Reviewerly/exHarmony huggingface dataset:
Knowledge Management Strategies and Green Innovation Practices: Empirical Evidence from the Malaysian Public Sector

This research study aimed to investigate the impact of knowledge management strategies, specifically knowledge creation, knowledge acquisition, knowledge sharing, and knowledge application, on green innovation practices. Knowledge management strategies encompass processes within organizations that involve the development and dissemination of knowledge to enhance business performance. Harnessing knowledge is crucial not only for competitiveness but also for fostering innovation in support of sustainability. Green innovation practices have garnered attention from scholars and policymakers due to their ability to address environmental concerns. Furthermore, organizations increasingly recognize the importance of green practices, prompting globally competitive entities to continuously enhance their competencies to mitigate environmental degradation and improve overall organizational performance. Therefore, this study identified knowledge management strategies as variables that can support green innovation practices. To conduct the study, a quantitative research approach was adopted, and data collection utilized a closed-ended questionnaire adapted from a prior study. The questionnaire was distributed among employees in the public sector within the Klang Valley region of Malaysia. Purposive sampling was employed to select participants, resulting in the successful collection and analysis of 256 data sets. Descriptive and inferential statistics were performed using SPSS software. The findings indicate that knowledge management strategies, encompassing knowledge creation, knowledge acquisition, knowledge sharing, and knowledge application, positively and significantly influence green innovation practices. Among these factors, knowledge sharing was found to have the most substantial impact on green innovation practices. These results confirm the importance of knowledge management practices for organizations seeking success in green innovation practices. Therefore, it is recommended that organizations support knowledge management activities to enhance the adoption of green innovation practices.
ELICITATION TECHNIQUE EMPLOYED BY EFL TEACHERS IN MOTIVATING JUNIOR HIGH SCHOOL STUDENTS TO SPEAK

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;This study was inspired by the lack of teaching techniques that could accommodate students’ speaking skills as the main indicator determining students’ success in learning English. The elicitation technique could be used by teachers to initiate students to speak during the learning process. A descriptive qualitative method was employed which aimed to investigate the types of elicitation techniques used, the way to implement them, and students’ responses to the elicitation techniques employed by the English teachers in motivating students to speak. There were two English teachers and ten students consisting of seventh and eighth grades involved as the participants in this study. Checklist, classroom observation, interview, and video audio recordings were conducted as the methods of data collection by means of observation checklist, observation sheet, interview guide, as well as video-audio recorders. The data were analyzed based on the category of elicitation techniques proposed by Cross (1992). The findings showed that there were 10 types of elicitation techniques used. Speech Prompts 44.5 (28.7%), WH Short-Answer Question 42.5 (27.4%), dan Yes-No Question 37 (23.9%) were the most frequent types used by the teachers in eliciting students to speak. In addition, it was also revealed that the elicitation techniques were employed by the teachers in all the teaching segments. Eight out of ten students positively responded by feeling motivated to speak in English. This indicated that the elicitation techniques used by the teachers could motivate students to speak in English.&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
FUNCTIONS OF SPEECH ACTS IN AUSTRALIAN MINISTER OF FOREIGN AFFAIRS SPEECH AT UNGA

&lt;p&gt;Sometimes, politicians may use speech acts in their political speeches that the audience may interpret in a variety of ways. Speech acts are used when a speaker expresses words or phrases to achieve specified objectives and it relates to pragmatics in some way. This research aims to find the most dominant and the functions of illocutionary acts found in Australian Minister of Foreign Affairs speech at UNGA. The theory used was from Searle and it was found that there were four illocutionary acts in the speech, namely representative, commissive, directive, and expressive. It was found that representative was the most dominant type of act in the speech. It occurred 54 times to inform and state the current condition and issues of the world. The second was commissive with 23 occurences, followed by directive with 14 occurences, and expressive with 6 occurences. While declarative was not found because there was not any declaration that could change the current condition.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
The Memento Tracer Toolset for Human-Guided Focused Crawling of Dynamic Web

