AI models help to learn about neurobiology and approaches assisting in building software programs. Numenta Platform for Intelligent Computing is of the top brain-inspired AI projects consisting of a set of learning algorithms. Learning algorithms are known for capturing different layers of neurons for neural networks in artificial intelligence. Visual pattern recognition, NLP, object recognition, and many more can be done by human brains with the help of the neocortex. This AI project helps the machines to approach and take over human-level activities efficiently and effectively. Neu is known as a C framework with a collection of multiple programming languages as well as multi-purpose software systems.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

Heavily shattered by the pandemic, the retail sector is on the lookout for innovation. Among the many technologies retailers focus on, artificial intelligence is an undeniable leader. The market of artificial intelligence solutions for retail is projected to reach $23.32 billion by 2027, quite a leap compared to $5.06 billion in 2021. Within AI, computer vision and image recognition have become notable areas of interest for the retail sector -- the global market of retail image recognition software is expected to grow at a CAGR of 22% and attain the value of $3.7 billion by 2025. Bringing image recognition into their technology mixes, retailers hope to optimize inventories, simplify checkouts, and boost customer experience.

The attention-based encoder-decoder framework is becoming popular in scene text recognition, largely due to its superiority in integrating recognition clues from both visual and semantic domains. However, recent studies show the two clues might be misaligned in the difficult text (e.g., with rare text shapes) and introduce constraints such as character position to alleviate the problem. Despite certain success, a content-free positional embedding hardly associates with meaningful local image regions stably. In this paper, we propose a novel module called Multi-Domain Character Distance Perception (MDCDP) to establish a visual and semantic related position encoding. MDCDP uses positional embedding to query both visual and semantic features following the attention mechanism. It naturally encodes the positional clue, which describes both visual and semantic distances among characters. We develop a novel architecture named CDistNet that stacks MDCDP several times to guide precise distance modeling. Thus, the visual-semantic alignment is well built even various difficulties presented. We apply CDistNet to two augmented datasets and six public benchmarks. The experiments demonstrate that CDistNet achieves state-of-the-art recognition accuracy. While the visualization also shows that CDistNet achieves proper attention localization in both visual and semantic domains. We will release our code upon acceptance.

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing event logs stored by the systems supporting the execution of a business process. In this paper, the problem of explaining deviations in business processes is first investigated by using features based on sequential and declarative patterns, and a combination of them. Then, the explanations are further improved by leveraging the data attributes of events and traces in event logs through features based on pure data attribute values and data-aware declarative rules. The explanations characterizing the deviances are then extracted by direct and indirect methods for rule induction. Using real-life logs from multiple domains, a range of feature types and different forms of decision rules are evaluated in terms of their ability to accurately discriminate between non-deviant and deviant executions of a process as well as in terms of understandability of the final outcome returned to the users.

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.

--Based on the analysis of the proportion of utility in the supporting transactions used in the field of data mining, high utility-occupancy pattern mining (HUOPM) has recently attracted widespread attention. Unlike high-utility pattern mining (HUPM), which involves the enumeration of high-utility (e.g., profitable) patterns, HUOPM aims to find patterns representing a collection of existing transactions. In practical applications, however, not all patterns are used or valuable. For example, a pattern might contain too many items, that is, the pattern might be too specific and therefore lack value for users in real life. T o achieve qualified patterns with a flexible length, we constrain the minimum and maximum lengths during the mining process and introduce a novel algorithm for the mining of flexible high utility-occupancy patterns. In addition, a utility-occupancy nested list (UO-nlist) and a frequency-utility-occupancy table (FUO-table) are employed to avoid multiple scans of the database. Evaluation results of the subsequent experiments confirm that the proposed algorithm can effectively control the length of the derived patterns, for both real-world and synthetic datasets. Moreover, it can decrease the execution time and memory consumption. HE initial motivation for frequent pattern mining (FPM) was to analyze the shopping behavior of customers using transactional databases and recommend frequently purchased patterns to customers [1], [2], [3], [4], [5]. In this case, researchers believed that the item is binary and whether an item appears in a transaction is considered primary. However, frequent purchase patterns are occasionally less profitable than infrequent purchase patterns with high profits, which poses a fundamental problem. Hence, the discovery of high-utility patterns that consider not only the internal utility (e.g., quantity) but also the external utility (e.g., profit, interest, or weight) [6], [7], [8], [9] has gained substantial research attention. Moreover, a framework called high-utility pattern mining (HUPM) [10], [11] was proposed to address this practical issue. In contrast with frequent pattern mining (FPM), the lack of a downward closure property makes HUPM more difficult and intractable. This research was partially supported by National Natural Science Foundation of China (Grant No. 62002136), Guangzhou Basic and Applied Basic Research Foundation (Grant No. 202102020277). Wensheng Gan is with the College of Cyber Security, Jinan University, Guangzhou 510632, China.

Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://github.com/rajeswar18/MILe

This piece was a finalist for the inaugural Gradient Prize. Machine Learning is a powerful technique to automatically learn models from data that have recently been the driving force behind several impressive technological leaps such as self-driving cars, robust speech recognition, and, arguably, better-than-human image recognition. We rely on these machine learning models daily; they influence our lives in ways we did not expect, and they are only going to become even more ubiquitous. Consider a couple of example machine learning models: 1) Detecting cats in images 2) Deciding which ads to show you online 3) Predicting which areas will suffer crime, and 4) Predicting how likely a criminal is to re-offend. The first two seem harmless enough.

Experts across multiple states and regions are working on cutting edge science like'call recognition' software to help the shy and elusive Eastern Bristlebird recover the devastating Black Summer bushfires. The state-of-the-art deep learning AI pattern recognition tool is one of eight new recovery projects that have received funding through the Morrison Government's multiregional species coordinator. Minister for the Environment Sussan Ley said the projects will cover a range of recovery actions including the use of scientific surveys to record sightings of the birds to improve understanding of subpopulations and habitat connectivity. "Eastern Bristlebirds are a very secretive bird but can be easily recognised by their melodic song and alarm-call, which is why we are developing new listening tools to support the identification and recovery of this endangered species," Minister Ley said. "By creating a tool that automatically and accurately detects the bird's calls from remote field recordings, and updating radio-transmitter attachment methods, we will be able to track remaining and translocated populations to support their recovery in the future. "We will also be using highly-skilled volunteer scientists to collect data that will inform the future recovery actions we need to take to support the recovery of the Bristlebird across its entire range." Other projects for the Eastern Bristlebird will focus on enhancing recovery through habitat restoration, health and disease research, and support for the establishment of a new genetically viable population in Victoria as a safety net in case of extreme weather events or the spread of disease. "One of the key learnings from the Black Summer bushfires was a need for coordinated on-ground action, monitoring and research, across the entire range of a species, to support its recovery," Minister Ley added. "That is why the Australian Government's $200 million investment in bushfire recovery for wildlife and their habitats is seeing states, territories and stakeholders continuing to work together to support the recovery of ecosystems over a year on from the devastating bushfires.