Reviewerly/exHarmony huggingface dataset:
Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation

Reinforcement learning has gained significant interest in modern industries for its advancements in tackling challenging control tasks compared to rule-based programs. However, the robustness aspect of this technique is still under development, limiting its widespread adoption. This problem has become more pronounced as users switch to training simulations to reduce costs, resulting in a reality gap that negatively affects real-world performance. One popular method employed to mitigate this problem is randomizing uncertain parameters of the environment during training. Nevertheless, this approach requires expert knowledge to determine the appropriate range of randomization. On the other hand, there is a technique that introduces fine-tuning of agents by adapting their policies to new environments. However, it is challenging to adapt policies when the new environment requires shifting them off their initial distribution. These obstacles limit the practical utilization and popularization of both techniques. Our study proposes a hybrid approach that handles these issues by fine-tuning agents trained with domain randomization through additional real-world training. To assess the efficacy of our approach, we conducted experiments involving a rotary inverted pendulum, augmented with an extra weight not represented in the simulation. Additionally, we employed simulated environments including a cart pole, a simple pendulum, a quadruped, and an ant robot scenario. These environments were given in two distinct versions with mismatching parameters to imitate a gap between training and testing conditions. The results demonstrate that adding as few as twenty to fifty additional real-world training episodes can significantly enhance the performance of agents trained with domain randomization. Moreover, including fifty to two hundred additional episodes can elevate it to a level comparable to those fully trained in the real world. Our study concludes that achieving efficient simulation-to-reality transfer is feasible with domain randomization and relatively small amounts of real-world training.
Tracking an Underwater Target in a Large Surveillance Region with Sensor Location Uncertainty

This paper provides an integrated solution of sensor selection and multi-sensor tracking to localize an underwater target using passive angle-only measurements received from sonobuoys, floating in a large surveillance region. It is evident that sonobuoys, deployed via airdrop in the surveillance region, undergo drift due to the influence of sea currents. To accurately capture this behavior, the positions of the sonobuoys are modeled using a stochastic difference equation. Due to some physical limitations, only a few selected sensors are allowed to send the measurement data for an interval of time, and they are chosen by solving an optimization problem whose cost function is formulated based on the Fisher information matrix. The effect of uncertainty in sensor location on measurement noise covariance matrix is calculated and the deterministic sample (SRF) is modified with the deterministic support points so that it can be applied to a system with a nonlinear process model. The combined method of sensor selection and target localization is used to track a target moving in (i) a nearly straight path, and (ii) taking a turn with a constant but unknown turn rate. The tracking performance of the developed method is compared with the conventional Gaussian filters in terms of root mean square error (RMSE), averaged normalized estimation error squared (ANEES), percentage of track divergence, and execution time. The proposed methodology with the SRF provides an improved result when other existing Gaussian filters are compared.
Distributed Service Choreography Framework for Interoperability Among Prosumers and Electric Power System

The electric power system is transforming from a strict hierarchy into a distributed scheme that consists of prosumers and other service providers. Some services require interactions more complex than request-response. To reach functional interoperability, such services necessitate an agreement about what actions happen, in which order, and in which conditions. Such agreements can be modeled as service choreographies, a concept familiar from microservice architectures (MSA). However, the management of choreographies is demanding, especially in inter-organizational (or actor-to-actor) schemes, if changes occur frequently or the number of participants and choreography instances is high. This article proposes Discografia, a framework to support the distributed execution of service choreographies modeled with the standard Business Process Model and Notation 2.0 (BPMN2). Beyond distributed execution, certain operations must be centralized, specifically for security as well as lifecycle and execution support. The framework is proven with a software prototype to demonstrate applicability. The prototype coordinates the charging of electric vehicles (EV) among competing aggregators operating in urban charging hubs (UCH). Additionally, the concept is evaluated with theoretical calculations that suggest reduced manual effort in communicating choreography specifications. Thus, this paper argues that Discografia concept for distributed service choreographing facilitates inter-organizational interoperability, an essential factor in future power system.
Pengembangan buku saku fisika dengan fitur QR Code untuk meningkatkan pemahaman konsep optika geometri

