Reviewerly/exHarmony huggingface dataset:
Exposition Text Material Module Development in Class X Based on Approach Problem Based Learning

This research aims to produce an Expository Text Material Module in Class X Based on the Problem Based Learning Approach. The research method used is a quantitative descriptive research method and Research and Development development which refers to the Borg and Gall model. The development stages
Recursive Bayesian Estimation Search with Environmental Constraints and Psychological Beliefs and Biases

In the paper, we consider a modification of the Recursive Bayesian Estimation technique and incorporate the Fast Sweeping Method to extend recent work in search applications with an algorithm capable of calculating optimal trajectories in the context of multiple targets and searchers. In addition to providing a computational overview of the algorithm, we demonstrate how incorporating knowledge, deception, and belief biases into the algorithm alters the optimal trajectories of the searchers. Finally, we present Monte-Carlo simulations of how these psychological factors influence the mean probability that the searchers detect the target. We will discuss the implications of the findings, current limitations and future extensions of the model, and potential applications to decision support.
Digitalization of the Quranic Reading Learning Program for Collage Students, Indonesia

This study aimed to describe the planning of an online Quranic Reading Learning (QRL) program for students, explain the implementation of the online QRL program, and reveal the students’ response on it. This study used a qualitative research approach. Data were collected using interviews, questionnaires, and observations that were conducted from July to October 2022. The results showed that the online QRL program was carried out as a response to the transition in lecturing mode from online to hybrid and fully offline. This program was attended by 4000 students programmed Islamic religious education courses and were guided by 50 teachers. Learning was carried out in eight online meetings. It progressed through a class grouping test, a grand opening, online learning, and a final evaluation. The identified problems were the large number of participants in a group, unequal assistance for individual participants, network constraints, noise that classical learning caused, and unclear articulation. The advantage of this program was the efficient cost, time, and place. All participants showed a positive response to the use of this program and suggested that it should be continued in the next semester with various improvements.
ASSISTANCE IN INVOICE DATA COLLECTION PT. GAPURA ANGKASA FOR THE EFFICIENCY OF FINANCIAL MANAGEMENT

Procurement of goods must have a good financial system to support all activities carried out by the company. The developed financial system must also have controls aimed at managing incoming goods, used goods, stock, transaction processing results, and transaction results reports. The purpose of this service activity is to assist in collecting invoice data in support of the efficiency of the financial management system used at PT. Gapura Angkasa. So it is necessary to use a web-based information system so that the transactions carried out can be well organized and clear. PT. Gapura Angkasa Denpasar Branch in collaboration with PT. Garuda Indonesia and PT. Angkasa Pura which is engaged in the field of ground handling services and other business activities that support the aviation business at airports. At first the procurement of goods in and out at PT. Gapura Angkasa is still manual, so a web-based information system is needed that can simplify and speed up service. This system can only be accessed by employees or officers at PT. Gapura Angkasa. To design this procurement system, structural design is used, namely by using an Enttity Relationship Diagram (ERD), making Data Flow Diagrams (DFD), making billing and recording transactions using Data Invoices EXCEL, and MySQL as data storage databases.
TRAINING AND MENTORING OF LITERACY AND NUMERACY ACTIVITIES THROUGH THE USE OF ICT-BASED BULLETIN INTEGRATED CHARACTER EDUCATION (BICE) MEDIA IN ELEMENTARY SCHOOL 9 MAMBORO

SDN 9 Mamboro is one of the schools in Palu City that has launched a literacy and numeracy program. Observations conducted by the service team showed that the literacy and numeracy practices at this school were not optimal. The implementation is still at the habituation stage, directing students to read for 15 minutes at the beginning of the lesson and the implementation is not routine. The book collection and the number of students visiting the library were also lacking, the reading corner was inadequate, the learning methods used were conventional and most teachers did not have the necessary skills to create ICT-based learning media. Based on these observations, the service team and the principal agreed to provide training and assistance in literacy and numeracy activities through the use of bulletin integrated character education (BICE) media. The expected goal of this activity is to improve students' literacy and numeracy skills. The average pretest score of literacy and numeracy skills was 38.29 and increased to 84.92 during the posttest.
Motion2language, unsupervised learning of synchronized semantic motion segmentation

