Reviewerly/exHarmony huggingface dataset:
Meta-transfer Learning for Person Re-identification in Aerial Imagery

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery; this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 $$\%$$ and a mean average precision (mAP) of 38.02 $$\%$$ , demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model.
Exploration on the Teaching Reform of Data Structure Course under the Background of "Si Xin" and Engineering Education Certification

Under the background of "Si Xin" and engineering education certification, taking the major of computing science and technology of the College of Information Science and Technology of Hebei Agricultural University as an example, in view of the problems existing in the teaching of data structure courses in the past, a result-oriented data structure course teaching system was built from four aspects: revising the syllabus, reforming teaching methods, improving the assessment and evaluation system, and establishing a continuous improvement mechanism, and scientifically quantified the teaching effect of data structure courses, so as to improve the teaching quality.
[Geochemical Background and Baseline Value of Soil Chemical Elements in Hebei Province].

Geochemical background and baseline values are important parameters for understanding the geochemical characteristics of soil elements, but the research degree of these two parameters is lacking in Hebei Province. Therefore, data from the multi-purpose regional geochemical survey and land quality geochemical assessment in Hebei Province from 2004 to 2018 were collected, covering approximately 71% of the land area of the whole province. Based on the data of surface soil and deep soil, scientific and robust methods including median value and median absolute deviation were used to calculate the geochemical background values, geochemical baseline values, as well as variation ranges of 54 indexes (Ag, Al2O3, As, Au, B, Ba, Be, Bi, Br, CaO, Cd, Ce, Cl, Co, Cr, Cu, F, Fe2O3, Ga, Ge, Hg, I, K2O, La, Li, MgO, Mn, Mo, N, Na2O, Nb, Ni, P, Pb, pH, Rb, S, Sb, Sc, Se, SiO2, Sn, Sr, Th, Ti, Tl, U, V, W, Y, Zn, Zr, total carbon (TC), and organic carbon (Corg)) in Hebei Province and 11 prefecture-level cities. The change rate in geochemical background for each index was also calculated. The results showed that the geochemical background and baseline values of most soil chemical elements in Hebei Province were lower than those nationwide, but the values of Ba, Br, Cl, MgO, Na2O, P, pH, S, Sr, and TC were higher, with CaO being the highest. Compared with those in north China, there was no significant difference in the geochemical background and baseline values for the 54 indexes, with the ratios of 0.83-1.17 and 0.79-1.19, respectively. Significant changes in the geochemical background for Corg, Hg, N, P, S, and Se were observed in Hebei Province, indicating that these indexes were greatly influenced by human factors. Preliminary analysis suggests that coal burning emissions and agricultural chemical use were two very important inducing factors.
UDA-DP: Unsupervised Domain Adaptation for Software Defect Prediction

Software defect prediction can automatically locate defective code modules to focus testing resources better. Traditional defect prediction methods mainly focus on manually designing features, which are input into machine learning classifiers to identify defective code. However, there are mainly two problems in prior works. First manually designing features is time consuming and unable to capture the semantic information of programs, which is an important capability for accurate defect prediction. Second the labeled data is limited along with severe class imbalance, affecting the performance of defect prediction.In response to the above problems, we first propose a new unsupervised domain adaptation method using pseudo labels for defect prediction(UDA-DP). Compared to manually designed features, it can automatically extract defective features from source programs to save time and contain more semantic information of programs. Moreover, unsupervised domain adaptation using pseudo labels is a kind of transfer learning, which is effective in leveraging rich information of limited data, alleviating the problem of insufficient data.Experiments with 10 open source projects from the PROMISE data set show that our proposed UDA-DP method outperforms the state-of-the-art methods for both within-project and cross-project defect predictions. Our code and data are available at https://github.com/xsarvin/UDA-DP.
Test of Causal Nonlinear Quantum Mechanics by Ramsey Interferometry with a Trapped Ion

