Reviewerly/exHarmony huggingface dataset:
Class Based Thresholding in Early Exit Semantic Segmentation Networks

We propose Class Based Thresholding (CBT) to reduce the computational cost of early exit semantic segmentation models while preserving the mean intersection over union (mIoU) performance. A key idea of CBT is to exploit the naturally-occurring neural collapse phenomenon. Specifically, by calculating the mean prediction probabilities of each class in the training set, CBT assigns different masking threshold values to each class, so that the computation can be terminated sooner for pixels belonging to easy-to-predict classes. We show the effectiveness of CBT on Cityscapes and ADE20K datasets. CBT can reduce the computational cost by $23\%$ compared to the previous state-of-the-art early exit models.
Learning Single-Index Models with Shallow Neural Networks

Single-index models are a class of functions given by an unknown univariate ``link'' function applied to an unknown one-dimensional projection of the input. These models are particularly relevant in high dimension, when the data might present low-dimensional structure that learning algorithms should adapt to. While several statistical aspects of this model, such as the sample complexity of recovering the relevant (one-dimensional) subspace, are well-understood, they rely on tailored algorithms that exploit the specific structure of the target function. In this work, we introduce a natural class of shallow neural networks and study its ability to learn single-index models via gradient flow. More precisely, we consider shallow networks in which biases of the neurons are frozen at random initialization. We show that the corresponding optimization landscape is benign, which in turn leads to generalization guarantees that match the near-optimal sample complexity of dedicated semi-parametric methods.
Integrated Brier Score based Survival Cobra -- A regression based approach

Recently Goswami et al. \cite{goswami2022concordance} introduced two novel implementations of combined regression strategy to find the conditional survival function. The paper uses regression-based weak learners and provides an alternative version of the combined regression strategy (COBRA) ensemble using the Integrated Brier Score to predict conditional survival function. We create a novel predictor based on a weighted version of all machine predictions taking weights as a specific function of normalized Integrated Brier Score. We use two different norms (Frobenius and Sup norm) to extract the proximity points in the algorithm. Our implementations consider right-censored data too. We illustrate the proposed algorithms through some real-life data analysis.
PLATAGAM: a videogame structured as a didactical material for divulgation of some of the benefits of the Gamma Radiation produced in the Cobalt-60 Multipurpose Irradiator at IPEN’s facilities

This paper aimed at the conception and implementation of a platform videogame designed to make known some of the benefits of the use of Gamma Radiation produced in the Cobalt-60 Multipurpose Irradiator, built with Brazilian technology at IPEN’s facilities. Unity 3D game engine and some other free softwares for image edition were used. Four missions await the player, illustrating the following benefits of gamma radiation irradiation: on foods and beverages to retard deterioration and interrupt microorganisms proliferation; on tilapias (a kind of fish), which skins are used in bandages for burns; on surgical materials as a sterilization procedure; and, finally, on museum items, for eradication of plagues on the collection. The authors hope that the offering of this didactic material will aid some effective Science Learning, mainly in Nuclear Technology.
Particle Swarm Optimization with Long and Short Term Memory in Feature Selection

&lt;p&gt;Taking each iteration of Particle swarm optimization (PSO) algorithm as a time node, the change of population in PSO algorithm can be regarded as a time series model. Particle population learns and evolves in multiple time nodes, which can be regarded as a dependent behavior on leader particles. In the traditional particle swarm optimization algorithm, this dependence behavior is independent of time, and its consideration standard is only the fitness value of particles. We deeply study the leadership mechanism of PSO algorithm in order to find a more robust leadership mechanism and improve the ability of PSO algorithm to explore the solution space, by extending the dependence behavior in the time dimension, we propose an improved PSO algorithm with long-term and short-term memory ability. In order to verify its performance, in the experimental part, we select 32 public data sets in UCI data to find the optimal feature subset. In a large number of feature selection experiments. The experimental results proofed that the performance of proposed algorithms is better than some state of the art algorithms. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Method for Measuring the Angular Velocity of a Target by Laser Information Field and the Engineering Design

&lt;p&gt;To address the problem that the data related to the relative motion angular velocity of the target encapsulated in the fire control system cannot be obtained in real time during training, this paper proposes to use a laser transmitter to emit multiple concentric laser pulses to form a laser information field, measure the position of the vehicle in the information field by detecting the movement of the vehicle in the information field and the laser frequency value, and then calculate the relative angular velocity of the target. This method simulates the effect of real operation training and is used to measure the relative angular velocity of the target movement in the laser information field to determine the tracking smoothness and aiming accuracy of the operator in the process of tracking the target, and can be applied to the design of a training system for operator training.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Some Support Vector Regression Machines with Given Empirical Risks Partly

