All aboard early learning center: All Aboard Early Learning Center,
All Aboard Early Learning Center,
About the Provider
Description: All Aboard Early Learning Center, is a Licensed Child Care Center in Columbia MD, with a maximum capacity of 70 children. This child care center helps with children in the age range of 6 weeks through 17 months, 18 months through 23 months, 2 years, 3 years, 4 years, 5 years, 5 years to 15 years. The provider does not participate in a subsidized child care program.
Program and Licensing Details
- License Number:
252082 - Capacity:
70 - Age Range:
6 weeks through 17 months, 18 months through 23 months, 2 years, 3 years, 4 years, 5 years, 5 years to 15 years - Enrolled in Subsidized Child Care Program:
No - District Office:
Region 6 – Howard County / Carroll County - District Office Phone:
(410) 750-8771 (Note: This is not the facility phone number.)
Inspection/Report History
Busy Bees Child Development Center …
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Busy Bees Child Development Center – Chula Vista CA DAY CARE CENTER
Where possible, ChildcareCenter provides inspection reports as a service to families. This information is deemed reliable,
but is not guaranteed. We encourage families to contact the daycare provider directly with any questions or concerns,
as the provider may have already addressed some or all issues. Reports can also be verified with your local daycare licensing office.
Date | Type | Regulations | Status |
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2021-05-27 | Mandatory Review | 13A.16.06.09C | Corrected |
Findings: A teacher had 3,5 hours of training on site from 5.2020 to 4.2021. Please submit a correction plan. |
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2021-05-27 | Mandatory Review | 13A.16.06.12B(1) | Corrected |
Findings: An aide had 1.5 hours of training on site at time of inspection from 9.2019 to 08.2020. Please submit a correction plan to OCC. |
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2020-12-03 | Full | 13A. 16.03.02A | Corrected |
Findings: Two children did not have the parents or medical portion of the health inventory. Three children did not have the parents portion of the health inventory. Please submit a correction plan to OCC. |
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2020-12-03 | Full | 13A.16.03.02E | Corrected |
Findings: A child was missing the lead screening. Please submit a correction plan to OCC. |
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2020-12-03 | Full | 13A.16.03.04C | Corrected |
Findings: Four children did not have doctors information on emergency cards. Two children did not have doctors information on emergency cards and did not have updated emergency cards. Please submit a correction plan to OCC. |
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2020-12-03 | Full | 13A.16.06.04A(1) | Corrected |
Findings: A staff member started in June 2020 and did not have a medical on site. Please submit a correction plan to OCC. |
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2020-12-03 | Full | 13A.16.10.04A | Corrected |
Findings: Room 2 had Lysol wipes and spray sitting by the sink in the classroom. Room 5 had diaper cream sitting below the diaper table Please submit a correction plan to OCC. |
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2019-05-13 | Mandatory Review | 13A.16.06.09B(1) | Corrected |
Findings: One teacher was observed in the files with having 6 clock hours. Provider will send 6 additional clock hours in to OCC. |
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2019-05-13 | Mandatory Review | 13A.16.03.05B | Corrected |
Findings: Outdated staffing patterns were observed. Provider must update staffing patterns. |
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2019-05-13 | Mandatory Review | 13A. 16.03.06A(2) | Corrected |
Findings: Four staff were no longer working at the center and OCC was not notified within 5 working days. Provider must notify OCC of changes in staff within five working days. |
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2018-07-12 | Complaint | 13A.16.03.05E | Corrected |
Findings: Owner did not have a sub log for the two substitutes that were in rooms 4A, 4B and 5. |
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2018-07-12 | Complaint | 13A.16.07.02B | Corrected |
Findings: Owner was made aware of an incident that took place over a year ago and did not report it to the Office of child care. |
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2018-07-12 | Complaint | 13A.16.07.03B(1) | Corrected |
Findings: The owner reports she was made aware of an incident by staff which occurred over a year ago that a staff person grabbed the jaw of a child and fed the child. The owner did not see the incident; however, she was informed by staff. |
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2018-07-12 | Complaint | 13A.16.08.01A(2)(a) | Corrected |
Findings: LS observed one sub in the 4A room with 2 infants and one aide in 4B with 2 toddlers and 1 two year olds. Supervision in room 4B did not have a qualified teacher to provide adequate supervision. |
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2018-07-12 | Complaint | 13A.16.08.02B | Corrected |
Findings: LS observed one sub in the 4A room with 2 infants and one aide in 4B with 2 toddlers and 1 two year olds. Room 4B did not have a qualified teacher to meet staffing requirement. |
If you are a provider and you believe any information is incorrect, please contact us. We will research your concern and make corrections accordingly.
