dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODOLOGICAL CONSIDERATIONS, DIAGNOSTIC HURDLES AND TREATMENT OPTIONS Methodological Considerations for Research As it has been emphasized throughout this review, the prevalence of EA in AD is highly variable across studies, which we suspect is the result of methodological choices. In fact, several points of divergence create a snowball effect of inconsistencies in the results. One such point is the diagnostic criteria of Alzheimer's disease. While most papers follow NINCDS-ADRDA or IWG-2 criteria, some authors base the diagnosis on MRI markers  (26) , or do not differentiate between AD and other types of dementia  (116, 118, 129, 209, 210). Finally, retrospective or prospective population-based studies or projects using datasets from large memory clinics or databases cannot verify the diagnostic method for all patients in the cohort  (122, 123, 127, 128, 131, 132, 211).
However, contrary to preclinical studies  (218, 219) , there are no recommendations or guidelines for EA detection in AD patients, considering the many abnormalities and specificities already present on EEGs. Such recommendations would greatly facilitate the diagnosis of epilepsy or subclinical EA in AD patients. In their article, Sen and colleagues  (220)  have already made a step toward formulating such guidelines for neuropsychological data collection. They suggest standardized, culture-independent, short batteries of tests, with baseline evaluation and longitudinal tracking. Based on snippets from existing studies and an ongoing clinical study (ClinicalTrials identification: NCT03923569), we suggest some methodology-unifying considerations concerning the measurement and detection of epileptic events in AD in Textbox 1 in the hope of amplifying data on mechanisms that have already been suggested but which have still not been replicated.
RESEARCH OBJECTIVES AND QUESTIONS In order to allow the drone to fly autonomously during the IARC flight competition, the objective is to investigate the many methods in which deep learning and computer vision may be utilised to extract robot position data from live raw images acquired on a drone. This will be done using live photos obtained from the drone. In order to acquire location information from raw images, one of the methods that is being researched is the identification of objects and the positioning of these components inside photographs. This category of surgical procedures encompasses a variety of recently discovered methods, including all of them. Deep learning has been shown to be the most successful solution to such challenges, and it is the first step in building this study on the most recent and cutting-edge approaches for developing neural networks that are both accurate and effective when detecting things.
Constructing and running a specialised sensor network in the current world requires a significant investment of time, the majority of which is spent on the creation of a sensor dataset. As a direct consequence of this, our primary objective will be to gather trustworthy training data before we actually begin the process of building the network of locations. This will take place before we begin the process of constructing the network. Even though the emphasis of this discussion is on the implementation of deep learning in response to the challenge of object recognition, there will be a need for a sizeable quantity of more conventional computer vision techniques to be used in order to prepare the way for the deep learning solution to operate at its absolute best. In point of fact, the objective is to demonstrate that deep learning may be used to find a solution to the challenge of object identification.
A number of research have been conducted in an effort to investigate the feasibility of encoding the knowledge that artificial intelligence systems acquire about the world in official languages. By applying principles of logical reasoning to the data, a computer is able to automatically reason about phrases that are supplied in these formal languages. An method to artificial intelligence that has seen rising levels of adoption over the last several years is known as the knowledge base approach. To this far, none of these efforts have led to the achievement of a significant milestone. One of the most well-known instances of such an effort coming from the general public is the creation of Cyc. (Lenat and Guha, 1989). A claims database and an inference engine are both included in the Cyc system as individual components.
CycL is the name of the programming language that was used to construct the approval database. These observations were made by a team of human auditors who are now engaged in evaluating the data behind the scenes. The procedure is laborious and takes a considerable amount of time. The issue that mankind has is coming up with formal rules that are sufficiently complicated to offer an accurate account of the cosmos. To give you just one example, Cyc was unable of comprehending the narrative of a guy called Fred who shaved first thing in the morning. Fred's belief that the inference engine "FredWhileShaving" entity comprised electrical components was based on the fact that he owned an electric shaver. In spite of the fact that he was aware people do not include any electrical components, he arrived to the same conclusion.
At the time that this article was written, the third wave of popularity for neural networks was still going strong, although throughout this wave, the primary emphasis of research on deep learning had shifted significantly. The third wave began with an emphasis on newly developed unsupervised learning methods and the capacity of deep models to generalise effectively from small datasets. Today, however, there is a greater interest in more sophisticated algorithms, much more established forms of supervised learning, and the capacity of deep models to make use of big datasets. labelled datasets.
