Reviewerly/exHarmony huggingface dataset:
Scene Semantic Guidance for Object Detection

In the real world, objects often have ambiguous or indistinctive appearances but they tend to co-vary with other objects and particular scene. In this paper, we exploit scene context to provide a global semantic guidance for object detection. We present a simple but effective Scene Semantic Guidance (SSG) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of detectors. Specifically, to explicitly model scene semantic context, we propose a scene semantic embedding module which leverages an auxiliary task of multi-label classification to learn object-level scene concept. Further, to adaptively incorporate the scene semantic context into the object feature, we propose a semantic consistency guidance module which can strengthen the discrimination of the object feature. Comprehensive experiments on MS-COCO benchmark demonstrate that the proposed SSG framework is effective and generalizable, leading to consistent improvements upon typical detectors, including Faster R-CNN, RetinaNet, and FCOS.
Research on fusion navigation of mobile robots based on map and no map

A robot fusion navigation system based on a map without a map is introduced. The A* algorithm is used for global planning, and the DWA algorithm is used for self-adaptation and dynamically adjust the navigation route. Experiments show that the fusion navigation system has higher scene adaptability and can complete navigation more efficiently. The new navigation mode of integrated navigation without map and map is introduced in detail. Can be more dynamic and flexible to complete navigation tasks on unknown map terrain. This project can automatically switch the behavior mode between the known and unknown map navigation. The A* algorithm is mainly used for global planning in areas with maps, two-dimensional radar scanning in unknown areas[1], real-time feedback of map roadblocks, and improved DWA algorithm for self-adaptation, dynamically planning routes, and providing the most efficient navigation route.
Performance Evaluation of Artificial Neural Networks Applied in the Classification of Emotions

Facial expressions are always manifested by people. For the human being, it is easy to recognize emotions. Technological advances applied to the recognition of expressions are growing rapidly and, in the same way, interest in research on this topic. However, at the computational level, it is a complicated task, some of the expressions of human beings are similar, for this reason, the computer can be confused at the moment of recognition. The use of machine learning models specifically artificial neural networks that have a good performance in emotions recognition is required to detect automatically feelings. This research shows the performance analysis of artificial neural networks applied to emotion datasets. The FER2013 and JAFFE datasets were used, a preprocessing of the data was carried out. For the classification, a comparison was made between artificial neural networks (Perceptron, VGG, and a Convolutional Neural Network). Optimal results were obtained in the detection of emotions.
Four Tier Test (FTT) Development in The Form of Virtualization Static Fluid Test (VSFT) using Rasch Model Analysis to Support Learning During the Covid-19 Pandemic

Abstract This study aims to develop a valid and reliable test instrument. The test instrument developed is a Four Tier Test (FTT) in a Virtual Static Fluid Test (Test) on Static Fluid material using the Rasch model analysis. The method used in this research is the method of research and development with the 4D model. This research produces an educational product in the form of an FTT in the form of VSFT. The items developed in this study amounted to 20 items. The validation results from several experts gave an average score of 88.13% with very good criteria. The CVR value for each item is 1, while the CVI value is 1 with very good criteria. The validation results from several educational practitioners gave an average score of 89.58% with very good criteria. The results of the Rasch Model analysis showed that the test instrument is accepted, the reliability of all the items has good criteria, all the items are valid and divided into three difficulties, also each item is not biased towards gender differences. The results of the analysis of student misconception showed that only 4.68% of the students understood the whole concept, 23.42% of students only understood partially, 58.95% of students have a misconception, and 12.95% of students did not understand the concept at all. The conclusion of this study is that the FTT instrument in the form of VSFT is valid and reliable as an assessment instrument during the Covid-19 period, and also able to analyze student conceptions.
Learning to Navigate in the Gaussian Mixture Surface

In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving the state-of-the-art over well-known datasets. Cross-Entropy (CE) loss function is commonly used to enhance the discriminative power of the deep learned features, encouraging the separability between the classes. However, observing the activation map generated by these models in the hidden layer, we realize that many image regions with low discriminative content have a high activation response and this could lead to misclassifications. To address this problem, we propose a loss function called Gaussian Mixture Centers (GMC) loss, leveraging on the idea that data follow multiple unimodal distributions. We aim to reduce variances considering many centers per class, using the information from the hidden layers of a deep model, and decreasing the high response from the unnecessary areas of images detected along the baselines. Using jointly CE and GMC loss, we improve the learning generalization model overcoming the performance of the baselines in several use cases. We show the effectiveness of our approach by carrying out experiments over CUB-200-2011, FGVC-Aircraft, Stanford-Dogs benchmarks, and considering the most recent Convolutional Neural Network (CNN).
Hierarchical Software Design Methodology for High Concurrency and Fine-Grained Permission Control Scenarios

