Reviewerly/exHarmony huggingface dataset:
Meningkatkan Pengenalan Huruf Hijaiyah Dengan Menggunakan Alat Peraga Edukatif Melalui Media Stik Es Krim Dan Kertas Origami Di TK Bina Edukasi Pekanbaru

This research is motivated by the lack of children's ability to recognize hijaiyah letters and the low ability of children to recognize hijaiyah letters, distinguish hijaiyah letters and have not been able to pronounce hijaiyah letters correctly. The purpose of this study was to determine the improvement of children's ability to recognize hijaiyah letters with educational props through the media of ice cream sticks and origami paper through the CAR research method (Classroom Action Research). This research was conducted in two cycles and each cycle was conducted in two meetings. In order for this classroom action research to be successful without any obstacles that interfere with the smooth running of the research, the researcher arranges the stages that are passed in classroom action research, namely: 1) Action planning/preparation, 2) Action implementation, 3) Observation, and 4) Reflection. The subjects in this study were B2 grade students with 5 female and 9 male students in the first semester of the 2022/2023 academic year in Kindergarten Bina Edukasi, Rejosari Village, Tenayan Raya District, Pekanbaru City.While the object of this research is the use of educational props through the media of ice cream sticks and origami paper to improve the ability to recognize hijaiyah letters in children. The research data were collected through teacher observation techniques and children's activities and documentation. After the data is collected through observation, the data is processed using the percentage formula.The results of this study are an increase in the learning process carried out by the teacher and the child's ability to recognize the concept of numbers. Observation of children's activities also increased, this can be seen from the initial data to cycle II meeting 2. Initial data was 43.21%, Cycle II meeting 2 obtained an average percentage of 85.38%. So that the overall increase in children's activities to recognize hijaiyah letters obtained an average percentage of 42.17%.
A pixel-enhanced deep convolutional neural network for classifying immunohistochemistry images

Understanding protein subcellular location in immunohistochemistry (IHC) images is conducive to investigating textured background at the protein tissue level and analyzing the interaction mechanism of the protein molecule. At present, the mainstream IHC image classification models are mainly designed based on classical machine learning and general deep learning, which expose the shortcomings of gradient dispersion, low information richness, and weak discrimination. In this paper, the IPA net is developed to classify IHC images based on Interactive Pointwise Attention (IPA), whose advance can be concluded in the following: firstly, patch splitting is adopted to split IHC images to dilute tissue diversity, bubbles, noise and other interference factors; secondly, the IPA unit consisting of interactive computation is proposed to modify pointwise pixels and enrich the feature richness. The experimental results demonstrated that IPA net reaches97.33%, 97.28%, and 97.24% in accuracy, precision, and recall, respectively, which out perform other baseline Convolutional Neural Networks (CNNs). Moreover, feature maps were visualized to investigate spatial distribution information, which provides a theoretical basis for analyzing protein subcellular pattern.
Bus Stop Information Data Collection and Analysis Technology Based on Web Crawler Technology

In this study, we use the means of web crawler to obtain the information data of bus stops in specific cities by using 8684 website and Baidu map as the data platform, and conduct data cleaning and visual analysis on the detailed data of the stops to analyze the distribution of urban stops. Finally, based on this, a city bus stop information collection and analysis software is formed to make up for the incomplete data acquisition of existing software. It can realize the collection of bus stop information data in different cities, which is of great significance in the field of traffic planning and big data analysis.
A high-capacity reversible data hiding based on improved rhombus prediction

Reversible data hiding algorithms based on prediction error histogram of rhombus prediction need excellent prediction performance to achieve more embedding capacity. However, the cost of improving the prediction accuracy is to reduce the number of prediction pixels, which results in a reduction in embedding capacity. It means high prediction accuracy and large embedding capacity become two contradictory conditions. In this paper, we proposed a reversible data hiding algorithm based on improved rhombus prediction, which can break through the upper limit of prediction pixel numbers and increase the embedding capacity. The proposed algorithm utilizes the characteristics of reversible data hiding process to carry out the second stage reversible embedding based on the traditional rhombus prediction. It can embed the additional data into the context pixels of the first stage rhombus prediction so that the embedding capacity will be increased significantly. At the same time, reusing the reversible data hiding characteristics can ensure the lossless recovery of the carrier image and secret information. Experiments show that the algorithm can improve embedding capacity significantly and ensure the high visual performance of marked images.
Implementation of single-scan star centroid extraction algorithm based on FPGA

