Reviewerly/exHarmony huggingface dataset:
Developing a methodology for Co-creation in aviation industry using extended reality technologies

Abstract: In the recent times the concept of Co-creation, involving stakeholders, customers, and partners in the design, development, and delivery of products and services, has gained importance in various industries, including aviation. This PhD research paper presents the concept of co-creation in the aviation sector, focusing on its key elements and the potential of extended reality (XR) technologies as enablers. The paper highlights collaboration, interaction, and user experience as the essential elements of co-creation and discusses existing co-creation platforms and tools. Traditional co-creation platforms have limitations, prompting the proposal to leverage XR technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) as future co-creative platforms. These immersive technologies offer the potential to enhance collaboration, visualization, and engagement in a virtual environment. The study explores how XR tools can aid digital co-creation methods and whether XR systems can induce effective co-creation sessions. In this context, the research develops a multi-user real-time XR environment that fosters collaborative and interactive co-creation aiming to enhance engagement, communication, and idea generation. This paper includes use-case scenarios for adapting such environments in view of investigating the potential of XR tools to improve the quality and novelty of ideas, reduce communication challenges, and decrease time and costs in the aviation industry's co-creation process.
Spatiotemporal Optimal Trajectory Planning for Safe Planar Manipulation of a Moving Object

This article explores a specialized problem of safely manipulating a moving object on a surface concerning moving obstacles with an industrial manipulator. To address this challenge, we formulate it as an optimal trajectory optimization problem to yield a safe, smooth, and locally time-optimal trajectory. Specifically, we propose a task-oriented trajectory planning algorithm that enables reaching and controlling a moving target on the surface as quickly as possible without specifying the reaching moment. Then we formally construct a spatiotemporal safe manifold to handle time-varying obstacles on the surface. The initially planned trajectory is projected onto this manifold, generating a safe but nonsmooth and time-consuming result. Finally, we leverage this result as an initial estimate for the optimization problem to efficiently generate a preferable trajectory while keeping the constraints not violated. Simulations and practical experiments in a production environment demonstrate the approaches presented. The results confirm the effectiveness of our algorithms, which offers an efficient solution to the challenge of manipulating a moving object on a surface.
Smart Geometry Learning in Authentic Contexts with Personalization, Contextualization, and Socialization

This study aimed to investigate the effectiveness of smart mechanisms (i.e., personalization, contextualization, and socialization) on geometry learning supported by Smart-UG, a mobile application with the integration of augmented reality (AR) to facilitate authentic learning and provide learners with some features in measuring and applying geometric concepts in a real-world environment. To investigate the impact of the technology with smart mechanisms on student learning achievements (i.e., geometry ability (GA) and problem-solving skill (PSS), we conducted a quasi-experiment with the participation of fifth-grade students (n = 52). They were divided into two groups (i.e., experimental and control group) and carried out two stages of learning activity with an independent experiment which is underpinned by a set of problem-solving activities. Stage 1 aimed to investigate the impact of personalization and contextualization mechanisms, while stage 2 focuses on assessing socialization's impact on geometry learning. The results showed that the experimental group students achieve good performance in geometry measurement and problem-solving compared to those in the control group. It implied that our proposed application with smart mechanisms can benefit learners to improve their geometry understanding and skill. A positive attitude and high intention by the students toward Smart-UG also signify system utilization in the future. Therefore, this study has the potential to be applied in more complex geometry learning with enhanced smart mechanisms.
REHABILITASI BAGI PECANDU NARKOTIKA OLEH POLDA METRO JAYA UNTUK MENEKAN PEREDARAN GELAP NARKOTIKA DI JAKARTA