Quantum mechanics requires the time evolution of the wave function to be linear. While this feature has been associated with the preservation of causality, a consistent causal nonlinear theory was recently developed. Interestingly, this theory is unavoidably sensitive to the full physical spread of the wave function, rendering existing experimental tests for nonlinearities inapplicable. Here, using well-controlled motional superpositions of a trapped ion, we set a stringent limit of $5.4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}12}$ on the magnitude of the unitless scaling factor ${\stackrel{\texttildelow{}}{\ensuremath{\epsilon}}}_{\ensuremath{\gamma}}$ for the predicted causal nonlinear perturbation.
Private Collection Matching Protocols

We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. Existing privacy-preserving cryptographic primitives cannot solve PCM problems efficiently without harming privacy. We propose a modular framework that enables designers to build privacy-preserving PCM systems that output one bit: whether a collection of server sets matches the client's set. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server elements. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PCM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost.
Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer

This paper introduces a deep learning approach to photorealistic universal style transfer that extends the PhotoNet network architecture by adding extra feature-aggregation modules. Given a pair of images representing the content and the reference of style, we augment the state-of-the-art solution mentioned above with deeper aggregation, to better fuse content and style information across the decoding layers. As opposed to the more flexible implementation of PhotoNet (i.e., PhotoNAS), which targets the minimization of inference time, our method aims to achieve better image reconstruction and a more pleasant stylization. We propose several deep layer aggregation architectures to be used as wrappers over PhotoNet, to enhance the stylization and quality of the output image.
Pengembangan Model Pembelajaran Project Based Learning (PjBL) dalam Akuntansi Keuangan

The development of the Project Based Learning (PjBL) learning model in financial accounting courses aims to increase student HOTs in analyzing elements of financial statements. The model used is ADDIE, in the MSME Accounting management course. The model is applied for one semester. The effectiveness test results show that it is very effective, and the practicality results also show a very practical score so that the PjBL development model in financial accounting can be used without revision.
SiMAIM: identifying sockpuppets and puppetmasters on a single forum-oriented social media site

With the Internet becoming indispensable in our lives, social media has become an integral part of our lives. However, with this has come the phenomenon of a single user registering multiple accounts (sockpuppets) to advertise, spam, or cause controversy on social media sites, where the user is called the puppetmaster. This phenomenon is even more evident on forum-oriented social media sites. Identifying sockpuppets is a critical step in stopping the above-mentioned malicious acts. The identification of sockpuppets on a single forum-oriented social media site has seldom been addressed. This paper proposes a Single-site Multiple Accounts Identification Model (SiMAIM) framework to address this research gap. We used Mobile01, Taiwan's most popular forum-oriented social media site, to validate SiMAIM’s performance. SiMAIM achieved F1 scores between 0.6 and 0.9 on identifying sockpuppets and puppetmasters under different datasets and settings. SiMAIM also outperformed the compared methods by 6–38% in F1 score.
Development Of Educational Crossword Puzzle Games In Arabic Vocabulary Learning For Madrasah Aliyah Students

This research examined the production of an educational crossword puzzle game used to facilitate Arabic vocabulary learning in Madrasah Aliyah. Using the Research and Development Approach by Borg and Gall, the authors used 4 stages to produce the product: exploration, product development, testing, and finalization. After analyzing the data using methods, the results show a score of 5 (very good category) and validated media experts with a score of 4.8. (Very good category), and limited trials with a score of 4.4 (very good category) and field trials with a score of 4.5 (good category). Further research is needed to expand this vocabulary learning technique beyond this small dataset.
Malicious or Benign? Towards Effective Content Moderation for Children's Videos

Online video platforms receive hundreds of hours of uploads every minute, making manual content moderation impossible. Unfortunately, the most vulnerable consumers of malicious video content are children from ages 1-5 whose attention is easily captured by bursts of color and sound. Scammers attempting to monetize their content may craft malicious children's videos that are superficially similar to educational videos, but include scary and disgusting characters, violent motions, loud music, and disturbing noises. Prominent video hosting platforms like YouTube have taken measures to mitigate malicious content on their platform, but these videos often go undetected by current content moderation tools that are focused on removing pornographic or copyrighted content. This paper introduces our toolkit Malicious or Benign for promoting research on automated content moderation of children's videos. We present 1) a customizable annotation tool for videos, 2) a new dataset with difficult to detect test cases of malicious content and 3) a benchmark suite of state-of-the-art video classification models.
Analisis Daya Tampung Waduk Sebagai Pengendali Banjir di Kawasan Sungai Wanggu