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Programs
| For more information call 786-955-6549
Introducing
All Aboard Learning Centers offers two educational learning programs that caters to the early development of every child while monitoring their progress.
Scroll Down To Discover More
All Aboard Learning Centers prepares your child with an early head start for school readiness through developmental screenings provided by programs like ELC and VPK that focus on increasing your child’s opportunity to be successful.
Early Learning Coalition
Voluntary Prekindergarten
ELC
Program
Early Learning Coalition
The ELC can help at-risk and low-income working families with necessary childcare costs by providing child care tuition assistance through the School Readiness programs. The Coalition is responsible for overseeing
and funding early learning programs using federal,
state, and local funding sources. the Coalition currently serves over 4500 children in its two early learning programs – School Readiness and Voluntary Prekindergarten. Its purpose is to implement and monitor readiness and VPK programs to help children receive quality comprehensive learning guidance appropriate for their specific learning needs. VPK is FREE to all 4 and 5 year old children.
VPK
Program
Voluntary Prekindergarten
VPK or Voluntary Prekindergarten gives young children an excellent advantage by providing a head start for them in preparation for elementary school by enhancing their pre-reading, pre-math, language and social skills. By facilitating with the development of essential skills that children need to excel in reading and comprehension students will more likely become successful in their educational career. VPK learning environments offer high-quality programs that include high literacy standards, developmentally appropriate curricula, manageable class sizes, and qualified teachers thus ensuring your child’s readiness when it’s time to enter into kindergarten. The Voluntary Prekindergarten program is FREE to all 4 and year old children.
Parent VPK Handbook
Learn more about VPK. Click here to download and learn more about VPL resources.
FL. Dept of Children & Families
Partners with local communities to protect vulnerable children and families while promoting economic self sufficiency.
FL Dept of Education
The Department of Education serves as the single repository of education data & administer a statewide reading initiative for Florida’s public schools.
Olga Trofimova: Students motivate me to further develop my business – News of Yakutia
YAKUTIA.INFO. Olga Trofimova from the Neryungri district has always liked working with children, so she decided to get a job as an English teacher at a local school. After some time, Olga decided to open her own center for the study of foreign languages. In an interview, she told why she decided to become an entrepreneur, and also shared plans for business development.
— Tell us about yourself – what did you do before starting a business?
— I graduated from the Institute of International Relations, studied diplomacy. I did not want to work in my specialty, so, fortunately, I became a teacher. Before entrepreneurial activity, I didn’t do anything, I tried myself in different directions. However, she mainly worked with children: first in local organizations of additional education, and then as an early development instructor and head of the pedagogical department in one of the private centers in Dalian, China. A year later, she returned to Russia and got a job at a public school. Then she decided to start a career as a tutor, subsequently opening her own center for the study of foreign languages.
— At what point did you realize that you need to develop in business?
— In general, from an early age I thought that children should be taught differently and treated differently. Sometimes I imagined and visualized what my school would look like. I wanted to create an interesting and modern educational project. Of course, at the beginning I thought about it for a long time, I was afraid and did not understand where I needed to start, although at that time I had already been working with children for a long time.
Some students came to me to study individually, and many parents left positive feedback about my activities. In the end, everything happened somewhat spontaneously and unexpectedly, but I’m glad that everything coincided so well. In August, our center turned three years old. During this time, children have achieved many successes in learning a foreign language, and the most important thing is that they themselves like to gain new knowledge and improve their English.
— Did you encounter any difficulties in starting your business? If yes, how did you deal with them?
— During the pandemic, it was not clear how to work further and in what direction we need to develop. We also did not know whether we should abandon the lease of the premises and go completely online. At that time, we were solving problems as they came, doing all the lessons online and looking for new business opportunities. Any difficulties only harden you, because it is thanks to this self-isolation that we managed to revise the business project, to see the potential of our center.
— Could you tell us more about other business projects you are currently working on?