&lt;p&gt;There are often some prior requirements about empirical risk in regression problems. To meet these requirements, this paper firstly proposes two novel support vector regression machine models in which part of empirical risks are given. One is a support vector regression machine in which partial empirical risks are given (PSVR), and the other is a model in which unilateral partial empirical risks are given (UPSVR). For the samples with given empirical risk levels, PSVR meets the requirements by some inequality constraints about empirical risk levels, while for the other samples without empirical risk requirement, PSVR uses the same strategy as the tradition support vector regression (SVR) to meet the requirement of empirical risk. UPSVR is similar to PSVR, except that the inequality constrains of empirical risks are unilateral. Secondly, the dual problems and the solving methods of PSVR and UPSVR are given. Finally, the effectiveness and superiority of PSVR and UPSVR are verified by the experiments on four artificial datasets. Both PSVR and UPSVR achieve better regression performance than the traditional models respectively. At the same time, PSVR is less sensitive to the trade-off coefficient C between empirical risk and confident risk compared with SVR. Thus, PSVR can select parameter C faster and more conveniently. PSVR and UPSVR are the extensions of the traditional models. When the set of samples with given empirical risks is empty, they degenerate into the traditional models. PSVR and UPSVR are suitable for the scene with prior requirements of empirical risk. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Predicting Credit Accessibility Based on Social Capital and Artificial Intelligence

&lt;p&gt;Social capital is a significant factor that determines the success of a business, company or individual. The paper presents a new method to predict the aquaculture household&amp;rsquo;s ability to access to credit, based on social capital and artificial intelligence. Social capital data is collected through surveys of aquaculture households, and then replying on artificial intelligence algorithms to build predictive models. The prediction results show that our proposed method can accurately predict households accessing the formal credit market. This is the basis for the planning and development of aquaculture in potential areas. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
DP-Kmeans and Beyond: Optimal Clustering with a new Clustering Validity Index

&lt;p&gt;The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by dropping the random selection of the initial clustering centers and continuous updating of the new centers. Due to the unsupervised learning feature, the number of clusters and the quality of data partitions generated by the clustering algorithm cannot be guaranteed. In order to evaluate the results of the DP-Kmeans algorithm, this paper proposes the SII, a new clustering validity index based on the Sum of the Inner-cluster compactness and the Inter-cluster separateness. Based on the DP-Kmeans algorithm and the SII index, a new method is proposed to determine the optimal clustering numbers for different datasets. Experimental results on ten datasets with different distributions demonstrate that the proposed clustering method is more effective the existing ones. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
YEREL METEOROLOJİK VERİ KAYITLARI İLE SEKBANDEMİRLİ KIRSAL BÖLGESİNDE GÜNLÜK VE MEVSİMSEL FOTOVOLTAİK ISI ADASININ İNCELENMESİ VE DEĞERLENDİRİLMESİ

Photovoltaic Power Plants have a considerable share among solar energy conversion technologies toward environmentally sustainable and economically feasible electricity production. However, when a rural region's land surface formed by natural soil types is covered by a Photovoltaic Power Plant (PVPP)'s dark-colored solar modules in large numbers, an artificial albedo (reflectivity) change is expected on that surface. Because of the heat exchange between these modules and the air surrounding them due to albedo alteration, the region's natural weather conditions may experience Photovoltaic Heat Island Effect (PVHIE) as a result of external and time-dependent air temperature oscillations caused by the warming-cooling cycles of solar modules. To observe and analyze a possible PVHIE trend, it has been conducting a field study project since October 2017 for a PVPP near the Sekbandemirli rural region in the Kutahya city of Turkey. The weather data, including air temperature and wind (direction and speed) at every 10-minute and hourly intervals, are collected by the three weather monitoring stations installed at the specific locations inside and outside the PVPP field. The plant's hourly average power output and module temperature data can also be monitored. After conducting statistical, correlational, and graphical analyses, the results show some temporal PVHI formations at the PVPP field center daily and on a seasonal basis. The plant center's air temperature tends to be warmer (up to the 6°C difference) during daytimes and colder (up to the (-3)°C difference) during nighttimes.
Parallel Operation of Transformers with Different Voltage Ratio

The power transformers can operate in parallel when changing the sources for the consumer supply without interrupting the power supply.The article describes the way transformers with different voltage ratio until 0.5% of the geometric mean of the voltage ratio can operate in parallel without damaging the windings of the transformers due to the equalisation current.There are studied also parallel operation of transformers with different power with power ratio of maximum 1:3 and of the transformers with different impedance voltage with maximum ratio until ±10% of the arithmetic mean and the maximum power limit for each transformer due to his impedance voltage.The possibility for parallel operation of transformer with different voltage ratio was determined through mathematical calculation.In the article is presented an example for parallel operation of two transformers of 25MVA and different voltage ratio 110/20 kV and 110/21 kV.
THEORETICAL AND METHODOLOGICAL BASIS OF THE AESTHETIC DEVELOPMENT OF HIGH SCHOOL STUDENTS IN THE CONDITIONS OF A CHILDREN'S MUSIC SCHOOL

