Reviewerly/exHarmony huggingface dataset:
Optical image and SAR image registration based on improved SIFT-Like algorithm

Although optical image has high resolution, it is extremely susceptible to the impact of harsh environments which will cause the loss of spectral details. SAR image has strong penetrating power to vegetation, clouds and snow, but they will be disturbed by coherent speckle noise. In addition, the imaging mechanism of optical image and SAR image is different, and the gray information is vastly different, which may lead to the failure of the performance of the two image registrations. In this letter, we propose an improved SIFT-Like image registration algorithm. Different gradient operators are used to calculate the gradient of optical image and SAR image respectively, and the corresponding scale space is constructed, and then the feature descriptor is constructed. Experimental results on optical and SAR image pairs show that the proposed algorithm in this letter has a significant improvement in the correct matching rate and registration accuracy compared with other algorithms.
On the quantum complexity of integration of a function with unknown singularity

In this paper we study the quantum complexity of the integration of a function with an unknown singularity. We assume that the function has $r$ continuous derivatives, with the derivative of order $r$ being H\"older continuous with the exponent $\rho$ on the whole integration interval except the one singular point. We show that the $\ve$-complexity of this problem is of order $\ve^{-1/(r+\rho+1)}$. Since the classical deterministic complexity of this problem is $\ve^{-1/(r+\rho)}$, quantum computers give a speed-up for this problem for all values of parameters $r$ and $\rho$.
The Effects of Self-Efficacy on Students’ Reading Comprehension

Self-efficacy is concerned with people’s beliefs in their capabilities to do something including reading. Reading comprehension is a part of English study measuring understanding of English. The objectives of this research were (1) to investigate the students’ self-efficacy in reading comprehension (2) to investigate the students’ reading comprehension and (3) to investigate whether there are the effects of students’ self-efficacy on their reading comprehension. This research applied Ex Post Facto. The sample of 33 students was selected by using the cluster random sampling technique. Data collection used reading tests and questionnaires. The data were analyzed by using SPSS 23. The result showed that the percentage of the students’ self-efficacy was in the high category, and the students’ reading comprehension was in the good category. There was no effect of self-efficacy on reading comprehension in this research.
Using STEM Development Worksheets at Free Fall Montion Concept to Toward the Generic Science Skills

This study aims to analyze about the eligible, characteristics, and skill upgrade generic science for students at STEM (Science, Technology, Engineering, and Mathematics) worksheets. Evaluating student responses at STEM learning worksheets. This research is adopted by ADDIE method. It is consisting into of five stages such as analysis, design, development, implementation and evaluation. The results of study show percentage 87.80% excelent category at feasibility test. Second, the main characteristic of development student worksheet is based on STEM (Science, Technology, Engineering, and Mathematics) based learning to improve science generic skills. Third, the result of study is in science generic skills at MA Nurus Sunnah Semarang the average percentage is 80% good category with small-scale and large-scale is at Al Fattah Terboyo High School Semarang the average percentage is 85.35% excelent category at skill upgrade, it is used by students of class X MIPA SMA/MA as a supporting learning resource in the learning process. Based on text above, it can be conclude that the student are able to understand about the learning as well
A feature lightweight method in optimized acoustic encoder

This paper is based on end-to-end speech recognition based on convolutional neural network technology. The problem that convolutional neural networks are difficult to balance accuracy and model size is analysed and studied. A new acoustic encoder is proposed to optimize the extraction of speech features. The effectiveness of the proposed method is verified by an end-to-end speech recognition model. The new acoustic encoder focuses the Global context information with the local information obtained by convolution, Global context information is added. The convolution depth is increased while the convolution kernel size is reduced. It also reduces the amount of parameter calculation. At the same time, RNN-Transducer is used as the model architecture to jointly optimize acoustic features and text features, in order to obtain more effective context information. On the LibriSpeech dataset, the improved acoustic encoder achieves 9.83% word error rate and 4.61% sentence error rate with only 7M model. Compared with the baseline model of convolutional neural network, the word error rate is relatively reduced by 0.52%, and the sentence error rate is relatively reduced by 1.22%.
Path planning of mobile manipulator based on improved RRT algorithm

