Reviewerly/exHarmony huggingface dataset:
Developing Public Speaking Ability For the Teachers of SMP Jamiah Al-Aziziyah Batee Iliek Samalanga

Authors:
raw_author_name: Saiful Bahri
raw_affiliation_string: Institut Agama Islam Al-Aziziyah Samalanga Bireuen Aceh

Abstract: Teachers are the backbone of an educational institution. The progress or setback of an educational institution is determined by the role of teachers. Through observations and interviews conducted with the Headmaster of SMP Jamiah Al-Aziziyah Batee Iliek, it was found that the majority of teachers lack proficient public speaking skills. Based on this issue, it is necessary to organize activities to enhance public speaking skills, which are crucial for the teachers of SMP Jamiah Al-Aziziyah Batee Iliek. The method of implementing these activities is through lecture-based methods. The stages of the activities include pre-implementation, implementation, and evaluation. Technically, a theoretical explanation related to public speaking skills is provided, and participants then engage in practical exercises. After the lectures, participants are assigned tasks and provided guidance to create speech scripts on themes relevant to the school's needs. The results of the community service activities aimed at enhancing public speaking skills have shown improvement among the participants. According to the participants' questionnaire responses, there was an increase in understanding the criteria for effective speakers, with 20% previously not understanding and 80% having an average level, which improved to 40% understanding and 60% having a high level of understanding. During the practical exercises, all participants successfully overcame confidence issues while speaking. The results of the task to compose speech scripts were quite satisfactory, with an average score of 77.5 out of 100.
Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

Authors:
raw_author_name: Daijun Ding
raw_affiliation_string: College of Applied Science, Shenzhen University, Shenzhen 518052, China
raw_author_name: Xianghua Fu
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Xiaojiang Peng
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Xiaomao Fan
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Hu Huang
raw_affiliation_string: Shenzhen Graduate School, Peking University, Shenzhen 518055, China
raw_author_name: Bowen Zhang
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China

Abstract: Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings.
Spatial Relation Awareness Module for Phase Unwrapping

Authors:
raw_author_name: Chiori Azuma
raw_affiliation_string: Computermind Corp., Shinjuku Kokusai Building 4F, 6-6-2 Nishi-Shinjuku, Shinjuku-ku, Tokyo 160-0023, Japan; Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
raw_author_name: Tomoyoshi Ito
raw_affiliation_string: Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
raw_author_name: Tomoyoshi Shimobaba
raw_affiliation_string: Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

Abstract: Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range (−π,π]. Various methods have been proposed for phase unwrapping. In particular, methods using convolutional neural networks (CNNs) have been extensively researched because of their high robustness against noise and fast inference speed. However, conventional CNN-based methods discard the local position information and relationships between pixels in the convolution process, resulting in poor phase-unwrapping performance. To obtain better phase unwrapping results, we propose a module that combines a global convolution network, which applies convolutional layers with a kernel size equivalent to that of the feature maps, and CoordConv, which acquires the positional relationships between pixels. We validated the performance of the proposed method by comparing it with a quality-guided path algorithm and deep learning-based phase unwrapping methods and found that the proposed method is highly robust against noise.
A Hierarchical Orthographic Similarity Measure for Interconnected Texts Represented by Graphs

Authors:
raw_author_name: Maxime Deforche
raw_affiliation_string: Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
raw_author_name: Ilse De Vos
raw_affiliation_string: Flanders AI Academy (VAIA), Kasteelpark Arenberg 10/2440, 3001 Leuven, Belgium
raw_author_name: Antoon Bronselaer
raw_affiliation_string: Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
raw_author_name: Guy De Tré
raw_affiliation_string: Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium

Abstract: Similarity measures play a pivotal role in automatic techniques designed to analyse large volumes of textual data. Conventional approaches, treating texts as paradigmatic examples of unstructured data, tend to overlook their structural nuances, leading to a loss of valuable information. In this paper, we propose a novel orthographic similarity measure tailored for the semi-structured analysis of texts. We explore a graph-based representation for texts, where the graph’s structure is shaped by a hierarchical decomposition of textual discourse units. Employing the concept of edit distances, our orthographic similarity measure is computed hierarchically across all components in this textual graph, integrating precomputed similarity values among lower-level nodes. The relevance and applicability of the presented approach are illustrated by a real-world example, featuring texts that exhibit intricate interconnections among their components. The resulting similarity scores, between all different structural levels of the graph, allow for a deeper understanding of the (structural) interconnections among texts and enhances the explainability of similarity measures as well as the tools using them.
Design of a Generic Dynamically Reconfigurable Convolutional Neural Network Accelerator with Optimal Balance

