Reviewerly/exHarmony huggingface dataset:
Special Issue on Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area

Earthquakes and tsunami disasters happen suddenly, devastating cities in just a few minutes. For decision makers to manage such disasters, they need current information on the status of their cities, but all information is collected separately and on paper now. All disaster-related information should be integrated to support decision making. To develop such an integrated system, Peru and Japan launched a new SATREPS project, “Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area,” in 2021. The project consists of four groups. Group 1 is related to earthquake and tsunami hazards, estimating the level of shaking and predicting the damage due to tsunami. Group 2 is related to damage detection/estimation, using sensing devices to estimate the level of damage to buildings and other infrastructure. Group 3 is related to understanding the damage over a wide area through satellite images, and also to the integration of all information from Groups 1 to 3. Group 4 is related to developing the capacity to utilize the integrated expert system. This special issue has been organized to present the progress accomplished in the first two years and to share the project’s achievements. It covers the fields of earthquake hazards, building and infrastructure damage estimation, damage estimation with satellite imagery, and human loss estimation.
WORD-FORMATION PROCESSES IN LOC’S SITE POEM: MORPHOLOGICAL STUDY

This study aims to find out the processes of word formation in ten poems in the LOC’s Site Poem. Word-formation processes is the study in morphology. The method used is qualitative research. The data are from the ten poems in the LOC’s Site Poem. The results of the study are that there are 193 (one hundred and ninety three) data found in the LOCs Site Poem. The affixation process including inflectinal and derivational process by 87.56% (eighty seven point five six percent), compounding process by 9.84% (nine point eight four percent), and acronym by 2.07%(two point oh seven percent). The dominant word formation process appear is affixation of inflectional process. The compounding is the combination of noun plus noun and adjective or adverb plus noun.
Review for Optimal Human-Gesture Design Methodology and Motion representation of Medical Images using segmentation from depth data and gesture recognition

Human gesture recognition and motion representation has become a vital base of current intelligent human-machine interfaces because of ubiquitous and more comfortable interaction. Human-Gesture recognition chiefly deals with recognizing meaningful, expressive body movements involving physical motions of face, head, arms, fingers, hands or body. This review article presents a concise overview of optimal human-gesture and motion representation of medical images. This paper surveys various works undertaken on human gesture design and discusses various design methodologies used for image segmentation and gesture recognition. It further provides a general idea of modeling techniques for analysing hand gesture images and even discusses the diverse techniques involved in motion recognition. This survey provides an insight into various efforts and developments made in the gesture/motion recognition domain through analyzing and reviewing the procedures, datasets, recognition rates and approaches employed for identifying diverse human motions and gestures for supporting better and devising improved applications in near future.
Identify Novel Elements of Knowledge with Word Embedding

As novelty is a core value in science, a reliable approach to measuring the novelty of scientific documents is critical. Previous novelty measures however had a few limitations. First, the majority of previous measures are based on recombinant novelty concept, attempting to identify a novel combination of knowledge elements, but insufficient effort has been made to identify a novel element itself (element novelty). Second, most previous measures are not validated, and it is unclear what aspect of newness is measured. Third, some of the previous measures can be computed only in certain scientific fields for technical constraints. This study thus aims to provide a validated and field-universal approach to computing element novelty. We drew on machine learning to develop a word embedding model, which allows us to extract semantic information from text data. Our validation analyses suggest that our word embedding model does convey semantic information. Based on the trained word embedding, we quantified the element novelty of a document by measuring its distance from the rest of the document universe. We then carried out a questionnaire survey to obtain self-reported novelty scores from 800 scientists. We found that our element novelty measure is significantly correlated with self-reported novelty in terms of discovering and identifying new phenomena, substances, molecules, etc. and that this correlation is observed across different scientific fields.
A context-aware model with a pre-trained context encoder for dense video captioning

The task of dense video captioning is to generate descriptions for multiple events in an untrimmed video. Previous works generate descriptions for each event, and then concatenate the individual sentence descriptions into a final paragraph, often leading to inconsistency and redundancy problems. To overcome these limitations, we propose a context-aware model that leverages context event information from both past and future events to provide conditional dependence on the current event description. Moreover, context event information is encoded by a strong pre-trained context encoder and is incorporated into the captioning module through a gate-attention mechanism. Experimental results on both YouCookII and ActivityNet datasets demonstrate that our model outperforms the existing context-aware and pre-trained model by a margin.
Design and implementation of remote piano teaching based on IPSecV6 security simulation

