Reviewerly/exHarmony huggingface dataset:
Integrating Symbolic Planning and Reinforcement Learning for Following Temporal Logic Specifications

Teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments is a challenging problem. We consider that user defines every task by a linear temporal logic (LTL) formula. However, some causal dependencies in complex environments may be unknown to the user in advance. Hence, when human user is specifying instructions, the robot cannot solve the tasks by simply following the given instructions. In this work, we propose a hierarchical reinforcement learning (HRL) framework in which a symbolic transition model is learned to efficiently produce high-level plans that can guide the agent efficiently solve different tasks. Specifically, the symbolic transition model is learned by inductive logic programming (ILP) to capture logic rules of state transitions. By planning over the product of the symbolic transition model and the automaton derived from the LTL formula, the agent can resolve causal dependencies and break a causally complex problem down into a sequence of simpler low-level sub-tasks. We evaluate the proposed framework on three environments in both discrete and continuous domains, showing advantages over previous representative methods.
Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy

When optimization is applied in real-world applications, optimal solutions that do not take into account uncertainty are of limited value, since changes or disturbances in the input data may reduce the quality of the solution. One way to find a robust solution and consider uncertainty is to formulate the problem as a min-max optimization problem. Min-max optimization aims to identify solutions which remain feasible and of good quality under even the worst possible scenarios, i.e., realizations of the uncertain data, formulating a nested problem. Employing hierarchical evolutionary algorithms to solve the problem requires numerous function evaluations. Nevertheless, Evolutionary Algorithms can be easily parallelized. This work investigates a parallel model for differential evolution using SciPy, to solve general unconstrained min-max problems. A differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, the design level optimization is parallelized. The performance of the algorithm is evaluated for a different number of cores and different dimensionality of four benchmark test functions. The results show that, when the right parameters of the algorithm are selected, the parallelization can be of high benefit to a nested differential evolution.
Research on Computed Tomography Image Reconstruction Algorithm Based on Optical Flow Equation and Target Matching

Image is the main source of human access to information. Due to the development needs of aerospace, biomedical engineering, industrial detection, culture and art, pattern recognition and military, image processing has attracted more and more attention, and has gradually developed into a promising new discipline. For different processing purposes, digital image processing can be divided into geometric processing, arithmetic processing Image coding, image enhancement, image restoration, image reconstruction, image segmentation and image analysis. Based on the adaptive image matching and tracking algorithm, a comprehensive algorithm combining particle prediction and template image update is studied to predict the position of nonlinear and non Gaussian problems. Aiming at the problem of large amount of calculation of the algorithm, an update combination method is adopted. Computed tomography technology is widely used in medical and industrial non destructive testing. Reconstruction algorithm is the core, and incomplete angle reconstruction is a hot and difficult problem in the research field of reconstruction algorithm in practical application This paper introduces the basic theoretical conclusions and common algorithms of sparse optimization. Then, the application of sparse optimization theory in image incomplete angle reconstruction is summarized, and its main research results and the role of sparse optimization are introduced; Finally, the research of incomplete angle reconstruction based on sparse optimization is prospected.
Analysis of Waste Transportation Volume in Jakarta Province using Linear Regression and Random Forest Regression

The accumulation of waste volume in the river waters of DKI Jakarta is still a significant problem that cannot be solved optimally because the population continues to increase every year, so the tonnage of waste also increases as well as some residents of DKI Jakarta still throw garbage into the river. In predicting the level of waste volume, the DKI Jakarta Provincial Environment Agency must make decisions, so it is necessary to carry out a prediction stage regarding the increase in waste in the future. For this reason, this research performs a prediction stage by utilizing two machine learning algorithms: Linear Regression and Random Forest Regression. The experiment used historical data on waste volume transportation from January to June 2021. The experimental results showed that the Random Forest Regression had the lowest error values ​​of 0.82 and 0.81, with a training and testing data ratio of 80%:20%. On the other hand, Linear Regression has an error value of 0.83 and 0.82 at a ratio of 80%:20%. The analysis discussed in this study can be a reference for predicting and taking the necessary actions to prevent an increase in the volume of waste in DKI Jakarta Province.
An Evaluation of Cross-Project Defect Prediction Approaches on Cross-Personalized Defect Prediction