One of the learning media by utilizing the collaboration between books and technology is realized in the form of a pocket book with a QR Code feature in which various kinds of content are available, such as illustrated images, educational videos, and online simulations. This research has the aim of developing a product in the form of a Physics pocket book with a QR Code feature on the subject of optical instruments as a book to support students in understanding the concept of geometric optics. The research and development method uses the ADDIE model which consists of 5 stages, namely Analysis, Design, Development, Implementation, and Evaluation. However, this research is limited only to the design stage. The product feasibility test is carried out by 10 people who have studied optical physics and are currently studying physics. The results obtained in the pocket book feasibility test show that the average value of all indicator items in the presentation aspect, content aspect and language aspect has a value above 3.4. Based on the analysis results obtained, the physics pocket book product with the QR Code feature on the subject of optical instruments to improve understanding of concepts is included in the very feasible category. It is recommended that further research is needed to develop a physics pocket book with a QR code feature to improve understanding of concepts on the topic of optical instruments.
The Robot Wrote My College Papers

As chatbot technology has advanced significantly over the past few years, it has become more and more prevalent in higher education. Chatbots can simulate human communication and engage in natural conversations. Universities and colleges use chatbots to streamline enrollment and create a virtual “one-stop-shop” for queries. In the meantime, students discovered with excitement that chatbots can help them complete homework assignments. Academic integrity has become a growing concern with students' increasing reliance on chatbots. This chapter summarizes the history of chatbots, discusses the current state of the technology, examines their limitations, and investigates how chatbots can be applied as useful administrative, academic, and pedagogical tools in higher education.
Supply Chain Information System for Sustainability and Interoperability of Business Service

With the realization of environmental and social sustainability in developing and using apparel products and services, stakeholders – particularly consumers- are more concerned regarding these issues in business operations. In order to address new developments and changing trends, apparel businesses are compelled to identify and implement innovative and sustainable solutions for regular activities. This chapter assesses how the textile and apparel supply chains can comply with the United Nations' sustainable development goals. In particular, verifying the source of raw materials and maintaining visibility of merchandise products and related services while moving through the value-chain networks is challenging and maintains interoperable business sustainability. Information systems play a vital role in maintaining operational sustainability. This chapter presents a blockchain-based Internet of Things (IoT) infrastructure powered by service-oriented computing architecture as a solution for information processing for maintaining sustainable supply chain operations.
Supply Chain Information System for Sustainability and Interoperability of Business Service

Recent Semantic Web Technology developments indicate possible advancements in supply chain management. In particular, the innovative business process automation based on SWT attracted much interest from the logistics, manufacturing, packing, and transportation industries. This technology combines a set of new mechanisms with grounded knowledge representation techniques to address the needs of formal information modelling and reasoning for web-based services. This chapter provides a high-level summary of SWT to help better understand this technology's impact on broader enterprise information architectures. In many cases, it also reuses familiar concepts with a new twist. For example, "ontologies" for "data dictionaries" and "semantic model" for "data model." This chapter presents the usefulness of a proposed architecture by applying theory to integrating data from multiple heterogeneous sources, which entails dealing with semantic mapping between source schema and Resource Description Framework (RDF) ontology, which are described declaratively using a specific query language (i.e., SPARQL) queries. Finally, the semantics of query rewriting are further discussed, and a query rewriting algorithm is presented.
A Conceptual Framework for Blockchain-Based, Intelligent, and Agile Supply Chain