Because complex software usually faces high concurrency and complex permission control scenarios, and there are problems with low code reuse rate and chaotic function module structure in the development process of its access control module, so providing software design methodology for such scenarios is helpful to improve software productivity and software quality play an important role.
The application of author’s academic influence in expert retrieval

The evaluation index of author’s academic influence is an important research content in the field of science metrics, which is helpful to dig out the high-level researchers in the field of scientific research. Based on the analysis and research of the commonly used indexes of authors’ academic influence, a new evaluation index of authors’ academic influence is proposed. The new evaluation model not only examined the author’s h index, but also the influence of citation and author’s signature are examined on the evaluation effect. Aiming at the low performance of the current expert retrieval model, the new evaluation model was applied to the re-ranking method of expert retrieval and the retrieval experiment was carried out. The experimental results show that the new evaluation index is better than other influence evaluation indexes in many evaluation indicators. At the same time, compared with the existing expert retrieval models, it is found that the new evaluation model can improve the performance of expert retrieval much better.
Multimodal Product Identification

This technical report describes the overview of our approach to the "Watch and Buy: Multimodal Product Identification Challenge". Specifically, we tackle this problem with a three-stage framework, i.e., product detection, retrieval and classification. For the product detection, we leverage the performance by Cascade R-CNN and deformable convolution to alleviate the impact of image distortion. For the product retrieval, we enhance the Multiple Granularity Network (MGN) with global and local context through IBN, SE and Non-local blocks. The task of product classification suffers from fashion variation. To this end, we propose to fuse the global feature of the integral images and local feature of products. Experiments demonstrate that our works could achieve competitive performance with the state-of-the-art methods and our overall approach achieves a F1 score of 0.648, ranking the second place in the final challenge.
I LOVE MATHEMATICS: PENGGUNAAN ALAT PERMAINAN EDUKATIF UNTUK MENGENAL KONSEP BILANGAN PADA ANAK USIA DINI: Penelitian Tindakan di Kelompok Usia 5-6 Tahun

This study discusses the use of I Love Mathematics media in improving the ability to recognize the concept of numbers for early childhood. The issue that underlies this research is the ability to recognize the concept of numbers for children aged 5-6 years has not been optimally stimulated. The ability of children's number concepts consists of the child's ability to mention numbers, recognize number symbols and use number symbols to count. The purpose of this study was to determine the increase in the ability to recognize the concept of numbers for children 5-6 years old through the use of I Love Mathematics media. This research was conducted using action research methods with 3 cycles 3 action of 6 steps. Data collection techniques through observation techniques, field notes, anecdotal notes and documentation. The results showed that the ability to recognize the concept of numbers for children aged 5-6 years increased in each indicator through the use of I Love Mathematics Media. The ability to recognize numbers 1-10 children achieved 93.1% on the very good criteria, the ability to recognize the addition of numbers 1-10 got 83.3% on the very good criteria. The ability to recognize the subtraction of numbers 1-10 obtained 83.3% on very good criteria. I Love Mathematics media can improve the ability to recognize the concept of numbers for children aged 5-6 years and other improvements.
An Attempt to Pick Teleseismic P Wave Arrival Using Envelope and Artificial Neural Network Algorithm