Context: Just-in-time software defect prediction (JIT SDP) helps to prioritize fault-prone commits for efficient software quality assurance. As each commit can be attributed to each developer, JIT SDP can also be personalized to each developer as a personalized defect prediction. A question is whether the commit data of other developers, namely, cross-personalized data, are still valuable for prediction. Cross-project defect prediction (CPDP) approaches are a promising answer. Objective: To clarify the effectiveness of cross-personalized defect prediction with CPDP approaches. Method: An experiment with 23 CPDP approaches was conducted on 9 project datasets. Results: Some CPDP approaches using cross-personalized data were often better than the personalized defect prediction using one’s data. Conclusion: It is recommended to use the CPDP approach to achieve better predictions. Turhan09 is our recommendation.
Energy-based Models for Deep Probabilistic Regression

It is desirable that a deep neural network trained on a regression task not only achieves high prediction accuracy, but its prediction posteriors are also well-calibrated, especially in safety-critical settings. Recently, energy-based models specifically to enrich regression posteriors have been proposed and achieve state-of-art results in object detection tasks. However, applying these models at prediction time is not straightforward as the resulting inference methods require to minimize an underlying energy function. Furthermore, these methods empirically do not provide accurate prediction uncertainties. Inspired by recent joint energy-based models for classification, in this work, we propose to utilize a joint energy model for regression tasks and describe architectural differences needed in this setting. Within this framework, we apply our methods to three computer vision regression tasks. We demonstrate that joint energy-based models for deep probabilistic regression improve the calibration property, do not require expensive inference, and yield competitive accuracy in terms of the mean absolute error (MAE).
Designing Secured Cafe Network with Security Awareness Domain and Resource (SADAR) by Simulation using Cisco Packet Tracer

Computer networking has a huge part of life today, which its participation not only in one side of life but in almost all aspects of life, especially in the fields of education, organization and business. The purpose of a computer network related to connecting every device for providing information and data services through the borderless concept of anytime, anywhere and anyone. On the other hand, the use of a network in the business sector is very critical and fundamental to assist the implementation in the respect of accelerate growth and reducing cost. In this case, the network in the cafe not only become the secondary service to the customer but also enable the business to be sustained, which many customers browse and utilize Internet during their visit, also cafe's effort to broaden the range of its business. Despite, the accessibility become widespread to the network, but at some point, the demand for Internet access in cafe such as for gaming, accompanying and streaming remain huge. Thus, this study want to design a computer network using the Cisco Packet Tracer software through the concept of SADAR (security awareness domain and resource) in order to increase security compliance. This study also utilized and configure VLAN and DHCP connected to gigabit Ethernet as standard, while the testing will used PING and TRACEROUTE in order for mapping as desired.
Pelatihan Penggunaan Aplikasi Quizlet untuk Pembuatan Soal Matematika Pada Materi Geometri di SMP

Based on the results of interviews at partner schools, namely SMPN 27 Padang, it can be seen that the learning process carried out by teachers during the COVID-19 Pandemic is a learning process using throught WhatsApp by giving assignments to students. Therefore, the proposer and partners work together in overcoming these problems through the Community Service (PKM) program. This PKM program aims to increase teachers' knowledge about the application of online learning models, so training on the use of the Quizlet Application for Making Mathematics Problems in Geometry Material been given. The method used in achieving these goals is a simulation method and practice using the Quizlet application. The result of this community service is to increase teacher knowledge in using the Quizlet application in the mathematics learning process.
Breaking Hardware Boundaries of IoT Devices via Inverse Feature Completion

Privacy-preserving collaborative learning enables resource-constrained edge devices (e.g., Internet of Things (IoT) devices and smartphones) to build a knowledge-shared model while keeping individual data locally, achieving privacy preservation by designing an effective communication protocol. However, the learning paradigm raises high requirements for aligned input features of models, which is hard to realize in complicated IoT scenarios with various monitoring indicators. In this article, we propose a novel collaborative learning framework that is tolerant of IoT devices with unaligned feature spaces. Local bilevel optimizations for both model parameters and input features are performed iteratively in the training phase, in which the internal correlations of local sensor data provide additional guidance for the feature inference and completion. The scheme breaks hardware boundaries among various IoT devices in collaboration with the assistance of model inversion inference, which gains a new perspective on the utilization of model confidentiality and requires minimal modifications to the existing collaborative learning process. The framework achieves significant improvement compared with state-of-the-art methods, as we demonstrate through extensive simulations on real-world data sets.
Attention-based Deep Residual U Neural Network on Brain Tumor Segmentation Algorithms