Traditional supply chain administration regularly depends on centralized frameworks, manual forms, and data silos. This could lead to wastefulness, lack of transparency, and expanded risk of extortion and falsification. As businesses proceed to expand globally and customer requests increments, there is a developing requirement for more prominent straightforwardness, effectiveness, and security in supply chain administration. Using blockchain technology, businesses can see the movement of goods throughout the supply chain in real-time. It enables data sharing among all parties involved, provides a source of truth, and fosters trust among stakeholders. Blockchain technology can offer assistance in computerizing and streamlining supply chain forms, counting acquirement, inventory administration, and procurement. This may lead to taking a toll on investment funds and expanded operational proficiency. In this chapter, a conceptual system for intelligent supply chains based on blockchain is given. It shows the causal connections of the compelling components in these smart supply chains.
Introduction to Unsupervised Learning

This chapter provides an overview of unsupervised learning, first describing the basic principles of unsupervised learning, followed by the basic problems and fundamental methods of unsupervised learning.
Perceptron

This chapter first introduces the perceptron model, then describes the learning strategy of the perceptron, especially the loss function, and finally presents perceptron learning algorithms, including the primitive form and the dual form, and proves the algorithm's convergence.
Cooperative management of an emission trading system: a private governance and learned auction for a blockchain approach

Abstract Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory, there is little research on using blockchain in carbon emission trading schemes (ETS). This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology. Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security. Considering the augmentation of information interaction during the auction process brought about by blockchain, the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction. In particular, implementing smart contracts can provide a swift and automatic settlement. The simulation results of the proposed system demonstrate the following: (1) fine-grained access is possible with a second delay; (2) the average annual compliance levels increase by 2% when bidders’ learning behavior is considered; and (3) the blockchain network can process more than 350 reading operations or 7 writing operations in a second.
Extracting Book Titles From Book Recommendation Videos Using a Deep Learning Approach

Extracting text from images and videos is an emerging field of research with a wide range of applications, including video search, video editing, and translation. Nowadays, book promotion videos in different languages are shared on social media and especially on YouTube. In this study; It is recommended to take book titles through book promotion videos. The developed system takes video as input and separates the names of the books. The viewer can select the desired book by clicking on the detected book titles and watch the relevant part of the video. This application result in time saving by the viewer. In order to achieve this application, a deep learning-based system was developed to retrieve the names of books from videos. YOLO-based method was used in the study. Different YOLO algorithms were used in the study, and YOLOv5 was found to be more successful. This study contributes to the field of text extraction and video analysis by developing a deep learning-based approach to extract book titles from book promotion videos.
Efficient Hybrid Zoom Using Camera Fusion on Mobile Phones

DSLR cameras can achieve multiple zoom levels via shifting lens distances or swapping lens types. However, these techniques are not possible on smart-phone devices due to space constraints. Most smartphone manufacturers adopt a hybrid zoom system: commonly a Wide ( W ) camera at a low zoom level and a Telephoto ( T ) camera at a high zoom level. To simulate zoom levels between W and T , these systems crop and digitally upsample images from W , leading to significant detail loss. In this paper, we propose an efficient system for hybrid zoom super-resolution on mobile devices, which captures a synchronous pair of W and T shots and leverages machine learning models to align and transfer details from T to W. We further develop an adaptive blending method that accounts for depth-of-field mismatches, scene occlusion, flow uncertainty, and alignment errors. To minimize the domain gap, we design a dual-phone camera rig to capture real-world inputs and ground-truths for supervised training. Our method generates a 12-megapixel image in 500ms on a mobile platform and compares favorably against state-of-the-art methods under extensive evaluation on real-world scenarios.
Reconstruction of Machine-Made Shapes from Bitmap Sketches

We propose a method of reconstructing 3D machine-made shapes from bitmap sketches by separating an input image into individual patches and jointly optimizing their geometry. We rely on two main observations: (1) human observers interpret sketches of man-made shapes as a collection of simple geometric primitives, and (2) sketch strokes often indicate occlusion contours or sharp ridges between those primitives. Using these main observations we design a system that takes a single bitmap image of a shape, estimates image depth and segmentation into primitives with neural networks, then fits primitives to the predicted depth while determining occlusion contours and aligning intersections with the input drawing via optimization. Unlike previous work, our approach does not require additional input, annotation, or templates, and does not require retraining for a new category of man-made shapes. Our method produces triangular meshes that display sharp geometric features and are suitable for downstream applications, such as editing, rendering, and shading.
Shaping an Image of Science in the 21st Century: The Perspective of Metamodernism

