Human Effort Research Articles

Artificial intelligence legal personality and accountability: auditors’ accounts of capabilities and challenges for instrument boundary

PurposeThe study aims to identify the practical borders of AI legal personality and accountability in human-centric services.Design/methodology/approachUsing a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.FindingsThe study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.Research limitations/implicationsThe insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.Practical implicationsThe findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.Social implicationsThe outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.Originality/valueThis paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

WORKSHOP PENYUSUNAN MEDIA PEMBELAJARAN BERBASIS PENDIDIKAN INKLUSIF DI GUGUS 1 KEC. KOPANG KAB. LOMBOK TENGAH

Education is one of human efforts to improve the dignity and standard of life. This improvement effort is carried out by developing competence, knowledge, attitudes, and the need for education which is the right of all citizens, and this is guaranteed by the 1945 Constitution. Implementing inclusive education is one way to make this happen. District Government Central Lombok, through the Department of Education and Culture, in collaboration with Innovation Indonesia and FKIP UNRAM, is holding a series of workshops, one of which is strengthening teacher competency in selecting and utilizing inclusive education-based learning media. The workshop was held in Gugus 1 Kec. Kopang with a total of 8 elementary schools and 32 lower class teachers as participants. Activities were carried out in October 2019 through workshops and field assistance. The results of the activity show that the objective of the activity was achieved, namely strengthening the competency of elementary school teachers in selecting and utilizing inclusive education-based media in the classroom. The pretest and posttest results show that the teacher's conceptual understanding has increased.

System dynamics-multiple the objective optimization model for the coordinated development of urban economy-energy-carbon system

The deepening conflict between human societal progress and efforts to combat climate change necessitates immediate coordination among the Economy-Energy-Carbon (EEC) management. The study applies IPCC accounting methods to compute the historical carbon emissions in the Chengdu-Chongqing economic circle from 2005 to 2021. It assesses the developmental efficiency of various subsystems in the EEC system by employing the rank-sum ratio. Taking a typical city in the region (Chongqing) as a case study, this study integrates System Dynamics (SD) with NSGAIII to develop an EEC multi-objective optimization model. It utilizes the VIKOR model to select the optimal solution. Based on this, the analysis explores the coupled and coordinated state of the EEC system from 2022 to 2030 under the optimal solution mode. Research findings indicate that: (1) The overall carbon emissions exhibit a slight increase, where carbon sources surpass the carbon sink created by land use. (2) Compared to results from other preference-based scenarios, in the scenario where subsystem efficiencies are all optimal, the minimum cumulative carbon emissions amount to 1520.52 million tons, with the economy maintaining a favorable status. Moreover, the overall coupling coordination of the EEC is in a relatively favorable state, and it tends to improve annually. (3) To attain the coordinated development of the EEC system by 2030, the energy structure of the representative city must decrease coal consumption by 26.17%, maintain the construction land ratio below 3.7%, and guarantee forest coverage exceeds 57.77%. This study, utilizing complex system simulations and optimization analyses across various scenarios, offers pertinent recommendations for attaining urban low-carbon development while upholding societal advancement.

Kajian Filosofis Tentang Keadilan Sosial Dari Pemikiran Plato

Philosophical study of social justice from Plato's thought Throughout the history of philosophy, the topic of justice has been a very important topic. Theoretical thinkers such as utilitarianism, intuitionism, eudaimonism, perfectionism, liberalism, communitarianism, and socialism have discussed this topic. The core of political research from the time of Plato to the present is justice. Questions of fairness cannot be determined by the standards used to determine whether something is fair. Different versions of answers regarding the meaning of justice are often considered unsatisfactory, so they cannot be separated from the debate which ultimately constructs the meaning of justice itself, even into a relative formulation. This problem ultimately encouraged many groups to take an alternative path by returning the meaning of justice to those who set the laws and regulations and to judges who will develop their own considerations. This article will discuss the concept of justice from the perspective of Pre-Christian philosophers, especially Plato. Philosophically, there are two ways of expressing justice: First, the view that justice means harmony between the implementation of rights and the implementation of obligations according to the "balance of law" clause, namely "the scope of rights and obligations". The view of legal experts basically holds that justice is harmony between legal certainty and legal comparability. There are even those who argue that law must be combined with justice for it to truly have legal meaning. This is related to the answer that law is part of human efforts to create ethical coexistence in the world. Only through a just legal system can society live peacefully towards happiness, because the essence of law is to create fair rules in society.

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles.

Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community.

A deep learning-based strategy for producing dense 3D segmentations from sparsely annotated 2D images.

Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths.

Host population structure and rare dispersal events drive leptospirosis transmission patterns among Rattus norvegicus in Boston, Massachusetts, US.

