Reviewerly/exHarmony huggingface dataset:
HCS-R-HER: Hierarchical reinforcement learning based on cross subtasks rainbow hindsight experience replay

Sparse reward feedback from the environment is the main challenge for learning goal-oriented tasks based on reinforcement learning. The lack of sufficient exploration also leads to the inability of the agent to robustly learn strategies, especially for hierarchical task control of continuous action space continuum robots, which are more difficult to explore. In this paper, we propose a hierarchical reinforcement learning framework, HCS-R-HER, to accelerate learning by reusing empirical data across subtasks. It uses an upper-level controller, meta-controller, to integrate the underlying targets, and a set of lower-level controllers, controllers, responsible for performing atomic operations. The Oracle perspective mechanism can skip the process of unfinished subtasks, which helps speed up the learning of the meta-controller. The CS-R-HER framework is used to improve the sparsity of the data and accelerate the learning of controllers. Our approach can solve complex tasks or hierarchical tasks more effectively, especially for continuum robot motion environments in continuous action space. Our method is the first time to apply HER to data augmentation for hierarchical tasks and to implement a framework where multiple subgoals are learned together.
AN ANALYSIS OF DIRECTIVE ILLOCUTIONARY ACTS PERFOMED IN “ROMEO AND JULIET” MOVIE: PRAGMATICS APPROACH

. It is impossible to rule out the execution of the directive action while waiting for the interlocutor to perform an act. The speaker must take directive action in order to persuade the interlocutor to do something. This study is concerned with the implementation of directive speech acts and seeks to identify the various types and forms of directive speech acts. The goal of this research was to help readers understand the discussion of linguistics, particularly directive acts. In practice, this research should assist speakers in carrying out their intentions through directive actions. Thus, by comprehending the directive speech act, the interlocutor should be able to grasp the speaker's intention. The purpose of this research was to analyze the types and forms of directive acts in Romeo and Juliet movie. It was a qualitative research. The theory applied in determining the types of directive acts by Searle &amp; Vanderveken (1985). The method used in data collection was the observation method. From the results of observations found 20 directive act utterances. The results of the research conducted on the collected data, found five types of directive acts, namely asking, ordering, forbidding, permitting, and praying. Of all these types, the type of directive that appeared the most was asking because the characters in the movie Romeo and Juliet conveyed a lot of utterances that demanded the interlocutor to provide information. On the other hand, the data showed that the type that appeared the least was praying because there were only a few prayer scene in the movie and the characters rarely pray outside that scene. The forms of directive act were interrogative, imperative, declarative.
Detection of Anomalous Behavior of Manufacturing Workers Using Deep Learning-Based Recognition of Human–Object Interaction

The increasing demand for industrial products has expanded production quantities, leading to negative effects on product quality, worker productivity, and safety during working hours. Therefore, monitoring the conditions in manufacturing environments, particularly human workers, is crucial. Accordingly, this study presents a model that detects workers’ anomalous behavior in manufacturing environments. The objective is to determine worker movements, postures, and interactions with surrounding objects based on human–object interactions using a Mask R-CNN, MediaPipe Holistic, a long short-term memory (LSTM), and worker behavior description algorithm. The process begins by recognizing the objects within video frames using a Mask R-CNN. Afterward, worker poses are recognized and classified based on object positions using a deep learning-based approach. Next, we identified the patterns or characteristics that signified normal or anomalous behavior. In this case, anomalous behavior consists of anomalies correlated with human pose recognition (emergencies: worker falls, slips, or becomes ill) and human pose recognition with object positions (tool breakage and machine failure). The findings suggest that the model successfully distinguished anomalous behavior and attained the highest pose recognition accuracy (approximately 96%) for standing, touching, and holding, and the lowest accuracy (approximately 88%) for sitting. In addition, the model achieved an object detection accuracy of approximately 97%.
Perceived usability of collaborative modeling tools

