On Graph Matching Using Generalized Seed Side-Information

At the nexus of artificial intelligence, robotics, image processing, and natural language processing (NLP), quadruped AI assistants provide a revolutionary method of interacting with machines. Intending to give quadruped AI assistants emotional intelligence, this literature review methodically looks over and compiles the body of research, concentrating on how to use natural language processing and image processing together. The review seeks to offer a comprehensive grasp of the developments, challenges, and possible uses in this diverse subject. The initial section of the survey provides an overview of quadruped robots and highlights how they are integrated with image processing technology to provide visual perception. It explores the various locomotion techniques, demonstrating how these robots use picture data to improve their capacity to navigate and adapt to different situations. The conversation also touches on sensor technologies, highlighting their function in obtaining and deciphering visual data for intelligent interaction. In addition, the research delves into how quadruped AI assistants include natural language processing and examines how these robots interpret and react to instructions in human language. One of the main topics of debate is how sentiment analysis and emotional recognition methods might help these assistants become more emotionally intelligent. Finally, this review of the literature offers a comprehensive viewpoint on how natural language processing and image processing are integrated in quadruped AI assistants, providing academics and practitioners with an outline for developing emotional intelligence in these robots. Compiling knowledge from robotics, artificial intelligence, image processing, and natural language processing is essential to creating emotionally competent quadruped AI assistants and ensuring their smooth incorporation into human-centered settings.This research also investigates the integration of Natural Language Processing and Image Processing for Emotional Intelligence in Quadruped AI Assistants and thoroughly assesses the gyroscope functioning on these robotic systems

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, it is an attractive alternative to make use of the cloud to store massive data graphs and conduct the complex graph matching. To protect the privacy, data graphs are usually encrypted before being outsourced to the cloud. A few schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. The time complexity of judging subgraph isomorphism is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n!n^{2})$</tex-math></inline-formula> at most, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of vertices in the data graph. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. The first cloud server can prune some impossible matched vertices from the data graph, and get a possible matched matrix to represent which vertices in the data graph might match with the vertices in the query graph. The second cloud server performs the related matrix operations based on its stored information and the information sent by the first cloud server. The two cloud servers jointly verify the correctness of the matched matrix. The graph user can directly and quickly extract the matrix of the subgraph isomorphic to the query graph from the data graph matrix based on the matched matrix, and further recover this subgraph. No subgraph isomorphism operation is involved in the procedure of subgraph extraction for the graph user. The time complexity of subgraph extraction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2})$</tex-math></inline-formula> in our scheme, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of vertices in the query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.

An induced matching in a graph is a set of edges whose endpoints induce a \(1\)-regular subgraph. It is known that every \(n\)-vertex graph has at most \(10^{n/5}\approx 1.5849^n\) maximal induced matchings, and this bound is best possible. We prove that every \(n\)-vertex triangle-free graph has at most \(3^{n/3}\approx 1.4423^n\) maximal induced matchings, and this bound is attained by every disjoint union of copies of the complete bipartite graph \(K_{3,3}\). Our result implies that all maximal induced matchings in an \(n\)-vertex triangle-free graph can be listed in time \(O(1.4423^n)\), yielding the fastest known algorithm for finding a maximum induced matching in a triangle-free graph.KeywordsMaximum Induced MatchingTriangle-free GraphsMaximum Independent SetTwin SetsPolynomial DelayThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.

Scientists today have completely different ideas of what machines can learn to do than we had only 10 y ago. In image processing, speech and video processing, machine vision, natural language processing, and classic two-player games, in particular, the state-of-the-art has been rapidly pushed forward over the last decade, as a series of machine-learning performance records were achieved for publicly organized challenge problems. In many of these challenges, the records now meet or exceed human performance level. A contest in 2010 proved that the Go-playing computer software of the day could not beat a strong human Go player. Today, in 2020, no one believes that human Go players—including human world champion Lee Sedol—can beat AlphaGo, a system constructed over the last decade. These new performance records, and the way they were achieved, obliterate the expectations of 10 y ago. At that time, human-level performance seemed a long way off and, for many, it seemed that no technologies then available would be able to deliver such performance. Systems like AlphaGo benefited in this last decade from a completely unanticipated simultaneous expansion on several fronts. On the one hand, we saw the unprecedented availability of on-demand scalable computing power in the form of cloud computing, and on the other hand, a massive industrial investment in assembling human engineering teams from a globalized talent pool by some of the largest global technology players. These resources were steadily deployed over that decade to allow rapid expansions in challenge problem performance. The 2010s produced a true technology explosion, a one-time–only transition: The sudden public availability of massive image and text data. Billions of people posted trillions of images and documents on social media, as the phrase “Big Data” entered media awareness. Image processing and natural language processing were forever changed by this new data resource … [↵][1]1To whom correspondence may be addressed. Email: donoho{at}stanford.edu. [1]: #xref-corresp-1-1

Graph matching, set covering and partitioning problems are both theoretically and practically important in decision support systems. Numerous situations have been modeled as graph matching, set covering and partitioning problems. These situations include applications in the areas of task assignment, marriage problem, airline crew scheduling, truck deliveries or vehicle routing, political redistricting, etc. Relational database systems currently support these applications merely by providing some programming languages which are used to write the programs associated with the data. These programming languages are complete as opposed to existing query languages like SQL. These languages, since they are complete, have the usual complexity of a powerful language and lack the simplicity of query languages. This increases application development effort. Relational operators and the query language SQL are extended in this present work in order to enable these problems to be stated and solved directly by the relational database systems leading to benefits of improved data independence, increased productivity, and better performance. The relational operators are extended with six operators, i.e., match, maxmatch, cover, mincover, partition, and minpartition. Properties of these operators are described. Some of these operators may possibly take a very long time in finding a solution. Genetic algorithms are proposed. These algorithms are bounded by a polynomial time. These algorithms therefore enable DBMS to be capable of responding to queries involving proposed operators in a given time constraint. The database operations and query languages extended in this research provide a powerful tool for users as a database‐query tool which supports some decision support systems for information retrieval from a database.