This is despite the fact that training these models has become easier as a result of these changes. Learning algorithms of today are capable of matching human performance on difficult tasks. The most important development that has taken place in recent times is that we are now able to provide these algorithms with the necessary hardware and support for them to be successful. The total amount of the underlying datasets has become noticeably larger throughout the course of time. The most important factor in the progression of this phenomenon is the widespread use of digital technology in contemporary culture. Since more and more of what we do in modern times takes place on computers, a greater proportion of this activity is being tracked. Because our computers are becoming more and more networked with one another, it is becoming simpler to centralise this information and organise it into a dataset that is appropriate for applications that use machine learning.
The advent of "Big Data" has made machine learning much simpler as a result of the substantially decreased cost of statistical prediction. Statistical prediction requires looking at relatively small quantities of data and then effectively generalising those findings to fresh data. Because of this, machine learning becomes far more precise. As of the year 2016, a supervised deep learning algorithm will normally perform pretty well with about 5,000 category-labeled instances, and when trained on a dataset with at least 10 million labelled examples, it will meet or surpass human performance. Since 2016, this has been a general rule of thumb that may be seen spreading on the internet. An important field of research that focuses on how we can make the most of big unlabeled sample sets that may be acquired either unsupervised or unsupervised, working successfully with smaller datasets is an area of study that is becoming more essential.
This technique was established and is primarily based on our knowledge of the human brain, statistics, and applied mathematics. In recent years, it has seen significant growth, not just in terms of popularity, but also in terms of its utility. The development of more powerful computers, datasets of a greater size, and techniques of network training that are more in-depth may be credited for the majority of this advancement. In the years to come, deep learning will be confronted with a number of obstacles and presented with a number of chances that will enable it to push new limits and continue to advance.
EVOLUTIONARY NEURON NETWORKS (CNNS) Since its discovery, over two decades have elapsed since convolutional neural networks were first put into use. In 1990, Yann is credited with becoming the first person to successfully use them. He was the brains of the machine that eventually became known as LaNet, which is capable of correctly categorising various kinds of numbers. Although it has been around for a while, it has only very recently become mainstream as a result of recent advancements in graphics cards and interfaces that make it possible for several networks to operate in parallel on the same dataset. The creation of AlexNet in 2012 was the spark that sparked interest in CNNs, and various enhancements have been presented in the following years illustrating the usefulness of CNNs. AlexNet was the spark that kindled interest in CNNs.
In order to construct AlexNet, the initial step was to link 5 convolutional layers, which was then followed by the addition of 3 fully connected layers. This is a lot of parameters and neurons for such a flat mesh, as we will see in a moment. In all, there are sixty million parameters and five hundred thousand neurons. The topology of the network is quite dense. This may lead to a method that is not entirely convincing but is reliable for categorising photographs in order to establish whether or not they include ground robots. This makes it possible for slower software to determine with a high level of precision whether sections of a picture include robots and which do not contain robots. This segmentation may be utilised further to offer reliable data for a network that can genuinely recognise the various robots that are working.
GOOGLE LAUNCH MODEL Additionally, a team from Google took part in ImageNet 2014 and emerged victorious with a network conclusion. Since its inception, GoogleNet has been subjected to a variety of modifications and enhancements. The most recent version of the system is referred to as the Inception v4 model  [43]. Architecturally, there is no way that the standard model that AlexNet uses can be compared to it in any manner. This is accomplished by the use of a layered setup that is referred to as the starting module, which drastically decreases the necessary parameter set. In spite of the complexity of its structure, it employs a factor of twelve less parameters than AlexNet does while yet achieving a far greater degree of accuracy. The fact that this tactic might be tough to completely comprehend and far more challenging to modify is undoubtedly the most significant drawback associated with using it.
This may be shown by presenting a 2x2 filter, which is represented by four little boards. These boards, when merged, will achieve maximum pooling of all components that make up the input piece. The completed collection of components may then be pieced together in a variety of different configurations to represent the feature map that will be generated in a specific region of the input picture. The authors of the research took use of the chance to build a per-pixel segmentation method that was capable of segmenting a picture in 15 seconds, even when executed in a Matlab CPU programme that had not been optimised. This was accomplished by following the instructions in his picture. On the other hand, while executing highly optimised Cuda code on a graphics card, the amount of time it took to execute the same thing with the typical floating window approach was 492 seconds, which is 32 times slower.