Reservoir Boulevard is part of the flood control system in the city of Kendari, like other areas in Indonesia. Wanggu Reservoir is located in Lepo Lepo Village, Baruga District, Kendari City. The reservoir has a storage volume of 267.142m3. danluas DAS wanggu 348,75 km2. This flood control reservoir functions to accommodate flood overflow from the Wanggu River when it rains with high intensity. So in this study to determine the capacity of the reservoir capacity from year to year, a design rain analysis was carried out with return periods of 2, 5, 10, 25, 50, 100 years. It is necessary to analyze the design rainfall to find out whether the reservoir can reduce flooding for up to 100 years or not. In the rain analysis this plan uses the log person type III method and the flood discharge analysis uses the Rational Method. From the research results, it was found that the flood discharge with a return period of 100 years is 9,670m3/the.
CenterPoint-SE: A Single-Stage Anchor-Free 3-D Object Detection Algorithm With Spatial Awareness Enhancement

Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and latency. Due to poor perception of spatial features, the accuracy of the existing single-stage anchor-free detection algorithms with low latency are difficult to be implemented into autonomous vehicles. Therefore, we focus on enhancing the spatial perception ability of the anchor-free detection network based on CenterPoints. In this paper, we propose a single-stage anchor-free 3-D object detector CenterPoint-Space-Enhancement (CenterPoint-SE) algorithm and construct an efficient 3-D backbone network to extract fine-grained spatial geometric features by introducing a spatial attention mechanism and residual structure. At the same time, a powerful spatial semantic feature fusion module, the enhancement of feature fusion (EF-Fusion), is designed. In addition, we add a lightweight IoU prediction branch to improve the algorithm’s perception of various object sizes. Finally, we add a foreground point segmentation auxiliary training branch to enable the 3-D backbone to obtain object boundary features. We use the ONCE dataset to train and validate the proposed model, and the results showed that the proposed CenterPoint-SE achieves 70.33 mAP and an inference speed of 17.15 FPS, outperforming other methods.
Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm

The pandemic that hit the world has greatly impacted our life. But after some time, it seems that it will be going to end because the vaccine has already been made. In response to this, some people expressed their opinions about this vaccination on social media, for example, in the form of tweets on Twitter. The authors use those opinions or tweets as sentiment analysis material to determine the assessment of this vaccination. The tweet data in this study was obtained through data crawling using the Twitter API with the Python programming language. The variables used in this case are public tweets and their sentiments. This sentiment analysis process uses the Classification method with the Naive Bayes Classifier and will be compared with the XGBoost Classifier algorithm. The results of this study indicate that people are more likely to respond positively to this vaccination. In this case, the Naive Bayes Classifier got better performance with 0.95 from ROC - AUC Score and 134 ms in runtime compared to the XGBoost Classifier algorithm with 0.882 in ROC - AUC Score and 1 minute and 59 seconds in runtime.
Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization

Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts which words in source code are the most important for code summarization. Next, we augment a baseline neural code summarization approach using our model of human attention. We observe an improvement in prediction performance of the augmented approach in line with other bio-inspired neural models.
Formal models of differential framing effects in decision making under risk.

