Reviewerly/exHarmony huggingface dataset:
Strategi Bauran Pemasaran Syariah (Studi Kasus Sayuran Hidroponik Hani Farm Samarinda)

The 7P marketing mix strategy is a combination of product, price, promotion, place, people, physical evidence, and process. In each mix has a role to succeed in marketing achievements. In every important marketing mix strategy implemented based on sharia marketing. Hani Farm was founded in 2019 and is a producer of hydroponic vegetables that are cultivated and managed by Islamic boarding schools where students play an active role. Hani Farm was also identified as using a marketing mix strategy, with increased turnover even though there was a decrease when PPKM occurred so it is important to study the marketing mix strategy of Hani Farm Samarinda from a sharia marketing perspective.&#x0D; The research method is qualitative research. Data collection techniques through observation, interviews, and documentation. Data analysis techniques are data reduction, data presentation, then drawing conclusions. Based on the results of Hani Farm Samarinda's marketing mix research from a sharia marketing perspective; Hani Farm's products are vegetables and vegetable delivery services. The price of vegetable products is determined based on market prices and service product tariffs using the average rate in one area of ​​Samarinda City. Promotion is done by sales promotion, publicity, and personal selling. The place is located at the KH Harun Nafsi Islamic boarding school which has easy access to consumers. Human resources are equipped with knowledge and coaching and are supervised by field coordinators at work. The process is carried out following standard rules and the ordering process is made easy to understand and work on. In the marketing mix strategy, Hani Farm has carried it out with sharia marketing values, namely shiddiq, amanah, fathanahtabligh and. As well as the principles of sharia marketing, namely strategy, needs, halal, welfare, and mutual agreement. And the characteristics of sharia marketing are theistic, ethical, realistic, and humanistic.
Letter From the President [President’s Message]

Hello and nice to see you again! My name is Mariko Burgin, and I am the IEEE Geoscience and Remote Sensing Society (GRSS) President. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.
HDSRNet: A Simple Segmentation-free Method for Unconstrained Handwritten Digit String Recognition

Handwritten digit string recognition (HDSR) is one of the most challenging tasks in the area of off-line handwritten optical character recognition. The main challenge comes from the segmentation errors caused by several factors such as touching, breaking, complex background, and unknown length of string. In this paper, we propose a new segmentation-free HDSR system using a one-stage object detector based on a convolutional neural network (CNN). This is a simple, efficient, and lightweight algorithm. This work describes the following: (1) the problem of HDSR can be solved successfully by CNN-based object detectors which determine both locations and classifications for objects and it makes us completely free from the complicated segmentation; (2) the network for HDSR does not need to be too heavy and its depth can be determined suitably by the size of receptive field; (3) it is efficient to use single-scale and single-anchor method rather than multi-scale and multi-anchor one, in the detector for HDSR. The experiments conducted on the benchmark datasets (CVL, ORAND-CAR) show that the proposed method achieves excellent performance compared to other state-of-the-art methods.
Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries

A binary’s behavior is greatly influenced by how the compiler builds its source code. Although most compiler configuration details are abstracted away during compilation, recovering them is useful for reverse engineering and program comprehension tasks on unknown binaries, such as code similarity detection. We observe that previous work has thoroughly explored this on x86-64 binaries. However, there has been limited investigation of ARM binaries, which are increasingly prevalent.In this paper, we extend previous work with a shallow-learning model that efficiently and accurately recovers compiler configuration properties for ARM binaries. We apply opcode and register-derived features, that have previously been effective on x86-64 binaries, to ARM binaries. Furthermore, we compare this work with Pizzolotto et al., a recent architecture-agnostic model that uses deep learning, whose dataset and code are available.We observe that the lightweight features are reproducible on ARM binaries. We achieve over 99% accuracy, on par with state-of-the-art deep learning approaches, while achieving a 583-times speedup during training and 3,826-times speedup during inference. Finally, we also discuss findings of overfitting that was previously undetected in prior work.
E’Teacher Digitalization Supports Graduate Competence in Digital Economy