Aiming at the problems of the basic RRT algorithm in path planning of mobile manipulator, such as undirectivity, repeated search and node redundancy, an improved RRT algorithm was proposed. Firstly, the heuristic search idea is introduced, and the target points are extended to sampling points with certain probability to enhance the target orientation of the algorithm. Secondly, node rejection mechanism is introduced to cover the search area to avoid repeated search to generate new nodes. Finally, the path is pruned and smoothed. The experimental results show that the improved RRT algorithm has stronger search ability, greatly reduced path sampling points, reduced search time, and higher success rate.
Just Do It! Exploring the musician's (use of) bodily performance

In the 21st century an increasing number of composers and performers include elements in their work that activate the musicians’ voice and body in ways not related to playing an instrument. The musician’s moving, dancing, touching, speaking, singing, shouting, grimacing, sounding body opens an novel and still expanding palette of expression – but its use also requires new skills that are currently not an explicit part of higher musical education or standard musical performance practice. Just Do It! is an artistic research project that seeks to explore the possibilities of bodily performance in an expanded field of music and to detect, define and describe some of the skills and knowledge needed to realize them. The project’s practice-based methodology is rooted in the creation of five new solo pieces for accordion and body and a concerto for ensemble and accordionist written by James Black, Marcela Lucatelli, Philip Venables/Ted Hufmann, Jennifer Walshe, Louise Alenius and Andreas Borregaard.
Bio-Inspired Optimization Technique for Feature Selection to Enhance Accuracy of BC Detection

The common type of cancer that results in death across the world is Breast Cancer (BC). It is necessary to detect cancer in its earlier stages when it is more treatable and can be effectively managed The detection of BC can be carried out by employing a variety of different Machine Learning (ML) approaches in the diagnostic process. This study proposes a ML-based strategy for doing automated BC analysis. There are several steps in tumor detection, and feature extraction (FE) is one of them. The tumor condition's existence in an image can be determined using the powerful Gray Level Co-occurrence Matrix (GLCM) feature descriptor identification approach, in addition to the Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) Feature Selection (FS) techniques are employed Techniques from the realm of ML, such as Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) algorithm, are used throughout the data training and testing phases for tumor classification. The outcome of both optimized FS techniques is given to the ML models for identifying BC. From the experimental result, it is identified that the ACO with SVM gives greater accuracy of 97.4% than all other techniques.
Skills of the 21 Century and Education 4.0: the curriculum of the discipline – 011 Educational, pedagogical sciences

The academic discipline "Skills of the 21 Century and Education 4.0" is aimed at forming a systematic view of post-graduate students about Education 4.0 and readiness to use its trends in their own professional activities; mastering by post-graduate students the mechanisms for developing skills of the 21 century in the student pedagogical community, in particular in the context of the implementation of Education 4.0. The content of the academic discipline "Skills of the 21 Century and Education 4.0" consists of three interrelated blocks: lecture (study of the didactic and methodological foundations of the concept of Education 4.0 and questions of formation and development of students' skills of the 21 century), seminar sessions (consideration of these issues in the context of the Ukrainian education system) and independent work (deepening the knowledge of educational applicants in the problems of the discipline).
Analisis Kemampuan Mengapresiasi Puisi Aku Karya Chairil Anwar pada Siswa Kelas VIII Sekolah Menengah Pertama

Abstract: An Analysis of The Ability to Appreciate My Poems By Chairil Anwar pthere are Class VIII Studentsof Junior High School. This study aims to describe the ability to appreciate the poetry Aku by Chairil Anwar in grade VIII junior high school students. Approach to qualitative research with caulitative descriptive research penis. The pupulation in this study was 288 students of SMPN 36 Makassar City spread across 9 classes. The sample was determined to be 32 students, of which there were 12 women and 15 men. Teknik data collection in the form of tests. As for the data analysis techniques used in this study, namely quantitative descriptive analytical techniques. Based on the results of the study, it can be seen that the ability of students to appreciate poetry was 26 people or 81% who obtained a score of 75 and above, with an average score of 85.28% (Very good) and in terms of all aspects of physical elements there were 23 people or 71% who obtained a score of 75 and above with an average score of 81.18% (Very good). Thus the ability to appreciate poetry in class VIII Sstudents of Junior High School ecolah is sufficient.
Secure Auditing and Deduplicating Data in Cloud