In this paper, we investigate building a sequence to sequence architecture for motion-to-language translation and synchronization. The aim is to translate motion capture inputs into English natural-language descriptions, such that the descriptions are generated synchronously with the actions performed, enabling semantic segmentation as a byproduct, but without requiring synchronized training data. We propose a new recurrent formulation of local attention that is suited for synchronous/live text generation, as well as an improved motion encoder architecture better suited to smaller data and for synchronous generation. We evaluate both contributions in individual experiments, using the standard BLEU4 metric, as well as a simple semantic equivalence measure, on the KIT motion-language dataset. In a follow-up experiment, we assess the quality of the synchronization of generated text in our proposed approaches through multiple evaluation metrics. We find that both contributions to the attention mechanism and the encoder architecture additively improve the quality of generated text (BLEU and semantic equivalence), but also of synchronization.
The Inverse Problem for Neural Networks

We study the problem of computing the preimage of a set under a neural network with piecewise-affine activation functions. We recall an old result that the preimage of a polyhedral set is again a union of polyhedral sets and can be effectively computed. We show several applications of computing the preimage for analysis and interpretability of neural networks.
A Comparative Study of Relasional and NoSQL database for Big Data Analytics

The Database technology suite is rapidly evolving and consistently answering solutions user needs, one of which is the ability of the database to accommodate one piece of information quite a lot, the ability of the Database to accommodate data must be balanced together speed in loading the information needed by the user. In these fast-paced times today’s sophisticated information technology forces us to be able to serve data requests as well as process data quickly and efficiently, but if a system has accommodated too much a lot of data will certainly experience a time constraint in processing the data. The research method used is the type of library research (Library Research). The data obtained is sourced from the literature. NoSQL database discussion literature searched using keywords: SQL, NoSQL, databases from various publication databases. The results of the study are comparisons between relational databases and NoSQL involving factors&#x0D; Such as data models, scalability, data flexibility, and query and analytics capabilities. Databases Relational is more suitable for structured data and strict consistency, whereas database NoSQL is better suited for unstructured data and prioritizes scalability and flexibility.
Penggunaan Teknologi Multimedia Pada Pembelajaran Literasi Anak Usia Dini Pada TKIT Ilmi Khoir

The purpose of this study is to define the use of multimedia technology in literacy learning in early childhood, including the implementation of the use of multimedia technology in literacy learning in children aged 5-6 years. This research method uses qualitative research. Interview techniques and document reviews are used in the collection of information. The validity of the data is obtained by data triangulation. Data analysis techniques are carried out with interactive models. The results of the study proved that: the application of the use of multimedia technology in literacy learning shows that learning becomes more conducive and directed. By utilizing multimedia technology, children become easier to understand learning and learning becomes more effective.
An Optimized Control Strategy for Image-Based Visual Servoing Using Polar Coordinates

Image-based visual servoing is a crucial technique in robotic systems that enables accurate manipulation and control through visual feedback. In scenarios involving substantial rotational motion or changes in orientation, traditional Cartesian-based visual servoing methods may encounter limitations in terms of control performance. To address this challenge, we propose a novel approach that use coordinates for visual servoing control. By reparametrizing the control inputs in terms of polar radius and polar angle, our method offers a more intuitive and efficient way to handle rotations and angular variations. In addition, we further study the polar-based IBVS control by conducting experiments using a Proportional (P) controller as well as the Levenberg-Marquardt (LM) algorithm respectively, and the experimental results indicate that the proposed method demonstrates a faster convergence speed and better robustness.
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need

Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (i.e., trainable vectors) with frozen LLM, where the LLM parameters were not updated (i.e., frozen) and only the vectors of soft prompts were updated, known as prompt tuning. We added additional soft prompts as a prefix to the input layer, which were optimized during the prompt tuning. We evaluated the proposed method using 7 clinical NLP tasks and compared them with previous task-specific solutions based on Transformer models. Results and Conclusion The proposed approach achieved state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one unified generative LLM. Our approach outperformed previous task-specific transformer models by ~3% for concept extraction and 7% for relation extraction applied to social determinants of health, 3.4% for clinical concept normalization, 3.4~10% for clinical abbreviation disambiguation, and 5.5~9% for natural language inference. Our approach also outperformed a previously developed prompt-based machine reading comprehension (MRC) model, GatorTron-MRC, for clinical concept and relation extraction. The proposed approach can deliver the ``one model for all`` promise from training to deployment using a unified generative LLM.
Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models