Authors:
raw_author_name: Ke Han
raw_affiliation_string: School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Si Han
raw_affiliation_string: School of Information Management for Laws, China University for Political Science and Law, Beijing 100091, China
raw_author_name: Yingqi Luo
raw_affiliation_string: School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple different network models on one hardware platform, but there is a lack of generic acceleration architectures that support standard convolution (CONV), depthwise separable CONV, and deconvolution (DeCONV) layers in such complex scenarios. In response, this paper proposes a more versatile dynamically reconfigurable CNN accelerator with a highly unified computing scheme. The proposed design, which is compatible with standard CNNs, lightweight CNNs, and CNNs with DeCONV layers, further improves the resource utilization and reduces the gap of efficiency when deploying different models. Thus, the hardware balance during the alternating execution of multiple models is enhanced. Compared to a state-of-the-art CNN accelerator, Xilinx DPU B4096, our optimized architecture achieves resource utilization improvements of 1.08× for VGG16 and 1.77× for MobileNetV1 in inference tasks on the Xilinx ZCU102 platform. The resource utilization and efficiency degradation between these two models are reduced to 59.6% and 63.7%, respectively. Furthermore, the proposed architecture can properly run DeCONV layers and demonstrates good performance.
Sunda Script Detection Using You Only Look Once Algorithm

Authors:
raw_author_name: None Asriyanik
raw_affiliation_string: Universitas Muhammadiyah Sukabumi
raw_author_name: Agung Pambudi
raw_affiliation_string: Universitas Muhammadiyah Sukabumi

Abstract: The Sundanese script is a writing system used in the Sundanese language, one of the regional languages of West Java, Indonesia. This study investigates the use of the YOLO v8 algorithm for the real-time video detection of Sundanese script. Various versions of YOLO v8, including YOLO v8n, v8s, v8m, v8l, and v8x, were tested to determine the most effective model. After a comprehensive evaluation involving the analysis of mean Average Precision (mAP), F1-Confidence, and precision, the study selected the YOLO v8s model as the primary detection method. YOLO v8s demonstrated superior performance with the highest mAP of 98.835%, an F1-Confidence of 98%, and a precision of 76,2%. This choice was based on a balance between high accuracy and computational efficiency. The results indicate significant potential for object recognition technology in the learning and preservation of Sundanese script.
Web-Based Tuition Payments Information System At Al-Wafi Kindergarten

Authors:
raw_author_name: Suci Ramadani
raw_affiliation_string: STMIK KAPUTAMA

Abstract: The tuition payment system at AL-WAFI Kindergarten is still done manually, that is, recording is done using a tuition payment notebook. Which can cause errors in recording SPP payment data or mistakes in calculating SPP payments, the process of recording and making SPP payment reports takes a long time. Several other problems were also found that were often complained about, namely the difficulty of collecting separate SPP payment data in several financial books. This slows down the payment process, recording and reporting payments. This causes the process related to SPP payments to not run optimally. To overcome all the problems above, the author proposes to design and build supporting applications such as database systems so that processing of tuition payments is more effective and efficient, and can also simplify operational activities in schools. This system was designed and built using the PHP (Hypertext Preprocessor) programming language and MYSQL as the database.
Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method

Authors:
raw_author_name: Pandi Barita Nauli Simangunsong
raw_affiliation_string: Politeknik LP3I Medan
raw_author_name: Paska Marto Hasugian
raw_affiliation_string: Politeknik LP3I Medan

Abstract: This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.
Implementation of the Laplacian of Gaussian Algorithm in Edge Detection Image Processing of Zebra Cross Damage on Highways in the Langkat Regency Area

Authors:
raw_author_name: Ratih Puspadini
raw_affiliation_string: STMIK KAPUTAMA
raw_author_name: Melda Pita Uli Sitompul
raw_affiliation_string: STMIK KAPUTAMA

