Reviewerly/exHarmony huggingface dataset:
Design and implementation of intelligent soccer-picking robot

To address the time-consuming and laborious task of picking up soccers, an intelligent soccer-picking robot is designed and implemented, which consists of three main parts: mechanical, electronic, and software design. The robot employs a four-wheel drive and hydraulic shock absorber structure to ensure fast and stable operation. The main body structure of the robot is built with aluminum alloy and PLA materials to reduce weight effectively. The Kendryte K210 chip is the main control chip to reduce power consumption and cost. A monocular camera is used to construct a map, and the YOLOX object detection model is used to detect various objects on the soccer field. The artificial potential field algorithm is improved to achieve optimal path planning for the robot. The robot is tested 80 times on a real soccer field, with a successful pickup rate of 92.5%, meeting the actual needs of soccer-picking and filling the gap in the soccer-picking robot market.
Service Pattern Optimization: Focusing on Collaboration in Service Ecosystems

The service pattern is an abstraction of the business relationship among various participants from the service ecosystem in four aspects: workflow, data flow, resource flow, and value flow. In order to optimize service patterns, it is necessary to consider the collaboration between participants as well as the interaction among different servers. The existing works either optimize the former by adjusting service orchestration, such as business process optimization and workflow optimization, or focus on the latter through adjusting service distribution, such as cloud service distribution optimization and edge service deployment optimization. However, the prevalence of service ecosystems and distributed computing has begun to make multi-user, multi-server scenarios commonplace, placing greater importance on fast and effective optimization of service patterns. In this work, we summarize the constraints and objectives and formally define the service pattern optimization problem. Beyond that, we propose a service pattern optimization-oriented confidence aware recurrent simulated annealing algorithm (PooCa). Experiments conducted on an existing dataset show that our method outperforms the other three baselines on the overall dataset as well as on the eight subsets. Also, our method can reduce the number of search iterations by 41.15% on average with the same search space. We also carry out case studies on the online travel booking service pattern and investigate factors that make patterns perform better.
Melanoma Classification via Hybrid Saliency and Conditional Random Field with Bottleneck to Optimize DeepLab

Neural networks overcome drawbacks of vision tasks by becoming convolutional in a wide range of layers. The salient map is affected by multilevels of strong pixels (superpixels) in global images and that is dependent on the hard threshold for their dividing. Deep neural networks have been established for saliency prediction of segmentation because the feature extraction must be suited to the input data. The convolutional neural network (CNN) also endures conflict between spatial pattern and a likeness of salient objects. Semantic segmentation is one of the approaches to continue classification based on these features. Therefore, upgrading the extraction process can be of use in saliency. In this work, we optimize DeepLab based on an atrous convolutional and a conditional random field (CRF) with a bottleneck in the semantic segmentation method, which serves for classification. The backbone of deep feature extraction is atrous convolution and the bottleneck based on CRF for hybrid saliency in the encoder-decoder system. The classification results are compared with some approaches for saliency prediction of recent deeper methods in an ISIC 2017 dataset. The results give better values not only for saliency prediction for segmentation but also for training and testing for classification.
Multimodality in the Hypergraph Lambek Calculus

The multimodal Lambek calculus NL♦ is an extension of the Lambek calculus that includes several product operations (some of them being commutative or/and associative), unary modalities, and corresponding residual implications.In this work, we relate this calculus to the hypergraph Lambek calculus HL.The latter is a general pure logic of residuation defined in a sequent form; antecedents of its sequents are hypergraphs, and the rules of HL involve hypergraph transformation.Our main result is the embedding of the multimodal Lambek calculus (with at most one associative product) in HL.It justifies that HL is a very general Lambek-style logic and also provides a novel syntactic interface for NL♦: antecedents of sequents of NL♦ are represented as tree-like hypergraphs in HL, and they are derived from each other by means of hyperedge replacement.The advantage of this embedding is that commutativity and associativity are incorporated in the sequent structure rather than added as separate rules.Besides, modalities of NL♦ are represented in HL using the product and the division of HL, which explicitizes their residual nature.
Mobile Learning American Service as Digital Literacy in Improving Students' Analytical Skills

This research aims to produce mobile learning media as digital literacy to improve students' skills in service and arrangement in the American style. This research uses data analysis techniques with the Content Validity Ratio (CVR) method. Data analysis used two methods: qualitative analysis for quantitative data and qualitative analysis. The Feasibility of material content for mobile learning has gone through the formative evaluation stage with a one-to-one expert assessment including material experts, instructional design, media, and language with the results feasible to use. A one-to-one test was conducted by 3 participants with an average overall percentage of the results of the student's assessment was 94.1% which means very good. The limited group test was conducted on 8 students, and all students stated that the media had very good quality, with an average percentage of 99%. The last user trial of this formative evaluation was a field trial with 30 Catering students. Field Group Test, the last trial of the last formative evaluation, obtained an average percentage of 87.46%, which can be categorized as good with a very positive response to the media used. Based on the results of user testing through one-to-one, small group, and field trials, it can be concluded that mobile learning American service as digital literacy is feasible to use.
DisCoCat for Donkey Sentences