AI-powered coding assistant tools have revolutionized the software engineering ecosystem. However, prior work has demonstrated that these tools are vulnerable to poisoning attacks. In a poisoning attack, an attacker intentionally injects maliciously crafted insecure code snippets into training datasets to manipulate these tools. The poisoned tools can suggest insecure code to developers, resulting in vulnerabilities in their products that attackers can exploit. However, it is still little understood whether such poisoning attacks against the tools would be practical in real-world settings and how developers address the poisoning attacks during software development. To understand the real-world impact of poisoning attacks on developers who rely on AI-powered coding assistants, we conducted two user studies: an online survey and an in-lab study. The online survey involved 238 participants, including software developers and computer science students. The survey results revealed widespread adoption of these tools among participants, primarily to enhance coding speed, eliminate repetition, and gain boilerplate code. However, the survey also found that developers may misplace trust in these tools because they overlooked the risk of poisoning attacks. The in-lab study was conducted with 30 professional developers. The developers were asked to complete three programming tasks with a representative type of AI-powered coding assistant tool, running on Visual Studio Code. The in-lab study results showed that developers using a poisoned ChatGPT-like tool were more prone to including insecure code than those using an IntelliCode-like tool or no tool. This demonstrates the strong influence of these tools on the security of generated code. Our study results highlight the need for education and improved coding practices to address new security issues introduced by AI-powered coding assistant tools.
FastPart: Over-Parameterized Stochastic Gradient Descent for Sparse optimisation on Measures

This paper presents a novel algorithm that leverages Stochastic Gradient Descent strategies in conjunction with Random Features to augment the scalability of Conic Particle Gradient Descent (CPGD) specifically tailored for solving sparse optimisation problems on measures. By formulating the CPGD steps within a variational framework, we provide rigorous mathematical proofs demonstrating the following key findings: (i) The total variation norms of the solution measures along the descent trajectory remain bounded, ensuring stability and preventing undesirable divergence; (ii) We establish a global convergence guarantee with a convergence rate of $\mathcal{O}(\log(K)/\sqrt{K})$ over $K$ iterations, showcasing the efficiency and effectiveness of our algorithm; (iii) Additionally, we analyze and establish local control over the first-order condition discrepancy, contributing to a deeper understanding of the algorithm's behavior and reliability in practical applications.
CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models

Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects. Existing solutions often require customly tailored functions for each of these problems, leading to sub-optimal results, especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conduct extensive experiments across a wide variety of scenarios, each involving unique combinations of objects, attributes, and scenes. These experiments effectively showcase the versatility, efficiency, and flexibility of our method in working with both latent and pixel-based diffusion models, including Stable Diffusion and Imagen. Moreover, we publicly share our source code to facilitate further research.
"What's important here?": Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfaces

Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to see how LLMs can be used to retrieve and locate important elements for a user given query (i.e. task description) in a web interface. In contrast with prior works, which primarily focused on autonomous web navigation, we decompose the problem as an even atomic operation - Can LLMs identify the important information in the web page for a user given query? This decomposition enables us to scrutinize the current capabilities of LLMs and uncover the opportunities and challenges they present. Our empirical experiments show that while LLMs exhibit a reasonable level of performance in retrieving important UI elements, there is still a substantial room for improvement. We hope our investigation will inspire follow-up works in overcoming the current challenges in this domain.
Motion Planning for Multiple Mobile Manipulator System in Complex Flipping Manipulation