Leptospirosis (caused by pathogenic bacteria in the genus Leptospira ) is prevalent worldwide but more common in tropical and subtropical regions. Transmission can occur following direct exposure to infected urine from reservoir hosts, such as rats, or a urine-contaminated environment, which then can serve as an infection source for additional rats and other mammals, including humans. The brown rat, Rattus norvegicus , is an important reservoir of leptospirosis in urban settings. We investigated leptospirosis among brown rats in Boston, Massachusetts and hypothesized that rat dispersal in this urban setting influences the movement, persistence, and diversity of Leptospira . We analyzed DNA from 328 rat kidney samples collected from 17 sites in Boston over a seven-year period (2016-2022); 59 rats representing 12 of 17 sites were positive for Leptospira . We used 21 neutral microsatellite loci to genotype 311 rats and utilized the resulting data to investigate genetic connectivity among sampling sites. We generated whole genome sequences for 28 Leptospira isolates obtained from frozen and fresh tissue from some of the 59 Leptospira -positive rat kidneys. When isolates were not obtained, we attempted Leptospira genomic DNA capture and enrichment, which yielded 14 additional Leptospira genomes from rats. We also generated an enriched Leptospira genome from a 2018 human case in Boston. We found evidence of high genetic structure and limited dispersal among rat populations that is likely influenced by major roads and/or other unknown dispersal barriers, resulting in distinct rat population groups within the city; at certain sites these groups persisted for multiple years. We identified multiple distinct phylogenetic clades of L. interrogans among rats, with specific clades tightly linked to distinct rat populations. This pattern suggests L. interrogans persists in local rat populations and movement of leptospirosis in this urban rat community is driven by rat dispersal. Finally, our genomic analyses of the 2018 human leptospirosis case in Boston suggests a link to rats as the source. These findings will be useful for guiding rat control and human leptospirosis mitigation efforts in this and other urban settings.

Assessing Reptile Conservation Status under Global Climate Change

Global climate change drives variations in species distribution patterns and affects biodiversity, potentially increasing the risk of species extinction. Investigating the potential distribution range of species under future global climate change is crucial for biodiversity conservation and ecosystem management. In this study, we collected distributional data for 5282 reptile species to assess their conservation status based on distributional ranges using species distribution models. Our predictions indicate that the potential distribution ranges for over half of these species are projected to decrease under different scenarios. Under future scenarios with relatively low carbon emissions, the increase in the number of threatened reptiles is significantly lower, highlighting the importance of human efforts. Surprisingly, we identified some endangered species that are projected to expand their distribution ranges, underscoring the potential positive effects of climate change on some special species. Our findings emphasize the increased extinction risk faced by reptile species due to climate change and highlight the urgent need to mitigate the effects of habitat degradation and human activities on their potential distribution in the future.

Approximate ground truth generation for semantic labeling of historical documents with minimal human effort

Deep learning approaches have shown high performance for layout analysis of historical documents, provided that enough labeled data is available. This is not an issue for generic tasks such as image binarization, text graphics separation, or text line and text block detection but can become an impediment for more specialized tasks specific to one or a few books only. This paper addresses layout analysis of medieval books with rich and complex layouts, for which no labeled data is initially available. The proposed strategy consists of training an initial model with artificial data created to reflect the rules a deep neural network should learn. Then, the model is iteratively fine-tuned by mixing the artificial data with real data obtained by previous predictions, post-processed, and manually selected by an expert user. Such a strategy needs less human effort than manual ground truthing. The approach is qualitatively and quantitatively assessed and shows that the system converges to an accurate model that finally produces approximate ground truth stable and good enough to train a final model to solve the targeted task with high accuracy.

Design and Development of Multipurpose Mechanical Machine

This project introduces the concept of a multi-purpose machine that can perform multiple operations at the same time with the same speed. This machine is mainly used in manufacturing industries and is designed to be economically and efficiently used. It is an automatic machine that is powered by a motor and runs with current. It uses the same shaft as a motor and is powered by pulleys. This machine is designed to be portable and can be used to cut in different places and on different materials. It is also suitable for remote locations and workshops. This machine requires very little human effort and is cost-effective. Keyword: Multipurpose, Mechanical, Machine, Design, Development

Period neizvjesnosti i nesigurnosti: Pravni i politički status Kosova u periodu od 1989. do 1991. godine