Narcotics become an extraordinary threat for human life. The development of drugs trafficking is very fast due to the increasing number of users or addicts. Nowadays, based on data from National Narcotics Agency (BNN), there are almost five million narcotics addicts in Indonesia. These narcotics addicts are mostly spread in big cities such as Jakarta, Surabaya, Medan, Makassar and Bandung. Jakarta as the capital city of Indonesia, center of economy, trade and business is the city with the largest population of narcotics addicts. There are so many narcotics dealers carry out their illicit actions in Jakarta. It can be confirmed that there are always narcotics dealers in every subdistrict. The narcotics dealers do not stop offering these illicit narcotics to buyers who come timeless. Even the law enforcement seems unable to suppress the narcotics traficking. In fact, it often happens that the narcotics users change their status to be narcotics dealers after the law enforcements are conducted by the law enforcement officials. Therefore,it is neccessary to find the right solutions to suppress the drugs trafficking so that it does not become a very dangerous threat anymore namely by decreasing the narcotics user, one of the right ways is rehabilitation for the narcotics users.
CUSTOMER LIFETIME VALUE PREDICTION USING THE REGRESSION MODEL

Customer Lifetime Value (CLV) is an indispensable measure that businesses employ to approximate the forthcoming monetary worth of their consumers.The ability to foretell CLV can furnish businesses with the necessary insights to make well-informed decisions regarding their customer acquisition and retention approaches.Within this manuscript, we delve into the exploitation of regression analysis, a supervised machine learning method, to predict CLV.We expound upon the various steps involved in CLV prediction with regression, including data preparation, feature selection, train test split, model building, evaluation, tuning, and usage.Additionally, we proffer a case study that exemplifies the application of regression analysis to customer data from a virtual retailer.Our findings demonstrate that regression analysis is a highly accurate method of predicting CLV and that the model can help businesses discern and maintain high-value customers.Our study highlights the potential of machine learning techniques to advance the prediction of CLV, and we encourage businesses to explore these methods in their customer analytics endeavors.
PELATIHAN PUBLISH OR PERISH, VOSVIEWER, DAN MENDELEY PADA MAHASISWA MBKM RISET FEB UNSOED

MBKM Riset is one of the programs currently being implemented at FEB UNSOED with the goal of providing opportunities for students to increase their capacity, role, and participation, particularly in research activities that are relevant to their competencies.Some of the difficulties encountered by participants, such as how to identify research gaps, determine research titles, locate and manage references, and analyze and evaluate scientific publications, must be addressed immediately.The goal of this service activity is to provide training and assistance to all participants on how to optimize the use of Publish Or Perish, Vosviewer, and Mendeley in the preparation of research and scientific articles so that higher quality research can be produced.This activity's method consists of five stages: pre-survey, situation analysis and identification of partners' problems, problem-solving methods, PKM activity implementation, and evaluation.During the training and mentoring, participants were extremely enthusiastic.Participants felt the need for additional training as a result of the discussion, so that they could complete research results as efficiently as possible given the MBKM Riset timeline.
TCANet: three-stream coordinate attention network for RGB-D indoor semantic segmentation

Abstract Semantic segmentation plays a vital role in indoor scene analysis. Currently, its accuracy is still limited due to the complex conditions of various indoor scenes. In addition, it is difficult to complete this task solely relying on RGB images. Since depth images can provide additional 3D geometric information to RGB images, researchers chose to incorporate depth images for improving the accuracy of indoor semantic segmentation. However, it is still a challenge to effectively fuse the depth information with the RGB images. To address this issue, a three-stream coordinate attention network is proposed. The presented network reconstructs a multi-modal feature fusion module for RGB-D features, which can realize the aggregation of two modal information along the spatial and channel dimensions. Meanwhile, three convolutional neural network branches are used to construct a parallel three-stream structure, which can, respectively, process the RGB features, depth features and combined features. On one hand, the proposed network can preserve the original RGB and depth feature streams, simultaneously. On the other hand, it can also contribute to utilize and propagate the fusion feature flow better. The embedded ASPP module is used to optimize the semantic information in the proposed network, so as to aggregate the feature information of different scales and obtain more accurate features. Experimental results show that the proposed model can reach a state-of-the-art mIoU accuracy of 50.2% on the NYUDv2 dataset and on the more complex SUN-RGBD dataset.
Geostatistics