Abstract: With the widespread adoption of cloud computing, ensuring the security and integrity of data stored in the cloud has become a critical concern. This paper presents a novel approach for achieving secure auditing and deduplication of data in cloud environments. The proposed method addresses the challenges of data integrity verification and duplicate elimination while preserving the privacy of the stored data. By leveraging cryptographic techniques, including homomorphic encryption and Merkle hash trees, the system enables efficient and privacy- preserving auditing of data integrity. Additionally, a deduplication mechanism is integrated to eliminate redundant copies of data, reducing storage costs and improving efficiency. The proposed solution provides end-to-end security guarantees, ensuring that data remains confidential, unaltered, and readily available for authorized users. Extensive experimental evaluations demonstrate the effectiveness and efficiency of the proposed approach, making it a promising solution for secure data management in cloud environments
Implicit aspect based sentiment analysis for restaurant review using LDA topic modeling and ensemble approach

The internet is a global web that links millions of people, and therefore, it has become the hub of modern-day social life [1].It is widely appreciated for providing an open platform where individuals can connect and express their opinions on anything and everything in their environment.However, it can be difficult for an individual to trawl through each online resource.Therefore, approaches for evaluating this collection of documents are essential.
Microrobot Path Planning Based on the Multi-Module DWA Method in Crossing Dense Obstacle Scenario

A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.
Landslide Monitoring and Detection

The project titled "Landslide Monitoring and Detection" aims to detect and monitor potential landslides using a combination of sensors and machine learning algorithms. The sensors used in this project are MQ, rainfall, vibration, soil moisture, DHT, and water flow sensors. These sensors collect data that is sent to an Arduino Uno, which then sends it to a Node MCU. The Node MCU sends the data to a Java server using NetBeans IDE, which stores the data in a text file. The machine learning algorithms used in this project are KNN, random forest, XGBoost, and decision tree. These algorithms are applied to the data in the text file, with a given threshold value based on training data, to determine which algorithm provides the best accuracy. The algorithm with the highest accuracy is then used as the result for landslide detection and monitoring. The use of multiple sensors and machine learning algorithms improves the accuracy of landslide detection and monitoring. The sensors provide real-time data that is continuously monitored and analyzed by machine learning algorithms. The results of this project can aid in preventing and mitigating the effects of landslides, which can cause significant damage to infrastructure and loss of life. This project has the potential to be implemented in areas prone to landslides, providing early warning systems and improving disaster management strategies.
A Multitask Cross-Lingual Summary Method Based on ABO Mechanism

Recent cross-lingual summarization research has pursued the use of a unified end-to-end model which has demonstrated a certain level of improvement in performance and effectiveness, but this approach stitches together multiple tasks and makes the computation more complex. Less work has focused on alignment relationships across languages, which has led to persistent problems of summary misordering and loss of key information. For this reason, we first simplify the multitasking by converting the translation task into an equal proportion of cross-lingual summary tasks so that the model can perform only cross-lingual summary tasks when generating cross-lingual summaries. In addition, we splice monolingual and cross-lingual summary sequences as an input so that the model can fully learn the core content of the corpus. Then, we propose a reinforced regularization method based on the model to improve its robustness, and build a targeted ABO mechanism to enhance the semantic relationship alignment and key information retention of the cross-lingual summaries. Ablation experiments are conducted on three datasets of different orders of magnitude to demonstrate the effective enhancement of the model by the optimization approach; they outperform the mainstream approaches on the cross-lingual summarization task and the monolingual summarization task for the full dataset. Finally, we validate the model’s capabilities on a cross-lingual summary dataset of professional domains, and the results demonstrate its superior performance and ability to improve cross-lingual sequencing.
How the pharma industry can reinvent vulnerability management

Organisations continue to be breached through the exploitation of known vulnerabilities for which patches were available. Keeping track of the threats is not easy, but this is an area where companies need to up their game, especially in the pharmaceutical sector.
Exploring Pedagogical Approach in EFL Courses Based on Bilingual Parallel Corpus