This article discusses the features of the education system, information technology, the concept of the digital economy, and the relationship between the digitalization of the economy and the quality of education.The goal is to increase graduates' competence in using digital economy technology and to learn the role of education in developing the digital economy.The methodology assesses educational components and innovations on the development of the digital economy, improves learning programs, and describes the characteristics of teacher competence in the application of the education system-design methodology, improving e-learning resources into a more productive process, and perspective of their use.The analysis results that it is necessary to improve the digital adaptability of the education system to socioeconomic conditions in the pedagogical approach.Findings: Potential intensity depends not just on the inspiration that drives the growing experience in increasing the competitive advantage of education and plays a role in teachers' sustainable development, professional skills, and competence.Originality: Significance of the prospects for realizing e-education in economic, geological, and social disparity conditions.The end is that the components of e-schooling, satisfactorily coordinated into the preparation framework, can give expanded intensity and variation of experts inside the digital economic system.
Simulation Tools for Urban Search and Rescue Robotics

Real world experiments are critical for validating performance of new concepts and algorithms in robotics field.Yet, experiments tend to be too expensive in terms of time and resources of a research team.Moreover, it is not feasible to conduct thousands of complex experiments with a physical robot in a real environment.To check new ideas, preliminary evaluate new algorithms and interaction protocols, on first stages of a research project it is reasonable to start within a simulation.To produce relevant results, a simulator should provide adequate models of robots and environments with realistic physical properties.This paper presents an overview of our experience in using robot operating system (ROS) with Gazebo and Webots simulators for urban search and rescue robotics projects and considers constructing new models of mobile robots and complicated environments, algorithm validation and comparative analysis.
Multi Chaotic Flow Direction Algorithm for Feature Selection

Feature selection is a crucial pre-processing step used to remove redundant information from original datasets while preserving the accuracy and processing time of classifier.The feasibility of using metaheuristic search algorithms (MSAs) such as Flow Directional Algorithm (FDA) to solve feature selection problems is one of the active research topics.Similar with other MSAs, FDA also employs conventional initialization scheme that generates initial solutions in random basis.The absence of intelligent mechanisms in conventional initialize scheme tends to generate initial populations in local optima, hence compromising the performance of algorithm to handle datasets with complex features.In this paper, a modified algorithm known as Multi Chaotic Flow Directional Algorithm (MCFDA) is proposed to solve feature selection problems with enhanced performances by leveraging the strengths of multiple chaotic maps for population initialization.A total of 12 datasets from UCI Machine Learning Repository are selected for performance evaluation of MCFDA and another four peer algorithms to solve feature selection problems.The proposed MCFFA is revealed to deliver best performances by solving 7 out of 12 datasets with the best mean classification accuracy and 6 out of 12 datasets with the least numbers of selected features.
Human Detection with Uprisen Angle of a Camera for the Service Robot

In order to improve the intelligent service robot, the visual perception is a crucial.This paper presents the human detection for a service robot.The camera is installed at the robot by uprisen angle around 30 degree.Two feasible algorithms for the real-time detection between Haar cascade and Single Shot Detector (SSD) algorithm are compared.This research collects the training data as 1,000 images by the uprisen angle and the different views of the human, within 60 cm of user range.between a camera and human.The result shows that our proposed model of SSD method is higher performance than another by 0.933 of the average of IoU.Therefore, the proposed method is suitable to apply for the service robot.
A Fundamental Study on Car Sickness Using Data Science

Car sickness often occurs with dizziness and discomfort accompanied by vomiting and headaches lasting several days in severely affected person.Car sickness has been studied from various standpoints on countermeasures and onset mechanisms, but a general measure has not been established yet.In this study, all dataset of the motion that occurs in the car and the head of the person sitting and/or driving in the car are collected based on data science.By validating the data, the characteristics of person who has experience of car sickness are tested.This study proposes a new measure aiming at development of motion sickness countermeasures that do not depend on car performance and find that the quick rotational motion of the head generated in the car could be considered as one of the factors that cause car sickness.
Super Resolution Reconstruction Model Based on Attention Mechanism and Generative Adversarial Network

The purpose of image super resolution reconstruction is to recover high frequency details from low resolution images containing little information, so as to improve the visual effect of images.Based on the traditional SRGAN algorithm, this paper by importing the residual network structure and combining the channel attention mechanism constructs the basic network module, and this method is named CA-SRGAN.The proposed CA-SRGAN replace the base block of SRGAN with a residual dense block based on the channel attention mechanism.Test in Set5, Set14 and BSD100 data sets, the results of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of CA-SRGAN algorithm are better than those of the traditional SRGAN algorithm, and the details of the reconstructed image are clearer.The whole show better robustness and comprehensive performance.
A Study of Experiential Learning Activities using Model Materials for the Kicking Motion

