Reviewerly/exHarmony huggingface dataset:
Distinctive Image Captioning via CLIP Guided Group Optimization

Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions. In this paper, we focus on generating distinctive captions that can distinguish the target image from other similar images. To evaluate the distinctiveness of captions, we introduce a series of metrics that use large-scale vision-language pre-training model CLIP to quantify the distinctiveness. To further improve the distinctiveness of captioning models, we propose a simple and effective training strategy that trains the model by comparing target image with similar image group and optimizing the group embedding gap. Extensive experiments are conducted on various baseline models to demonstrate the wide applicability of our strategy and the consistency of metric results with human evaluation. By comparing the performance of our best model with existing state-of-the-art models, we claim that our model achieves new state-of-the-art towards distinctiveness objective.
Using new technology to discover archeological remains

The aim of discovering remains from underground mainly to protect the remains and to recognize and study them, by this new technology fact concluded about the archeological sites and the centuries passed over. Using new techniques and equipment’s during excavation and digging is to protect the pieces of the remains and all possible information’s about them and the possibility maintain them. And avoid any possible damage to them
Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform

The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.
The Satisfaction Index of the Central Java Persani Association towards the Implementation of the Virtual Gymnastics Championships

The purpose of this study was to determine the user satisfaction index with the implementation of a virtual-based gymnastics championship held in the provincial environment, Central Java.The research method is a survey research method using a questionnaire as a data collection instrument.Respondents used were from 25 City/Regency Governments throughout Central Java.The results of this study indicate that the average satisfaction index of the Central Java Association entry is 3.2 points (enough).The satisfaction index for the residencies of Banyumas, Semarang, Pati, and Pekalongan came in at 3.4 points.The satisfaction index for the residences of Kedu and Surakarta came in at 3.0 points.This study concludes that from the five dimensions, the overall satisfaction of the Central Java gymnastics association is in the sufficient zone.The satisfaction zone is suitable for the residencies of Banyumas, Semarang, Pati, and Pekalongan residencies, while it is in the moderate category for the residences of Kedu and Surakarta.
The Student Health Literacy Using Teaching Materials in Science, Technology, Engineering, Art, and Mathematics (STEAM)

The study aims to describe learning activities and health literacy for students using science teaching materials with STEAM.Classroom Action Research was carried out in three cycles for Master Program in Elementary School Teacher Education students, Faculty of Education and Teacher Training, University of Bengkulu in the first semester 2021/2022.The lecturer with observation sheet instruments collected the learning activity.Test instruments used to measure health literacy aspects of content, attitude, context, and performance reports for competency aspects.The success indicators include (1) lecturers and students' activities in the learning process using STEAM materials that shown a good category; (2) the students' percentage shown at least 80% in the good category for the health literacy aspects of content, context, competence, and attitudes.Based on the actions, It can be concluded that (1) lectures used STEAM IPA teaching materials could increase student activity into an excellent category in the third cycle; (2) The students' percentage who achieved good health literacy for content aspects in the first cycle was 58.33%, the second cycle reached 75%, and the third cycle reached 83.33%; (3) the context in the first cycle by 62.5%, the second cycle it reached 83.33%, and the third cycle reached 91.67%; (4) the attitude aspects in the first cycle was 70.8%, the second cycle reached 79.16%, and the third cycle reached 83.33%; and (5) the competency aspects in the first cycle is 75%, in the second cycle it reaches 83.33%, and the third cycle reaches 91.67%.
Decision-Making Analysis of Selected Subjects for Comprehensive Reform of College Entrance Examination Based on SEU Model