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
Blockchain-Assisted Privacy-Preserving Data Computing Architecture for Web3

Web3 has received a lot of attention since its emergence. It aims to provide users with more diverse and vivid web services as well as the complete control over their own data. To support the development of Web3, privacy-preserving decentralized data computing schemes need to be studied. In earlier works, blockchain was used for trusted data sharing. However, due to the lack of computing attribute, blockchain is not capable enough to ensure the trustworthiness of the distributed computing process. Besides, the distributed computing method requires a large amount of data transmission and the current privacy protection researches seldom consider the problem of user privacy. In this article, we design a blockchain-assisted privacy-preserving distributed data computing architecture to break-through the limitations of existing researches. The proposed architecture ensures the secure and trustworthy computing with state channel and computing sandbox. We also design a sandbox location obfuscation method based on onion routing technology, making it difficult for attackers to identify the sandbox location or infer user privacy. Our solution fully considers the characteristics of Web3 and can well support the diverse Web3 applications.
Spatial Data Aggregation

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
PECULIARITIES OF TEACHERS’ INTERACTION WITH TALENTED HIGH SCHOOL STUDENTS: ANALYSIS OF QUALITATIVE RESEARCH RESULTS

The article reveals different aspects of teachers' work with talented high school students.Despite the ongoing research that has been conducted in Ukraine since 2007 and was devoted to the education of talented children, the topic of interaction between teachers and talented high school students remains undiscovered.This article is the result of the analysis of materials obtained in the course of qualitative research -a survey of teachers who teach in high schools and gymnasiums in different regions of Ukraine.The opinions of teachers about their interaction with talented children in the context of various issues are considered: the criteria for determining talented students; strategy of educational support of the student after the identification of his special abilities; interaction with the parents of talented high school students, the difficulties of teaching and educating talented students; important factors to ensure timely and effective assistance to such children.Conclusions are made about the causes of the main problems arising in the interaction of teachers with talented students.It is revealed that the majority of teachers pay attention to the high intellectual level of students as the main criterion of endowment.At the same time artistic and sports endowments, as well as communication and leadership endowments, go unnoticed.The most problematic area is the identification of general and special endowment of high school students, highlighting individual segments with specific abilities of one of the child's talents.A number of moral, volitional and emotional qualities of high school students, which are extremely important as indicators of being gifted, remain outside of attention.It is a style of mastering social experience and originality of unconventional approach in solving challenges, situations, and problems.It is a unique energy potential -the student's ability to work for a long time to solve a problem, to redistribute psychological energy, directing it to solve the task at hand.
“The Relief is Amazing”: An In-situ Short Field Evaluation of a Personal Voice Assistive Technology for a User Living with Dementia

We present a first short field evaluation of IntraVox, a smart home assistive technology that has the potential to support older adults with dementia living independently at home. Based on sensor data, IntraVox uses a personalized human voice to send prompts and reminders to end-users to conduct daily life activities. During a short field study of seven days, IntraVox was installed in the home of an end-user with advanced dementia to prompt a lifestyle change. Additional feedback was collected from their family supporter and three carers. Results show that IntraVox has the potential to prompt end-users with complex needs into changing their actions. In particular, the family supporter found that IntraVox was “100% successful” in that it allowed the family more time together rather than focusing on caregiving, and the relief afforded by the system was considered “amazing”. Thus, we argue the system has the potential to improve the quality of life of both the end-users and their carers. These preliminary findings will inform future larger studies that will assess the usability and feasibility of such systems.
EmoClock: Communicating Real-Time Emotional States Through Data Physicalizations

Expressive interfaces that communicate human emotional state (e.g., level of arousal) are beneficial to many applications. In this work, we use a research-through-design approach to learn about the challenges and opportunities involved in physicalizing emotional data derived from biosignals in real-time. We present EmoClock, a physicalization that uses a clock as a metaphor to communicate arousal and valence derived from biosignal data and lessons learned from its evaluation.
A Survey on using Clustering to Enhance Search Engine Performance

