Reviewerly/exHarmony huggingface dataset:
Defect and deformation comprehensive measurement method based on dual-wavelength speckles

Composite materials are widely used in aviation, aerospace, and other fields. Poor service conditions, insufficient stiffness or strength of materials and other problems can cause deformation and defects of composite materials, affecting their service life and safety. Optical detection methods can measure composite materials' surface deformation and internal defects separately. However, there is a lack of comprehensive measurement methods for defects and deformation in these optical detection methods. It causes the problems of complicated detection steps, inefficiency in engineering, and is not conducive to in-depth analysis of the detection results. This paper proposes a comprehensive defect and deformation measurement method based on dual-wavelength speckles, which is beneficial for solving the above problems. The measurement system includes four parts: the control module, the speckle binocular vision module for 3D morphology measurement, the speckle interferometry module for internal defect detection, and the data processing module. Firstly, the system should perform trinocular calibration. Then, the control module controls the speckle interferometry module and the speckle binocular vision module to emit lasers with different wavelengths. Speckles of different colors and sizes are formed on the surface of the composite material specimen. The cameras corresponding to the two modules are controlled to capture speckle images. The data processing module processes the speckle images separately to obtain defect and morphology information. It then uses the information fusion algorithm to integrate the 2D defect and 3D morphology information to complete the measurement. Composite material specimens with internal defects of different sizes and shapes were measured. Finally, the comprehensive detection of both internal defects and 3D morphology of composite materials was realized with this method.
Patch Clustering for Representation of Histopathology Images

Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between ($50\% - 90\%$), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small ($10\%-50\%$) portion of patches. The results showed that LBP features can outperform deep features. By selecting only $50\%$ of all patches after SOM clustering and GMM patch selection, we received $65\%$ accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was $69\%$.
Web-Based Clothing Sales Information System at Target Factory Outlet

Website “Sistem Informasi Penjualan Pakaian Berbasis Web Pada Target Factory Outlet” aimed at helping Target Factory Outlet to expand market share and increase the credibility of Target Factory Outlet itself. The results obtained are websites with the domain name ‘targetfo.store’ which have been hosted can be accessed by website visitors. The making of the website uses System Development Life Cycle (SDLC) method which is carried out in several phases namely analysis, design, implementation, installation, testing, and maintenance. This website uses several design tools, namely Flowchart, Data Flow Diagram, Entity Relationship Diagram, Context Diagram and Relationship Between Tables. Testing this website uses Black Box Testing and User Acceptance Test(UAT) method. This website is created using HTML and PHP programming languages and uses MySQL as a Database Management Software (DBMS). The website ‘targetfo.store’ has the advantage of having goods import reports, sales reports and financial reports that can be seen by Target Factory Outlet owner.
Specification Decision Support System at The Faculty Of Engineering Muhammadiyah University Bengkulu Using Method Simple Additve Weighting (SAW)

The title of this research is a specialization decision support system at the Faculty of Engineering, University of Muhammadiyah Bengkulu using the Simple Additive Weighting (SAW) method. The main problem is how to make an information system in decision making at the Faculty of Engineering, University of Muhammadiyah Bengkulu. This decision support system uses the SAW method. The SAW method was chosen because it determines the weight for each art or criterion, then proceeds with a ranking process that will select the best alternative from a number of existing alternatives. Based on the SAW discussion carried out with data on the value of a student named Andi Prasetio NPM 19552051, the ranking results for Robotics specialization with a value of 56.1 Multimedia specialization with a value of 45.2 Network Specialization with a value of 45.2 and the largest value is Programming specialization with a value of 56 ,6. thus the chosen alternative is Programming specialization. It is hoped that the existence of this specialization decision support system can help provide recommendations for students of the Faculty of Engineering, University of Muhammadiyah Bengkulu in determining the specialization to be taken.
Machine Learning Strategies for Medical Assessment