The article reveals the problem of aesthetic development of high school students in the conditions of a children's music school. The author investigated the problems of musical and aesthetic development of students in the context of history and modernity; Pedagogical conditions aimed at the development of the studied definition in students of the children's music school are determined; features of adolescence are revealed. The methodological base of the research consists of dialectical and systemic methodologies used in the field of psychological and pedagogical research. General scientific and logical methods of analysis, synthesis, induction and deduction, historical and comparative research of the problem are used. The perspective of further research consists of new trends and directions that require further development and improvement: the study of the relationship between the processes of musical and aesthetic development and self-development of high school students; identifying prospects for the development of extracurricular educational institutions.
Notes about Winning Strategies for Some Combinatorial Games

We study the theory of combinatorial games and find winning strategies for players. The algorithmic implementation of the winning strategies for the game TacTix is presented and the software implementation for this game in Python programming language is implemented. The program has a console interface and allows one to check the winning strategies in practice.
GEOCHEMICAL AND MINERALOGICAL STUDY OF THE CRETACEOUS SEDIMENTS IN KERI KERI, FIKA, GONGILA, AND BIMA SANDSTONE FORMATION BORNU BASIN, NORTH-EASTERN NIGERIA

The mineralogical and geochemical composition of Cretaceous and Tertiary sediments of the Bornu Basin, northeastern Nigeria penetrated by Kanadi Wells, have been studied towards deducing aspects of their derivation and deposition. The sediments comprising the Bima Sandstone Formation, sandy-shale Gongila Formation, and, clayey and shaley Fika Formation are mainly composed of quartz (35-58%) and kaolinitic clay (0-50%) with the quartz sympathetically decreasing up the succession. The basal Bima Sandstone Formation is notably arkosic with 25% albite feldspars. A significant abundance (15-25%) of barite also characterized the sandy units of the Bima and Fika Formations. The SiO2/Al2O3 ratios of 5 to 6 underline the general sandy nature of the sediments while the calcareous nature of Bima and Gongila Formation has reflected in their higher (NaO+K2O)/(MgO+CaO) ratios of 1 to 2. In addition, the occurrence of significant 2-3% SO3 constituents showed the important occurrences of barite in the Bima, Gongila, and lower parts of, Fika Formations. The sediments are mostly shallow water deposition in tropical-like paleoenvironment(s).
Automated Labeling Infrastructure for Failure Analysis

Abstract The development of intelligent assistants helping Failure Analysis (FA) engineers in their daily work is essential to any digitalization strategy. In particular, these systems must solve various computer vision or natural language processing problems to select the most critical information from heterogeneous data, like images or texts, and present it to the users. Modern artificial intelligence (AI) techniques approach these tasks with machine learning (ML) methods. The latter, however, require large volumes of training data to create models to solve the required problems. In most cases, enterprise clouds store vast volumes of data captured while applying various FA methods. Nevertheless, this data is useless for ML training algorithms since it is stored in forms that can only be interpreted by highly-trained specialists. In this paper, we present an approach to embedding an annotation process in the everyday routines of FA engineers. Its services can easily be embedded in existing software solutions to (i) capture and store the semantics of each data piece in machine-readable form, as well as (ii) provide predictions of ML models trained on previously annotated data to simplify the annotation task. Preliminary experiments of the built prototype show that the extension of an image editor used by FA engineers with the services provided by the infrastructure can significantly simplify and speed up the annotation process.
METHODOLOGICAL FOUNDATIONS OF THE STUDY OF CONTEMPORARY THEATER AND DECORATION ART

Formation of a complex of methods of scientific knowledge of the phenomen on of art of artistic design of theater performances in order to identify scientifically grounded ways to improve scenography and increase the theoretical and methodological level of scenographic creativity of professional training of theater and artistic personnel. From the point of view of the methodology, the analysis of the art of contemporary stage has become complex and relies on philosophical, aesthetic, art, cultural, sociopsychological and historical approaches in combination with applied theatrical science. Among the methods of analysis, the leading place in the study of transformational problems of the stage is given to the system-structural method. From this point of view, the scenography can be considered as a figurative-plastic system consisting of certain artistic elements (fine arts, design architecture, costume architecture, design, etc.), and at the artistic and synthetic level as a subsystem of the general system of theatrical art, which is in relation to the scenography by the context and source of artistic influences that determine the forms and content of theatrical and decorative creativity.
Penerapan Model Direct Instruction untuk Meningkatkan Keterampilan Games Edukasi Siswa Kelas XII

