Reviewerly/exHarmony huggingface dataset:
GPT-based Knowledge Guiding Network for Commonsense Video Captioning

Video-based commonsense captioning aims to generate captions for the video content while providing multiple commonsense about the underlying event. Existing methods utilize video features to explore and generate commonsense containing latent semantics. However, this process needs to overcome the complex semantic gap between visible videos and invisible commonsense, which is not supported by the limited knowledge in existing video captioning datasets. To this end, we propose a novel GPT-based Two-stage Knowledge Guiding Network (TKG-Net), which uses GPT to augment datasets knowledge and introduces a cross-attention mechanism to fuse multimodal knowledge. Specifically, to augment knowledge, we set prompts and finetune GPT to imagine and reason based on the video content description at the first stage. At the second stage, to prevent over-reasoning caused by the loss of visual features in GPT, TKG-Net extracts high-level semantic representations of commonsense knowledge and fuses them with video features in a cross-attention mechanism for multimodal semantic interaction. Our experiments on the large-scale Video-to-Commonsense dataset manifest significant improvements over the previous state-of-the-art approach on all metrics.
Realistic Depth Image Synthesis for 3D Hand Pose Estimation

The training of depth image-based hand pose estimation model typically relies on real-life datasets which are expected to be 1) largescale and cover a diverse range of hand poses and hand shapes, and 2) always come with high-precision annotations. However, existing datasets in reality are rather limited in the above regards due to multitude practical constraints, with time and cost being the major concerns. This observation motivates us to propose an alternative approach, where hand pose model is primarily trained with synthesized hand depth images that closely mimicking the characteristic noise patterns of a specific depth camera make under consideration. It is achieved by firstly mapping a Gaussian distributed variable to certain specific non-i.i.d. (independent and identically distributed) depth noise pattern, and then transforming a “vanilla” noise-free synthetic depth image to a realistic-looking image. Extensive empirical experiments demonstrate that our approach is capable of generating camera-specific realistic-looking hand depth images with precise annotations; comparing to entirely relying on annotated real images, a hand pose model with better performance is obtained by using only a small fraction (10%) of annotated real images as well as our synthesized images.
Ego-Aware Graph Neural Network

In scientific exploration and daily life, inter-connectivity between entities is ubiquitous, such as species via the food webs, people among social networks, and products and customers in e-commerce. Graphs and networks are natural constructs to model such linkages, which can capture the relational information in addition to features provided by individual nodes. Graph neural networks (GNNs) are a powerful computation tool for reasoning about interconnected data and can help us make better use of the information imbued in data collectively. GNNs have found many successful applications, such as predicting drug side-effects, classifying diseases, and predicting the function of proteins. Most existing GNN methods are designed for datasets with moderate node features, where individual node features provide limited information, and assortative graph datasets, where nodes and their neighbours are more likely to be similar. However, for graphs with rich node features, or for the disassortative graphs, where nodes and their neighbours tend to be different, the message passing process in GNNs might be susceptible to inferences from the neighbourhood, leading to performance deterioration. To address this issue, we find that ego networks contain an extra layer of information to further distinguish different ego nodes. Based on that, we propose a generic GNN model that can better utilize structural information of nodes' proximity to extract informative messages and resist contradictory ones from their neighbourhood. We analyze how node features and graph structure can influence the performance of GNN models. Experimental results provide insight on how our method outperforms the baselines. The presented model paves the way for incorporating ego networks' structural information into the learned graph representations, which brings GNNs with better performance and higher robustness over different datasets.
Feature Point Matching Based on Multi-Scale Local Relative Motion Consistency

Matching feature points from image pairs with significant visual changes and repetitive patterns remains a challenging problem due to the presence of mismatches in the putative corresponding point sets. In this paper, we propose a novel feature matching algorithm called Multi-scale Local Relative Motion Consistency (MLRMC) to accurately find matched feature points. The MLRMC algorithm exploits the multi-scale relative motion information between putative matches and their neighboring points to effectively remove outliers with accuracy and robustness. By clustering the relative motion, coincident relative motion among the matches is obtained. The similarity of each putative match is evaluated by considering both the consensus of relative motion and the consensus of neighborhood elements. To achieve accurate and robust feature point matching, a two-step outlier removal strategy is employed. Experimental evaluations are conducted using three datasets to verify the accuracy and robustness of our proposed method, MLRMC. The results demonstrate that our method outperforms six other classic feature matching methods.
A Virtual Reality Training System for Automotive Engines Assembly and Disassembly

Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this work, we developed a multi-layer structured Virtual Reality (VR) system to provide students with training in automotive engine (Buick Verano) assembly and disassembly. We designed the VR training system with The VR training system is designed to have several major features, including replaceable engine parts and reusable tools, friendly user interfaces and guidance, and bottom-up designed multi-layer architecture, which can be extended to various engine models. The VR system is evaluated with controlled experiments of two groups of students. The results demonstrate that our VR training system provides remarkable usability in terms of effectiveness and efficiency. Currently, our VR system has been demonstrated and employed in the courses of Chinese colleges to train students in automotive engine assembly and disassembly. A free-to-use executable file (Microsoft Windows) and open-source code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LadissonLai/SUSTech_VREngine</uri> for facilitating the development of VR systems in the automotive industry. Finally, a video describing the operations in our VR training system is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/yZe4YTwwAC4</uri> .
Reflection Intensity Guided Single Image Reflection Removal and Transmission Recovery

Single image reflection removal (SIRR) aims at eliminating unwanted interference caused by the reflection of transparent or smooth surfaces and obtaining an estimation of a clear transmission layer. Existing data-driven methods typically rely on decomposing the observed image into transmission and reflection layers, which neglects the physical generation principles of an image with reflections, thus leading to unsatisfactory results, especially in strong reflection regions. To address this issue, in this work, we analyze the imaging process of reflection image from the physical perspective and derive a conclusion that the physical quantity: illuminance of the reflection layer determines the reflection intensity. Then a two-stage reflection intensity-guided network (RINet) is proposed for reflection removal and transmission recovery. The key lies in the first stage are the parallel modules that generate the reflection intensity map and transmission layer. In the second stage, besides utilizing such intensity map as the guidance, we additionally calculate the gradient field as the other prior to facilitate the final reflection removal. Specifically, we design a dual-flow joint learning module (JLM) comprised of a transmission recovery branch and a gradient optimization branch that jointly optimizes image structures and details by exploiting the interactions between transmission and gradient features. In particular, guided by the reflection intensity map, the transmission recovery branch can dynamically focus on removing reflections. Equipped with the two-stage framework, our RINet constitutes a divide-and-conquer process to achieve effective transmission recovery and reflection removal. Experimental results on public datasets demonstrate the superiority of the proposed method over recent state-of-the-art methods.
Twitter sentiment analysis using ML and DL algorithms

In the ever-changing internet world, people still love to spend time on social media and share their thoughts and content. But with online fame and popularity also comes hateful comments and the risk of becoming a victim of cyberbullying. Many users target particular people through social media, using text and other communication means to spread hate and violence. Twitter (now X) is the most widely used platform, where these cases occur more frequently than on any other social media site, forcing moderators to label tweets as misleading information or suspend popular celebrities to stop them spreading hateful comments. New ways to detect these hateful comments and ban them before they are spread across the globe are needed. In this paper, we implemented traditional algorithms in machine learning and compared their performance with our proposed CNN architecture along with other deep learning models such as LSTM, a combination of CNN-LSTM and BERT transformer model.
Factors influencing the implementation success of blockchain technology: A systematic literature review

Despite the significant attention given to blockchain technology, there needs to be more understanding of the related organizational challenges to adoption. This research provides a systematic literature review (SLR) to comprehensively explore the current literature and answer the following three research questions: 1) Which organizational theories are used to examine blockchain technology in supply chain management (SCM)? 2) What is the value of blockchain technology for SCM? 3)What are the organizational capabilities that influence the success of blockchain technology implementation in supply chains? Through the SLR, we identify the organizational theories applied to investigate the impact of blockchain technology on SCM and examine the main drivers of blockchain deployment. The study also investigates specific dimensions of blockchain technology capability, laying the groundwork for further research on this important emerging research area.
Behavioural analysis of COVID-19 vaccine hesitancy survey: A machine learning approach