Multiple robot systems are favored for object manipulation and transportation, especially for large objects. However, in more complex manipulation such as flipping, these systems encounter a new challenge, configuration disconnectivity of manipulators. Grasping objects by manipulators will impose closed-chain constraints on the system, which in turn limits the feasible motions of manipulators and further compromises the configuration connectivity. Multiple mobile manipulator systems show much more flexibility in object manipulation with the mobility of the mobile platform and have the potential to address the above problem. In this paper, a novel planning framework is proposed for complex flipping manipulation by incorporating platform motions and regrasping. Firstly, two types of trajectories, mobile manipulator planning and regrasping planning, are classified and can be assigned different priorities for different tasks. Secondly, corresponding planning methods are designed for each type of trajectory. Specifically, in mobile manipulator planning, the configuration of the platform is determined through optimization to ensure connectivity when the manipulator approaches configuration boundaries. In regrasping planning, closed-chain constraints are temporarily disregarded and the manipulation capabilities are prioritized to facilitate subsequent planning. Finally, the structure of the overall planning framework is provided. Experimental results demonstrate that the proposed planner efficiently plans the motions of the system to accomplish flipping manipulation. Additionally, a comprehensive experiment emphasizes the significance of our planner in extending the capabilities of multiple mobile manipulator systems in complex tasks.
Where exactly does contextualization in a PLM happen?

Pre-trained Language Models (PLMs) have shown to be consistently successful in a plethora of NLP tasks due to their ability to learn contextualized representations of words (Ethayarajh, 2019). BERT (Devlin et al., 2018), ELMo (Peters et al., 2018) and other PLMs encode word meaning via textual context, as opposed to static word embeddings, which encode all meanings of a word in a single vector representation. In this work, we present a study that aims to localize where exactly in a PLM word contextualization happens. In order to find the location of this word meaning transformation, we investigate representations of polysemous words in the basic BERT uncased 12 layer architecture (Devlin et al., 2018), a masked language model trained on an additional sentence adjacency objective, using qualitative and quantitative measures.
Mean estimation in the add-remove model of differential privacy

Differential privacy is often studied under two different models of neighboring datasets: the add-remove model and the swap model. While the swap model is frequently used in the academic literature to simplify analysis, many practical applications rely on the more conservative add-remove model, where obtaining tight results can be difficult. Here, we study the problem of one-dimensional mean estimation under the add-remove model. We propose a new algorithm and show that it is min-max optimal, achieving the best possible constant in the leading term of the mean squared error for all $\epsilon$, and that this constant is the same as the optimal algorithm under the swap model. These results show that the add-remove and swap models give nearly identical errors for mean estimation, even though the add-remove model cannot treat the size of the dataset as public information. We also demonstrate empirically that our proposed algorithm yields at least a factor of two improvement in mean squared error over algorithms frequently used in practice. One of our main technical contributions is a new hour-glass mechanism, which might be of independent interest in other scenarios.
Learning Robust Policies for Uncertain Parametric Markov Decision Processes

Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a better cost. We propose a method for verifiably safe policy synthesis for a class of finite state models, under the presence of structural uncertainty. In particular, we consider uncertain parametric Markov decision processes (upMDPs), a special class of Markov decision processes, with parameterised transition functions, where such parameters are drawn from a (potentially) unknown distribution. Our framework leverages recent advancements in the so-called scenario approach theory, where we represent the uncertainty by means of scenarios, and provide guarantees on synthesised policies satisfying probabilistic computation tree logic (PCTL) formulae. We consider several common benchmarks/problems and compare our work to recent developments for verifying upMDPs.
Adaptive Shape-Servoing for Vision-based Robotic Manipulation with Model Estimation and Performance Regulation

This paper introduces a manipulation framework for the elastic rod, including shape representation, sensorimotor-model estimation, and shape controller. Until now, the manipulation of the elastic rod has faced several challenges: 1) shape learning from high-dimensional to low-space dimensional; 2) the modeling of robot manipulation of the elastic rod; 3) the determination of the shape controller. A novel manipulation framework for the elastic rod is presented in this paper, which only uses the input and output data of the system without any prior knowledge of the robot, camera, and object. The proposed approach runs in a model-free manner. For the approximation of the sensorimotor model, adaptive Kalman filtering (AKF) is used as the online estimation. Model-free adaptive control (MFAC) is designed according to the obtained differential model of robot-object configuration and then is combined with the performance regulation requirement to give the final format of the shape controller. Hence, the proposed approach can enhance the autonomous capability of deformation object manipulation. Detailed simulation results are conducted with a single robot manipulation to evaluate the effectiveness of the proposed manipulation framework.