— We are planning a new project to open a children’s snowboarding school. In general, the business has already been launched, but with the team we are trying to implement it on a larger scale, to create all the necessary conditions for students for comfortable and productive learning. In addition, I would like to expand the scope of services of the center, giving children the opportunity to study not only English. For example, a Spanish teacher from Costa Rica is currently cooperating with us, and his lessons are held exclusively in English, which allows the children to fully immerse themselves in the environment and pump not only their level of English, but also learn a new language for themselves. Now we are looking for opportunities to move our foreign teacher to Russia, where he will fully work with children in an offline format.
— Where do you get the energy and inspiration to implement all your business ideas?
— Of course, these are students, they motivate me to further develop my business. The initial goal of the project was to create a favorable, friendly environment in which the child would be interested and fun to study. I wanted to show them that studying is an exciting, exciting process, where every time you learn something new about this world.
Guys always inspire us to do something big. Even this project to launch a snowboarding school would not have appeared without them. They showed me that this is a cool and vibrant sport that gives a lot of positive emotions and impressions. I immediately wanted to tell everyone about how amazing snowboarding can be. This is how the desire to open our own school appeared, so that our children could have fun and usefully spend their free time.
— What advice would you give to aspiring entrepreneurs?
— Don’t be afraid to take the first step! If an idea seems crude or unpromising, then you may be being overly critical of yourself. First of all, you have to like the job. I am sure that if a novice entrepreneur loves his work and makes every effort to develop his business, then he will succeed. We live in a time of happy opportunities, because never before has a simple person had as many chances to improve his life as he has now. I am always guided by the following credo: “I see the goal, I believe in myself.”
Digital intelligence in a video camera. Risks of its absence on board
The lack of high-quality hardware analytics on board video surveillance cameras carries certain limitations and risks. Let’s see what are the advantages of cameras with built-in analytics based on deep learning, in which areas their application is most effective. Let’s also talk about what awaits the intelligent video analytics industry on devices in the near future.
The field of deep learning evolved from the concept of “artificial neural networks” in the 1980s. In the early years of this branch of artificial intelligence (AI), neural networks were modeled after the human brain, which is known to have more than 100 billion neurons.
Development of deep learning technologies
A key limitation of early systems was the complexity of network training. Hardware technology was too slow to properly train a neural network that could solve meaningful problems from the real world.
Since 2000, the neural network research community has begun to attract the attention of industry laboratories to the work of deep learning networks. Over the past few years, real applications of this technology have covered many areas, including handwriting recognition, language translation, automatic games (chess, go), object classification, face recognition, analysis of medical images, the creation of fully autonomous cars, and many others.
According to research market leader Omdia, 117 million professional camcorders were sold worldwide in 2020. 42% of them had a resolution of more than 4 Mpc. And by 2025, the market share of such cameras is projected at 74%. At the same time, by the end of this year, Omdia predicts that the number of installed video cameras will exceed 1 billion. These cameras will generate a huge amount of information. The question arises: what to do with this impressive array of data? After all, this information will be useful only if it can be processed and analyzed in a timely manner.
This is precisely the problem that deep learning algorithms help to solve, which significantly expand the capabilities of hardware analytics on video cameras. With their help, you can teach devices to better filter unnecessary data, which in the world of big data can save time, money and human resources.
Limitations and risks for cameras without deep learning-based hardware analytics on board
Of the 117 million cameras shipped globally in 2020, only 16% had deep learning-based analytics functionality. The remaining 84% of cameras sold impose the following restrictions and risks on owners.
Data security
For further analysis and storage, the camera transmits the video stream to the server. This, in turn, increases the number of nodes and the amount of data at risk.
Increased network load
Analysis performed on the server requires all camera data to be transmitted to the data center for analysis, resulting in a much greater need for an expensive network with adequate bandwidth.
Increased costs for electricity and data processing
In some cases, data transmission over the network for centralized processing can be completely eliminated, while in other cases the amount of data involved in the entire system makes centralized processing impractical, especially using improved compression codecs, more cameras and higher resolution.
In addition, streaming large amounts of video and then analyzing it with GPUs on a server will require a significant investment. In the case when data analysis takes place on the camera itself, there is no need for constant video streaming and expensive infrastructure.
Data transfer and processing delay
In scenarios that require real-time control, such as face recognition for access or real-time alerts, and especially in critical situations, there is simply no alternative to image analytics directly on the camera itself.
For the task of tracking objects and individuals over large areas, multiple cameras also achieve significant benefits, as systems benefit from the development of computational power for deep learning-based analysis on board the camera. And, as a result, this leads to a reduction in the delay and time required to process data from the video image.