Abstract Seismic events detection and phase picking play an essential role in earthquake studies. Typical event detection is done visually or manually on recorded seismogram by choosing a series of higher amplitude signals recorded on at least 4 stations. More sophisticated methods have been used in event detection and picking with additional attributes such as Short Time Average over Long Time Average (STA/LTA). This method is based on average number sampled at multiple predefined windows. However, STA/LTA is dependent on the window size which becomes its drawback. In this study, we explore one derivative attribute, popularly known as envelope or instantaneous amplitude. It has been extensively used in seismic reflection and refraction method. In principle, this method uses the Hilbert Transform to calculate complex seismic trace and take the magnitude of complex seismic trace as envelope amplitude that can be used to analyze P wave arrival time. We employed one of the machine learning methods, Artificial Neural Network (ANN). The ANN method works by analyzing various inputs and training them to recognize patterns in P wave arrival signals. We started our study by applying envelope attribute to synthetic data with noise addition. We found that with noisy data the envelope attribute still gives a clear signal for first-time arrival. Next, we trained 300 seismograms of teleseismic events recorded on IRIS-US networks and tested our trained program on 20 seismograms as a blind test. To compare performance between the two methods, we calculated the difference between the results of automatic picking and manual picking. The final calculation shows an average deviation of 0.355 seconds. Twenty-five percent of testing data (5 samples) has a deviation above 0.5 seconds, and 75% of the remainder (15 samples) already had a deviation under 0.5 seconds. The more significant deviations of the P wave picks are likely due to noisy signals in the data set and complex arrival signals. This study shows that the combination of envelope attribute and machine learning method is promising to distinguish teleseismic P wave arrival and automatically pick them.
Secure Featurization and Applications to Secure Phishing Detection

Secure inference allows a server holding a machine learning (ML) inference algorithm with private weights, and a client with a private input, to obtain the output of the inference algorithm, without revealing their respective private inputs to one another. While this problem has received plenty of attention, existing systems are not applicable to a large class of ML algorithms (such as in the domain of Natural Language Processing) that perform featurization as their first step. In this work, we address this gap and make the following contributions:
Local Image Analysis of Malaysian Herbs Leaves Using Canny Edge Detection Algorithm

Machine vision helps a lot with the latest recognition technology. To get the best recognition results, the initial phase of image processing should be done as best as possible. This phase involves the production of an image map of the resulting image using edge detection. The Canny method is often used because of its performance of producing meticulous strong edges but this method is sensitive to changes in image intensity when involving complex images such as image leaves and results in a lot of noise at the edges of the resulting image. This is because in this method the threshold value is selected empirically on the image globally. In this study, local image analysis will be discussed along with its impact on the resulting image edge results. In addition, a set of data from herbal leaves in Malaysia has also been produced by containing ground truth images for each herbal image available. The results from this study found that the locally analysing image approach has outperform the global approach of the conventional Canny method. Findings from this study may help the identification system in the future.
How financial services firms can mitigate the next wave of attacks

Every organisation operating in the world today is a potential victim of cybercrime. However, financial services (FS) firms are a particularly tempting target for those looking to make a profit. This is neither a surprising nor a new concept. Throughout history, the FS sector has been relentlessly targeted by criminals, whether it's the traditional bank robbers of old or the hackers of today, stealing millions through phishing and ransomware attacks.
Control Application Based on Multi-objective Simulated Annealing Algorithm

The application of drones is a recent hot research topic. We need to use drones based on some optimization algorithms to find the best path for simulation data. We will use Monte Carol to simulate the initial solution to establish a multiobjective simulated annealing algorithm to arrange the UAV and balance two main goals: safety and cost. We also use other factors as parameters, including the size and frequency of the destination. We applied the algorithm to the simulated data and got the solution result. In addition, we conducted a sensitivity analysis on the outer loop parameters of the simulated annealing algorithm. At the same time, the model was evaluated, and its development prospects were forecasted.
Multi-Modal Abstractive Summarization based Transformer using Video Transcripts

In this paper, we propose a MASTF methodology, which is a Multimodal Abstractive Summarization based on Transformer. Neural network models applied in the field of generative summaries utilizing conventional multi-modals were techniques utilizing hierarchical attention based on circulating neural networks. Although transformers showed excellent performance in various natural language processing fields, including generative summaries, there were no cases of application in multimodal-based generative summaries. Thus, in this paper, we use transformers to improve the performance of multimodal image subtitle generation summary models. Transformer-based models outperform hierarchical attention-based models by 24.17% on ROUGE-L basis and 10.52% on combining speech and text.
Digital Differentiation as a Form of Pedagogical Creativity