Subfield of AI, is going to change the healthcare industry forever. However, it is not yet considered part of standard of care, especially when it comes to the treatment of specific patients. Whether or not data-driven methods are being used to support clinical making a Call (CDS). To date, there has been no comprehensive analysis of how research in machine learning and other data-driven techniques might effectively contribute to clinical care and what kinds of support they can bring to doctors. In this study, we investigate the potential contributions to clinical decision support systems of two data-driven fields: machine learning and data visualization. Here, we survey the research on three distinct CDS and how heuristic knowledge, machine learning, and visualization are now being used to analyse and improve them. Predictive modelling for alerts has been the subject of extensive study, although this technology is not yet integrated into CDS systems. Interactive visualizations and machine learning inferences are gaining popularity as a means to organize and review patient data, however these methods are still in the prototype stage and have not been implemented. We still lack CDS systems that could take use of prescriptive machine learning (e.g., individualized therapy suggestions). Possible explanations for the slow adoption of data-driven approaches in CDS are offered, along with suggestions for future study in this area. Clinical decision assistance; Visual analytics; Machine Learning.
Aspek Kepribadian Tokoh Pada Novel Si Anak Kuat Karya Tere Liye Dan Implikasinya Terhadap Pembelajaran Bahasa Indonesia DI SMA

The purpose of this research is to describe the personality aspects of the characters in Tere Liye's The Strong Child novel. Describe the implications of research results in learning Indonesian in high school. This research use desciptive qualitative approach. The source of the data in this study is in the form of sentences in the novel Si Anak Strong by Tere Liye. The form of data used in this research is the structure of the character's personality and the dynamics of the character's personality. The data collection technique used in this study is a reading and note taking technique. The data analysis technique used is descriptive qualitative. The results of this study can be found in the form of personality structure and personality dynamics. (1) In the form of personality structure there are 22 data. (2) In the form of personality dynamics there are 25 data. The implications of the research results for Indonesian language learning are applied to basic competencies X KD 3.8 interpreting the author's view of the novel's life. Class Xll high school level semester 2.
From Civic Space to the Internet: Legislating rights to peaceful assembly during pandemic

With the social distancing measures implemented during Pandemic Covid 19, many peaceful assemblies have attempted to restructure, equip, and empower themselves creatively for online gathering. This incident emphasises the importance of digital tools, such as the Internet and ICTs, in exercising online rights to peaceful assembly and association. Given the above, this article attempts to deliberate on transforming rights to the peaceful assembly from offline to online and the legal challenges attached. The authors argue that the right to peaceful assembly and association is vital to ensuring enhanced transparency and accountability in implementing human rightsÂ policies within countries.
Exploration on Modular Curriculum Construction of Economic and Management Majors in the Digital Age

Under the background of digital economy, digital literacy has become one of the basic qualities that economic and management talents should have. Digital thinking and digital skills should be integrated into the construction of modular courses of economic and management professional groups. Focusing on the new business philosophy of "soft business and hard technology", the comprehensive education mode of "post-course-competition-certificate" and the sustainable development of multi-specialty "collaborative symbiosis", this paper puts forward the construction system of "three-dimension and three-order" modular courses for economic and management majors. Three dimensions include management knowledge, digital skills and professional literacy. Three levels include primary general digital ability, middle professional digital ability and advanced innovative digital ability. Finally, this paper proposes the implementation path: integrating digital tools into teaching resources, building teaching team across borders, deepening the integration of industry and education, establishing a quality system.
Exploring the Cultivation Model of Innovative Talents for Undergraduate Students Based on the Evaluation of Innovative Ability