In a contemporary world facing countless multifaceted crises and challenges, science can still serve as one of the most powerful tools to deal with the ordeals of our time. However, the scientific community needs to provide space for reflection on novel ways of developing its centuries-old heritage and unlocking its potential for the benefit of the world and humanity. The purpose of this article was to deliberate on the image of contemporary science within the framework of the new philosophical paradigm of metamodernism. Following historical strands related to metamodernism and science, the authors encircled the general features and elaborated the main philosophical principles of metamodernism. The main task was to identify elements of contemporary science that conform to the philosophical principles of metamodernism. Thus, several features of science and research, such as the structure of science, scientific truth, metanarratives of science, scientific thinking, system of science, interaction of scientific disciplines, dialogue of science with society and politics, open science, digitalisation of science, etc., were interpreted through the perspective of the ontological, epistemological, axiological, and methodological principles of metamodernism. This article ends with a summary of the main points of the discussion and practical implications of the presented ideas.
A novel Bayesian-based INS/GNSS integrated positioning method with both adaptability and robustness in urban environments

Achieving higher accuracy positioning results in urban environments at a lower cost has been an important pursuit in areas such as autonomous driving and intelligent transportation. Low-cost Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) integrated navigation systems have been an important means of fulfilling the above quest due to the complementary error characteristics between INS and GNSS. The complex urban driving environment requires the system sufficiently adaptive to keep up with the time-varying measurement noise and sufficiently robust to cope with measurement outliers and prior uncertainties. However, many efforts lack a balance between adaptability and robustness. In this paper, a novel positioning method with both adaptability and robustness is proposed by coupling the Mahalanobis distance method, the Variational Bayesian method and the student’s t-distribution in one process (M-VBt method). This method is robust against non-Gaussian noise and priori uncertainties, plus adaptive against measurement noise uncertainty and time-varying noise. The field test results show that the M-VBt method (especially the Mahalanobis distance part) has significantly improved the system performance in the complex urban driving environment.
KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.
Volumetric CT Segmentation with Mask Propagation Using Segment Anything

Medical imaging is both an interesting and challenging field for researchers, mainly due to their lack of labeled data, especially in segmentation tasks. Many interactive system has been introduced to help streamline the annotation workflow, and most recently Segment Anything Model (SAM) has been a breakthrough as a foundation model in interactive segmentation using prompts. In this paper, we adapt SAM, a 2D segmentation for 3D organ interactive segmentation task, then we propose several performance improvement strategies and achieve a comparable result of 0.8694 of mean DSC in a full-supervised setting with a small amount of data. Furthermore, in the process of finding a method to reduce the human effort when using our algorithm, we also develop a novel method inspired by a beam search algorithm, stemming from the NLP domain, which can run volumetric segmentation for one target (i.e. the liver) with minimal human manual input, and still achieve an interesting result of 0.9410 Liver's DSC, allow it to be integrated to annotation system to aid medical expert.
Weapon Detection Using Deep Learning

Nowadays, security and safety issues are very complex and tend to be related to criminals using weapons to commit crimes, posing many potential risks to society. Recently, Deep Learning models have been researched and applied to Computer Vision problems. This article focuses on training a weapon detection system using the YOLO - 5, 7, 8 model and the Swin Transformer model combining Mask R-CNN, Cascade Mask R-CNN, Mask RepPoints V2 for detecting weapon in the images and providing early warning. Object recognition solution The research focuses on 3 main objects of weapons: Pistols, Rifles, and Knives. Due to limited available data, the WeaponData_VN dataset was built and described.