This paper is to examine the feasibility of bilingual parallel corpus as a data-driven approach targeting to comprehensively enhance English competence of EFL (English as a foreign language) learners. In particular, corpus turns out to be functional of assisting EFL learners in acquiring high-frequency words and the collocation and colligation, indicating that corpus can be considered to be optional in pedagogy, as well. Bilingual parallel corpus, more importantly, provides learners with an ideal tool to focus on the translation techniques based on the function of comparing the same words or similar conceptions in corpora of two languages, providing authentic texts to expound translation theories, terminologies and techniques. In addition, corpus implies the use of improving learners comprehension of original texts, further the reading proficiency. It also helps to extend the scope of reading based on the large quantity of authentic materials.
Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on comparing a Deep-RL technique based on the Deep Q-Network (DQN) algorithm with a second one based on the Double Deep Q-Network (DDQN) algorithm. We use 24 laser measurement samples and the relative position and angle of the agent to the target as information for our agents, which provide the actions as velocities for our robot. By using a low-dimensional sensing structure of learning, we show that it is possible to train an agent to perform navigation-related tasks and obstacle avoidance without using complex sensing information. The proposed methodology was successfully used in three distinct simulated environments. Overall, it was shown that Double Deep structures further enhance the problem for the navigation of mobile robots when compared to the ones with simple Q structures.
Innovation Practices Track: Testability and Dependability of AI Hardware and Autonomous Systems

Testability and dependability (e.g., safety) of AI hardware (e.g., GPU, AI accelerators) and AI-based autonomous systems has been emerging as an important R&D topic in order to address increasing resiliency. In this session we will invite the industry experts to discuss the various aspects of this new field.
Survey on Diverse Image Inpainting using Diffusion Models

Image inpainting (or Image completion) is the process of reconstructing lost or corrupted parts of images. It can be used to fill in missing or corrupted parts of an image, such as removing an object from an image, removing image noise, or restoring an old photograph. The goal is to generate new pixels that are consistent with the surrounding area and make the image look as if the missing or corrupted parts were never there. Image inpainting can be done using various techniques such as texture synthesis, patch-based methods, and deep learning models. Deep learning-based Image inpainting typically involves using a neural network to generate new pixels to fill the missing parts of an image. Different network architectures can be used for this purpose, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks(GANs), Transformer-based models, Flow-based models, and Diffusion models. In this work, we focus on Image Inpainting using Diffusion models whose task is to provide a set of diverse and realistic inpainted images for a given deteriorated image. Diffusion models use a diffusion process to fill in missing pixels, where the missing pixels are iteratively updated based on the surrounding context. The diffusion process is controlled by a set of parameters, which can be learned from data. The advantage of diffusion models is that they can handle large missing regions, while still producing visually plausible results. The challenges involved in the training of these models will be discussed.
An Improved Path Planning Algorithm Based on RRT*

RRT* algorithm has some shortcomings, such as slow convergence speed and long path length. To solve these problems, an improved algorithm based on RRT* is proposed. By optimizing the sampling part of RRT* algorithm, the algorithm guides the random tree to grow towards the target point, which greatly improves the planning speed. Then, a path optimization strategy is proposed to reduce the redundant inflection points in the path and reduce the cost of the path. Finally, the effectiveness of the algorithm is verified by simulation experiments.
Research on Computer Information Security Technology Based on DES Data Encryption Algorithm

With the continuous development of computer technology, people pay more and more attention to data information security. Traditional cryptographic algorithms can no longer meet the needs of computer communication. Based on DES encryption algorithm as the core idea to design a highly confined conditional memory (MAC), which is used to prevent unauthorized disclosure of important files and sensitive content, it uses ciphertext computing method for secret communication and combines it with ordinary channel to achieve protective measures for data information security. In this paper, a computer information security technology based on ciphertext computing is designed using DES encryption algorithm, which mainly uses triple DES (3DES) encryption algorithm to encrypt the original plaintext data to protect important files and sensitive information so that they can be used for storage and transmission after encryption, which can ensure the integrity of data.