Online collaborative creation of models is becoming commonplace. Collaborative modeling using chatbots and natural language may lower the barriers to modeling for users from different domains. We compare the perceived usability of two similarly online collaborative modeling tools, the SOCIO chatbot and the Creately web-based tool. We conducted a crossover experiment with 66 participants. The evaluation instrument was based on the System Usability Scale (SUS). We performed a quantitative and qualitative exploration, employing inferential statistics and thematic analysis. The results indicate that chatbots enabling natural language communication enhance communication and collaboration efficiency and improve the user experience. Chatbots need to improve guidance and help for novices, but they appear beneficial for enhancing user experience.
Maritime Low-light Image Enhancement by Joint En-decoder Architecture and Feature Enhancement Network

In low-light conditions, images captured by marine imaging equipment suffer from degradation problems like low brightness and colour distortion. Most existing low light enhancement methods are not well to restore detailed information. To address this issue, we propose the joint en-decoder architecture and feature enhancement network (JEFNet). The framework consists of the encoder to extract features, the MidNet to enhance features and the decoder to reconstruct images. Furthermore, the spatial attention module is employed for fine detail preservation. By exploiting feature enhancement and spatial information, JEFNet is capable of improving the image reconstruction quality. Extensive experiments on synthetic and authentic images verify the superiority of the JEFNet against the state-of-the-art methods.
Forward Translation to Mix Data for Speech Translation

End-to-End speech translation means that using a model to translate speech in one language into text in another language. Currently, the main challenge in the field of speech translation is data scarcity. Existing works solve this problem by using text information or applying data augmentation. However, these works only focus on the exploitation of a single corpus, ignoring the full use of existing human-labeled different-sources data. In this paper, we introduce a simple method to solve the data scarcity problem: training a model with simply mixed data and applying the forward translation method to expand the training set. We perform experiments on covost v2 French-English and mTEDx French-English. Our experiments demonstrate that combining the mixture of speech translation corpora with forward translation can yield a better result than the method without mixing.
Materialistic: Selecting Similar Materials in Images

Separating an image into meaningful underlying components is a crucial first step for both editing and understanding images. We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area. Our proposed approach is robust to shading, specular highlights, and cast shadows, enabling selection in real images. As we do not rely on semantic segmentation (different woods or metal should not be selected together), we formulate the problem as a similarity-based grouping problem based on a user-provided image location. In particular, we propose to leverage the unsupervised DINO [Caron et al. 2021] features coupled with a proposed Cross-Similarity Feature Weighting module and an MLP head to extract material similarities in an image. We train our model on a new synthetic image dataset, that we release. We show that our method generalizes well to real-world images. We carefully analyze our model's behavior on varying material properties and lighting. Additionally, we evaluate it against a hand-annotated benchmark of 50 real photographs. We further demonstrate our model on a set of applications, including material editing, in-video selection, and retrieval of object photographs with similar materials.
A Demonstration of Morphing Identity: Exploring Self-Other Identity Continuum through Interpersonal Facial Morphing

We explored continuous changes in self-other identity by designing an interpersonal facial morphing experience where the facial images of two users are blended and then swapped over time. To explore this with diverse social relationships, we conducted qualitative and quantitative investigations through public exhibitions. We found that there is a window of self-identification as well as a variety of interpersonal experiences in the facial morphing process.
GAAMmf: genetic algorithm with aggressive mutation and decreasing feature set for feature selection

Abstract This paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM), one of the genetic algorithms (GAs) used for feature selection. The modification proposed in this study expands the original GAAM’s capabilities by allowing not only feature selection but also feature reduction. To obtain this effect, we applied the concept of ranks used in the non-dominated sorting genetic algorithm (NSGA) and the concept of penalty term used in the Holland genetic algorithm. With those two concepts, we managed to balance the importance of two competing criteria in the GAAM fitness function: classification accuracy and the feature subset’s size. To assess the algorithm’s effectiveness, we evaluated it on eleven datasets with different characteristics and compared the results with eight reference methods: GAAM, Melting GAAM, Holland GA with a penalty term, NSGA-II, Correlation-based Feature Selection, Lasso, Sequential Forward Selection, and IniPG (an algorithm for particle swarm optimisation). The main conclusion drawn from this study is that the genetic algorithm with aggressive mutation and decreasing feature set (GAAMmf) introduced in this paper returned feature sets with a significantly smaller number of features than almost all reference methods. Furthermore, GAAMmf outperformed most of the methods in terms of classification accuracy (except the original GAAM). In contrast to Holland GA and NSGA-II, GAAMmf was able to perform the feature reduction task for all datasets, regardless of the initial number of features.
A hyperintensional approach to positive epistemic possibility