In the image segmentation task, due to the complex distribution and variable shapes of brain tumors, conventional networks have poor accuracy in detecting small objects. Moreover, the computation cost of full convolutional network is high, and it is difficult to design a deep network as well. In this paper, we propose a new segmentation network called Attention-based Deep Residual U-Net (ADRU-Net). Res-Net50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> replaces the fully convolutional network as the backbone feature extractor of U-Net <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> , which can not only better learn deep features, but also avoid gradient disappearance. Both can greatly improve segmentation performance. At the same time, ADRU-Net introduces 6 Coord Attention modules <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</sup> between the networks, which realizes the fine segmentation of edges without increasing the computational load too much. In practice, the introduction of Coordinate Attention not only optimizes the segmentation of tumor edges, but also makes the segmentation efficient especially in small objects. Experiments show that compared with other U-Net variant networks, ADRU-Net obviously improves the convergence speed, and can treat small objects well, which achieves a more effective segmentation with low cost. Finally, Our network proposed in this paper improves the mIoU by 5.13% and the mPA by 5.96% in the LGG Segmentation Dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</sup> .
Multi-objective path planning based on deep reinforcement learning

Before an intelligent robot needs to go to multiple target areas to perform different tasks, it is essential to plan a safe and shortest path for the completion of the task. In this paper, the deep reinforcement learning method is introduced and the hierarchical Twin Delayed Deep Deterministic policy gradient (HTD3) algorithm is proposed in view of Twin Delayed Deep Deterministic policy gradient (TD3) algorithm, so that the intelligent robot can learn the optimal multi-objective path planning policy independently. The main idea of this paper is to hierarchically process multiple objectives according to their priorities, that is, each objective has one layer. Firstly, a state vector representing different layers is defined to layer the targets with different priorities, and the switching conditions between layers are given. Secondly, a unified hierarchical state representation and hierarchical reward function are defined for all targets, which can change automatically on the basis of the hierarchical state vector. Finally, a novel HTD3 algorithm is put forward via combining the above layered method and TD3 algorithm. We train and evaluate the proposed algorithm in two-dimensional environment and three-dimensional environment respectively, in which the simulation results show that the proposed HTD3 algorithm can effectively complete multi-objective path planning in various environments.
Pengembangan Media Komik Foto untuk Meningkatkan Keterampilan Menulis Narasi Peserta Didik Sekolah Dasar

The state of Indonesia has a writing skill level of about 84% of the total population, but only 12 books are published for one million Indonesians per year, which ranks 60 below Thailand out of 61 countries in the overall world ranking. Purbalingga Regency itself, on the portal page of the Communication and Informatics Service, stated that around 4% or 6000 people in Purbalingga cannot read and write. The illiterate community places Purbalingga in 26th out of 35 districts in Central Java. Students at the time of learning to write narratives still tend to be passive, and less focused, even teachers still useless interesting methods. The teaching and learning process has not used appropriate techniques, methods, and learning models. Based on the results of interviews with 15 students in the Sultan Agung cluster, Karanganyar district, it was found that out of 20 students, 15 children liked comics. The research objective is to develop and produce narrative writing instructional media for elementary school students. The research method is interview, observation, and document with 3 stages: preliminary study, model development, dan model test. The results showed that the condition of learning narrative writing skills for elementary school students was still not optimal and based on the t-test the use of photo comic media products proved effective to improve the narrative writing skills of fifth-grade elementary school students.
A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset

The application of deep learning (DL) methodology in the differentiation of benign and malignant lesions has drawn wide attention. However, it is extremely hard to acquire medical images with biopsy labeling, which leads to the scarcity of datasets. This is contrary to the requirement that DL algorithms need large datasets for training. To effectively learn features from small tumor datasets, a Bagging Strategy-based Multi-scale gray-level co-occurrence matrix (GLCM)-Convolutional Neural Network (BSM-GLCM-CNN) is proposed to boost the classification performance. Specifically, instead of feeding the raw image to the CNN, GLCM is used as the input of the designed model. As a texture descriptor, GLCM has the advantages of effectively representing lesion heterogeneity and of the same size for all input samples given the gray level. This work creatively partitions the GLCM to three groups to make full use of certain scale information of each group. When fusing the multi-scale texture information, the concept of bagging strategy in ensemble learning is used to improve the classification performance, where multiple base Learners are generated. Final classification results are obtained by integrating the multi-scale base Learners with the voting mechanism. Experimental results show that the proposed BSM-GLCM-CNN can successfully distinguish colonic polyps in a small dataset. The proposed method achieves an improvement from 68.00% Area Under Curve (AUC) to 90.88% AUC over other state-of-the-art models. The experimental results demonstrate the great potential of the proposed method when challenged by small pathological datasets in the medical imaging field.
Least-Squares Estimation of Keypoint Coordinate for Human Pose Estimation