Under the background of the comprehensive reform of the college entrance examination, there are many main factors that affect the decision-making of the elective subjects, such as the relevant documents required by the elective subjects, the scoring method of the elective subjects, the number of elective subjects in the province, etc., integrating senior high school career planning Decision-making, For high school students in provinces with comprehensive reform of the college entrance examination, the SEU model can be used to analyze and make decisions.Based on the comprehensive reform mode of the college entrance examination and the classification of the subject scope, this paper will use the SEU model as the theoretical basis to model and analyze the decision-making of the selected subjects.
TOWARD A NEW GEOLOGIC AND GEOPHYSICAL MODEL FOR BUCHAREST, ROMANIA, BASED ON STANDARDIZED AND INTEROPERABLE DATABASES

In the past decades, the necessity for detailed earthquake microzonation studies was recognized for Bucharest city first because significant damage occurred here during the last major earthquakes, as well as for other densely populated cities in Romania exposed to major seismic risk due to the intermediate-depth earthquakes occurring in Vrancea zone and local site effects. Numerous studies established different approaches incorporating non-linearity analyses, which generally did not consider groundwater level changes. For this purpose, notably, numerical models are most suitable. These models require a good knowledge of the local geological conditions (especially of the uppermost unconsolidated units), information about the geotechnical parameters of these units, and a hydrogeological model of the investigated area. These models required first the construction of a robust database which should contain all the information about geological strata in the area down to at least 100 m depth or more. The database should include geology or geometric data of the layers and other geophysical and geotechnical related details like the characteristic density and average seismic velocity for each layer, as well as the water table variation in the area. All of them have proved to be important in a scientific assessment of the local seismic hazard according to the modern procedures able to have an output in the city's local seismic hazard (microzonation). Most of this information can be obtained from geotechnical investigations and surveys that have already been carried out in the most densely populated areas. In the last 25 years, a lot of valuable information has been gathered by different research institutes, and only part of them was made available through publication. Other parts of the dataset will be acquired beginning now, according to new methods designed not to disturb the daily activity in the city and to respect the green spaces and general environment. The present study aims to establish a numeric database, organized from all this information in a GIS environment, capable of giving an appropriate image of the Bucharest surface and shallow geologic layers until 100 � 150 m depth. Due to the rapid changing of physical properties on the surface and in-depth, it is required that we have verified values in a grid of 100 m x 100 m and with an error of +/- 1 m depth. The geologic layers database, together with the database of the static and dynamic properties of the sedimentary rocks, that is already accomplished, will serve as valuable input for further studies aiming at a thorough quantification of local seismic hazard in Bucharest city.
Explaining Non-Entailment by Model Transformation for the Description Logic EL

Reasoning results computed by description logic systems can be hard to comprehend. When an ontology does not entail an expected subsumption relationship, generating an explanation of this non-entailment becomes necessary. In this paper, we use countermodels to explain non-entailments. More precisely, we devise relevant parts of canonical models of ontologies that serve as explanations and discuss the computational complexity of extracting these parts by means of model transformations. Furthermore, we provide an implementation of these transformations and evaluate it using real ontologies.
Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the Earth

Subject of study. Results of comparative analysis and testing of the application capabilities of several neural network detection algorithms, programming interfaces, and machine learning libraries for real-time analysis of graphical data from scanning thermal imaging surveying systems are presented. Method. The availability of programming interfaces for integration and adaptation of algorithms into the developed software, data processing rate, and object detection accuracy were selected as the main criteria for assessing the detection algorithms. These criteria were evaluated using practical experiments involving training and running neural network algorithms on test software using computers with different configurations. Main results. Modern neural network algorithms were demonstrated to enable image data processing with detection accuracy for specified object classes, which is sufficient for the automation of image recognition aimed at real-time processing of images from scanning thermal imaging surveying systems. Practical significance. The results of the investigation and tests presented in this study can be advantageous and reduce the time required for developers to find base neural network algorithms suitable for a practical implementation of automation aimed at image processing. Implementation of the considered algorithms in the developed software enables image analysis and processing in real time during surveillance owing to a reduction in the amount of data processed by the operator, thus enabling the removal of the post-processing stage from the technological sequence of the surveillance.
Intelligent Standard is Just in Time