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance.

Frequent subgraph mining refers usually to graph matching and it is widely used when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structural semantic graph matching to discover a set of frequent subgraphs. It uses both similarity measures. An approximate structural similarity function based on graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function. Both structural and semantic filters contribute together to prune extracted frequent sets. Indeed, new hybrid structural-semantic frequent subgraph mining approach will be suitable to be applied to several applications such as community detection and social network analysis.

A dynamical systems approach to weighted graph matching

A graph can be exactly specified by the spectrum and corresponding eigenvectors of its adjacency matrix. This provides a solid foundation for spectrum based graph matching. However, most previous methods ignore the spectral multiplicity, which may significantly affect the matching accuracy. In this paper, we address the problem of minimizing the matching error when graph spectral multiplicity is involved. We first model spectral multiplicity by the sub-eigenspace rotation matrix R, and integrate R into the spectrum based graph matching model. We then focus on the exact graph matching problem, and show how to establish the vertex-to-vertex correspondence by iteratively optimizing the sub-eigenspace rotation matrix R and the permutation matrix P. A reliable matching initialization method is proposed to make this process converge rapidly. Finally, we extend the approach to the inexact graph matching problem by optimally warping two graphs to the same size. The proposed approach is robust and efficient. We support our approach with numerical experiments and demonstrate its effectiveness in the practical application of uncalibrated stereo matching.

We propose a novel video event retrieval algorithm given a video query containing grouped events from large scale video database. Rather than looking for similar scenes using visual features as conventional image retrieval algorithms do, we search for the similar semantic events (e.g. finding a video such that a person parks a vehicle and meets with other person and exchanges a bag). Videos are analyzed semantically and represented by a graphical structure. Now the problem is to match the graph with other graphs of events in the database. Since the query video may include noisy activities or some event may not be detected by the semantic video analyzer, exact graph matching does not always work. For efficient and effective solution, we introduce a novel sub graph indexing and matching scheme. Sub graphs are grouped and their importance is further learned over video by topic learning algorithms. After grouping and indexing sub graphs, the complex graph matching problem becomes simple vector comparison in reduced dimension. The performances are extensively evaluated and compared with each approach.

Mobile devices are characterized by limited and non-scalable resources such as low processing power, insufficient primary memory and short battery life. Resource intensive applications such as augmented reality, artificial intelligence, artificial vision, object tracking, image processing and natural language processing are becoming popular. These applications are unable to execute effectively on resource constraint mobile devices. In the last few years considerable research has been done on Mobile Cloud Computing to extend the capabilities of mobile devices. Various models and frameworks have been proposed to offload resource intensive components of applications to cloud for efficient execution. This paper surveys recent computation offloading frameworks to determine and analyze the factors which affect the wide acceptance of these frameworks.

Deep Learning is a significant tool that communicates with the computer to perform task as a natural human being. Deep learning algorithms exhibits a vital role in defining the features and can assist the large number of processes for the data which are structured or unstructured. In recent years, this concept is receiving quite a lot attention for its exceeding achievements in human-level performance. In deep learning, systematic and rapid development of diverse lifestyle, including marketing, policing, finance, manufacturing and education modeling which creates pioneering technologies such as autonomous systems. Deep learning being a subset of machine learning is tackling problems with advanced features. Deep Learning is basically a neural network with processing units consisting of multiple layers with which it has efficaciously applicable to a wide set of problems in the areas such as image processing, image recognition and natural language processing. This paper gives a brief review on the different approaches, algorithms used in deep learning techniques from the beginning to the present scenario. The main aim of this study is to reveal the advantages features of the handsome approaches utilized in the deep learning process. Deep learning methods are contributing a diversity of assistances not only to online learners but also to institutions that advance in current eLearning platforms. This survey will afford an overview of the key ideas on the concept, approaches, advantages and impact of deep learning in the area of research.

Pattern recognition is the study of automatic processing and interpretation of patterns using computers through mathematical techniques, where the environment and the objects are collectively referred to as “patterns”. It studies the problem of classifying samples by their attributes using computational methods. The main research directions of pattern recognition are computer vision, image processing, and natural language processing. In this paper, we discuss the application of a variant of reinforcement meta-learning, which combines experience replay and meta-learning in reinforcement learning, to the problem of continuous learning in pattern recognition. It also compares its accuracy with current solutions such as online learning for the continuous learning problem in pattern recognition. It mainly consists of reinforcement learning, reptilian meta-learning and experience replay in SGDM optimizer. It is finally concluded that for the problem of continuous learning in pattern recognition, its ability to preserve past learned knowledge with rapid learning of current knowledge is higher than the current online learning. Based on this, the effect of different parameters on the performance of this variant of reinforcement meta-learning is explored.

Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables static analysis of the robustness of a DNN model against the input as a whole, but also allows the identification of the vulnerable segments of a sequential input (e.g. audio input) by runtime analysis. It is worth noting that DeepMutation++ specially features the support of RNNs mutation testing. The tool demo video can be found on the project website https://sites.google.com/view/deepmutationpp.