Abstract The received view says that possibility is the dual of necessity: a proposition is (metaphysically, logically, epistemically etc.) possible iff it is not the case that its negation is (metaphysically, logically, epistemically etc., respectively) necessary. This reading is usually taken for granted by modal logicians and indeed seems plausible when dealing with logical or metaphysical possibility. But what about epistemic possibility? We argue that the dual definition of epistemic possibility in terms of epistemic necessity generates tension when reasoning about non-idealized agents and is a problem of concern for most hyperintensional epistemic logics that alleviate the problem of logical omniscience. The tension is particularly evident when knowledge is taken as a primitive to define other epistemic concepts, such as justification and belief, as done in the knowledge-first tradition. We propose a non-dual interpretation of epistemic possibility, employing a hyperintensionality filter similar to the one that makes the corresponding epistemic necessity operator hyperintensional. We employ the proposed semantics to model Stalnaker’s belief as epistemic possibility of knowledge and provide a sound and complete axiomatization for a hyperintensional version of his bimodal logic of knowledge and belief.
Opportunities and tools of the Central Scientific Agricultural Library (CSAL) for scientific editors and researchers

The work of the CSAL with scientific agricultural journals is considered. The library has been a collector and caretaker of scientific agricultural journals, ensuring the completeness and safety of sets of scientific agricultural journals from the moment of its creation. It is emphasized that the information resources of the library that provide information support for scientific research can be used by scientific editors for reference purposes. The library’s stock having over 3 million items is a reliable source of various information. The content of the stock is disclosed in electronic information resources of its own generation: bibliographic, abstract and full-text, presented on the library’s website, which has long been a virtual double of the library. The library distributes information presented in scientific journals by publishing information about it in the bibliographic index, abstract database, in the Electronic Scientific Agricultural Library, by presenting the contents of journals in the electronic catalog of journals on its website. The results of monitoring the demand by users of scientific journals by subject and year of publication are presented. The work of the AGRIS center at the CSAL on integration of the results of scientific research published in agricultural journals into the world scientific information space is presented. The role of AGRIS in obtaining and exchanging scientific specialized information is described. It is emphasized that this is the only free international database on agriculture and food that provides abstract and full-text information, and the volume of full-text information is increasing every year.
Single‐image snow removal algorithm based on generative adversarial networks

Abstract The effect of snowfall on an image is not only the interference of snow particles but also snow streaks and masking effects (similar to haze). Snowy weather severely reduces the accuracy of computer vision systems. There is a lot of interest in how to effectively remove snow while preserving as much of the original image information as possible. Based on this, the authors propose an effective Generative Adversarial Network (GAN) snow removal algorithm for single images to solve the snow removal failure problem caused by irregular snow particles and snow streaks. Specifically, the authors improve the original GAN network as follows: A novel Transformer module, the Contextual Transformer (CoT) module, is adopted in the residual modules based generator. It effectively uses the contextual information of the snow streaks neighbourhood to restore the texture and information in the noisy image as much as possible based on the focus on snow streaks features. Also, using learnable Regionalized Normalization (RN‐L), potentially corrupted and undamaged regions are automatically detected for separate normalization, and global affine transformations are performed to enhance their fusion. In addition, a multi‐scale discriminator is used in the discriminator to make the discrimination more adequate and retain more details. Extensive experiments have shown that the authors’ GAN network snow removal algorithm outperforms various current networks on snow removal studies in terms of evaluation metrics on both synthetic and real datasets.
Neural Free‐Viewpoint Relighting for Glossy Indirect Illumination