This research holds great significance not only for the Albanians of Kosovo in the former Yugoslavia but also for the entire Balkan region during a highly important historical period between 1989 and 1991. It gives a succinct history of Kosovo, detailing Serbia's legal persecution and the nonviolent protests of the Albanians. This research combines a variety of official documents, local and worldwide literature, as well as international actions and judgments, using an analytical approach to the sources to create a thorough comprehension of the events and their causes. During the late 1980s and early 1990s, the Serbian state undertook a series of constitutional and legal changes that resulted in the limitation of Kosovo's autonomy. These restrictive changes caused a series of peaceful civil and political reactions from the Albanian population in Kosovo. Conscious of their position, the Albanians of Kosovo strive to assert their rights and preserve their cultural identity. This research aims to describe the constitutional and legal acts of the Serbian state as well as the political and civic responses of the Albanians in Kosovo. The study examines the cause-and-effect link between the oppressive acts of the Serbian government (cause) and the responses of the Albanian community (effect), illuminating the intricate dynamics that molded the course of the area. The research attempts to provide a nuanced and comprehensive view of the period of political and legal uncertainty by evaluating these changes within the specified timeframe. The methodology used in this research is based on material analysis, employing descriptive, historical, and analytical methods. The expected results of this research emphasize the challenges that Kosovo faces in the context of political and legal insecurity. The research highlights human rights violations, socio-political struggles, and efforts to preserve the identity of the Albanian community. The study also shows the reactions of the international community to these events and the impact of external actors on the situation in Kosovo. In conclusion, this research presents the answers to the research questions or hypotheses. It also explains why the years between 1989-1991 were a period that led to the beginning of an end for the system of the former Yugoslavia. It is evident that these years marked a critical and significant turning point in the disintegration of the former Yugoslavia. The legal repressions and political turmoil experienced by Kosovo during this period were decisive in the dissolution of the so-called Socialist Federal Republic of Yugoslavia (SFRY). Understanding the events between the years 1989 and 1991 is crucial from a future analytical standpoint to comprehend the larger historical framework of Kosovo and the Balkans since these events had an impact on the neighboring nations as well. By concentrating on this significant time period, which had a significant influence on Kosovo's history and its road to liberation and independence, this research seeks to add to scholarly discourse. Recognizing the struggles and resiliency of the Albanian community over these years might provide important lessons for tackling problems and boosting stability in the area as Kosovo develops and moves forward.

Magzā Surah Al-Naba’ [78] Perspektif Adi Hidayat: Analisis Tafsir Audiovisual di Youtube

The Qur'an and al-Sunnah as guidelines for Muslims in carrying out all activities in the world are not only to obey the commands and stay away from Allah's prohibitions. But as a sign of life in the world is temporary and there will be life after death. The news of the afterlife was announced by Allah in the Qur'an in Surah al-Naba' [78]. With the passage of time, the method of da'wah has also developed. One of them is the form of da'wah through social media, which has become a new field for researchers, not least in the field of Al-Qur'an and interpretation. The author is interested in examining Adi Hidayat's perspective on revealing the news of the truth of life after death as one of the magzā surah al-Naba' [78] broadcast through Adi Hidayat's official YouTube account. In order to achieve this goal, this research uses a qualitative descriptive method with a content analysis approach. The results of this study are in the form of maghzā surah al-Naba' [78] from Adi Hidayat's perspective, which is the truth of the existence of life after death and Allah's answer to human beings the difference between those who disbelieve and those who believe. Adi Hidayat's ability to identify the meaning contained in the Qur'an makes it easier for listeners to understand and practice the Qur'an in their daily lives. Thus, human efforts to prepare as well as possible for the afterlife are the proof of the belief in the afterlife.

EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images.

Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model's capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model's receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements' efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation.

Pharmacological characterization of sex differences in the effects of dopaminergic drugs on effort-based decision making in rats.

Motivational dysfunctions related to effort exertion are common in psychiatric disorders. Dopamine systems regulate exertion of effort and effort-based choice in humans and rodents. Previous rodent studies mainly employed male rats, and it is imperative to conduct studies in male and female rats. The present studies compared the effort-related effects of IP injections of the dopamine antagonists ecopipam and haloperidol, and the vesicular monoamine transport-2 inhibitor tetrabenazine (TBZ), in male and female rats using the fixed ratio 5/chow feeding choice task. Ecopipam (0.05-0.2mg/kg) and haloperidol (0.05-0.15mg/kg) induced a low-effort bias, decreasing lever pressing and increasing chow intake in males and females in the same dose range. With lever pressing, there was a modest but significant dose x sex interaction after ecopipam injection, but there was no significant interaction after administration of haloperidol. In the first study with TBZ (0.25-1.0mg/kg), there was a robust sex difference. TBZ shifted choice from lever pressing to chow intake in male rats, but was ineffective in females. In a second experiment, 2.0mg/kg affected choice behavior in both males and females. TBZ increased accumbens c-Fos immunoreactivity in a sex-dependent manner, with males significantly increasing at 1.0mg/kg, while females showed augmented immunoreactivity at 2.0mg/kg. The neural and behavioral effects of TBZ differed across sexes, emphasizing the importance of conducting studies in male and female rats. This research has implications for understanding the effort-related motivational dysfunctions seen in psychopathology.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

BoNuS: Boundary Mining for Nuclei Segmentation With Partial Point Labels.