China has built a huge standard system with symbiotic development of national standards, industry standards, local standards, group standards and enterprise standards.The role of standards in supporting various fields is becoming more and more obvious.In this paper, we introduce the background of standard intelligence, the progress of international intelligent standard, the current situation of domestic standard intelligence policy, and the progress of domestic intelligent standard work.Finally, we put forward several aspects of work that need to be solved urgently in order to achieve the goal of standard intelligence.
Cheesecloth: Zero-Knowledge Proofs of Real-World Vulnerabilities

Currently, when a security analyst discovers a vulnerability in critical software system, they must navigate a fraught dilemma: immediately disclosing the vulnerability to the public could harm the system's users; whereas disclosing the vulnerability only to the software's vendor lets the vendor disregard or deprioritize the security risk, to the detriment of unwittingly-affected users. A compelling recent line of work aims to resolve this by using Zero Knowledge (ZK) protocols that let analysts prove that they know a vulnerability in a program, without revealing the details of the vulnerability or the inputs that exploit it. In principle, this could be achieved by generic ZK techniques. In practice, ZK vulnerability proofs to date have been restricted in scope and expressibility, due to challenges related to generating proof statements that model real-world software at scale and to directly formulating violated properties. This paper presents CHEESECLOTH, a novel proofstatement compiler, which proves practical vulnerabilities in ZK by soundly-but-aggressively preprocessing programs on public inputs, selectively revealing information about executed control segments, and formalizing information leakage using a novel storage-labeling scheme. CHEESECLOTH's practicality is demonstrated by generating ZK proofs of well-known vulnerabilities in (previous versions of) critical software, including the Heartbleed information leakage in OpenSSL and a memory vulnerability in the FFmpeg graphics framework.
Unbalanced Optimal Transport, from Theory to Numerics

Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.
FACTORS AFFECTING SECOND LANGUAGE ACQUISITION IN THIRD-GRADE STUDENTS IN SABILUL HUDA ISLAMIC PRIMARY SCHOOL

There are several aspects that influence the acquisition of English by third-grade children during online schooling. Respectfully, this research is eager to prove the psychological and social aspects that affect second language acquisition. This study uses qualitative descriptive method and the researcher as a participant observer. The data in this study are third-grade students of Sabilul Huda Islamic Primary school while online schooling. The data are collected through interviews, observation method, and documentation. Then, theory of second language acquisition is involved in analyzing data to answer the research problems. In the final result, several factors that impact third-grade students to acquire English verbs are psychological and social aspects; this influences online learning during the covid-19 pandemic. The process of learning and teaching English also influences the acquisition of English in third-grade students during online schooling. Learning English material appropriately and can be understood by third-grade students is a supporting factor for them to acquire English verbs. Keywords: second language acquisition, psychological and social factors
The Influence of Individual Personality Traits on the Result Creative Work

The article deals with the analysis of the process of students’ creative activity in connection with a set of individual personality traits. The process of teaching students showed that the result of the final product of creative activity is directly affected by the individual characteristics of a person. The stage of inspiration and creative drive alternates with the stage of loss of interest and motivation when creating the author’s concept, which characterizes the main problem of the presented article. The purpose of this work is to find and determine the reasons for the lack of motivation for learning among students of various educational institutions (college, university), courses and areas of study (design, advertising), where creative disciplines are introduced into the curriculum. The classical method of a sociological survey was taken as the main research method, according to the results of which a number of reasons were identified that affect the lack of motivation among students of creative profiles. The decisive issues were the lack of faith in the future, the denial of any teacher on the basis of their own convictions, which automatically creates a reflection on the discipline taught, the desire to replace the learning process with entertainment activities. The factors influencing the formation of a successful personality are identified: self-discipline, motivation, inspiration, an example of a teacher (as an example of an error-free algorithm of actions). As well as the influence of the typology of individuality (based on the theory of C. Jung: introversion and extraversion) on building one’s own image and the result of creative work. Introverts tend to stick to a laconic style and achromatic colors in both clothing and work. Extroverts, on the other hand, prefer bright colors and extravagant style.
Development of Network-Based Popular Writing Teaching Materials