Machine and deep learning algorithms have become widely employed in natural language processing tasks that involve text categorisation. Behavior analysis has proven helpful for solving human problems, while sentiment analysis is helpful to extract opinions. This paper aims to develop a behavioral analysis tool for solving problems rooted in behavior by leveraging survey comment data. The study categorises behaviours based on well-known behavioral barriers and employs Natural Language Processing (NLP) techniques to comprehend and interpret the comment data. Pre-processing techniques are applied to extract information from the texts. Subsequently, feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF) word vectorization, converting the texts into numerical values. Various Machine Learning (ML) techniques, including Multinomial Naive Bayes, XGBoost, Linear Support Vector Classifier, and Multi-Layer Perceptron Classifier, are utilized with the vectorized input and corresponding labels. Sentiments from the survey comments are extracted using a pre-trained hugging face transformer model based on the RoBERTa platform. However, limited availability of data from the health sector due to privacy and legal reasons restricts its use. Nevertheless, the model can be used to personalise prevention plans, including lifestyle and early medical interventions. It can be used in academia and e-commerce industry to identify trends and patterns by analysing user traffic and online behaviour, enabling the development of personalised recommendations. These examples highlight the potential for research to contribute to the digital economy across diverse fields.
Stabilization of TS Fuzzy Systems via a Practical Observer

This paper focuses of the analysis and study of the advanced approach for the asymptotic behaviors of perturbed fuzzy systems via an observer design. We introduce the notion of global practical exponential stability for Takagi-Sugeno fuzzy systems and by using common quadratic Lyapunov function and parallel distributed compensation controller techniques we study the asymptotic stability of the solutions of such systems by optimizing the number of rules. The proposed approach for stability analysis is based on the determination of the bounds of perturbations that characterize the asymptotic convergence of the solutions to a closed ball centered at the origin.
THE INFLUENCE OF SOCIETY 5.0 ON THE SAVI LEARNING MODEL AND STUDENT CHARACTER VALUES ON LEARNING OUTCOMES FOR CLASS VII MTS NEGERI 2 ASAHAN

This research was conducted at MTS Negeri 2 Asahan which is located at Jl. Melur No. Ex. Kisaran Naga, Kisaran Timur District, Asahan Regency, North Sumatra postal code 21219. The time of the study was carried out from February 2022 to March 2022. The research method was quasi-experimental. The data analysis technique used is ANOVA. The results of this study are: 1) There are differences in student learning outcomes of class VII MTS Negeri 2 Asahan students who are treated with the SAVI model using video and the SAVI model using power point. It is obtained from Fh = 7.55 and Ft = 4.09, and the conclusion is Fh &gt; Ft. 2) The results of the study where Fh = 22.07 and Ft = 4.09 where the conclusion is Fh &gt; Ft. so it can be concluded that there is an interaction between the SAVI model and the character values of class VII MTS Negeri 2 Asahan students. 3) In this study, the Tukey test was continued, where the conclusion was obtained: there was a significant difference because Q count &gt; Q table. Obtaining Q table = 2 :20 = 2.95 and Qh = 3.76. So it can be concluded that the learning outcomes of students who have high character values taught by the SAVI model and video media are better than students who have high character values taught by the SAVI model and power point media. 4) In this study, the Tukey test was continued, where the conclusion was obtained: there was a significant difference because Q table = 2 : 20 = 2.95. In conclusion, there was a significant difference because Q count &gt; Q table. Obtaining Qh = 9.54.
THE EFFECTIVENESS OF THE WHATSAPP GROUP APPLICATION USE PROGRAM FOR EDUCATORS DURING THE COVID-19 PANDEMIC

This research was conducted based on the background of problems in the Education sector during the Covid-19 pandemic. Where the implementation of face-to-face learning is limited, it is a condition where students and teachers cannot meet in-person to maintain social distancing and physical distancing during this pandemic which makes learning to be done through online learning. Where so far teaching and learning activities are carried out conventionally, namely face to face. In online learning, we recognize the terms synchronous and asynchronous learning. The use of various applications during online learning is considered more effective and efficient because it makes it easier for users to access all kinds of information and to stop the spread of the virus. This study seeks to see how the effectiveness of the WhatsApp group (WAG) application program on the performance of educators at Wachid Hasyim I High School Surabaya during the Covid-19 pandemic. The theory used is the theory of program effectiveness and the theory of performance. This type of research is descriptive qualitative using data collection techniques, namely interviews, observations, documentation, and questionnaires. The data analysis used is the collection, reduction, display, and verification. The results of this study are first, the program using the WhatsApp group application at SMA Wachid Hasyim 1 Surabaya as the main media in online learning activities is said to be effective and efficient because the WhatsApp group application is very quota-efficient, easily accessible anywhere, and helps facilitate the delivery of information so that it can be accessed easily. coordination related to teaching and learning activities is carried out. Second, regarding the effectiveness of the program using the WhatsApp group application on the performance of educators at SMA Wachid Hasyim I Surabaya during the pandemic, it is said to have not been effective. Because the use of the WhatsApp group application as the main media for online learning activities is not optimal in delivering material and greatly affects the performance of educators.
COMMUNITY EMPOWERMENT IN THE ARCHIVES AWARENESS MOVEMENT IN WONOKROMO SURABAYA

