Reviewerly/exHarmony huggingface dataset:
A Critical Study of Composition Algorithms in Differential Privacy

Differential privacy has emerged as a prominent framework for safeguarding individual privacy in the context of data analysis and statistical computations. With the proliferation of data-driven applications and the necessity to share sensitive information while preserving confidentiality, the study of composition algorithms in differential privacy becomes increasingly vital. This paper presents a critical examination of the various composition techniques employed to combine multiple privacy-preserving computations while upholding the integrity of the overarching privacy guarantees. The paper begins by outlining the fundamental principles of differential privacy and its significance in contemporary data-centric environments. It subsequently delves into a critical analysis of sequential composition, parallel composition, and advanced composition theorems. The inherent strengths and limitations of each technique are scrutinized; emphasizing their practical implications in preserving privacy across different scenarios. The study extends beyond basic composition strategies to explore intricate facets of composition algorithms. Post-processing and renewal-based composition methodologies are evaluated in the context of preserving privacy amidst evolving data landscapes. The paper also investigates adaptive data analysis and hierarchical composition, elucidating their roles in addressing complex privacy challenges that arise in multi-agent and multi-level environments. Through a meticulous survey of literature and practical implementations, this paper unveils the nuanced interplay between composition algorithms and differential privacy mechanisms. It highlights the need for judicious application of composition techniques while considering the dynamic interactions between privacy parameters, data characteristics, and adversarial behaviours. Furthermore, the paper underscores the significance of incorporating advanced composition theorems to yield more precise privacy bounds, providing a comprehensive understanding of the intricacies involved.
Implementasi Metode Collaborative Filtering Based Untuk Sistem Rekomendasi Buku Fiksi

A book is the result of someone's work in the form of a collection of papers containing articles intended for publication. One of the benefits of books is that they can open people's horizons and can educate reason, thoughts and faith. Indonesia is ranked 60 out of 61 countries with reading interest problems causing low public interest in reading. One of the factors that can be taken into consideration for interest in reading in Indonesia is the difficulty in finding books to read because the many kinds of books in circulation make it difficult for readers to decide which books to read, besides that readers only want to read books with the best reputation. The purpose of creating a book recommendation system is to make it easier to find fiction-type books to read. The data used in this design are book data and rating data from Kaggle. This design uses one of the recommendation system methods, namely collaborative filtering. Collaborative filtering is a recommendation method that calculates similarity between items by users to make choices. The system will recommend 5 books according to the book title that the user will input.
PERANCANGAN DASHBOARD PERFOMA SALES DI CV MIRKO SEJAHTERA ABADI

Dashboard is design for CV Mirko Sejahtera Abadi aims to improve the sales performance of CV Mirko Sejahtera Abadi. Making the dashboard can make CV Mirko Sejahtera Abadi easier to know their sales every month and also, they sales performance every month. The design of this dashboard uses the Waterfall model System Development Life Cycle (SDLC) development methodology. The database used in this application is MySQL. The programming languages used are HTML and PHP.
IMPLEMENTASI AES UNTUK KEAMANAN APLIKASI FORMULIR ONLINE

Google Forms software is an online application which users can create form for various purpose. The application can store information or data that has been provided by the form fillers. The form fillers are merely identified by their institutional email's domain or those with the access link to the make response for each question provided in an online form. The use of third-party applications certainly reduces the sense of trust in the security of the data provided. Therefore, a digital form application design was created with the Advanced Encryption Standard (AES). The aim is to maintain the security of the data provided by the form filler and ensure that the fillers are those with the authority.
Penerapan Metode K-Means Clustering Untuk Menentukan Pola Penjualan Kue Pada Alfaza Bakery

The home industry is a type of small-scale business activity that is often found in villages and around houses, both in urban and rural areas. Starting from an association of the same people who studied to pursue the field of making cakes and bread and who then wanted to expand the sales area and create jobs for residents. the obstacle faced is ignorance of the products that are purchased the most and in which areas certain products run out the fastest, it is necessary to do data mining analysis using the clustering method. The K-Means method is a data clustering method using observation based on the similarity of the objects studied. A cluster is a collection of data that has similarities in its members or is different from other groups, clusters are used to minimize variation within a cluster and maximize variation between clusters, in other words data that has attribute similarities between one another and attribute differences to other clusters, determines the right cluster by using the elbow method which can maximize the quality of clusters so that the clusters are more varied. The results of testing this study with the elbow method obtained the right number of 4 clusters, then the clustering results with the most sales were obtained in cluster 3, cluster 1 with moderate sales, cluster 0 with few sales and cluster 2 with the least sales.
Pengembangan Sistem Informasi Penjualan Di Toko Karunia Dengan Framework Spring MVC