This study was aimed to produce network-based teaching materials for writing learning materials that were feasible to use.The teaching material was based on the explanation of the planning and initial development of written teaching materials using the network according to students and lecturers' needs.This study was motivated by populer writing learning's unavailability, which made it easier for students in that the teaching material contained subject matter that can be more easily studied and observed by students.According to Reeves, this development used the research method Design-Based Research (DBR) model based on research procedures.Data collection in this study was carried out in closed groups of 30 students and 2 lecturers who teach Populer Writing Skills in the Indonesian Language and Literature Education Program, Universitas Negeri Jakarta.This research was a descriptive qualitative study with data for approximately 6 months.Data were collected through interviews, documentation and observation.The data were analyzed through data reduction, data presentation, and data conclusion by utilizing data analysis techniques.After a validation test by experts to ensure the test, the product design was declared worthy of being implemented in the Populer Writing Skills course.In general terms, the product was suitable for teaching material that can help students carry out network-based learning.The development reflection of this product, namely producing teaching materials in the form of a website by relying on the Padlet website for network-based populer writing learning which was designed consisted of three learning activities, namely introduction to populer writing skills, introduction to types of populer works, and writing populer works online.
Polynomial Chaos-Based Procedural Generation of Synthetic Training Data in Machine Learning for Automated Acoustic Monitoring

In additive manufacturing such as powder bed fusion the acoustic monitoring taking care of timely process termination in case of failure is commonly achieved by ear and therefore highly susceptible to human bias. Solutions based on machine learning algorithms need large datasets for training purposes which are not readily available. Additionally, capturing high-quality audio samples and providing respective material parts are expensive both in terms of time and cost. To overcome this problem, this work proposes a method by which the required synthetic datasets are obtained by way of procedural generation. Here, synthetic data implies the substitution of measured audio data by equivalent virtual and artificial samples from 3D acoustic simulations. In order to cover process variations as well as consider the variability of multiple input parameters, a design-of-experiments based on the theory of generalized polynomial chaos is conducted. Additionally, the polynomialchaos method is extended through use of a decision tree so that the prevalence of specific critical events may be accounted for.
Give noise a voice so it can be better understood.

The purpose of the European Outdoor Noise Directive (OND) 2000/14/EC is to provide to the buyer of a machine relevant noise information so an informed buying decision favoring low noise machinery will be made. This in return should incentivize manufacturers over time to produce quieter machinery. However, the results of this strategy have yet to meet the expectations. Multiple issues have been recognized and investigated over the last years. As a result two guides were published, one for manufacturers to provide correct noise information and one for buyers in their purchases. Efforts are made to resolve issues with measurement standards. The actual noise label applied on OND-machines is also one of the identified issues at time of purchase. And for market surveillance it provides no real help. So what could be done to improve the situation? What key parts in the process involving different stakeholders and procedures should be addressed? This paper proposes a technical solution to provide understandable noise information to buyers in an effective way by leveraging mobile communication technology and by taking in account insights about human buying behavior. The solution could also help making market surveillance more effective.
Facial Emotion Recognition Based on Textural Pattern and Histogram of Oriented Gradient

In the past few years Emotion detection from images has become very popular due to its immense applications fields in day-to-day life. Various computer visions techniques are applied to detect facial emotions but it’s a very challenging task in real time scenario. Most of real-life images are taken in the poor illumination condition and it fails to achieve good recognition accuracy. LBP and LTP both the textural feature descriptor come into the picture to overcome such condition. Histogram of Oriented Gradient (HOG) detects the edges and corners from the images very efficiently. Textural feature descriptor captures the local pattern. HOG and textural feature descriptor capture different types of information of the image. In the proposed method textural image and HOG image fusion is performed to increase the accuracy for recognizing the facial expression from images. The performance of proposed method is validated on CK+ dataset.