Dynamic web content is a challenge to archive, because the underlying web technologies are constantly advancing. For example, new JavaScript frameworks and Scalable Vector Graphics web elements make the capabilities of archiving tools a moving target. Moreover, the order of execution of events on a web page can affect the page state and the crawling path. To date, a smart crawler capable of solving the daunting task of driving all action elements on a page with all possible traversal paths does not exist, due to the complexity and cost of operation. Consequently, conventional crawling usually does not activate interactive elements on the page, and thus preserves pages with missing information. In our approach, we use a pre-crawl curated web navigation session to record the navigation plan (or Trace) via our Chrome browser extension. We implemented a Trace-driven crawler that can take the navigation plan recorded in the Trace and apply it to a class of webpages with like HTML layouts and interactive options. The Trace-driven crawls are particularly useful for sites where software development work and scholarly research are now kept, such as GitHub, Slideshare, and Publons. Our crawler and other tools are open-sourced to help the web archiving community efficiently capture the valuable information now housed in the interactive web of productivity portals.
CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning

Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics. However, establishing the correspondence between humans and robots with different body structures is not straightforward due to the mismatches in kinematics and dynamics properties, which causes intrinsic ambiguity to the problem. Many previous algorithms approach this motion retargeting problem with unsupervised learning, which requires the prerequisite skill sets. However, it will be extremely costly to learn all the skills without understanding the given human motions, particularly for high-dimensional robots. In this work, we introduce CrossLoco, a guided unsupervised reinforcement learning framework that simultaneously learns robot skills and their correspondence to human motions. Our key innovation is to introduce a cycle-consistency-based reward term designed to maximize the mutual information between human motions and robot states. We demonstrate that the proposed framework can generate compelling robot motions by translating diverse human motions, such as running, hopping, and dancing. We quantitatively compare our CrossLoco against the manually engineered and unsupervised baseline algorithms along with the ablated versions of our framework and demonstrate that our method translates human motions with better accuracy, diversity, and user preference. We also showcase its utility in other applications, such as synthesizing robot movements from language input and enabling interactive robot control.
Semiotic Analysis on the Movie ‘Yaavarum Nalam’ (2009)

This study is a semiotic analysis of the 2009 movie “Yaavarum Nalam” using Saussure’s dyadic Theory. The ultimate aim of the horror genre is to bring the extreme emotion of deep fear inside the viewers. This research focuses on the Mise-en-scène the composition, lighting, costume, props, and any other object on frame can carry a meaning to the audience .The filmmaker’s structure their visualization with the layers of volatile metaphors in the mise - en - scene for the narrative. However deep the verbal information is, the visual representation of properties, acting, symbols, and the everything inside the frame plays a key role strengthens the narration. This research uses a qualitative method of analysis. The study is done analysing the signifier and the signified is interpreted using the elements of Mise – en – scene. The conclusions shows new camera angles introduced to represent the character’s feel in the situation, similar colors were used in both positive and negative aspects. How to cite this article:Swamynathan V. Semiotic Analysis on the Movie ‘Yaavarum Nalam’ (2009). J Adv Res Jrnl Mass Comm 2023; 10(1): 18-23. DOI: https://doi.org/10.24321/2395.3810.202304
PERANAN UAV (UNMANNED AERIAL VEHICLE) DALAM IDENTIFIKASI ANCAMAN LONGSOR TINGGI DI SUNGAI CI DURIAN, DAERAH CIGUDEG, BOGOR

Technological developments in the field of photography, especially in the manufacture of 3D models from a collection of aerial photographs can be quickly and effectively with Unmanned Aerial Vehicle (UAV). This research was conducted in CI Durian River as the target object of 3D modeling. The study area located in Bogor regency, West Java province has a morphology of hills to low hills with winding rivers and composed by the Bojongmanik formation (Tmb). The dominant rock units in the study area are sandstone and claystone. The process of data retrieval using Unmanned Aerial Vehicle (UAV) can be used to perform mapping quickly and accurately at a more affordable cost. This research uses descriptive and interpretive methods. Primary Data in the study is all the data acquired through drones. Secondary Data are data such as slope maps and topographic maps. Primary Data and secondary data can be a reference in detecting landslides in the study area. The tool used in this study is to use unmanned Aerial Vehicle (UAV) technology in the form of drones by utilizing aerial photography data which is then processed into Digital Elevation Model (DEM) data. The use of Ground Control Point (GCP) can certainly add accuracy to the creation of DEM data. The Data is a reference in the interpretation of the threat of landslides that occurred in the study area. Primary data in the form of slope can validate secondary data in the form of slope maps. The dominant factors that influence the threat of landslides in the study area include: topographic factors and steep slopes. Topography and slope factors are very steep slopes are the main factors of high landslide threat in the study area.
The TypeScript Compiler