In this paper, the connected component labeling and centroid extraction algorithm of single scan are implemented in FPGA. The scheme meets the requirements of high precision and real time of target extraction. This solution uses Block RAM to build dual-port ram instead of Look-Up-Table and register for feature data storage, the FPGA logic resource occupation is less; the connected component labeling method is used to process the labelling and pixel data in parallel, and the gray centroid of the target is calculated according to the stored information, the error is less than 0.045 pixels, the accuracy of the calculation results is improved; it only needs to scan the whole image once, reducing the processing time, at the same time, FPGA is used to combine several steps, such as threshold segmentation, connected component labeling and gray centroid calculation, processing 640x480 images at 50MHz clock can reach 124f/s, It meets the real-time requirement of centroid extraction.
Machine vision based for brake shoe steel back size inspection

The traditional dimensional measurement of gate tile steel back is still under the status quo of relying on manual work, which is not conducive to industrialization and automated production. In this paper, we rely on machine vision and image feature extraction to achieve contactless measurement of gate tile backs, preprocess the acquired images, binarization operation, edge detection using Canny operator, and finally obtain the product length and width parameters, square hole length and width parameters, central round hole diameter parameters using Hough transform and least squares method. The experimental results show that the method can accurately measure the index parameters of the gate tile steel backing products, and the detection error is within ±2mm. The method reduces the influence of human factors and environmental factors on the inspection of the gate tile steel backing, improves the measurement accuracy and speed of the quality inspection of the gate tile steel backing, and promotes the automation of the production of the gate tile steel backing to a certain extent.
A Study of Rousseau’s View of Happiness and Humam Education

The purpose of this paper is to examine the relationship between happiness and education, focusing on Rousseau's books. To this end, his view of happiness and the purpose of education were examined, focusing on the contents of happiness and education in his major works. Rousseau told children not to use competition, jealousy, and fear as a means of education. This is because self-love necessary for self-preservation turns into selfishness and becomes the source of unhappiness. He also made 'human and citizen' his educational goal. The teacher guaranteed the child's freedom as an authoritative leader and led the child to discover knowledge and value as a free being. This is because the balance of duty as a citizen and life as an individual is the foundation of happiness.
Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.
Video-based case formulation for individuals with persistent physical symptoms associated with indoor air or chronic fatigue

The paper presents a new algorithm for the image inpainting problem. The algorithm uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of these qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which expands the Split Bregman Iteration (SBI) procedure by supplying it with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.
A Study of Technological Knowledge Production and Dynamics in Universities