Searching the web seeking relevant documents or information becomes a difficult task.The reason for this problem is the availability of huge amounts of information on the web and poor indexing.So, it is necessary to manage the web resources to help people find the content they are interested in.As a result, the user needs a reliable information retrieval system to find and arrange the pertinent information.Many research studies are conducted to improve the performance of the web to find the most crucial information based on the query of the user.Some of them use ontology and semantic web to get the most relevant information to the user.Others use machine learning techniques, such as clustering to enhance the performance of the search engine.This paper provides a review about the search engine components, and the search engine index structure and ways to update it.This paper also reviews the clustering techniques, such as hard clustering techniques and overlapping clustering techniques, and the methods employed for labeling clusters.The different techniques that have been proposed to improved clustering techniques, cluster labeling and web search index are also discussed in this paper.
Combining Multiple Sentiment Analysis Dimensions into a Comprehensive Sentiment Metric

Despite its growth as a focus within natural language processing, sentiment analysis is often limited to the examination of a single dimension of sentiment-polaritywhich measures the relative positivity, neutrality, or negativity of the language of a text.The analysis presented here combines three additional sentiment dimensions-aspect, mood, and intensity-into a new sentiment metric.This novel metric provides a single sentiment score that includes all four dimensions and is more comprehensive than a polarity score alone.The usefulness of the new metric is demonstrated first by applying it to complaints filed with the U.S. Consumer Financial Protection Bureau and correlating the scores with the outcomes of the cases.The analysis demonstrates that consumers received better outcomes when sentiments expressed in their complaints had a more positive comprehensive score.Next, the new metric is applied to tweets sent by former U.S. president Donald Trump.The scores are shown to distinguish the tweets authored by President Trump from tweets authored by others that the president retweeted.The correlation between the comprehensive sentiment scores of these two types of tweets demonstrates that President Trump retweeted others' messages that were more negative in their sentiment expression than those he authored himself.
An Overview of Speech-to-Speech Translation Framework and its Modules

Speech is the most natural form of human communication and arguably the most efficient method of exchanging information.However, communication between people who only speak different languages is a very challenging task.Speech-to-Speech translation (S2ST) attempts to overcome this issue, making it one of the most promising research domains in speech and Natural Language Processing (NLP).This present article reviews the most recent S2ST systems employed for different languages in terms of their constituent modules, namely Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-To-Speech (TTS).Furthermore, the paper critically highlights the main advantages and disadvantages of state-of-the-art techniques in S2ST in order to provide researchers with an up-to-date picture of current systems and potential directions for future work.
Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback based User Simulator

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item, which often deviates from the real scenario. The user may have a clear single preference for some attribute types (e.g. brand) of items, while for other attribute types (e.g. color), the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable items under multiple combinations of attribute instances. Furthermore, previous works assume that users would provide clear responses to any questions asked by the system. And they also assume that users would be dedicated to the target item, that is, user would answer ”yes” to the attribute corresponding to the target item and answer ”no” to other attributes. However, users’ responses to attributes are not completely dependent on target items, but also influenced by users’ inherent interests. Besides, for some over-specific or equivocal questions, the feedback of user might not be clear (”yes”/”no”) and user might give some fuzzy response like ”I don’t know”. To address the aforementioned issues, we first propose a more realistic conversational recommendation learning setting, namely Multi-Interest Multi-round Conversational Recommendation (MIMCR), where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances. To effectively cope with MIMCR, we propose a novel learning framework, namely Multiple Choice questions based Multi-Interest Policy Learning. Moreover, we further propose a more realistic User-centric User Simulator with Fuzzy Feedback (UUSFF), which naturally calibrates the user response with additional fuzzy feedback based on user‘s inherent preference. To better match the new scenario UUSFF, we propose a simple but effective adaption method for different backbones. Extensive experimental results on several datasets demonstrate the superiority of our methods for the proposed settings.