Abstract Precomputed Radiance Transfer (PRT) remains an attractive solution for real‐time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new environment maps while changing viewpoint in real‐time. However, practical PRT methods are usually limited to low‐frequency spherical harmonic lighting. All‐frequency techniques using wavelets are promising but have so far had little practical impact. The curse of dimensionality and much higher data requirements have typically limited them to relighting with fixed view or only direct lighting with triple product integrals. In this paper, we demonstrate a hybrid neural‐wavelet PRT solution to high‐frequency indirect illumination, including glossy reflection, for relighting with changing view. Specifically, we seek to represent the light transport function in the Haar wavelet basis. For global illumination, we learn the wavelet transport using a small multi‐layer perceptron (MLP) applied to a feature field as a function of spatial location and wavelet index, with reflected direction and material parameters being other MLP inputs. We optimize/learn the feature field (compactly represented by a tensor decomposition) and MLP parameters from multiple images of the scene under different lighting and viewing conditions. We demonstrate real‐time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed rendering of challenging scenes involving view‐dependent reflections and even caustics.
Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing, language teaching, translation and speech therapy. The ever-growing Online Social Networks (OSNs) experience a vital issue to confront, i.e., hate speech. Amongst the OSN-oriented security problems, the usage of offensive language is the most important threat that is prevalently found across the Internet. Based on the group targeted, the offensive language varies in terms of adult content, hate speech, racism, cyberbullying, abuse, trolling and profanity. Amongst these, hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm, social chaos or violence. Machine Learning (ML) techniques have recently been applied to recognize hate speech-related content. The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection (GOARN-OSD) model for social media. The GOARN-OSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech. In the presented GOARN-OSD technique, the primary stage involves the data pre-processing and word embedding processes. Then, this study utilizes the Attentive Recurrent Network (ARN) model for hate speech recognition and classification. At last, the Grasshopper Optimization Algorithm (GOA) is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process. To depict the promising performance of the proposed GOARN-OSD method, a widespread experimental analysis was conducted. The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
Semi-supervised reference-based sketch extraction using a contrastive learning framework

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.
POTENTIAL OPPORTUNITIES OF BLOCKCHAIN TECHNOLOGY IN STREAMLINING THE MALAYSIAN COMMERCIAL OFFICE BUILDING OPERATION MANAGEMENT PROCESS

Managing commercial real estate is extremely complex and cumbersome and involves many stakeholders who provide access to and modify a variety of information. This led to some common issues with traditional commercial real estate management such as lack of transparency, third-party interference, the complexity of the agreements, record keeping and security, and absence of real-time data. Accordingly, blockchain technology is anticipated to address the issues of data openness, trustworthiness, and correctness in the global real estate market. This research aims to discover the potential opportunities that blockchain could offer in streamlining the commercial office building operation management process. A conceptual framework of blockchain-based technology adoption in the current Malaysian commercial office building operation management process was developed based on the literature review and focus group discussion (FGD). The framework was underpinned by four core operation management that emerge from the research findings including financial, administrative, technical, and promotional management.
Assessment and data systems in early childhood settings: theory and practice

"Assessment and data systems in early childhood settings: theory and practice." Education 3-13, ahead-of-print(ahead-of-print), pp. 1–2 AcknowledgmentsThe authors would like to express their deepest gratitude to Lembaga Pengelolaan Dana Pendidikan (LPDP/ Indonesia Endowment Fund for Education) under the Ministry of Finance of the Republic of Indonesia as the sponsor for the author's doctoral studies and the support of this publication. Ultimately, we thank the Indonesia Researchers for Language Learning and Teaching (iRecall) team for mentoring this article.
Analisis Kesulitan Membaca Peserta Didik Kelas II SDN 14/1 Sungai Baung

Reading ability is one of the basic language skills. By reading students will apply another ability that is speaking. And also students who are unable to read will find it difficult to fulfill their abilities. Learning to read is the most important part of life, because it is the beginning of our systematic knowledge of the learning process and one of the keys to success in following the process. When learning in class there is interaction in the learning process of teachers and students. Teachers and students are a very important part in determining the success of learning objectives. Reading is a skill that must be taught to children from an early age. If a child of primary school age cannot master early reading skills, he or she will experience many difficulties in learning various subjects in later grades.