Since the 18th CPC National Congress, China has implemented an innovation-driven development strategy to meet the urgent need for innovative and entrepreneurial talents in society. Deepening a series of innovation and entrepreneurship education reforms in colleges and universities is a critical measure to implement the innovation-driven development strategy more quickly. Meanwhile, cultivating high-quality specialists with international vision, innovative consciousness, innovative spirit, innovative ability, and practical ability is an important guarantee for implementing the national innovation development strategy. Thus, this paper first summarizes the main practices of cultivating innovative talents in economics majors and their effectiveness systematically and analyzes the main problems encountered in the process of cultivating innovative talents in economics majors. Then based on 498 questionnaires, it also explores the influencing factors of the innovation ability of undergraduate students in economics majors. The results show that the basic skills about studies are the main factor that affects the innovation ability of undergraduate students in economics majors, the innovation spirit is a secondary factor, and the influences of innovation skills and comprehensive quality are the weakest. Finally, the paths to promote the cultivation of innovative talents in economics majors are proposed in terms of optimizing talent cultivation goals, adjusting the curriculum structure system, strengthening practical teaching, playing the role of undergraduate mentorship, and relying on the construction of undergraduate research and innovation teams.
Freedom of Expression and the Need for Digital Literacy in Today’s Digital Age

This paper discusses the freedom of expression that must be supported by increasing literacy to face the digital era.Freedom of expression is an individual right guaranteed by the constitution.Differences in literacy levels will affect differences in individual control in the process of interpreting existing information, especially information circulating on social media.Access to information and social support may increase.Several cases were found in the realm of freedom of expression such as misuse of social media.This article discusses some of the new reorientations of learning that are considered to affect vision, responsibility, social sensitivity and logical ability, and honesty.This paper recommends the role of the government, the community, and parents to ensure freedom of expression and fulfill literacy needs so that all people are ready to face the digital era.
SBCDetector: a hybrid approach to detect second-order similarity or change

Software Configuration Management (SCM) involves tracking similarities/changes during software evolution. Efficient comparison for tracking requires two perspectives—Granularity: comparing the entities at file level, class level, and method level. Second, Robustness should be prominent to detect renaming and shifting that occur as a part of restructuring. Even GIT repository, which is widely used, allows such comparison with renaming and shifting details but is limited to file level only, along with its own limitation of default similarity criteria of above 50%. In this study, the proposed technique named SBCDetector detects similarity/change status with both perspectives that is lacking in the existing literature. Result shows that one-fourth of entities have been found renamed/shifted at three granularities for eight subject systems improving tracking, understandability, and onboarding. Hybrid technique involving fuzzy logic derives classification model with .99 f-score to detect first- and second-order similarity/change.
A hierarchical optimal path planning algorithm for collaborative air-ground inspection

Heterogeneous collaborative systems consisting of unmanned aerial vehicles/ground vehicles (UAV/UGV) have great potential for application in many fields. Among other things, UAV can provide a wide field of view and photography, while UGV can provide sufficient load capacity and act as relay stations. Rational planning of UAV/UGV paths based on actual tasks is an important issue to be addressed. This study proposes a hybrid unmanned system based on coordinated cooperation between UAV /UGV. In this system, the UGV is used as a mobile charging station to solve the UAV energy constraint problem. The UAV is used as a robotic system responsible for reaching the target point and solving the UGV functional constraint problem. We propose a path planning method for the study of two robots working together. A hierarchical path optimization strategy that combines classical and modern evolutionary methods. The method takes full advantage of the strengths of individual methods and algorithms in path selection and planning. Simulation results show that the collaborative UAV and UGV systems can improve the efficiency of performing tasks and that the hierarchical optimization strategy can improve the solution quality and stability of path planning.
Digitally Mapping Student Potential: SIMPOSIDU (Sistem Informasi Potensi Individu) For Guidance And Counseling Teachers

Teachers of guidance and counseling play a significant role in the process ofevaluating students' potential. Basically, assessing students' potential plays asignificant part in assisting them in achieving their full potential in four areas:personal, social, academic, and professional. In actuality, there are still a numberof issues with assessing students' potential, one of which is the teacher's stilllimited capacity to record and map student potential data. At the Klaten DistrictVocational High School, 40 Guidance and Counseling teachers received trainingon the Individual Potential information system (SIMPOSIDU), which enabled theteachers to systematically and comprehensively record student potential data. Theinformation is personal, social, educational, and other. The service makes itsimpler for teachers to map data about future pupils so they can assist kids inproblem-solving in accordance with their potential.
Research on Distributed Internet Terminal Cloud Online Technology