The aim of this research is to create a sales information system for Toko Karunia, which is a general store, by utilizing the Spring MVC framework. The research applied an agile approach with a scrum methodology for software development, using JavaScript as the programming language. The system development consists of features such as inventory management, sales tracking, customer management, and reporting. The implementation of this system provides a significant improvement in the store's operational efficiency and accuracy in sales processes. The outcome of this study can be beneficial for retail businesses with similar needs to enhance their sales processes using modern software technology.
Teacher readiness and understanding in implementing teaching English speaking skills using an ecological approach

The purpose of this research is to dig deeper into information about three vocational high schools teachers who have applied the ecological approach in their English teaching process. The ecological approach from the perspective of education is a learning approach that emphasizes contextual learning and focuses on the relationship between teachers, students, and the delivery of teaching materials. Applying the ecological approach to learning English at vocational high school can facilitate teachers in teaching English, especially learning English speaking for students. Teachers have an important role in education because a good education is influenced by teacher competence and teacher readiness in teaching. Speaking skills are one of the skills taught by teachers in schools, speaking skills in Indonesia do not receive enough attention, this is because students have not been able to communicate even in simple English. Based on this, the teacher's readiness to find an appropriate approach to the environmental conditions of students is very influential in teaching English speaking skills and an ecological approach can be a solution in improving students' English speaking skills. This research uses a qualitative research approach, and phenomenological as the research design. The participants in this study were English teachers in tenth grade from the three vocational high schools in Bandung district. This research shows that the ecological approach can improve student English speaking skills in three vocational high schools in Bandung district. It is caused by the teacher's activity who has applied the ecology approach indicator, namely coordination, internalization, media, and development.
Classification of the lunar surface pattern by AI architectures: Does AI see a rabbit in the Moon?

In Asian countries, there is a tradition that a rabbit (the Moon rabbit) lives on the Moon. As the origin of this tradition, usually, two reasons are mentioned. One reason is that the color pattern of the lunar surface is similar to the shape of a rabbit. The other reason is that both the Moon and rabbit are symbols of fertility because the Moon appears and disappears (i.e., waxing and waning) cyclically, and rabbits bear children frequently. Considering the latter reason, is the lunar surface color pattern not similar to a rabbit? Here, the similarity between rabbit and the lunar surface pattern was evaluated using seven AI architectures. In the test by CLIP, assuming that people look at the Moon in the early evening frequently, the lunar surface is more similar to a rabbit than a face at low latitude regions, while it can be classified as face as latitude increases, which is consistent with that the oldest literature about the Moon rabbit was written in India and that there is a culture of human's face in the Moon in Europe. Tested with ImageNet weights, ConvNeXt and CLIP sometimes classified the lunar surface pattern into rabbit with relatively high probabilities. Cultures are generated by our attitude to the environment. Both dynamic and static similarities may be required to induce our imagination.
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training

Learning object segmentation in image and video datasets without human supervision is a challenging problem. Humans easily identify moving salient objects in videos using the gestalt principle of common fate, which suggests that what moves together belongs together. Building upon this idea, we propose a self-supervised object discovery approach that leverages motion and appearance information to produce high-quality object segmentation masks. Specifically, we redesign the traditional graph cut on images to include motion information in a linear combination with appearance information to produce edge weights. Remarkably, this step produces object segmentation masks comparable to the current state-of-the-art on multiple benchmarks. To further improve performance, we bootstrap a segmentation network trained on these preliminary masks as pseudo-ground truths to learn from its own outputs via self-training. We demonstrate the effectiveness of our approach, named LOCATE, on multiple standard video object segmentation, image saliency detection, and object segmentation benchmarks, achieving results on par with and, in many cases surpassing state-of-the-art methods. We also demonstrate the transferability of our approach to novel domains through a qualitative study on in-the-wild images. Additionally, we present extensive ablation analysis to support our design choices and highlight the contribution of each component of our proposed method.
Privacy-Preserving 3-Layer Neural Network Training using Mere Homomorphic Encryption Technique