The research on human pose estimation has recently been promoted to a new high degree. As a result, existing methods that are widely used but flawed in theory must be rethought. Most researchers focus on enhancing network structure and data processing details, yet neglect to study encoding-decoding methods for keypoint coordinate. In this paper, we rethink recent encoding-decoding methods and further propose a new, elegant and reliable one. Our method is referred to as Least-squares Estimation of Keypoint Coordinate (LSEC), which is a plug-in and can be conveniently used in recent state-of-the-art (SOTA) human pose estimation models. LSEC is mathematically rigorous and unbiased, and it can compensate for the inherent bias introduced by the existing encoding-decoding methods. Besides, LSEC greatly improves the robustness of Gaussian heatmap based human pose estimation methods against adversarial attack by noise. Experiments demonstrate the effective performance and robustness of our proposed method. We will release the source code later.
Convolution Neural Network Based Covid -19 Face Mask Detection and Recognition

The coronavirus is devastating global health. Ac- cording to WHO guidelines, wearing a mask and keeping a 6-foot distance between people can help to prevent the spread of COVID 19. As a condition of the international COVID-19 outbreak, protective equipment, the most vital of which is a face mask, is required. Wearing a face mask in public is a good way to be safe. This project seeks to develop a real-time, GUI- based face detection and identification system using machine learning. Tensor Flow, Keras, Scikit-learn, and Open CV are used to develop a Convolutional Neural Network (CNN) model to make the technique as accurate as possible. Principal Component Analysis (PCA) and the HAAR Cascade Algorithm are two components of the proposed methodology. If the person in front of the camera is wearing a mask, the classification algorithm’s result will be displayed by a green rectangle overlaid around the region of the face; otherwise, it will be represented by a red rectangle superimposed around the area of the face.
Design of Virtual Assessment Application system based on Python GUI and Face Detection to Supervise Pupils during E-examination

Virtual Assessment is widely utilized in everyday life since it saves time and is the most accurate approach available, especially as the number of participants grows in today’s world. Students can take virtual tests that provide guidelines and tips to help them understand the material. Every technical student should have a fundamental understanding of the online examination system. Because of the rapid and precise grading system, all of the exams are held online. The flexibility of an online examination is greater than that of a written examination. It is primarily intended to encourage educational diversity. The integrity of the exam pattern is less likely to be compromised with online examination. For example, when compared to other examination systems, disposing of the online test setup is the most difficult. For the implementation in this exam platform technologies like Python GUI, Machine Learning, Image Processing, Firebase are used. Tkinter is used as python GUI toolkit(Frontend), Firebase as Backend application development, Spyder(Anaconda) is an IDE for python language, OpenCV for Image Processing and Haarcascade Classifier to detect the objects in the picture. The goal of this project is to provide the exam platform for students, so that the tests can be conducted in coordinated form without cheating and also to predict the accurate results of each student.
Medical Imaging Analysis using Computer-Assisted Technologies

Analysis of medical images using computer-assisted technologies has been on the rise in the recent past. Machine and deep learning have proven to be reliable in the biomedical field, providing solutions to the analysis of many health issues. Machine learning platforms are a great tool in obtaining and analyzing medical images especially when a considerable implementation effort is made. Following this, efforts have been put into research pertaining to the application of this technology to give reliable results as far as diagnosis and images are concerned. This paper looks at developing a model that will be used in medical imaging technology using machine learning. The main aim is to come up with a model that can be adopted and simplify medical imaging. This work provides a machine learning pipeline to be applied in medical imaging such as regression, segmentation, generation of images, and learning applications. The components of this model include data loading, augmentation of data, networks build-ups, loss functions, and evaluating matrices that take advantage of medical image analysis. This model will allow researchers and medical practitioners to adopt a fast and efficient method to be applied in medical imaging.
Order Learning Using Partially Ordered Data via Chainization

We propose the chainization algorithm for effective order learning when only partially ordered data are available. First, we develop a binary comparator to predict missing ordering relations between instances. Then, by extending the Kahn’s algorithm, we form a chain representing a linear ordering of instances. We fine-tune the comparator over pseudo pairs, which are sampled from the chain, and then re-estimate the linear ordering alternately. As a result, we obtain a more reliable comparator and a more meaningful linear ordering. Experimental results show that the proposed algorithm yields excellent rank estimation performances under various weak supervision scenarios, including semi-supervised learning, domain adaptation, and bipartite cases. The source codes are available at https://github.com/seon92/Chainization .