Firstly, through the research of the simulation platform, according to the characteristics of the hybrid structure video grid structure, a hybrid structure video grid node component library is established. With the advent of the era of big data, the ideological and political education in colleges and universities is facing many opportunities and challenges. Based on the application of big data technology, three new concepts of ideological and political education innovation in colleges and universities are proposed: quantitative integration, accurate prediction and individualized concepts and the use of Petri The shared synthesis technique of nets obtains the Petri net model of the whole system. At the same time, the concept of reachable task graph of Petri net corresponding to resource scheduling is given, and some important characteristics of resource scheduling such as throughput, load balance and scheduling time are analyzed by using the reachable task graph.
Design of Optimal Service Scheduling based Task Allocation for Improving CRM in Cloud Computing

Cloud computing is a service level computing that provide various service to the customers in order to establish an effective customer Relationship management (CRM). This offers various services like virtual machine, self-service provisioning, elasticity computing and storage (pay-as-you-go). Cloud computing provides shared services by enhancing resource management scalability, interoperability and prediction resources as a key to realize the resource utilization with high-performance management metrics. However, when the number of users increase, the process of task scheduling and service allocation using a traditional computing environment will degrade the CRM. In order to address this challenge, this research study proposes an Optimal Service Level Scheduling (OSLS) based task allocation design for improving CRM in cloud computing environment. The Adaptive Service Level Scheduling Algorithm (ASLSA) and the Support Level Load Balancer (SLLB) will reduce the workload in cloud computing environment in order to improve the Quality of Service (QoS) of CRM. This process will optimize the resource utilization in cloud platform based on the service requirement. It provides optimal scheduling features to CRM in order to improve the service optimality based on task and enhance the computational processes such as service load management, heterogeneous service delivery, pricing, resource pools and elasticity. The proposed system leverages high performance when compared to the existing models.
CSMPQ:Class Separability Based Mixed-Precision Quantization

Mixed-precision quantization has received increasing attention for its capability of reducing the computational burden and speeding up the inference time. Existing methods usually focus on the sensitivity of different network layers, which requires a time-consuming search or training process. To this end, a novel mixed-precision quantization method, termed CSMPQ, is proposed. Specifically, the TF-IDF metric that is widely used in natural language processing (NLP) is introduced to measure the class separability of layer-wise feature maps. Furthermore, a linear programming problem is designed to derive the optimal bit configuration for each layer. Without any iterative process, the proposed CSMPQ achieves better compression trade-offs than the state-of-the-art quantization methods. Specifically, CSMPQ achieves 73.03$\%$ Top-1 acc on ResNet-18 with only 59G BOPs for QAT, and 71.30$\%$ top-1 acc with only 1.5Mb on MobileNetV2 for PTQ.
Zero-shot Triplet Extraction by Template Infilling

The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on training data involving specific target relations, and are incapable of extracting new relations that were not observed at training time. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. We show that by reducing triplet extraction to a template infilling task over a pre-trained language model (LM), we can equip the extraction model with zero-shot learning capabilities and eliminate the need for additional training data. We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations. Experiments on FewRel and Wiki-ZSL datasets demonstrate that ZETT shows consistent and stable performance, outperforming previous state-of-the-art methods, even when using automatically generated templates. https://github.com/megagonlabs/zett/
Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of stable scenes, each containing multiple hidden objects. Given such cluttered scenes, we then train a Scene Exploration agent, using deep RL, to uncover hidden objects by interactively rearranging the scene. We show that our learned agents hide and discover significantly more objects than the baselines. We present quantitative results that prove the generalization capabilities of our agents. We also demonstrate sim-to-real transfer by successfully deploying the learned policy on a real UR10 robot to explore real-world cluttered scenes. The supplemental video can be found at https://www.youtube.com/watch?v=T2Jo7wwaXss.