Remote piano teaching is an important part of piano teaching curriculum construction music and art colleges. At the same time, as a new model and new normal, distance education conforms to the goal of cultivating new applied talents. IPSecV6 provides a good technical guarantee for the development of distance piano teaching with its characteristics and advantages of bandwidth guarantee, efficient transmission, safety and reliability. In the application design of IPSecV6 for remote piano experiment simulation, network security is the first technical element of campus network technology research from beginning to end. In view of this, the article analyzes and studies the remote piano teaching of IPSecV6 security simulation. The simulation results show that the scheme can provide network security guarantee of access control, data encryption and security verification for remote piano teaching. At the same time, this model also provides new ideas and reference for modern distance education teaching.
The Effects of Career Club Activities on University Students’ Career Decision-making Self-efficacy, Career Preparation Behavior and Career Competency

The purpose of this study is to verify the long term effects of career club activity programs on university students' career decision-making self-efficacy, career competency, and career preparation behavior. This study was conducted on 65 out of 113 participants in the career club activity program operated for 1st to 4th graders at D University in Busan, who conducted both pre- and post-examination. The results of this study are as follows. First, the career club activity program is effective in career decision-making self-efficacy. Second, the career club activity program is effective in career competency. Third, the career club activity program is an effective tool for career preparation. The results of this study show that club activity is effective in improving university students' career decision-making self-efficacy, career competency, and career preparation behavior. Therefore, it is necessary to open and reorganize career clubs for department-based career exploration for freshmen, career clubs for students to cultivate career design-based career skills, and experience-based employment clubs for seniors/job seekers. Furthermore, the study suggested that there is a need to link and reorganize club activities with credit systems such as the career semester system to foster student autonomy and initiative and revitalize career activities.
A Method for Applying Questioning Strategies for Self-reflection to Supplement Value Clarification Theory

This study proposed a method of applying self-reflection questions to the process of valuing the value clarification theory in order to supplement the problems of the value clarification theory and to seek a method of value education that can consider the social relational aspect. The value clarification theory has the advantage of allowing learners to freely form values, but it has limitations in that they can make self-centered value judgments because it overemphasizes individual learners' value choices. Human beings engage in relationships with others as beings in the world. If one only consider their own point of view in various value conflict situations, one may be limited in growing into a person who lives together with others in society. In order to compensate for these shortcomings, this study focused on the concept of self-reflection to understand others based on a correct self-awareness and explored ways to utilize self-reflection through self-awareness, discordance, and integration in class situations. Therefore, the content of self-reflection was composed in the form of an epilogue and applied to the process of valuing the theory of value clarification. It is expected that this work will ensure educational effectiveness by being used in various teaching and learning methods such as discussion, discussion, and writing.
The innovation mode and path analysis of English education based on big data

In the context of the era of big data, the education field has built a new intelligent experience system according to its own business needs, which can not only accelerate the development of intelligent education, but also continuously expand the application scope of big data technology. From the perspective of modern education, it is helpful to construct a good big data education environment for teachers and students by constructing a big data intelligent education system based on user roles and role needs and properly handling educational guidance and management of various disciplines. Therefore, on the basis of understanding the development status of big data technology, this paper deeply discusses the innovation mode and specific path of English education in the context of big data according to the technical concepts of artificial intelligence and cloud computing.
Analysis of newspaper articles related to children·youth suffrage