We demonstrate how to parse Geach's Donkey sentences in a compositional distributional model of meaning. We build on previous work on the DisCoCat (Distributional Compositional Categorical) framework, including extensions that model discourse, determiners, and relative pronouns. We present a type-logical syntax for parsing donkey sentences, for which we define both relational and vector space semantics.
An Approach to Integration of Multi-Stream Information Sets for Load Forecasting

Smart transport systems (ITS) rely on accurate and timely traffic flow forecast.Recent developments in graphbased neural networks have shown encouraging prediction results.However, several obstacles remain, particularly in graph building and model time complexity.In this research, we offer a multi-stream feature fusion strategy for extracting and integrating rich features from traffic data, and we use a data-driven adjacent matrix to generate graphs rather than a distance-based matrix.To produce the initial nearby matrix, we compute a Spearman rank correlation coefficients between monitor stations and fine-tune it while training.Another popular feature extraction method used in NLP jobs is the Bow.It is the most easy and customizable method for obtaining the characteristics of a document.BoW examines the histogram of The Word inside the text.The frequency of the words is used as a function in the set's training.The Count Victories is used to implement the Bow technique in this study.Victimization is the process of getting numerical vectors from a textual data collection.The frequency of words is tallied, showing that tokens have been counted and the token vectors have been created.The BoW assigns a value to each property depending on how frequently those traits occur.
Research on image recognition technology based on CNN

Convolution Neural Network (CNN) is an algorithm that takes an image as input, and then assigns weights and deviations to all aspects of the image to distinguish each other. The neural network can be trained by using a batch of images. Each image has a label to identify the true nature of the image. This paper gives the basic flow of CNN image recognition technology, and verifies the accuracy of its image recognition with the method of cat and dog image recognition. We used 1000 images for testing, and the recognition accuracy rate was as high as 94.5%, higher than the accuracy rate of 90.3% of SVM and 89.5% of KNN. The experimental results show that the image recognition technology based on CNN is a good image recognition technology, which can be widely used in real life.
The empirical study of Joomla CMS map extension and location performance

Thousands of extensions can be installed on the Joomla CMS with various functions. One of them is the map and location extension, which is useful for meeting the needs of content that display information in the form of visual maps and locations. The Phoca Maps and MX Maps extensions are two of the many map and location extensions available on the Joomla Extension Directory website, downloadable and widely used. This study aims to provide a reference for Joomla CMS users in terms of managing content related to maps and locations through empirical studies of the performance of Phoca Maps and MX Maps extensions. In measuring extension performance, Google Lighthouse is used to audit all quality aspects that support the performance of a web application. The research results found that, in general, the desktop and mobile performance of the Phoca Maps and MX Maps extensions was unsatisfactory. This can be seen from the performance testing results for First Contentful Paint, Speed Index, Largest Contentful Paint, Time to Interactive, Total Blocking Time, and Cumulative Layout Shift.
Development of a mobile Web library application for an institutional repository and investigation of its influences on learning

Purpose This study aims to examine the effects of the proposed mobile Web library application (MWLA) on the search experience and its impact on learners’ engagement, interaction and overall learning outcomes within an institutional repository. Furthermore, the study investigates learners’ acceptance of the MWLA system. Design/methodology/approach The study suggests implementing an MWLA with Algolia’s search service to improve the institutional repository and enhance learners’ access to reliable information. It involved an experiment with 85 undergraduate students divided into experimental and control groups (CGs), where the experimental group (EG) used MWLA for search tasks, and the CG used the traditional library website. The study evaluated the acceptance and learning behaviours of the EG towards MWLA, considering factors such as usefulness, ease of use, mobility, accessibility, satisfaction and intention to use. Findings The findings of this study provide empirical evidence that the EG, which used the MWLA, demonstrated superior performance compared to the CG across all institutional repository collections, resulting in improved learning outcomes. Participants were highly satisfied with MWLA and found it user-friendly and beneficial for improving search skills. MWLA’s portability and accessibility motivated active learner engagement. Originality/value The powerful search bar of MWLA significantly enhanced learners’ search efficiency, resulting in more effective retrieval of relevant materials. Moreover, learners who actively engaged with previews and full-text content, using appropriate keywords and syntax, achieved higher scores and were more likely to access previews, abstracts and full texts of documents using the sorting-by-year or by-advisor feature.
The Effect of Serial Image Media on Students’ Skill in Writing Descriptive Essays