This allows for the computation of the feature maps for the frame at time t=1. This then results in the generation of feature maps for the frame at time t = 1, which can be shown below. Calculating optical flow may be accomplished via the use of a variety of approaches; however, the approach known as FlowNet, which is based on neural networks, has been established as the industry standard. The efficiency of a detection method that is founded on ResNet-101 is therefore elevated as a result. decreases from 4. 05 FPS to 20. 25 FPS in terms of frame rate. Although the tested network projects include a significant amount of documentation, it does not seem that there is any code that can be accessed at this time. It would seem that this approach is capable of significantly increasing its speed, however doing so would reduce its accuracy.
JOLO YOLO undoubtedly meets the speed criteria for real-time operation, and its developers say it has very good accuracy on larger detection datasets such as VOC[9] and COCO  [24]. The accuracy of YOLO's detection of very small objects is the main concern of this technology. The research work acknowledges that it lacks precise position information and divides the image into a 7x7 grid with two detections in each cell. The final mesh may need to be able to identify many overlapping robots that might be problematic. To combat this, the number of cells can be increased to just over 7x7; However, it has the potential to greatly increase the speed of the forward pass. Decisions like this will need to be re-evaluated as we learn more about how accurately YOLO does the job of identifying the robot.
Therefore, in addition to YOLO, we investigated whether a solution combining segmentation and location extraction is a viable option as a final solution. However, this requires a fast method to extract robot positions from the segmentation image. As we look at strategies for creating a training dataset below, we'll cover the many approaches that can be used to achieve this. With these two main possibilities, we started the process of creating the training dataset described in it. Other techniques, such as SSD and YOLOv2, had previously received little attention in the field of object identification work, so they were identified only much later in the process. As a direct result, the focus was on developing a survey dataset for YOLO and a segmentation dataset for DeepLab. The implications of this will be discussed in more detail later.
SSDs Therefore, the technique of partitioning networks was mostly a topic of discussion at the time of writing. It turns out that we are not the only ones who consider the usefulness of such a strategy leading to the implementation of SSD networking. This is a simpler technique that creates recognition directly from feature maps, rather than splitting the process into segmentation and location extraction. It captures exactly what we want to achieve, but in a simpler way. It shows that SSD outperforms both YOLO and any segmentation-based solutions that can be derived from DeepLab in terms of speed and accuracy. The system can be trained and tested very easily as the entire source code can be viewed online. SSD is a strong competitor for robot identification and the ultimate goal will be to allow the SSD to be trained and tested with various input sizes to determine the configuration that provides the best speed and accuracy in robot identification.
METHODS OF RECOGNIZING RECORDS Now that we have a general idea of what we want our final solution to look like, we can begin to meet the requirements for it to perform at its best. The first thing you need to do before developing a sensor network is to collect a substantial amount of training data that simulates a variety of plausible real-world situations. We understand that one of our goals is to design a method that accepts camera images as input and processes them to produce a segmentation image or collection of detections. The vast majority of neural networks are created and evaluated using one of several standard datasets provided by various image processing competitions. (network photo 2015). However, segmentation and detection of different colored ground robots with poles added from time to time is not a standard task and requires a dataset of robots and their positions at different angles and directions.
Segmenting and detecting different colored ground robots with the occasional pole attached to them is no ordinary task. We noticed that Ascend's participation in last year's IARC competition resulted in a significant number of photos that, in many ways, reflect the types of tests we want to train the sensing network. The image extracted from the dataset is shown in Figure  3. 1. However, none of the currently available photos contain information about the robot's location. Therefore, the focus will be on ground-based robotic sensor technology, where recognition accuracy is far more important than computational speed to reliably generate a training dataset based on photos from the previous year's competition. This information is then used in training in the procedures covered. Given the importance of accuracy at this stage of the process and the lack of pre-developed strategies that can speed the work, a significant part of this is devoted to developing a reliable dataset for robot detection.
MMOD AND DLIB It is possible that the fast learning capabilities of the MMOD loss function are an interesting feature that deserves further investigation. By manually creating a small survey dataset and using it to train the CNN, we were able to train the CNN dlib to at least a certain level of accuracy. It will then be tested on many new records, the results will be carefully evaluated, minor errors will be quickly corrected and the data will be used for further training. Even at this point there are no performance limits, so multiple resizes can be done per image to create bounding boxes that fit the image perfectly. Unfortunately, this technique was found shortly after the dataset was created; Had it been discovered early, it would have been much easier to describe a robot that does not require a large sensor dataset for training.