In addition, there are objects where the processes are not critical. But these processes imply a comfortable user experience that can be compromised by a slow-responsive system. For example, retail POS systems or an automated checkout that has delays of several seconds to respond to each input can become simply unusable when the Internet connection speed drops or is not available at all.
Supply of equipment for video surveillance with integrated video analytics
Advantages of cameras with built-in deep learning analytics
Omdia predicts that by 2025, the number of cameras with built-in deep learning analytics will reach 64% of the total number of cameras sold worldwide. According to Moore’s Law, computing power combined with greater energy efficiency in such devices will increase. This will provide the following benefits.
Low latency and higher frame rate
You can perform real-time AI analysis tasks. You won’t need to set it to 10, 15, or 20 fps if your application requires higher values. This is in demand for critical applications, for detecting and tracking fast moving objects, as well as for adaptive automated systems and interfaces.
Higher accuracy and reliability
The higher the video resolution, the more processing power is required, so high resolution cameras (FHD, UHD) used to accurately detect fine details require more TOPS (trillion operations per second).
Greater processing power allows applications to use large, highly accurate modern neural network models. Models are getting bigger and more complex, also to improve the accuracy of the results.
The first generation of AI edge processors and accelerators (not to mention GPU-based edge products) were more limited in their ability to use such models. At least not without a noticeable decrease in accuracy.
Both high-resolution cameras and high-precision AI provide the opportunity to improve detection and classification. In turn, this allows more reliable alerts to users and operators.
More robust capabilities, richer applications
Greater computational power enables more objects to be detected and better identified, and multiple neural network models to be used simultaneously. Users’ capabilities are not limited to line crossing detection, motion detection that captures objects moving at a speed of up to 50 km/h, or elementary object detection that can identify up to 10 types of objects.
Capacities allow you to simultaneously perform all these operations. Today, most useful video analytics applications don’t just use one model. They contain several in order to get more meaningful data.
System-level cost savings
More powerful on-camera analytics capture larger areas of interest. With increased video streaming capability, users can install a higher resolution camera or a multi-sensor camera instead of multiple fixed low resolution cameras to cover the same area of interest.
Improved analytics accuracy will reduce the amount of video stored and the load on network bandwidth with metadata created directly on the device.
Moreover, users can achieve a better combination of the above. There is always a trade-off between resolution, frame rate, model size (or the complexity of the analytics application) and the cost of the solution.
Getting significantly more resources reduces the compromise that users have to make. For example, clients can analyze meaningful data for multiple video streams simultaneously at high resolution and frame rate in real time.
Revenue from sales of AI video camera chipsets
Deep learning video analytics applications
Deep learning video analytics applications can be broadly classified into the following types.
Security and Safety
Includes all video analysis functions used for security and safety. This includes the detection of objects, violations of the perimeter, abandoned objects, private zones (blurring of the face), detection of unauthorized passage through the checkpoint of several persons by one presented identifier (the so-called “train” passage), as well as recognition of numbers for access control.
Business Intelligence
Includes all analytics for business and operational efficiency purposes, applications such as heat maps, people counting, queue length monitoring, zone stop time, people flow, age and gender recognition.
Traffic Monitor
Includes applications designed to detect traffic violations, accidents and incidents on the roads, as well as manage traffic flow. Now most of the analytical functions on the cameras solve the problems of security and safety. However, by 2025 the balance will change as business intelligence increases its share of the functionality of such cameras. Projects using business intelligence will be the growth driver for this market segment as end customers realize the return on investment that such solutions provide.
Deep learning to improve video quality
Special mention needs to be made about using deep learning to improve video quality. Intelligent image processing technology uses powerful deep learning algorithms to evaluate specific scenes (rain and fog, backlight, anti-flicker scene adaptation, etc. ) and automatically adjusts exposure settings to ensure high image quality.
For outdoor applications, this technology automatically compensates for changing weather and lighting conditions, saving users time and labor.
Open platforms
A number of camera manufacturers have long developed open platforms for third-party application developers. As the performance of video cameras grows, application developers for such platforms have the opportunity to tailor new solutions to the needs of specific industries or customers. In turn, when using deep learning cameras on an open platform, users get a wide range of different applications.
Just as we use different applications as needed to solve our application problems, which can also change over time, users get the opportunity to use different modules simultaneously or sequentially at different stages of the project.
What lies ahead for the industry
Currently, in most cases, camera analytics is limited by the ability to detect an object and its movement.