Reviewerly/exHarmony huggingface dataset:
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to degraded online reinforcement learning performance. In this paper we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks and our results show it can jump-start online fine-tuning and substantially reduce - in some cases by an order of magnitude - the required number of environment interactions.
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using belief propagation as much as possible, and falls back to sampling-based SMC methods when exact computations fail. This algorithm thus implements automatic Rao-Blackwellization and is even exact for Gaussian tree models.
RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding

Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data. Through Prior research, it has been evident that NLU benchmarks are susceptible to manipulation by neural models, wherein these models exploit statistical artifacts within the encoded external knowledge to artificially inflate performance metrics for downstream tasks. Our proposed approach, known as the Recap, Deliberate, and Respond (RDR) paradigm, addresses this issue by incorporating three distinct objectives within the neural network pipeline. Firstly, the Recap objective involves paraphrasing the input text using a paraphrasing model in order to summarize and encapsulate its essence. Secondly, the Deliberation objective entails encoding external graph information related to entities mentioned in the input text, utilizing a graph embedding model. Finally, the Respond objective employs a classification head model that utilizes representations from the Recap and Deliberation modules to generate the final prediction. By cascading these three models and minimizing a combined loss, we mitigate the potential for gaming the benchmark and establish a robust method for capturing the underlying semantic patterns, thus enabling accurate predictions. To evaluate the effectiveness of the RDR method, we conduct tests on multiple GLUE benchmark tasks. Our results demonstrate improved performance compared to competitive baselines, with an enhancement of up to 2\% on standard metrics. Furthermore, we analyze the observed evidence for semantic understanding exhibited by RDR models, emphasizing their ability to avoid gaming the benchmark and instead accurately capture the true underlying semantic patterns.
GEAR-Up: Generative AI and External Knowledge-based Retrieval Upgrading Scholarly Article Searches for Systematic Reviews

Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources. Given the ever-increasing volume of published studies, applying existing computing and informatics technology can decrease this time and resource burden. Due to the revolutionary advances in (1) Generative AI such as ChatGPT, and (2) External knowledge-augmented information extraction efforts such as Retrieval-Augmented Generation, In this work, we explore the use of techniques from (1) and (2) for SR. We demonstrate a system that takes user queries, performs query expansion to obtain enriched context (includes additional terms and definitions by querying language models and knowledge graphs), and uses this context to search for articles on scholarly databases to retrieve articles. We perform qualitative evaluations of our system through comparison against sentinel (ground truth) articles provided by an in-house librarian. The demo can be found at: https://youtu.be/zMdP56GJ9mU.
Invited Session IV: Extended reality--applications in vision science and beyond: Recent developments in head-mounted eye tracking for the understanding of natural(istic) behavior

The scientific investigation of eye movements in natural or simulated and "naturalistic" environments has historically been operating at the limit of what head-worn eye tracking technology is capable of. In this presentation, I will review efforts by myself and collaborators at RIT to push these limits, and to increase the scope of scientific inquiry into natural visual and motor behavior. This talk will end with a brief discussion of emerging methods, most of which are aimed at resolving long standing limitations to video-based eye tracking. Most notably, as a consequence of USB transfer limits and stringent power budgets, video based eye trackers are restricted to either a high spatial resolution of the eye image which improves the spatial accuracy of the final gaze estimate, or a high temporal sampling rate (i.e., a high number of eye frames per second), but not both.
FOSTERING STUDENT’S VOCABULARY MASTERY THROUGH YOUTUBE CHANNEL MR. D AT TENTH GRADE OF SMA YATAMA

: This study aimed to fostering student vocabulary by utilizing Mr. D YouTube channel. This research The study was carried out in the SMA yatama academic year 2021/2022 to increase students' vocabulary skills through the use of the YouTube channel. This study's participants included 31 students. The method used in this study was Classroom Action Research (CAR). This study was conducted following Kurt Lewin model with the following procedures of the action research: planning, acting, observing, and reflecting. The study was carried out in two cycles. Each cycle consisted of three meetings. The data were gathered in this study through observation and test. The results of this research utilizing YouTube indicating that the students' vocabulary skills had improved. At the end of the cycle, getting high grades gradually. Criteria worth the minimum sharpness (KKM) value is 75. (seventy-five). The average student score on the Beginning exam is&#x0D; &#x0D; In the first cycle, the average student score is 70. In cycle two, the average student is 82. Furthermore, there is a reaction. Mr. D's YouTube channel has received great feedback from English students and teachers. Finally, the YouTube channel has helped students enhance their language&#x0D; &#x0D; Keywords: Vocabulary Mastery, YouTube Channel MR. D
On the practical applica tion of the Floyd-Warshall algorithm and its modifications