The aims of this study is to explain community empowerment in an archive awareness community movement campaign in one-no-chromo villages. Archives are records of events and have value in use as various types of information sources created by institutions, organizations, and individuals. Unfortunately, Wonokromo Village is not yet properly managed as a family archive. Judging by the frequent loss of family archives such as birth certificates, family cards, and diplomas. Therefore, we hope that the community of Wonokromo Surabaya Village will be able to manage and build the archive. This study is a descriptive qualitative study using observation, interview and documentation methods. The results of this study indicate: First, what you can do to educate people about the importance of archives is to enable, empower, and protect them. Together with the empowerment of the community, we create archive lists to implement and become more interested in family archives. Second, the lack of public understanding and awareness of the importance of archive awareness is a stumbling block. So, community empowerment for the archive awareness movement in Wonokromo output is good. Governments must continue to empower communities on the importance of archival awareness, especially in disaster-prone areas.
A Comparative Survey of Open-Source Platforms for Chat-Based Generative AI

Generative AI for text-based applications have gained in popularity with the release of ChatGPT in late 2022 by OpenAI. Users engage with the application via a web-based user interface or programmatically via the API. The details of the model internals and specifics on the training have not been made public, which poses several challenges in better understanding the system. It would be advantageous to have insight into the system to better understand the occurrence of bias and issues pertaining to privacy, among other concerns. These aforementioned issues could be better addressed if the code base used for training these large language models were made publicly available. Since the release of ChatGPT and its underlying technologies, there have been various open-source platforms that have become publicly available. This survey presents an analysis of four open-source platforms for chat-based generative AI. These specific platforms include Open Assistant, Colossal AI, ChatLLaMA and OpenChatKit. This survey evaluates each of the four open-source platforms for chat-based Generative AI based on evaluative criteria consisting of understandability, setup and configuration, community and support, regularity of code updates, as well as system compatibility and efficiency.
Instruction of Mathematics Teachers Toward Numeracy Management Program

Numeracy education stands as a pivotal component of the educational landscape, necessitating attention from both administrators and teachers. To facilitate the ongoing improvement of teachers’ numeracy instructional skills, it becomes imperative to confront the barriers hindering students’ attainment of educational objectives. The purpose of the study was to evaluate the numeracy instruction provided by mathematics teachers, with an emphasis on its impact on curriculum and pedagogy enhancement. Additionally, it identified issues in the management of numeracy instruction, forming the foundation for forthcoming numeracy management initiatives targeting elementary school teachers. This study employed a researcher-developed questionnaire to conduct a descriptive survey involving 30 elementary mathematics teachers from Marawoy Elementary School. Utilizing statistical tools, specifically the mean, responses were meticulously analyzed, while issues concerning numeracy management instruction were methodically extracted and interpreted thematically. The study affirmed the robust alignment between teachers’ knowledge content and pedagogical approaches with the diverse learning needs of their students. Notably, instructional materials, classroom management, and assessment techniques exhibited variation according to students’ learning capabilities. Unfortunately, suboptimal Internet connectivity and limited access to learning resources only marginally supported the integration of technology in the teaching and learning processes. Consequently, the implementation of a numeracy management program was recommended to bridge instructional gaps among teachers in the domain of numeracy.
Peningkatan Keterampilan Menyimak Melalui Metode Bermain Bisik Berantai Siswa Kelas V SDI No 147 Palalakkang Kecamatan Galesong Kabupaten Takalar

This research aims to describe the improvement of listening skills using the chain whisper playing method in class V students of SDI No. 147 Palalakkang, Galesong District, Takalar Regency. This type of research is Classroom Action Research (PTK). The research subjects were 10 people consisting of 5 male students and 5 female students. Data collection methods are carried out through observation and tests. This research instrument was carried out in two cycles. The results of the research after implementing the actions for two cycles were: (a) Increasing students' positive activity where in cycle I an average score of 46% was obtained, including the criteria for being quite active and in cycle II an average score of 85 was obtained. % includes very active criteria. From an average score of 46% in cycle I to 85% in cycle II. (b) Increased student learning outcomes after implementing the use of the chain whispering method where in cycle I the average score was 60.30 and in cycle II the average score was 95. Based on these results it can be concluded that the use of the chain whispering method was used. can improve the listening skills of class V SDI No. 147 Palalakkang, Galesong District, Takalar Regency.