An intriguing finding in the decision-making literature is that, when people have to choose between sure and risky options of equal expected value, they typically take more risks when decisions are framed as losses instead of gains (Tversky & Kahneman, 1981).This framing effect is robust and has important implications for health, finance, and politics.However, theoretical debate exists on the origins of this effect.Moreover, pronounced task-related, individual, and developmental differences exist in the magnitude of the effect.These two issues-theoretical debate and differential framing effectscan be solved together, as an adequate theory of the framing effect should both describe the effect itself and describe differences therein.Therefore, we compare four theories on their capacity to describe differential framing effects: cumulative prospect theory (CPT), fuzzy trace theory (FTT), dual process theory, and a hybrid theory (HT) incorporating elements from lexicographic theory and fuzzy trace theory.First, in a theoretical analysis and empirical review, we build on recent advances in the fields of decision making, brainbehavior relationships, and cognitive development.Second, in an empirical study, we directly compare these theories by using a new experimental task and new analytic approach in which we use hierarchical Bayesian model-based mixture analysis of theories.Taken together, results indicate that differential framing effects are best described by the notion that the majority of decision makers decide according to the hybrid theory, and a sizable minority according to cumulative prospect theory and fuzzy trace theory.We discuss implications of these results for our understanding of the framing effect, and for decision making in general.
Relevansi Verbal Bulliying dengan Kecerdasan Interpersonal Siswa

Some children often express mockery words known as verbal bullying. Verbal expressions of bullying are words of ridicule that can offend others. This study aims to determine the relationship between verbal bullying and the interpersonal intelligence of elementary level students in Gampong Teungoh Langsa. The method used in this study is a quantitative method to see the relationship between verbal bullying and interpersonal intelligence. Hasil of this study states that 1) The form of verbal bullying that occurs in children in Gampong Teungoh in 2021 is mocking, mentioning parents' names, giving inappropriate nicknames to friends such as the name of the comedian doyok, dono because his friend's teeth are boneng. This form of bullying is called verbal bullying, which is a form of bullying in the form of inappropriate words to be expressed to others. 2) The level of interpersonal intelligence can be seen from the results of the normality test that the significance value is 0.544 &gt; 0.05. Then it can be concluded that the residual value is normally distributed. 3) The relationship between verbal bullying and interpersonal intelligence where the correlation of the relationship between verbal bullying and interpersonal intelligence is a perfect correlation where the correlation value amounts to 0.998. This indicates that the higher the child's interpersonal intelligence, the higher his personality.
Penerapan Media Flipbook untuk Meningkatkan Literasi Membaca Siswa SD

Indonesia is one of the countries in Asia with low reading literacy skills. Therefore, the mastery of reading literacy is vital for students and the Indonesian people. This study aims to describe the process of implementing flipbook media that can improve the reading literacy of 2nd graders of elementary school and measure the improvement of their reading literacy after the application of flipbook media. The method used is Classroom Action Research. The data collection uses observation techniques and documentation, and the literacy tests uses test sheets and documentation. The subjects of this research are 17 elementary school students in the 2nd grade of Muhammadiyah Elementary School 8 Surabaya. Based on the study results, the data show that flipbook media was applied online at the habituation stage and was carried out for 15 minutes before reading. The application of online-based flipbook media can improve students’ reading literacy and learning activities of the 2nd-grade students of Muhammadiyah Elementary School 8 Surabaya. The increase in reading literacy results can be seen in the literacy test results carried out in 2 learning cycles. Before applying flipbook, the result of classical literacy completeness was 29.41%. In the first cycle, the acquisition of classical reading literacy mastery was 47.06%, while in the second cycle, the classical completeness was 82.35%. So, it can be concluded that the use of flipbook media can improve the reading literacy of the 2nd-grade students of Muhammadiyah Elementary School 8 Surabaya
Jaccard distance as similarity measure for disparity map estimation

High confidence in disparity map estimation is critical in several application fields. A novel framework that employs customized local binary patterns and Jaccard distance for stereo matching along stereo consistency checks is presented. The proposal contributes with a method that allows greater confidence in its estimates, without dependence on supervised learning, and capable of generating a dense map with low-cost filtering. The proposed framework has been implemented in CPU and GPU for parallel processing capability. First, Local binary patterns are obtained during the initial stage; then, the Jaccard distance is employed as a similarity measure in the stereo matching stage; subsequently, a matching consistency check is performed, and singular disparities are removed. A comparison among novel and state-of-the-art algorithms for sparse disparity map estimation is performed employing Middlebury and KITTI stereo Datasets where the quality criteria used were percentage of bad pixels (B), quantity of invalid pixels, processing time and running environments to put each framework into context, obtaining down to 2.07% bad matching pixels and performing better than state-of-the-art cost functions