In this manuscript, we consider the problem of privacy-preserving training of neural networks in the mere homomorphic encryption setting. We combine several exsiting techniques available, extend some of them, and finally enable the training of 3-layer neural networks for both the regression and classification problems using mere homomorphic encryption technique.
ANALISIS POTENSI PEMBANGKIT LISTRIK TENAGA PIKO HIDRO ARCHIMEDES SCREW TURBINE DI CURUG SAWER

People all across the world depend on electricity, which is now primarily produced by fossil fuel power plants. The world is currently switching to use a sustainable and affordable energy source, new renewable energy, or NRE. Water is a energy source of NRE, making it one of the potential sources of NRE around the world. Sukabumi, which is spread out across a large region and has a lot of hydropower potential, has a great chance of being used to run Piko-scale hydropower. Curug Sawer, a popular tourist destination with the potential of hydro power plants, is one of the PHPP potentials in the Sukabumi region. Based on CaSimir and Simulink MATLAB simulations, this study presents an examination of the potential for a pico hydro power plant with an Archimedes Screw turbine. With an area discharge of 0.67 m3/s and a Head height of 1.1 m, the natural reservoir area at Curug Sawer served as the study's source of hydropower. With these parameters, this PHPP can generate 4.67 kW of electricity and 110 kWh of power each day. The tourism destination curug sawer area might save Rp 1,768,680.00 per month on power because of this possibility.
ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts

In this paper, we argue that the next generation of robots can be commanded using only Language Models' prompts. Every prompt interrogates separately a specific Robotic Modality via its Modality Language Model (MLM). A central Task Modality mediates the whole communication to execute the robotic mission via a Large Language Model (LLM). This paper gives this new robotic design pattern the name of: Prompting Robotic Modalities (PRM). Moreover, this paper applies this PRM design pattern in building a new robotic framework named ROSGPT_Vision. ROSGPT_Vision allows the execution of a robotic task using only two prompts: a Visual and an LLM prompt. The Visual Prompt extracts, in natural language, the visual semantic features related to the task under consideration (Visual Robotic Modality). Meanwhile, the LLM Prompt regulates the robotic reaction to the visual description (Task Modality). The framework automates all the mechanisms behind these two prompts. The framework enables the robot to address complex real-world scenarios by processing visual data, making informed decisions, and carrying out actions automatically. The framework comprises one generic vision module and two independent ROS nodes. As a test application, we used ROSGPT_Vision to develop CarMate, which monitors the driver's distraction on the roads and makes real-time vocal notifications to the driver. We showed how ROSGPT_Vision significantly reduced the development cost compared to traditional methods. We demonstrated how to improve the quality of the application by optimizing the prompting strategies, without delving into technical details. ROSGPT_Vision is shared with the community (link: https://github.com/bilel-bj/ROSGPT_Vision) to advance robotic research in this direction and to build more robotic frameworks that implement the PRM design pattern and enables controlling robots using only prompts.
MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system's evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today's technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking

3D single object tracking with LiDAR points is an important task in the computer vision field. Previous methods usually adopt the matching-based or motion-centric paradigms to estimate the current target status. However, the former is sensitive to the similar distractors and the sparseness of point cloud due to relying on appearance matching, while the latter usually focuses on short-term motion clues (eg. two frames) and ignores the long-term motion pattern of target. To address these issues, we propose a mixed paradigm with two stages, named MTM-Tracker, which combines motion modeling with feature matching into a single network. Specifically, in the first stage, we exploit the continuous historical boxes as motion prior and propose an encoder-decoder structure to locate target coarsely. Then, in the second stage, we introduce a feature interaction module to extract motion-aware features from consecutive point clouds and match them to refine target movement as well as regress other target states. Extensive experiments validate that our paradigm achieves competitive performance on large-scale datasets (70.9% in KITTI and 51.70% in NuScenes). The code will be open soon at https://github.com/LeoZhiheng/MTM-Tracker.git.
Apportionable matrices and gracefully labelled graphs