When you run the TypeScript file every time, you are making changes to it. In this chapter, you learn about other ways to watch for and make changes.
Advanced Types

In this chapter, you learn about advanced types. These are special types that can be used in TypeScript. They include intersection types, type guards and discriminated unions, type casting, index properties, function overloading, and nullish coalescing.
Image Dehazing Using Generic Model Agnostic Convolutional Neural Network

The most frequent environmental factor that affects image quality and image analysis is fog. A coherent generative approach to image organization is suggested in this study. We require the construction of a fully convolutional neural network in order to recognize the haze pattern in the input photo and restore a clear, fog-free image. The suggested method is agnostic because it doesn’t take into account air scattering models. Surprisingly, even SOTS outdoor images created using atmospheric scattering models outperform current state-of-the-art image decluttering techniques. Many modern apps use visual data analysis to find patterns and make decisions. Intelligence and control systems are some instances where clear pictures are crucial for precise outcomes and dependable performance. Environmentally generated distortions, the most frequent haze and fog, could, however, have a considerable impact on such systems. Therefore, the challenge of recovering high-quality images from their fuzzy equivalents has received attention in the vision community. The dehaze problem is the common name for that issue. This work proposes a dehazeNN that simply focuses on creating a blur-free version of the input picture, offering a novel and more flexible approach to the dehaze problem. It constructs an encode and decode architecture using the most recent developments in deep learning in order to recover the clear image, while entirely removing the estimation problem. The method might also be able to spot intricate haze patterns that the atmospheric scattering model missed, which were present in the training data. A convolutional neural network called the generic model-agnostic convolutional neural network (GMAN) has been proposed for the elimination of haze and the restoration of clear images. An end-to-end deep learning system for photo denoising uses encoder-decoder networks.
Creating a Party App in ReactJS with TypeScript

React is the most popular JavaScript library. Almost all production projects are made in ReactJS or Angular. In Angular, you have to write your code in TypeScript, but in React that is not the case.
Creating a To-do List Project with TypeScript

With all the foundational learnings under your belt, you can now start building projects. This first project is a to-do list, which you will build using TypeScript (see Figure 7-6 for the final result).
Face detection algorithm under low-light based on feature recovery

Face detection detects and locates faces in images for face recognition, face tracking, and analysis applications. The performance of many advanced face recognition models deteriorates significantly when applied to low-light environments, hence face detection from low-light images is challenging. To solve the problem, this paper proposes a face detection method based on feature recovery, which includes two modules: feature recovery and feature extraction. The feature recovery module can obtain the face feature recovery image, which is fused with the original low-light face image to obtain the face feature image. On this basis, the feature extraction is trained for face detection. Finally, a face detection method suitable for low-light is obtained. It solves the difficulty of face detection under low-light. The experiment results carried out the overall detection precision increased by 18% on the DARK FACE test set, which verified the effectiveness of the proposed method.
Securing Cloud Storage Data Using Audit-Based Blockchain Technology—A Review

Cloud storage services in cloud computing have become very popular. Many organizations nowadays are using cloud services to outsource their data storage. Due to the cloud services provider’s lack of cloud storage security and to ensure data integrity, availability, privacy, data tampering, and data leakage, the newly emerging blockchain audit-based has become a very popular and future solution. Most of the existing schemes are based on traditional or identity-based public auditing. These kinds of audit schemes have a problem such as a certificate management or key escrow. They also do not support dynamic data updates and user identity tracking for group users. A trusted TPA is required for the existing public auditing schemes. To address the above data privacy, data privacy leakage in cloud storage, third-party auditor’s mistrust, and certificate management, it is to adapt a certificateless multi-replica and multi-data public audit scheme based on blockchain technology and blockchain and public key searchable encryption. All replicas will be stored in different cloud servers and at the same time, their integrity can be audited. All data also is protected by the encryption algorithm. Smart contract technology is used for controlling data access and sharing. Data transaction is automatically recorded on the blockchain. The adapted project work will discuss all methodologies used, their comparison, and their benefits. This paper reviews the application of blockchain-based audit technology for securing cloud storage data.
A Comparative Study on the Training Mode of Postgraduates Majoring in Higher Education between China and America