The paper considers variants of the Floyd – Warshall algorithm, i.e. algorithms for finding shortest paths in a weighted graph with given edge weights; in one execution of the algorithm, the lengths of shortest paths between all pairs of vertices are found. Although the standard versions of the algorithm do not return the paths found, these paths can be reconstructed using simple modifications of the algorithm. Variants of this algorithm can also be used to find the transitive closure of some binary relation, as well as the widest paths between all pairs of vertices of a weighted graph. At the same time, according to the authors, the study of the topic is far from complete; it is confirmed, in particular, by many publications on the Internet on “algorithmic” sites. Despite the fact that the cubic complexity of the algorithm for solving this problem is not improved (at least for standard input data representation options), one can strive to improve the specific running time of the algorithms. Moreover, we deliberately worsen the complexity of the algorithm implementations we are considering (actually multiplying it by the logarithm of the number of vertices); however, due to the possibility of a successful iteration of the edges, conducted in order of increasing their cost, we achieve a reduction in the average running time of the implemented programs.
The Conversion of Jakarta City Solid Waste into Electrical Energy

The volume of solid waste generated by DKI Jakarta is 7.54 tonnes (BPS DKI Jakarta, 2023), affecting air quality. The population in 2022 will reach 10,748,230 and motorized vehicles in Jakarta will reach 26,370,535. (BPS DKI Jakarta, 2023). The population of Jakarta during the day increases along with the number of people who work and study from Bogor, Depok, Tangerang and Bekasi. The use of conventional (fossil) fuels greatly affects the low air quality. Poor air quality affects public health. To reduce the negative impact, it is necessary to control pollution by handling waste and converting it into electrical energy. Converting waste into electrical energy can reduce the amount of waste generation, and obtain electrical energy for urban needs, including to drive public transportation such as buses, MRT and LRT. The research objective was to determine the conversion of municipal waste into electrical energy. The research method was carried out using a qualitative approach, while the results of the study found that the volume of municipal solid waste generation could be reduced and produce by-products other than electricity.
Kinematrix Field Theory Unifying Fundamental Forces and Particles

The Standard Model (SM) of particle physics successfully describes electromagnetic, weak, and strong forces but omits gravity. We introduce the Kinematrix Field Theory (KFT), integrating gravity into this fundamental force framework through a novel particle, the \textit{kinematon}. This gauge boson, existing in both massless and massive forms, advances the SM's scope to address enigmas like dark matter and neutrino masses. We detail the mathematical structure of the KFT and establish Feynman rules for kinematon interactions with fermions and gauge bosons. The KFT, synergizing with the Higgs mechanism and general relativity, aspires to a unified quantum theory of gravity. We explore the kinematon's phenomenological implications and its potential experimental signatures, forecasting significant advancements in fundamental physics and setting a course for future explorations in the SM.
Desain Modul Data Konsumen dan Pemasok untuk Sistem Informasi E-Commerce Ultra Mikro Usaha Mikro Kecil dan Menengah Berbasis Website

Given the rapid development of technology, it makes competition in the business world and requires UMKMs (Micro, Small and Medium Enterprises) to keep up with the times, so that they can achieve the desired targets in marketing and so that information is more quickly received by consumers. During this pandemic, many people lost their jobs and switched to running their own businesses to meet their daily needs so it is possible for people to open Ultra Micro UMKM Businesses with a capital of between 2 to 5 million rupiah. With the number of UMKMs developing in Bandar Lampung City, competition is created in the business world, a strategy for sales is needed that can make it easier for sellers and consumers to buy products. Web-based information systems are one way to compete creatively in the business world. This E-commerce Information System has a database of product sales, product suppliers, product data, and sales reports. The database of product data is products purchased through suppliers, then the product sales database is generated from products that have been sold through online sales carried out locally within the scope of Bandar Lampung City. This research method uses Black-box Testing and has involved 3 examiners and showed a good response to the suitability of UMKM E-Commerce needs.
Educational Paradigms of Communication of the 21st Century: Educational Challenges for the Specialists of the Future

Language has always been the most powerful and versatile means of communication. Then the pictures painted in caves by primitive people gave us much more detail about their lives and their habits. If we move through time the ability to represent and communicate in others' language has been enriched. We have moved to moving images and transmitted images. Since its inception, cinema has become a global entertainment. Unlike television, cinema has always introduced audiences into a virtual space. Cinema creates emotions with an extraordinary power to polarize audiences. Each of us has at least one film to which we owe this involvement, for example, mine was "Blade Runner." Blade Runner is a 1982 science fiction film directed by Ridley Scott, Metaverse The metaverse is a new adventure that we have thanks to a huge amount of hardware and software technologies; for example, VR, AI, and AR have generated ever larger and more detailed spaces, think of the movie Matrix. The Metaverse is Ali Baba's new cave: all the riches are there, but you need the magic word. We are now on the threshold of a new transition. The Metaverse is a space populated by many suitors, none of whom have the dowry to claim the princess as their bride. I belong to one group of suitors. Looking back to some visionary writers we have a shining example of the metaverse: Alice's Adventures in Wonderland. The book was published before the invention of cinema. Alice in Wonderland is a "metaverse" described by Luis Carrol, a famous Oxford scientist. Alice goes in and out of a paradoxical world, a world populated by characters who do not follow the natural rules to which we are accustomed. Language, however, is still the most versatile, and that is where we must start if we are to understand the new paradigms of communication.
Pengembangan Modul Service Advisor Talk Menggunakan Framework ERP Odoo 10 Pada PT. Tunas Dwipa Matra