&lt;p class="ContentAbstrak"&gt;The low skill of making educational games on Microsoft Power Point is the basis for this research. Last year's student skill was in the less category. The purpose of this research is to increase skills in making educational games using the Direct Instruction model. The research was conducted through 2 cycles, each consisted of two meetings. Student skill data was taken by the skill observation sheet instrument on indicators: (1) reading the steps for making educational games, (2) determining concepts, (3) making educational games, (4) completing assignments. The success indicator of research that least 24 students (75%) have good skills. The results showed skills from the 16 students (50%) in the sufficient category and 16 students (50%) in the less category in cycle 1, increasing to 28 students (88%) in the good category in cycle 2. Students were able to make educational games using tabs. inserts and hyperlinks. This result can be used by other teachers to fulfill learning requirements.&lt;/p&gt;
СRITICAL THINKING TECHNOLOGY AS A TOOL FOR INNOVATIVE ACTIVITY IN MATHEMATICS LESSONS

The article revealed the ways of applying critical thinking technology in mathematics lessons as a means of implementing innovative activities. The practical implementation of the technology of critical thinking in mathematics lessons is aimed to generalize the experience of scientists on this pedagogical problem and implement the educational process through the activities of students, during which they learned something new. In the process of research, the methodological base has been composed of a set of theoretical methods (analysis, synthesis, comparison, generalization), empirical methods (observation of the actions of participants in the educational process, surveys). During the research, we found out that this technology significantly contributes to the formation of students' readiness for innovative activities, which allows the child to be successful and competitive in later life.
Detecting states of ion channels on the cell membrane using neural networks

The problem of automating the process of analyzing the open states of channels on the membrane of a neuron of a living organism is considered. Taking into account that the registration of the electrical activity of the cell was made by the patch clamp method at various values of the applied potential, a division into intervals with a constant potential is carried out. Further, to eliminate noise, a notch filter, low-frequency and high-frequency Chebyshev filters are applied to the data. A neural network is applied to the normalized data, based on the results of which the data is changed and re-processed by the same neural network. As a result of the algorithm, the dynamics of channel states was obtained, which makes it possible to register up to several open channels simultaneously.
The Effectiveness of Using Quizizz in Numerational Literacy Skills of Fractional Materials in 3rd Grade Students of Elementary School

The purpose of its application in this study is to determine the effectiveness of using Quizizz game-based application media in numeracy literacy skills. The method implemented in this research is a combination method with Explanatory Sequential Designs. The data collection technique used is a matter of numeracy literacy test material assisted by Quizizz, e-questionnaire assisted by google form, and interview sheet. The subjects used were 25 students of class III C Ibnu Batutah SD IT Harum for the 2021/2022 academic year. The results of data processing, the average value obtained by students with Quizizz media on all indicators is 81.80 with a percentage of 80% of students belonging to the complete group, compared to the average value obtained by students who only use paper, which is 68.80 with a percentage of 40% of students belonging to the complete group. In further testing using SPSS version 25 through the paired sample t-test the value of sig (2-tailed) is smaller than 0.05, namely 0.000 &lt; 0.05, meaning is rejected and is accepted. The results of the open questionnaire and interview process showed that all students gave positive responses in using the Quizizz application media when working on questions. This study concludes that there is an influence in the use of Quizizz application media in numeracy literacy skills in third third gradients of SD IT Harum.
DEVELOPMENT OF DIGITAL COMIC AS A BIOLOGY LEARNING MEDIA

This study aims to develop and test the feasibility of digital comic-based learning media and to examine its effect on student’s learning outcome in cell material. Learning media was developed according to the 4D development model of Thiagarajan which consisted of four stages, namely define, design, develop and disseminate. Media was validated by contentl expert, media expert, and learning expert. The suggestion from biology teacher and students of class XI MIA 2 SMA Negeri 2 Percut Sei Tuan were asked. The data of study were obtained through validation instruments, questionnaires, and cognitive test. Data were analyzed qualitatively and quantitatively. The result of the study showed that the developed comic media was considered very feasible by material expert (94,38%), media expert (89,34%), and learning expert (96,00%). Comic was considered very feasible too both by biology teachers (93.22%) and by students (92.38%). The results of the paired sample t-test on the pre-test and post-test score of 0.00 &gt; 0.05 indicate that the post-test score is bigger than the pre-test score. The data showed that the result of cognitive test had increased significantly after using the developed digital comic media.