Given the assumption that traditional object recognition approaches, such as pattern matching, cannot provide sufficient reliability when used to identify small ground robots, it is necessary to construct the dataset in a different way. The method can be simplified by first separating the problem into its components, called the segmentation part and the location extraction part, respectively. This strategy has the advantage of generating data for segmentation and discovery, which can be considered a segmentation solution with the potential to be as or better than an end-to-end discovery network. Cam shift and average shift are interesting approaches that should be evaluated to determine whether they provide a direct answer to the location mining problem. If the results do not provide useful information, we will examine the possibility of using large filters to allow for more controlled removal of pixel centers of mass.
This mesh is done very often for each image. In a nutshell, Alex Net was an innovative CNN broadcast in 2012; As a result, it is already included in most deep learning frameworks in use today. Setup is child's play and starting training won't take as long as you might think. Now that the structure of the network has been determined, we can move on to collecting training data. It is known that deep learning requires large amounts of training data to successfully arrive at a precise but general answer to avoid overfitting. The purpose of the network is to classify robot images and blank photos into their respective categories. To ensure that every element of the arena, including the background areas outside the 20m x 20m grid, is correctly identified, the dataset containing the blank photos must accurately represent the various states.
The image shown in Figure  3. 1 represents a real situation and shows how messy a normal image can be. The image is taken from the dataset in question, which also includes many other photos that are not robots in sight. These images can be easily parsed into more manageable images with 64 x 64 pixel resolution and then used as training data. We will also design it so that there is some overlap between the side-by-side windows so that we can identify the different features we want to learn about in different parts of the extracted image. It is important to generate this data from photographs that capture a wide variety of possible outcomes, as these results can occur during a live study. As a result, we will select for each possible window the photos that should present the greatest difficulty in classifying them correctly.
The blank image portion of the dataset was created from this. The next step is to compile the database of robot photos we have collected. This time we are not taking advantage of a huge database of pre-existing photos that we can sort into hundreds of individual models. Since there is no easy way to systematically remove bot photos from the IARC registry without going through the lengthy manual deletion process, we need to find a more efficient option. One strategy to teach the network the basics of a ground robot would be to take a series of photos of different ground robots and change the camera angles and backgrounds for each photo. The Ascend NTNU team has access to a collection of ground robots that are great for photography in any setting and in any lighting. Since the network needs to be able to distinguish robots from blank photos, this strategy is a viable method for generating a dataset no matter what surface the robot is on.
It also creates high-resolution photos that can be easily adapted to almost any network by resizing, editing, or both. It is estimated that more than 400 photos were taken to capture the different ground robots from every imaginable angle. In technical preparation, we tried to make the dataset larger and more general by blurring, scaling and rotating the photos. This was done in order to create the dataset. The goal was to improve overall system performance without having to buy additional images. Blurring was found to be the only preprocessing strategy that had such a large impact that it would require further investigation and use. Other methods included various growth and rotation techniques. As a result, the dataset we finally agreed on contained a total of 68,000 blank photos and 1,600 robot images.
The relationship between the two may not seem equal; However, we must take into account that blank photos will need to cover a much larger number of potential outcomes than robot images, and will, on average, cover more than 95% of images. Next, we need to train AlexNet on the dataset we just created so we can assess how well it understands the general visual ideas needed to build ground robots. Prior to joining AlexNet, the best-performing dataset in the expertise study was randomly split into a training set and a validation set. Figure  3. 1 shows the result of the experiment. After a single period, it shows a very high test score of 99. 97% on the independent validation set. This can be taken as proof that the network is very efficient and extremely fast, but it can also be a cause for concern.
Of course, we do not over-edit the photos selected for the validation set, which is generally considered good. The difficulty is that approaching a near-perfect answer so quickly can mean that the data used for training and validation are very similar without necessarily replicating the realworld situation. This is worrisome as it may indicate that the data used for training and validation are very similar. If so, we have a problem that even if the network doesn't overfit the training data, the dataset as a whole can fit on it. Even if the network doesn't overfit the training data, it can still outrun the dataset. Another topic covered in the tech article was this, and the answer included both the good and the bad. One of its drawbacks was that the robot image was generic enough to include any robot placed right in the middle of the image, but it also had a number of extreme cases where it struggled.