PT. Tunas Dwipa Matra is the main dealer of HONDA motorcycles in Lampung which is engaged in the development of a network of sales, maintenance, repair and service of spare parts for HONDA motorcycles. A service advisor is someone who helps customers at a dealership to understand vehicle maintenance or repair options, answering customer questions before being scheduled with a technician. This study aims to help a service advisor to be able to access materials regarding understanding motor maintenance and repair as well as other program information. The method used in this study is an agile method with a scrum framework. Based on the research conducted, a service advisor talk menu is produced that can be used for workshop employees, especially a service advisor. Tests carried out in the study were Alpha Test, Beta Test, and Functional Test. Based on the test results, it can be concluded that the service advisor talk module can make it easier for a service advisor to get material and program information related to technical work.
Improved YOLOv7 Algorithm for Detecting Occluded Targets at Aircraft Turnaround Milestone

Abstract The aircraft and special vehicles in the aircraft turnaround milestone have the characteristics of large-scale difference, small spacing distance, etc., and many external interference factors result in many occlusion situations in the process of services. Aiming at the problem that existing airport detection algorithms are ineffective in detecting occluded targets in milestones, an improved YOLOv7 occluded target detection algorithm for aircraft turnaround milestones is proposed. The algorithm adds an efficient channel attention mechanism to the backbone network to suppress the interference information in the environment and improve the attention to the important channels and replaces the convolution in the feature fusion network with a double-pooled coordinate convolution, which is used to improve the model’s ability to extract the spatial information of the occluded target and the remaining features. The algorithm achieves an average detection precision of 89.7% and a recall rate of 88.3% on the aircraft turnaround milestone dataset covering numerous occlusion scenarios, which are 6.1% and 4.4% higher than the YOLOv7 algorithm, respectively. The experiments show that the algorithm can better cope with the task of occlusion target detection in aircraft turnaround milestones.
Sistem Penilaian Angka Kredit Pegawai pada Program Pelatihan Mandiri di BPKP Provinsi Lampung

The Financial and Development Supervisory Agency (BPKP) of Lampung Province routinely holds independent training programs for employees. However, activity data collection is still carried out conventionally which makes it difficult for employees to see the history of the training attended. In fact, these activities have credit numbers that must be recorded carefully because they are related to performance appraisal and have an impact on employee careers. Based on this problem, this research developed a credit scoring information system for self-training programs using a prototyping approach that runs on a web-based platform. The system can provide information about the implementation of independent training activities and employees can make attendance of training activities through the system. Employees can also see the history of the training that has been followed along with the credit score. System testing uses a user acceptance test with a Likert scale which achieves a customer satisfaction index of 92.5%, which shows that it is very satisfying for users and has functioned according to the standards desired by the BPKP of Lampung Province.
Some scientifically-methodical questions of forecasting of characteristics of complex systems

Scientifically-methodical questions of forecasting of characteristics of complex systems (СS) are examined. Necessary definitions are brought. The concept of forecasting system as basic scientifically-practical device of researches СS is entered. Its basic contents and features of process of forecasting of characteristics СS are considered. Research of spasmodic changes of characteristics СS is carried out. Formal definition of jump and a necessary condition of its realization is entered.
Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.
Penerapan Media Pembelajaran Dengan Aplikasi Ideal Dictionary Di SDN Megaluh

In the era of increasingly developing technology, almost all subjects use internet media. The android-based English Learning Application is a learning medium that is used interactively in understanding vocabulary that is easy for students to understand. The intended subject of this activity is a grade IV student of SDN Megaluh. The problem experienced by grade IV students of SDN Megaluh is that they do not get English lessons from the first to third grades due to the independent curriculum which results in them not getting English language learning. The purpose of creating this learning media is to make it easier for students to get to know some basic vocabulary in English. The methods used in this case are twofold, namely the lecture method and the question and answer method. Both of these methods are used to find out the needs of students. From the analysis with these two methods, an ideal dictionary application was created where this application will support learning. It is hoped that this application can later be developed again by SDN Megaluh to support English learning, especially for students who are still new to English lessons. Because this kind of application if developed in the future becomes a learning medium that is quite interesting for students.
Interdisciplinary Research Contexts for Continuous E-Learning

Introduction. The set of aspects under consideration is an interconnected logical system that shows the nature of modern education. Highlighting the classic provisions of the traditional, which justify and condition the indisputability of the transition to continuous education, in the conditions of digitalization and transformation. Purpose. The main purpose of the research is the study of interdisciplinary processes of interconnection in the conditions of continuous electronic learning, the study of the processes of mathematization of processes and phenomena; presentation of the electronic space of continuous learning, which stimulates practical work and an integrated problem-based approach to support the purposeful development of continuous electronic learning. Methods. The requirements, recommendations and conclusions are based on literature study, system analysis methods and generalizations. Research results are illustrated by software implementations.Results. Studies show that the totality of the considered aspects is an interconnected logical system that shows the nature of modern learning, highlighting the classic provisions of the traditional one, which justify and condition a qualitative and effective transition to continuous education, in the conditions of digitalization and transformations.