연구목적: 2011년부터 2022년까지 12년 동안 보도된 아동·청소년 선거권 관련 온라인 신문기사를 분석하여 사회적 흐름에 따른 현안을 살펴보고 아동·청소년의 참정권 확대를 위한 방향성을 제시하는 것이다. 방법: 아동·청소년 선거권과 관련된 신문기사 수집 후 2차에 걸친 정제과정을 통해 총 2,648건의 기사 중에서 338,389개의 키워드를 추출하였다. 키워드 빈도분석, N-gram 분석, TF-IDF 분석, CONCOR 분석을 실시하여 주요 키워드와 구조적 관계를 살펴보았다. 결과: 2011년부터 2022년까지 연도별 기사 중 2019년 12월 27일 공직선거법 개정안 의결일을 기준으로 개정 전·후로 현황을 분석한 결과, 첫째, 아동·청소년의 선거권에 대한 사회적 관심이 매우 부족한 것으로 나타났다. 둘째, 주요 키워드 분석 결과 선거 연령 하향 과정에서 아동·청소년 중심의 사회적 합의와 제도 마련이 미비하였으며, 법 개정만으로는 큰 변화가 없는 것으로 나타났다. 셋째, CONCOR 분석 결과 아동·청소년의 선거권을 확대하고 참여권 옹호 역할을 지속해야 할 필요성이 제기되었다. 결론: 본 연구는 지난 12년 동안 보도된 신문기사를 통해 아동·청소년의 선거권 관련 동향을 파악하였다는 점에서 의의가 있다. 본 연구 결과를 토대로 아동·청소년의 참정권 확대 방안에 대해 제언하였다.Objectives: The purpose of this study was to examine current issues related to social trends and suggest directions for expanding children·youth's right to vote by analyzing newspaper articles related to children·youth suffrage for 12 years from 2011 to 2022. Methods: After collecting newspaper articles related to children·youth suffrage, a two-stage refinement process was conducted, and a total of 338,389 keywords were extracted from 2,648 articles. Frequency analysis of keywords, N-gram analysis, TF-IDF(Term Frequency-Inverse Document Frequency) analysis, and CONCOR(CONvergence of iterated CORrelations) analysis were performed to examine the major keywords and their structural relationships. Results: First, an analysis based on yearly articles from 2011 to 2022, using the revision of the Public Official Election Act on December 27, 2019, as a benchmark for pre- and post-revision status, revealed a severe lack of social interest in granting suffrage to children·youth. Second, according to the analysis of the main keywords, it was found that there was a lack of social consensus and institutional preparation centered around children·youth during the process of lowering the voting age for elections, and it was evident that significant change could not be achieved through legal revision alone. Third, the CONCOR analysis results have raised the necessity of expanding the voting rights of children·youth and continuing to advocate for their right to participation. Conclusions: This study has significance in that it identified current issues related to the voting rights of children·youth. Based on the results of this study, recommendations were made for expanding the voting rights of children·youth.
Multimedia Forensics versus disinformation in images and videos: lesson learnt and new challenges

From the very beginning when photography appeared and then much more in the digital era, images and videos have been edited not only to improve the visual quality of what they represented but also to change what had been acquired to mystify the reality. This is often done in order to transfer a different meaning to the watcher and basically mislead his/her opinion. On the other side, during the years, the need for even more effective defense instruments able to detect such alterations has increased. This has become particularly crucial with the advent of deep-learning based techniques that have allowed to rather easily achieve realistic results in content manipulation (deepfakes) but also in multimedia synthetic generation. This talk will provide a look throughout the evolution of the various kinds of manipulations with a parallel focus on the diverse multimedia forensic techniques and approaches [1, 2]. An analysis will be carried out to try to understand how needs and solutions have been evolved [3] in order to fix the lesson learnt and to individuate future research challenges.
Lightweight in-harbor ship detection based on UAV aerial images

Using unmanned aerial vehicles (UAVs) to take aerial images and detect ships in harbors provides a new way of harbor monitoring. However, due to the limitation of computing resources and storage resources, ship detection on UAVs is very challenging, which puts a higher demand on model lightweighting. In this paper, we propose an efficient model lightweighting scheme based on knowledge distillation. We use two advanced large-scale models YOLOv7 and PP-YOLO as teacher models, and transfer the excellent detection ability of these two models to small-scale student models YOLOv7-tiny through knowledge distillation. This scheme not only greatly reduces the parameter scale and computation, but also retains the ship detection performance equivalent to that of large models.
Farm Book: An Interactive Mobile Application for Smart Farming

Smart farming increase the usage of mobile applications. There are many applications which have been built for clearing farmers doubts. A survey about the hike and fall in price of vegetables and fruits in agriculture gave us a clear vision about the market needs. The agronomists are facing the plant defects and diseases which leads to loss in production of fruits and vegetables which results in the cost variation and demand for agricultural products. The price issue pushed us a step forward into the work which should be rectified for the betterment of agriculture as well as farmers. After that, an environment has been created to overcome the agriculture related issues. The goal is to disseminate knowledge regarding appropriate farming practices through the co-design of an agricultural management application that would maximize the crop productivity of small-scale farmers. This research study has planned to create an atmosphere, where people can gather information about agriculture and provides the relevant solutions. Yes, the concept is interaction between farmers. This research study provides a mini stack overflow like structure in agriculture so that the farmers can interact between themselves and their doubts in farming can be solved easily. Once after getting many users in the app, statistical data will be provided and a concept of marketing so that the scarcity in cultivation can be minimized.
A-Eye: Computer vision and Deep learning based Smart-Glasses with Edge computing for Visually Impaired