In order to develop human resources for the realization of Society 5.0, it is necessary for students to acquire the ability to solve problems that transcend the frameworks of subjects.For this purpose, cross-curricular learning is attracting attention.In this study, we examine teaching materials that connect the content of measurement and control in technology education with studies in science, health and physical education.A simple mechanism model teaching material is proposed to allow students to learn about ball operation through experiential learning by conducting motion experiments on a model that simulates the kicking motion.
A Research on Image Defogging Algorithm Based on Image Enhancement

In order to solve the problem of low contrast image and loss of image details in the foggy weather, the image defogging technique is used to remove the noise in the image and improve the image contrast, so as to recover a clear and fog-free image.In this paper, we mainly introduce three image defogging algorithms: global histogram equalization, local histogram equalisation and the Retinex algorithm.The advantages and shortcomings of each algorithm are summarised through the study of the principles of each algorithm and the comparative analysis of the experimental result.
Making High Precision Single Balance in Active Learning Seminar for Hiroshima Univ. Monozukuri Junior Doctor Special Educational Program

New learning materials which can cultivate competency of young people are expected.STEM/STEAM educational materials in technology field are the candidate of them, so that we proposed Active Learning seminar of "Making High Precision Single Balance".The seminar took place in "Hiroshima Univ.Monozukuri Junior Doctor" with around 40 participants of young (11 -15 in age) students.We prepared a number of pre-fabricated parts of deferent sizes, giving them to young participants teams (about 5 people each) to assemble single balance, putting their own ideas in the design by selecting favorite parts by themselves.Majority of the students could understand key concept of the balance (rule of moment) and assemble single balance with accuracy of milligrams.We believe that the seminar acts as a competency nurturing content.
A Rule-Based Procedure for Graph Query Solving

We consider a core language for graph queries. These queries, which may transform graphs to graphs, are seen as formulas to be solved with respect to graph databases. For this purpose, we first define a graph query algebra where some operations over graphs and sets of graph homomorphisms are specified. Then, the notion of pattern is introduced to represent a kind of recursively defined formula over graphs. The syntax and formal semantics of patterns are provided. Afterwards, we propose a new sound and complete procedure to solve patterns. This procedure, which is based on a set of rewriting rules, is terminating and develops only one needed derivation per pattern to be solved. Our procedure is generic in the sense that it can be adapted to different kinds of graph queries provided that the notions of graph and graph homomorphism are well defined.
Semi-supervised instance object detection method based on SVD co-training

Abstract： Detecting indoor instance objects is useful for various applications.Traditional deep-learning methods require a large number of labeled samples for network training， making them time-consuming and labor-intensive.To address this problem， SVD-RCNN-a semi-supervised instance object detection net• work based on singular value decomposition (SVD) and co-training-is proposed.First， key samples are selected for manual labeling to pre-train SVD-RCNN， to ensure that it acquires more prior knowledge.Second， a convergence， decomposition， and finetuning strategy based on SVD is used to obtain two detec• tors with strong independence in SVD-RCNN to satisfy the requirements of co-training.Finally， an adap
STDPboost: A Self-Training Method Based on Density Peaks and Improved Adaboost for Semi-Supervised Classification

The self-training methods have been praised by extensive research in semi-supervised classification. Mislabeling is the main challenge in self-training methods. Multiple variations of self-training methods are recently proposed against mislabeling from the following one of two aspects: a) using heuristic rules to find high-confidence unlabeled samples that can easily be predicted correctly in each iteration; b) enhancing prediction performance by employing ensemble classifiers composed of multiple weak classifiers. Yet, they still suffer from the following issues: a); most strategies for finding high-confidence unlabeled samples heavily rely on parameters; b) almost all employed ensemble classifiers originally designed for supervised classifiers and may not be suitable for semi-supervised classification due to the limited number and unrepresented distribution of the initial labeled data; c) few can overcome mislabeling from the above two aspects at the same time. To advance the state of the art, a new self-training method based on density peaks clustering and improved Adaboost is presented and named as STDPboost. In the iterative self-taught process, a new density peaks clustering-based strategy is proposed to find high-confidence unlabeled samples and a new ensemble classifier named AdaboostSEMI and more suitable for semi-supervised classification is proposed to predict high-confidence unlabeled samples, which overcomes mislabeling and the mentioned shortcomings of existing self-training methods. Intensive experiments on benchmark data sets have proven that STDPboost outperforms 7 state-of-the-art self-training methods in average classification accuracy of KNN classifier and CART classifier with the percentages of the initial labeled data from 10% to 50% due to further alleviating mislabeling.
Towards Reliable Memory Management for Python Native Extensions

