The article describes the representations of space and time that allows not only to unambiguously inter-pret the effects of quantum mechanics, but also to build a model of the bases of physical reality, the development of which, according to the author, can lead to the creation of a unified physical theory of the microworld. Within the framework of this model, the discreteness of space and time is asserted, from which the proba-bilistic nature of elementary motion follows with necessity; geometry is distin-guished into the inextricably linked continual geometry of Being and the discrete geometry of Existing, and time becomes the absolute invariant of Existing.
Design and implementation of a drawing encryption tool based on image processing and PDF manipulation

This paper proposes a design approach for an encryption tool based on image processing and PDF manipulation to meet the encryption requirements of sensitive design drawings. The tool is developed using Python programming language and the OpenCV-Python library. Designers can choose encryption fields through a GUI, including modifying product name, removing stage markers, and changing approval date, among others. Additionally, there are two encryption modes available for selection: combined into an one encrypted PDF or each PDF encrypted separately. Batch encryption of all PDF drawings within a selected path can be completed very quickly, significantly reducing the time and workload required for manually encrypting multiple PDF drawings. This greatly improves encryption efficiency and eliminates tedious repetitive work. The program avoids human errors and ensures that sensitive information remains protected from unauthorized access.
Deep Learning on 3D Neural Fields

In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D data, NFs offer a solution to the fragmentation and limitations associated with prevalent discrete representations. However, given that NFs are essentially neural networks, it remains unclear whether and how they can be seamlessly integrated into deep learning pipelines for solving downstream tasks. This paper addresses this research problem and introduces nf2vec, a framework capable of generating a compact latent representation for an input NF in a single inference pass. We demonstrate that nf2vec effectively embeds 3D objects represented by the input NFs and showcase how the resulting embeddings can be employed in deep learning pipelines to successfully address various tasks, all while processing exclusively NFs. We test this framework on several NFs used to represent 3D surfaces, such as unsigned/signed distance and occupancy fields. Moreover, we demonstrate the effectiveness of our approach with more complex NFs that encompass both geometry and appearance of 3D objects such as neural radiance fields.
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning

We propose FLASH-RL, a framework utilizing Double Deep Q-Learning (DDQL) to address system and static heterogeneity in Federated Learning (FL). FLASH-RL introduces a new reputation-based utility function to evaluate client contributions based on their current and past performances. Additionally, an adapted DDQL algorithm is proposed to expedite the learning process. Experimental results on MNIST and CIFAR-10 datasets demonstrate that FLASH-RL strikes a balance between model performance and end-to-end latency, reducing latency by up to 24.83% compared to FedAVG and 24.67% compared to FAVOR. It also reduces training rounds by up to 60.44% compared to FedAVG and 76% compared to FAVOR. Similar improvements are observed on the MobiAct Dataset for fall detection, underscoring the real-world applicability of our approach.
Like Treating the Symptom Rather than the Cause - the Omission of Courses over Terrorism in NSA Designated Institutions

The National Security Agency (NSA) awards Center of Academic Excellence (CAE) designations to institutions that commit to producing cybersecurity professionals who will work in careers that reduce vulnerabilities in our national infrastructure. A review of the curricula in the 327 institutions and their degree programs reveal that only two programs offer a required course about terrorism. Given the fluid nature of terrorism and its threat to national infrastructure, the omission is concerning. It is recommended that NSA-certified cybersecurity programs begin implementing educational content that aim to teach about this emerging crime and justice issue. One suggestion is to embrace the interdisciplinary nature of cybersecurity, as exemplified in the success of the Cybersecurity Living and Learning Community (CLLC). Designing courses that educate about the social processes that leads to the growing problem of violence and terror directed towards marginalized communities and our nation's technological infrastructure, is another.