Lack of media used by teachers so far needs to be changed. It is intended that students are not only of high theoretical quality but also of high practical quality. The purpose of this study is to find out the effect of series image media on the writing skills of grade III students of Madrasah Ibtidaiyah (MI) Al-Ikhlasiyah, Depok. In this study there were populations, namely all students in class III A and III B with a sample of 40 students. In this study, the data collection technique used the pretest posttest. The data analysis technique used was Normality and Homogeneity tests. The results of the analysis of research data stated that the existence of a series of media images was very affect with the ability to write descriptive essays for grade III students of MI Al-Ikhlasiyah, Depok. This was seen based on the average score of pretest and posttest results that have been carried out in the experimental group and the control group. The average pretest score obtained by the experimental group was 69, and for the control class the average score was 67.75. After both classes received treatment, the experimental class obtained an average score of 80.75 while for the control class obtained an average of 76.00. The same as the results of calculations through the t-test. The calculation results in the experimental group resulted in a probability value of &lt;0.05, which is 0.000, which means that the series image media affect student learning outcomes.
Perancangan User Interface Pura Teluk Terima Dengan Metode Design Thinking

Bali is renowned as a tourist destination with its natural beauty and religious traditions, earning it the nickname "Island of a Thousand Temples." In this era, the dissemination of information related to Bali's holy temples has become increasingly difficult to find and confirm its authenticity. Therefore, by focusing on the research object of the "Pura Teluk Terima" and incorporating the development of the digital era, as well as addressing the issues faced by prospective visitors by involving several respondent samples, a design thinking method was implemented to design an information system. The outcome of this design is a prototype that allows visitors to gain a deeper understanding of the Pura Teluk Terima. Once the information system was ready for testing, it underwent usability testing using the System Usability Scale method, achieving an excellent score of 82.8125 with a grade of B
Agile Leadership Training Program for Head Nurses and its Effect on Their Innovative Behavior

Background: Organizations require agile leaders to put agility`s ideas and practices, healthleaders especially need to be agile. The head nurses` daily lives are filled with creativity andinnovation. The current study aimed to evaluate agile leadership training program for head nurses andits effect on their innovative behavior at Benha University Hospitals. Design: A quasi experimentaldesign was used. Setting: This study was conducted in (31 units) at Benha University Hospitals,which were composed of bed capacity (880). Subjects: A convenient sample (n =45) of all theavailable head nurses who were having at least one year of experience at the time of study. Tools:Were used four of data collection tools three of them namely; agile leadership knowledge, attitude,and skills questionnaires and innovative behavior scale. Results: Majority of head nurses hadsatisfactory knowledge level and a positive attitude during post and follow up program phases. Threequadrants of head nurses had high level of skills during immediately post and follow up programphases. Meanwhile, more than half of head nurses had high level of innovative behavior at post andfollow up program phases. Conclusion: There was highly statistically significant weak positivecorrelation between attitude of head nurse and their assistant regarding agile leadership and theirInnovative behavior. Recommendations: Give enough time and space to bring out their innovationand implement it. Participate in daily meetings and decision making.
The Mediation of Music in the Development of Intrapersonal and Action Skills in Early Teacher Education

Soft skills enable the individual to successfully face the problems and challenges of a complex society in constant flux and under constant uncertainty. Music, in its different forms of expression, is presented as an ideal tool for the development of intrapersonal and action skills such as adaptability, optimism and proactivity. On the basis of a project carried out by and for future teachers, qualitative research based on a case study has been carried out. Data collection: questionnaires, observations, student productions and interviews. The results reveal that music can be a mediating element for the development of soft skills. Specifically, it has been observed that music can help the individual to face and effectively resolve changing and novel situations that require self-efficacy, self-confidence and social support. Music can also improve the physical and socio-emotional well-being of individuals as well as helping them successfully face adversity.
Corpus Analysis of Reddit Conversations Related to the 2020 Olympic Games