대학교의 역할과 가치는 시대적 요구에 따라 변화하고 있다. 과거 교육, 연구, 봉사 중심의 대학교에서 교육과일치를 중심으로 하는 연구중심대학으로 그리고 연구와 기업가적 가치 생산을 요구받는 현재의 기업가적 연구중심 대학의 모습으로 변화하였다. 또한, 대학교가 생산하는 지식도 변화하여 왔다. 과거에는 대학교에서 과학지식(학술논문) 생산에 큰 힘을 쏟았다면, 최근에는 기술지식(특허) 생산에도 많은 관심을 보여주고 있다. 본고에서는 기업가적 연구중심대학들이 생산하는 기술지식의 속성을 정의하고 그 속성에 따라 국내 대학교들이 생산하는 기술지식의 방향성을 연구하였다. 국내외에서 진행된 기업가적 대학과 지식흐름의 관한 연구를 선행연구로 보여주었고, 국내 대학에서 등록한 대학특허를 기반하고 재조정하여 도전적 기술지식과 혁신적 기술지식을 수치로 나타내었다. 도전적 기술지식은 특허에서 과학문헌을 인용하여, 과학지식에서 기술지식으로 도전적으로 전환한 기술지식을 가리키며, 혁신적 기술지식은 해외의 특허를 인용하며, 해외의 기술지식을 먼저 국내로 도입한 기술지식을 가리킨다. 연구 결과, 과학기술중점대학교의 도전적, 혁신적 기술지식 생산은 국내 종합대학보다 많았고, 시간 변화에 따라 국내 대학들은 모두 기업가적 연구중심대학의 기술생산에 많은 결과를 만들고 있음을 확인하였다. 다만, 대학에서 암묵적으로 생산 및 전달되는 가치를 평가에 반영하지 못하였고, 해외 대학의 결과값도 포함하지 못한 점은 연구 한계점으로 남지만, 대학의 기술지식 속성을 고려한 새로운 장을 열었다는 점에 의의가 있다. The roles and values of universities are changing according to the needs of the times. The roles of universities in the past were to provide education, research, and service. Later, research-oriented universities emerged with the convergence of education and research. Currently, this role has been transformed into that of an entrepreneurial research-oriented university. The knowledge that universities produce has also changed. In the past, universities focused on producing scientific knowledge (academic papers), but recently they are showing a lot of interest in producing technical knowledge (patents). In this paper, the attributes of the technical knowledge produced by entrepreneurial research-oriented universities were defined, and the direction of the knowledge produced by domestic universities was studied according to its attributes. Research on entrepreneurial universities and knowledge flow conducted at home and abroad was used as a preceding study, and both challenging and innovative technical knowledge were expressed numerically based on the university patents registered by domestic universities. "Challenging" technology knowledge refers to technical knowledge that has transitioned from scientific knowledge to technical knowledge by citing scientific literature in patents. "Innovative" technology knowledge refers to technical knowledge that cites foreign patents and introduces foreign technical knowledge for the first time in Korea. As a result of this study, it was confirmed that the production of challenging and innovative technical knowledge by the Korea Institute of Science and Technology (KIST) was higher than that of domestic universities and that all domestic universities were delivering results in their roles as entrepreneurial research-oriented universities producing technology over time. Unfortunately, the technical knowledge of universities was analyzed only based on patent information, so the value implicitly produced and delivered by them could not be reflected in the evaluation. Also, overseas universities were not included. Nevertheless, this study is meaningful in that it opened a new chapter in the research of technical knowledge produced by universities.
Ceh'e3teekuu! —Listen—This is Arapaho Land

We know about the rapid loss of our world's plants, animals and wilderness but few of us are aware of the parallel, perhaps more rapid and equally devastating, extinction of our planet's human cultural diversity and ongoing cultural genocide. The Vision Quest-University of Wyoming (VQ-UWYO) project addresses the physical occupation of Arapaho lands and the concomitant erasure of Arapaho culture as evidenced by the critically endangered status of the Arapaho language. VQ-UWYO is an augmented reality (AR) mobile place-based learning game that teaches Arapaho language and culture on the UWYO campus. The affordances of mobile technology in general and mapping and locative technologies like the global positioning system (GPS) in particular are present-day extensions of the European colonial enterprise of map making which named, mapped and thereby claimed ownership of the entire world. VQ-UWYO uses these same technologies to push back against the tide of cultural genocide and re-anchor Arapaho place names, language and culture to the UWYO campus. The foundational assumption that the land that is now the UWYO campus was a terra nullius to be freely taken, occupied and used is being challenged in order to begin to heal generations and centuries of colonialization and oppression perpetrated on first peoples in general and upon the Arapaho Nation in particular. The VQ-UWYO project seeks new ways to speak the truths of the past so that we as individuals and as a campus community can finally stand on equal ground.
Unbiased Real-time Traffic Sketching