The goal of this chapter is to explain differentiation and digital differentiation and their intersecting points with pedagogical creativity, which become a necessity for 21st century teachers. In regards to pedagogical creativity, three main elements, which are creative teaching, teaching for creativity, and creative learning, will be discussed. Differentiation techniques and digital differentiation tools used for digitalizing in-class techniques will be explained. Further, an example of a digitally differentiated lesson plan will also be presented as a guide for teachers that require it.
Ignore the Extra Zeroes: Variance-Optimal Mining Pools

Mining pools decrease the variance in the income of cryptocurrency miners (compared to solo mining) by distributing rewards to participating miners according to the shares submitted over a period of time. The most common definition of a “share” is a proof-of-work for a difficulty level lower than that required for block authorization—for example, a hash with at least 65 leading zeroes (in binary) rather than at least 75. The first contribution of this paper is to investigate more sophisticated approaches to pool reward distribution that use multiple classes of shares—for example, corresponding to differing numbers of leading zeroes—and assign different rewards to shares from different classes. What’s the best way to use such finer-grained information, and how much can it help? We prove that the answer is not at all: using the additional information can only increase the variance in rewards experienced by every miner. Our second contribution is to identify variance-optimal reward-sharing schemes. Here, we first prove that pay-per-share rewards simultaneously minimize the variance of all miners over all reward-sharing schemes with long-run rewards proportional to miners’ hash rates. We then show that, if we impose natural restrictions including a no-deficit condition on reward-sharing schemes, then the pay-per-last-N-shares method is optimal.
Analysis of Cybersecurity in the Real Environment

Cybersecurity is a necessary consideration for information technology as well as Internet services. Cyberattacks cause detriment and harm not only to private enterprises but also to the government organizations as well. The attackers endeavor new techniques to challenge the security frameworks, use powerful tools and tricks to break any sized keys, security of private and sensitive data is in the stale mark. There are many advancements are being developed to mitigate these attacks. This document gives a complete insight into the wide area of world of cyber security trends, challenges in the field of cyber security and its state in AFA (Armed Forces Academy in Liptovský Mikuláš) environment. In the end, the findings from the state of cyber security at AFA related to phenomenon of remote work and security measures in the AFA are described.
The effectiveness of E-learning based learning models to improve primary school students’ numeracy ability during the Covid-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nana Sutarna, Reza Muhamad Zaenal, Nanan Abdul Manan; The effectiveness of E-learning based learning models to improve primary school students' numeracy ability during the Covid-19 pandemic. AIP Conf. Proc. 27 October 2021; 2438 (1): 020007. https://doi.org/10.1063/5.0071603 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Single Image Specular Highlight Removal on Natural Scenes

Previous methods of highlight removal in image processing have exclusively addressed images taken in specific illumination environments. However, most of these methods have limitations in natural scenes and thus, introduce artifacts to nature images. In this work, we propose a specular highlight removal method that is applicable to natural scene image. Firstly, we decompose the input image into a reflectance image and an illumination image based on Retinex theory, and show that the illumination image of natural scene is obviously different from that of commonly used experimental scene. Then, the smooth features of the input image are extracted to help estimate the specular reflection coefficient in chromaticity space. Finally, the space transformation is used to calculate the specular components and the highlight removal is achieved by subtracting the specular reflection component from the original image. The experimental results show that our method outperforms most of existing methods on natural scene images, especially in some challenging scenarios with saturated pixels and complex textures.
Symbiont AI and Embodied Symbiotic Learning

Various approaches have been explored to enable AI agents to assist human users in complex tasks. To date, these systems have been narrowly designed to tackle specific rather than general tasks, are brittle when taken outside of controlled environments, or can only be trained by technically proficient users. In this paper we propose a new type of general-purpose assistive agent called a Symbiont AI that is designed to support users with greater task flexibility and environmental resilience, and that can be trained on new tasks by non-technical users using a novel method we call Embodied Symbiotic Learning. Instead of programming an AI to perform tasks (either by explicit coding, demonstration, or machine learning) and then deploying the AI to assist a human, we form a Human-AI Symbiotic System in which an AI partners with a human and learns to assist the human in real-time, while the human learns to share the workload with the AI. Each partner develops a theory of mind of the other in real-time, so a partnership-specific set of expectations and communication norms emerges through the shared interactions. This allows the AI to be useful to the human even in open-ended or underspecified tasks. We also consider practical design considerations for such systems and present experimentally testable predictions that follow from this theory.