India, where a third of the world’s blind people reside, has about 12 million blind people, especially in comparison to a total of 39 million worldwide, according to the National Programme for Control of Blindness (NPCB). Visually challenged people, including children and elderly people with low vision, still have trouble performing everyday tasks, even in this world of rapidly developing technologies. The existing solutions provide a straight forward solution using Artificial Intelligence technologies. To take this technological solution to the next level we propose a solution with all the AI features seamlessly integrated together and a dedicated mobile application gives full control and independency to their life. The A-Eye (smart glasses) system that is being proposed consists of a wearable smart glass solution, a mobile application, and a cloud-based data management platform. This project shows how Mobile Edge Computing (MEC) can be used to process data on the mobile phone other than to process the camera’s video stream in the cloud or locally at the system level. The project’s use of Mobile Edge Computing (MEC) expands the scope of its ability to add and update an endless number of features without being constrained by hardware. Therefore, the suggested smart glass system can minimize latency and be highly scalable while using heavy and effective models to effectively mitigate the problem and assist visually challenged individuals.
ESSNet: Lightweight Semantic Segmentation Network

Semantic segmentation is widely used in fields such as self-driving, robot navigation and augmented virtual reality, which has extremely high requirements for the real-time reasoning and accurate segmentation of the network. Recently, semantic segmentation has made great progress in segmentation accuracy, but little attention has been paid to real-time reasoning. Therefore, it makes sense to propose a network to balance accuracy and real-time reasoning. In this paper, we propose a lightweight semantic segmentation network with Multiple Correction Confusion Mechanism (MCCM) and Lightweight Attention Mechanisms (LAM), named Lightweight Semantic Segmentation Network (ESSNet). MCCM selectively incorporates low-level feature to make the network segmentation contour clearer. LAM effectively captures the context information, making the feature representation more abundant. In general, different scale features confusion, and efficient attention mechanisms are adopted by LCANet to significantly improve its performance. To demonstrate the effectiveness of the network, we conducted experiments on the Cityscapes and CamVid datasets, and obtained MIOU of 68.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 74.8%, respectively, which is comparable to the state-of-the-art methods under the same experimental settings. In addition, on a single Titan RTX card, the network reached a forward reasoning speed of 136.4 FPS (480x480x3 image resolution).
Physical Backdoor Attack using Multi-Trigger to Same Class

Data poisoning is an attack on machine learning models that involves inserting a sample into the training data sets that does not influence the model during training but does affect it during testing. A backdoor attack aims to inject a hidden backdoor instance into deep neural networks (DNNs), causing the attacked models to perform well on benign samples while the prediction is maliciously modified if the attacker has initiated the hidden backdoor attack in neural networks. Previously, triggers were embedded digitally in data sets, therefore trigger settings were static and followed the same look and placement. Physical backdoor research is limited due to a lack of large datasets including actual photos of physical objects present with classification goals. These datasets need a significant amount of time and effort to construct. In this paper, we attempt to overcome the difficulties encountered by researchers during the physical backdoor attack. The presence of a physical trigger in an instance is very plausible, yet little study has been done on physical trigger-based attacks so far. We manually constructed a dataset with both a physical trigger and clean picture. The dataset will comprise trigger images with physical triggers, and we will be able to train the model with various sorts of triggers. The different trigger for the same class has been used to misclassify the same person using the different trigger to the target class. We have created a data set that will be used for facial recognition having a total of 1100 images of the clean images and physically triggered images with 3 volunteers including 2 physical triggers on them.
Axillary Lymph Node Metastasis prediction Using Deep Reinforcement Learning on Primary Tumor Biopsy Slides

The “Sentinel Lymph Node Biopsy” (SLNB) is a common practice for detecting metastasis in Breast Cancer patients. It has improved the accuracy of predicting “Axillary Lymph Node” (ALN) status, allowing some women to avoid “Axillary Lymph Node Dissection” (ALND). However, SLNB is an invasive technique that has the potential to induce problems such as “lymphedema” and numbness in the upper limbs. Furthermore, the frozen segment of the SLNB takes a significant amount of time, which extends the total duration of the surgery. In this research work, a Deep Reinforcement Learning (DRL) approach has been used to pre-operatively predict ALN involvement in early-stage Breast Cancer patients using Whole Slide Images (WSIs). The goal was to achieve accurate ALN involvement detection, which is crucial in determining the best treatment options and avoiding unnecessary surgery and related complications. A Convolutional Neural Network model based on Deep Learning was also used for ALN metastasis prediction. The results of the experimentation demonstrate that the DRL algorithm outperformed the CNN model in terms of accuracy, precision, recall, and F1-score, with values of 67.7121%, 65.1499%, 67.7121%, and 63.519% respectively.