In network measurement, sliding window measurement has the advantage of providing recent and timely measurement results. Recently, sketches have become the most popular method of conducting flow-level network measurements due to their favorable trade-off between small memory overhead and high measurement accuracy. However, it remains a challenge that no current sketches are able to support unbiased estimation toward flow size measurement, which can improve the performance of tasks including network diagnoses, delay measurement and heavy hitter detection. In this paper, we propose the first work that achieves unbiased flow size measurement in sliding windows, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unbiased Cleaning sketch (UC sketch)</b> . The key technique of the UC sketch is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unbiased Cleaning</i> which can remove outdated keys from the sliding windows in a balanced way. Besides, we significantly reduce the variance of flow size by two optimization techniques, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Linear Scaling</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Column Randomizing</i> . To prove the result, we conduct rigorous mathematical analysis and reasonable experiments. All related source codes are open-sourced at Github anonymously.
Enabling Encrypted Cloud Emails with Data Encryption using Advanced Encryption Techniques

Abstract: Cloud computing offers a flexible and convenient means of exchanging data, with several advantages for both society and individuals. With the widespread usage of cloud emails and frequent reports of large-scale email leakage occurrences, the security property known as forward secrecy has become desirable and necessary for both users and cloud email service providers to increase the security of cloud email systems. Typical techniques of attaining forward secrecy, such as Diffie-Hellman key exchange and forward-secure public-key encryption, have not been extensively authorized and used because they fail to meet the security and practicality requirements of email systems at the same time. We introduce a new cryptographic primitive called forward-secure puncturable identity-based encryption (fs-PIBE) in this paper to capture forward secrecy of encrypted cloud email systems without sacrificing practicability. It allows an email user to perform fine-grained decryption capacity revocation. In the standard model, we design a framework for encrypted cloud email systems and instantiate it with a concrete fs-PIBE structure that has constant ciphertext size and proved security. We enhance the proposed fs-PIBE scheme to provide end-to-end encryption and outsourced decryption, respectively, to improve the security and efficiency of the presented framework.
BIBBOX, a FAIR toolbox and App Store for life science research

Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.
Efektivitas Pembelajaran Online pada Siswa Selama Pandemi Covid 19 di Era Literasi Digital

The purpose of this research is to see how the online learning process for students, or children and society in the digital literacy era? How is it progressing? Can the children understand the learning process! Then what is the role of parents and their teachers in the learning process. The subjects of this study were children and their parents in Meruyung Village, Limo District, Depok, West Java. This research method is descriptive qualitative research with analytical descriptive approach. Data collection techniques used are interviews, observations / observations. The results of this study are in the form of data and information related to the effectiveness of online learning during the COVID-19 pandemic in the current digital literacy era.
ICDAR’23: Intelligent Cross-Data Analysis and Retrieval

Recently, there has been an increased interest in cross-data research problems, such as predicting air quality using life logging images, predicting congestion using weather and tweets data, and predicting sleep quality using daily exercises and meals. Although several research focusing on multimodal data analytics have been performed, few studies have been conducted on cross-data research (e.g., cross-modal data, cross-domain, cross-platform). The article collection “Intelligent Cross-Data Analysis and Retrieval” aims to encourage research in intelligent cross-data analytics and retrieval and contribute to the creation of a sustainable society. Researchers from diverse domains such as well-being, disaster prevention and mitigation, mobility, climate, tourism and healthcare are welcome to contribute to this Research Topic.
Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

Appearance-based dynamic Hand Gesture Recognition (HGR) remains a prominent area of research in Human-Computer Interaction (HCI). Numerous environmental and computational constraints limit its real-time deployment. In addition, the performance of a model decreases as the subject’s distance from the camera increases. This study proposes a 3D separable Convolutional Neural Network (CNN), considering the model’s computational complexity and recognition accuracy. The 20BN-Jester dataset was used to train the model for six gesture classes. After achieving the best offline recognition accuracy of 94.39%, the model was deployed in real-time while considering the subject’s attention, the instant of performing a gesture, and the subject’s distance from the camera. Despite being discussed in numerous research articles, the distance factor remains unresolved in real-time deployment, which leads to degraded recognition results. In the proposed approach, the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera. Additionally, the capability of feature extraction, degree of relevance, and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding (t-SNE), Mathew’s Correlation Coefficient (MCC), and the McNemar test, respectively. We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.