Many programming languages provide a C interface as a foreign function interface (FFI) for C developers to access the language, the Python language being one of these languages. Over the years, the Python C API has grown to be a challenge for the evolution of the Python ecosystem. In this paper, we implement a new Python FFI, we call CyStck, by combining a stack and light-weight handles, to support efficient garbage collection (GC) in Python native extensions. CyStck introduces execution time overhead while copying the fewest bytes for all benchmarks across the CPython boundary compared to the CPython API and HPy respectively. We also implemented a tool to automate the migration of extensions from the CPython C API to CyStck using advanced pattern matching and static analysis, with a success rate as high as 90% in some workloads.
Neural detection of spheres in images for lighting calibration

Accurate detection of spheres in images holds significant value for photometric 3D vision techniques such as photometric stereo.1 These techniques require precise calibration of lighting, and sphere detection can help in the calibration process. Our proposed approach involves training neural networks to automatically detect spheres of three different material classes: matte, shiny and chrome. We get fast and accurate segmentation of spheres in images, outperforming manual segmentation in terms of speed while maintaining comparable accuracy.
A data science and machine learning approach to continuous analysis of Shakespeare's plays

The availability of quantitative text analysis methods has provided new ways of analyzing literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download.
THE IMPLEMENTATION OF LITERATURE IN EXTRACURRICULAR “STUDENT CONVERSATION CLUB”

Literature is divided into two parts, namely fiction and non-fiction. Literature is not only studied within the scope of school, but there are those who study it outside the school class. One of them is extracurricular activities. The literature referred to in this extracurricular activity is fictional literature such as choirs, songs and plays. The results of previous studies state that literature such as music and poetry can improve the quality of a person's language, and not only improve language, literature can also provide space for us to express. The purpose and reason for the researcher to raise and examine this title is to provide an overview of how literature is applied in the extracurricular student conversation club and to find out what problems hinder the application of literature in the extracurricular. This research uses descriptive qualitative method. The objects of this research are coaches and extracurricular members of the 2022-2023 student conversation club with details of 1 coach and 3 members. The results of the study stated that the students' conversation club extracurricular activities had a good impact on improving students' language skills, especially in English.
Impact of ICT Integration on Students’ Performance in Mathematics in Public Upper Secondary Schools in Huye District, Rwanda

This study aimed to analyze ‘‘Impact of ICT integration on students’ performance in Mathematics in public upper secondary schools in Huye district, Rwanda. The study adopted a correlational research design. The target population size for this study was considered five schools consisting of 392 composed of 384 students and 8 mathematics teachers. A sample of 5 Mathematics teachers and 256 students from the five schools were selected for the study. The instrument used was a set of questionnaires; one for the Mathematics teacher and one for the students. By Statistical Package for Social Sciences (SPSS) version 25, descriptive and inferential statistics were used to answer the research questions and the Chi-Square analysis test was applied in testing the hypothesis formulated at a 0.05 level of significance. The outcome of the data collected revealed that the students taught with ICT tools performed better in Mathematics than their colleagues taught tools. The recommendations for higher performance of students in Mathematics were summarized in this study. These include: supporting all public upper secondary schools with sufficient ICT tools, to providing more training to Mathematics teachers on the use of ICT tools in teaching-learning Mathematics.
Pemanfaatan Metode Pembelajaran Bahasa Inggris melalui Media Lagu-lagu untuk Siswa

English is a language that the whole world has started to learn. Thus the language is also called an international language. In addition, this language has become one of the subjects taught to students in schools, institutions up to universities. Song media will motivate and increase students' learning interest in order to create a pleasant learning atmosphere so that students can understand the material easily. The purpose of this research is to improve English learning methods through song media. The results of this study are that learning English through songs can be a powerful means of improving students' memory and ability to speak English. Song media also influences students' pronunciation and listening skills. this can be proven by the ease with which songs are followed and remembered by students so that the language will stick with itself.