Abstract: Walking is part of the traveler's movement and is the simplest means of transportation, but it is in a weak position and prone to conflict or accidents when they mix with other modes of transportation. To protect pedestrians, special facilities are needed, one of which is a crossing place (zebra crossing) that is able to serve according to pedestrian needs. Based on Law No. 22 of 2009 concerning Traffic Polytechnic Land Transportation Bali 46 Cross and Road Transportation, article 131 paragraph (2), it is stated that "Pedestrians are entitled to priority when crossing the road at the crosswalk". One of the important meanings for human life is the Way. Roads are used as a means of transportation that has a very useful role in efforts to develop human life. In 2018, based on statistical data, the number of motorized vehicle users in Indonesia is increasing every year to reach 146,858,759 units. The impact that occurs is that there are many Zebra Cross roads damaged with conditions that are very troubling and worrying for road users. Among the causes of zebra crossing being damaged will be traffic accidents where the vehicle does not lag obeying the path of the vehicle following the predetermined lane. So this study detects image processing with the Laplacian of Gaussian algorithm with edge detection making it easier for the government to improve traffic signs of zebra crossing images on highways that are worthy of improvement so that accidents do not occur. The results of this study illustrate the image of being able to see damaged zebra crossings with calculations of the Laplacian of Gaussian algorithm.
An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

Authors:
raw_author_name: Qinglei Zhang
raw_affiliation_string: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
raw_author_name: Yunfeng Liu
raw_affiliation_string: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
raw_author_name: Jiyun Qin
raw_affiliation_string: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
raw_author_name: Jianguo Duan
raw_affiliation_string: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

Abstract: Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency.
ANALYSIS OF THE SPATIAL-SEMANTIC COHERENCE IN THE GETTY AAT VOCABULARY DATA STRUCTURE - AN EDUCATIONAL EXPERIENCE

Authors:
raw_author_name: V. Bonora
raw_affiliation_string: GeCo Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Firenze, Italy
raw_author_name: L. Fiorini
raw_affiliation_string: GeCo Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Firenze, Italy; La Sapienza University of Rome, P.le A. Moro, 5, 00185 Rome, Italy

Abstract: Abstract. The paper explores the integration of Geomatics and architectural surveying into the educational context of Geomatic for Built Heritage Conservation course within the Master on Architectural Design, in which a multidisciplinary teaching approach is applied, emphasizing the importance of holistic knowledge encompassing historical, geometric, material, and structural aspects for the conservation and restoration of architectural heritage.A central theme is the role of Geomatics and 3D modelling in historical built heritage documentation, assuming the creation of three-dimensional models as a foundation for integrating various thematic data, enabling interdisciplinary studies, and facilitating knowledge representation and communication.The paper delves into the challenges and strategies of semantic 3D modelling. It discusses the necessity for a flexible and layered informational model, integrating spatial, geometric, and semantic data. The complexity of semantic annotation is explored, considering the subjectivity and cultural context inherent in identifying and describing architectural elements. The educational approach employed in fostering critical thinking among students, particularly in interpreting cultural heritage issues related to documentation and recording, is presented.A case study involving Pitti Palace in Florence is considered. An attempt to implement the Getty Vocabulary on Art &amp;amp; Architecture Thesaurus for semantic annotation is described, emphasizing the need for a coherent alignment between the 3D model and controlled vocabulary hierarchy. The study concludes with insights from the student's experience, highlighting the difficulties in aligning spatial and semantic elements within existing controlled vocabularies and suggesting avenues for future developments in refining these vocabularies for enhanced spatial-semantic coherence in 3D modelling applications.
NEURAL RADIANCE FIELDS (NERF) FOR MULTI-SCALE 3D MODELING OF CULTURAL HERITAGE ARTIFACTS

Authors:
raw_author_name: V. Croce
raw_affiliation_string: LISPEN EA 7515, Arts et MÃ©tiers Institute of Technology, Aix-en-Provence, France
raw_author_name: A. Piemonte
raw_affiliation_string: Dept. of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
raw_author_name: G. Caroti
raw_affiliation_string: Dept. of Civil and Industrial Engineering, University of Pisa, Pisa, Italy

Abstract: Abstract. This research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts.
Efficient genetic algorithm for spam mail detection and classification

Authors:
raw_author_name: None C. Nagaraju

Abstract: E-mail is the quickest tool to convey information from one to others. Individual users and organizations have become more reliant on e-mails as technology advances. At the moment, all email inboxes are swamped with spam and these cause significant and diverse difficulties, such as the loss of crucial information and the theft of the recipient’s identity. Organizations may suffer significant losses. As a result, users cannot avoid spam emails, which come in various formats such as advertisements and messages. Spam filtering removes spam messages and prevents them from being accessed. This paper focuses on categorization of e-mails by using genetic algorithm. The proposed approach uses entropy to evaluate information gain, which is subsequently employed by the genetic algorithm classifier to choose important features from the spam database. The model performance is then assessed by using the testing dataset with all 57 features, a crossover probability of 0.3, and a mutation probability of 1.0 and obtained good accuracy.