To apportion a complex matrix means to apply a similarity so that all entries of the resulting matrix have the same magnitude. We initiate the study of apportionment, both by unitary matrix similarity and general matrix similarity. There are connections between apportionment and classical graph decomposition problems, including graceful labelings of graphs, Hadamard matrices, and equiangluar lines, and potential applications to instantaneous uniform mixing in quantum walks. The connection between apportionment and graceful labelings allows the construction of apportionable matrices from trees and a generalization of the well-known Eigenvalue Interlacing Inequalities. It is shown that every rank one matrix can be apportioned by a unitary similarity, but there are $2\times 2$ matrices that cannot be apportioned. A necessary condition for a matrix to be apportioned by unitary matrix is established. This condition is used to construct a set of matrices with nonzero Lebesgue measure that are not apportionable by a unitary matrix.
Invariant representation learning for sequential recommendation

Sequential recommendation involves automatically recommending the next item to users based on their historical item sequence. While most prior research employs RNN or transformer methods to glean information from the item sequence-generating probabilities for each user-item pair and recommending the top items, these approaches often overlook the challenge posed by spurious relationships. This paper specifically addresses these spurious relations. We introduce a novel sequential recommendation framework named Irl4Rec. This framework harnesses invariant learning and employs a new objective that factors in the relationship between spurious variables and adjustment variables during model training. This approach aids in identifying spurious relations. Comparative analyses reveal that our framework outperforms three typical methods, underscoring the effectiveness of our model. Moreover, an ablation study further demonstrates the critical role our model plays in detecting spurious relations.
Knowledge Graph Prompting for Multi-Document Question Answering

The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this paradigm in the scenario of multi-document question answering (MD-QA), a task demanding a thorough understanding of the logical associations among the contents and structures of different documents. To fill this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to formulate the right context in prompting LLMs for MD-QA, which consists of a graph construction module and a graph traversal module. For graph construction, we create a knowledge graph (KG) over multiple documents with nodes symbolizing passages or document structures (e.g., pages/tables), and edges denoting the semantic/lexical similarity between passages or intra-document structural relations. For graph traversal, we design an LLM-based graph traversal agent that navigates across nodes and gathers supporting passages assisting LLMs in MD-QA. The constructed graph serves as the global ruler that regulates the transitional space among passages and reduces retrieval latency. Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality. Extensive experiments underscore the efficacy of KGP for MD-QA, signifying the potential of leveraging graphs in enhancing the prompt design for LLMs. Our code: https://github.com/YuWVandy/KG-LLM-MDQA.
VBMO: Voting-Based Multi-Objective Path Planning

This paper presents VBMO, the Voting-Based Multi-Objective path planning algorithm, that generates optimal single-objective plans, evaluates each of them with respect to the other objectives, and selects one with a voting mechanism. VBMO does not use hand-tuned weights, consider the multiple objectives at every step of search, or use an evolutionary algorithm. Instead, it considers how a plan that is optimal in one objective may perform well with respect to others. VBMO incorporates three voting mechanisms: range, Borda, and combined approval. Extensive evaluation in diverse and complex environments demonstrates the algorithm's ability to efficiently produce plans that satisfy multiple objectives.
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters

Depth estimation in surgical video plays a crucial role in many image-guided surgery procedures. However, it is difficult and time consuming to create depth map ground truth datasets in surgical videos due in part to inconsistent brightness and noise in the surgical scene. Therefore, building an accurate and robust self-supervised depth and camera ego-motion estimation system is gaining more attention from the computer vision community. Although several self-supervision methods alleviate the need for ground truth depth maps and poses, they still need known camera intrinsic parameters, which are often missing or not recorded. Moreover, the camera intrinsic prediction methods in existing works depend heavily on the quality of datasets. In this work, we aimed to build a self-supervised depth and ego-motion estimation system which can predict not only accurate depth maps and camera pose, but also camera intrinsic parameters. We proposed a cost-volume-based supervision manner to give the system auxiliary supervision for camera parameters prediction. The experimental results showed that the proposed method improved the accuracy of estimated camera parameters, ego-motion, and depth estimation.