This paper presents the results of a data collection project focused on online Reddit conversations about the 2020 Olympic games. The study explored the use of theme-specific corpora from online sources for use in language education. The data collection and analysis process involved the use of Python scripts to gather 254,267 comments from the most upvoted Reddit posts related to the Olympics in the 2 months around the Games. The collected comments were then processed using NLTK (Natural Language Tool Kit) to identify parts of speech within each comment, and VADER (Valence Aware Dictionary and sEntiment Reasoner) to give each comment a sentiment score. The data was then analyzed for general insights and compiled into a word frequency list which ranked words based on their usage in the collected comments. 本稿では、2020年のオリンピックに関するオンラインRedditの会話に焦点を当てたデータ収集プロジェクトの結果を紹介する。本研究では、オンラインソースからテーマ別のコーパスを言語教育に利用することを検討した。データ収集と分析プロセスでは、Pythonスクリプトを使用して、大会前後の2ヶ月間にオリンピックに関連するRedditの投稿のうち、最も賛同票を得ている投稿から254,267件のコメントを収集した。収集したコメントは、NLTK（Natural Language Tool Kit）を用いて各コメント内の品詞を特定し、VADER（Valence Aware Dictionary and sEntiment Reasoner）を用いて各コメントに感情スコアを付与した。その後、一般的な洞察を得るためにデータを分析し、収集されたコメントでの使用に基づいて単語をランク付けする単語頻度リストにまとめた。
Towards a mobile system with a new wearable device and an AI application for walking and running activities

Recognizing human activities from mobile applications is a challenging task due to the complexity of the context. Several healthcare applications consider wearable devices, including in the orthopedic area. Considering this context, we proposed a novel mobile application that can recognize walking activities with data collected by new wearable sensors on the user’s leg. The wearable system collects the data, which is processed using Edge AI. Then, we propose to present the generated information as a digital twin considering the user’s movements based on the sensor data. For this work, the wearable device and AI movement classification are operational, while the mobile application is still in development.
Retracted: Application of Outlier Detection Model in Korean Language and Culture Communication System Based on Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/6414112.].
Retracted: Optimization of Online Education and Teaching Evaluation System Based on GA-BP Neural Network

[This retracts the article DOI: 10.1155/2021/8785127.].
Boosting Quantum Key Distribution via the End-to-End Loss Control

With the rise of quantum technologies, data security increasingly relies on quantum cryptography and its most notable application, quantum key distribution (QKD). Yet, current technological limitations, in particular, the unavailability of quantum repeaters, cause relatively low key distribution rates in practical QKD implementations. Here, we demonstrate a remarkable improvement in the QKD performance using end-to-end line tomography for the wide class of relevant protocols. Our approach is based on the real-time detection of interventions in the transmission channel, enabling an adaptive response that modifies the QKD setup and post-processing parameters, leading, thereby, to a substantial increase in the key distribution rates. Our findings provide everlastingly secure efficient quantum cryptography deployment potentially overcoming the repeaterless rate-distance limit.
An Entropy-based Data Reduction Method for Data Preprocessing

The primary task in data mining is to find potential patterns or to discover hidden and useful knowledge from given data sets. However, with the increasing data quantity and exploding complexity, the capabilities of dealing with massive data becomes very crucial. The data preprocessing module is an integral part of data mining procedure, which aims to optimize input data usability for subsequent tasks such as classification, clustering, association analysis as well as other data mining algorithms. In general, data preprocessing procedures can effectively reduce the computational complexity while as possible to ensure accuracy and efficiency of prediction or classification, but meanwhile it even can assist to extract some unknown knowledge before applying more advanced data mining algorithms. This research proposes a three-patterns feature variables technique and an entropy-based data reduction (EBDR) algorithm for data preprocessing based on information theory. The goal is to explore high-purity subsets in which the values of an attribute are directly linked to specific class labels. The results of experiments demonstrate the efficacy of EBDR algorithm on datasets of varying sizes.
Retracted: Development and Application of One Separation-Free Safety Tube on the Disposable Infusion Needle

[This retracts the article DOI: 10.1155/2020/6896517.].
A General Heterogeneous Hypergraph Neural Network for Node Classification

Recently, there has been a surge of interest in hypergraph neural networks (HNN) due to their excellent ability to represent non-pair-wise interactions, such as communities in social networks, partnerships in academic cooperation networks, and biochemical interactions in biological networks. Existing works, which classify nodes of hypergraphs, mainly focus on the homogeneous hypergraph. However, the non-uniform and heterogeneous hypergraph is more common in realistic scenarios. Worse yet, homogeneous hypergraph approaches show limited accuracy on the heterogeneous hypergraph. In this paper, we propose a multi-channel hypergraph convolution framework (HHNN) to classify the nodes in the large, non-uniform, and heterogeneous hypergraph. Our framework is composed of two modules: The first decomposition module transforms arbitrary heterogeneous hypergraph to bipartite-form hypergraph (bf-HG) so that we can introduce hypergraph structure more efficiently than the other methods. Second, we devise a multi-channel attention hypergraph convolution module (HHCNN), aiming at fusing various information from different kinds of nodes. We conduct experiments on four real-world heterogeneous hypergraph datasets, and the results show that our framework significantly improves the accuracy compared with six state-of-the-art approaches.