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in nuclei morphometry in histopathology images. However, manual annotation of tens of thousands of nuclei is tedious and time-consuming, which requires significant amount of human effort and domain-specific expertise. To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei. Specifically, we propose a novel boundary mining framework for nuclei segmentation, named BoNuS, which simultaneously learns nuclei interior and boundary information from the point labels. To achieve this goal, we propose a novel boundary mining loss, which guides the model to learn the boundary information by exploring the pairwise pixel affinity in a multiple-instance learning manner. Then, we consider a more challenging problem, i.e., partial point label, where we propose a nuclei detection module with curriculum learning to detect the missing nuclei with prior morphological knowledge. The proposed method is validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets. Experimental results demonstrate the superior performance of our method to the state-of-the-art weakly-supervised nuclei segmentation methods. Code: https://github.com/hust-linyi/bonus.

A UWB/IMU-assisted fingerprinting localization framework with low human efforts

A UWB/IMU-assisted fingerprinting localization framework with low human efforts

PiClick: Picking the desired mask from multiple candidates in click-based interactive segmentation

Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing. In such a task, target ambiguity remains a problem hindering the accuracy and efficiency of segmentation. That is, in scenes with rich context, one click may correspond to multiple potential targets, while most previous interactive segmentors only generate a single mask and fail to deal with target ambiguity. In this paper, we propose a novel interactive segmentation network named PiClick, to yield all potentially reasonable masks and suggest the most plausible one for the user. Specifically, PiClick utilizes a Transformer-based architecture to generate all potential target masks by mutually interactive mask queries. Moreover, a Target Reasoning module is designed in PiClick to automatically suggest the user-desired mask from all candidates, relieving target ambiguity and extra-human efforts. Extensive experiments on 9 interactive segmentation datasets demonstrate PiClick performs favorably against previous state-of-the-arts considering the segmentation results. Moreover, we show that PiClick effectively reduces human efforts in annotating and picking the desired masks. To ease the usage and inspire future research, we release the source code of PiClick together with a plug-and-play annotation tool at https://github.com/cilinyan/PiClick.

Towards an automated framework for Aero-Servo-Elastic Large Eddy Simulation of wind turbine wakes

During the last two decades, wind turbine wakes have been extensively studied in academia by implementing the Actuator Line method in numerous Large Eddy Simulation solvers tailored for atmospheric flows. However, and while being computationally affordable for ad hoc deep investigations, this approach remains barely used in industry. One of the leading causes is the complexity of the simulation process, which still involves several aspects to be carefully looked at to get valuable results in output. This paper aims to present a workflow that merges and automates the different steps required to conduct aero-servo-elastic Large Eddy Simulations of wind turbines. In particular, a strategy based on an Accurate Conservative Level Set function is used to flag the regions where wakes propagate. This allows to automatically derive refinement zones that cover the wakes at all times. This generic procedure can be seamlessly applied to various farm layouts and inflow conditions. To display the capabilities of the workflow, it is applied to several configurations, including one to seven wind turbines for different inflow conditions. It is observed that lower wind speeds require larger mesh to capture the wake dynamics adequately. Overall, the workflow offers the added advantage of significantly reducing the required human effort while standardizing the process. This is important from an industrial perspective, wherein parametric studies are usually carried out as part of the design process.

Analisis Permasalahan Anak Autisme Di PAUD Harsya Ceria

Special education means learning that is specifically designed to meet the unique needs of children with physical disabilities. Special education is a learning program prepared to meet the unique needs of an individual child. Education is a human effort to develop and guide personality in accordance with the values of society or as an effort to help students to increase knowledge and useful behavior patterns. Children with special needs are children who experience limitations or obstacles, whether physically, mentally, intellectually, socially or emotionally, such as: children with autism, hearing impairment, blindness, mental impairment, hearing impairment, physical impairment, etc. Harsya Ceria is a school with segregation services, where there are several children with different special needs. In making observations, the researcher focused on one child with autism, therefore the researcher tried to research this with the aim of finding out the problems of autistic children. There is a Harya cheerful preschool. The problems of children with special needs are of course different and varied, one of which is autism. They have the right to receive education like children in general. The method used in this research uses descriptive qualitative methods. Researchers collected descriptive data through interviews and observations. The results of this research show that children with autism have a variety of behaviors, therefore autism really needs attention from its caregivers. Harsya Ceria Paud provides good attention and needs to children with autism.