Under the background of the new era in the 21st century, the development of postgraduate education in Chinese colleges has made great progress, with postgraduates gradually becoming the "reserve army" of various talents, and the training mode of postgraduate becomes the core of the talent training process. As a pioneer in the development of higher education in the world, the United States has more educational experience in the training mode of postgraduate, which can be used as a reference for the postgraduate training of higher education in China. Based on the comparative study on the training mode of postgraduates majoring in Higher Education between China and US, according to the problems of the training mode of postgraduates majoring in Higher Education, this paper puts forward effective strategies and suggestions to improve the training mode of postgraduates, cultivate more high-quality talents and build China into a powerful country of talents.
MDM method for solving the general quadratic problem of mathematical diagnostics

The term mathematical diagnostics was introduced by V. F. Demyanov in the early 2000s. The simplest problem of mathematical diagnostics is to determine the relative position of a certain point p and the convex hull C of a finite number of given points in n-dimensional Euclidean space. Of interest is the answer to the following questions: does the point p belong to the set C or not? If p does not belong to C, then what is the distance from p to C? In general problem of mathematical diagnostics two convex hulls are considered. The question is whether they have common points. If there are no common points, then it is required to find the distance between these hulls. From an algorithmic point of view, the problems of mathematical diagnostics are reduced to special problems of linear or quadratic programming, for the solution of which there are finite methods. However, when implementing this approach in the case of large data arrays, serious computational difficulties arise. Infinite but easily implemented methods come to the rescue, which allow obtaining an approximate solution with the required accuracy in a finite number of iterations. These methods include the MDM method. It was developed by Mitchell, Demyanov and Malozemov in 1971 for other purposes, but later found application in machine learning. From a modern point of view, the original version of the MDM method can be used to solve the simplest problems of mathematical diagnostics. This article gives a natural generalization of the MDM-method, oriented towards solving general problems of mathematical diagnostics. The equivalence of the general problem of mathematical diagnostics and the problem of linear separation of two finite sets with the largest width of the margin is established.
Organization Of Ship Design Information Using Cramer’s V And Genetic Algorithm

In ship design, existing specifications are repeatedly modified in response to shipowner requests. In this process, there are components that are simultaneously changed for design-related reasons, but the relationships among components have been established based on the tacit knowledge of designers owing to a large number of ship components. In this study, we identify specifications that change simultaneously by using Cramer's V. By leveraging the macro-level granularity, the designers can perform efficient checks and create a Design Structure Matrix (DSM). Additionally, the components are grouped by performing DSM clustering, which reflects whether specification changes occur within or outside the department. A case study is conducted using the actual specifications of a merchant’s vessel to demonstrate the effectiveness of the proposed method. Consequently,accurate relationships among components are extracted, and the departments that must collaborate with each other are identified.
RAP Vol: Robust Adversary Populations With Volume Diversity Measure

Deep reinforcement learning (DRL) algorithms have made remarkable achievements in various fields, but they are vulnerable to changes in environment dynamics. This vulnerability easily leads to poor generalization, low performance, and catastrophic failures in unseen environments, which severely hinders the application of DRL in real-world scenarios. The robustness via adversary populations (RAP) algorithm addresses this issue by introducing a population of adversaries that perturb the protagonist. However, the low data utilization efficiency and lack of population diversity greatly limit the generalization performance. This article proposes robust adversary populations with volume diversity measure (RAP Vol) to address these drawbacks. In the proposed joint adversarial training framework, we use the training data to update all adversaries rather than only a single adversary, leading to a higher data utilization efficiency and a fast convergence speed. In the proposed population diversity iterative improvement mechanism, the vectors representing adversaries span a high-dimensional region. The volume of this region is utilized to measure and enhance population diversity via its square. The ablation experiments have verified the effectiveness of our proposed method in improving the robustness against variations in environment dynamics. Also, the influence of various factors (such as adversary population size and diversity weight) on the robustness has been investigated.
Using Artificial Intelligence to Generate Synthetic Health Data

Generating synthetic data enables making sensitive data sets available to the research community. This report utilizes two off-the-shelf methods to generate synthetic health data. One method, synthpop, is based on standard statistical techniques. The other, CTGAN, is a deep learning generative adversarial network. The authors compare the performance of the methods and discuss the reasons for which synthpop outperforms CTGAN on these data.