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The explosive growth of the Internet of Things (IoT) is pushing forward the paradigm of fog computing services today. An important service for most fog-enhanced applications is geographic range-match, which means the fog-nodes can accurately collect sensed data from IoT devices based on their location distances. However, due to the increasing attacking surfaces, outsourcing range query operations to untrusted fog-nodes inevitably raises new privacy concerns about query content and device location exposure. In this paper, we devise a new geographic range-match scheme for fog-enhanced services, which allows fog-nodes to securely collect range-based sensed data while protecting the location privacy of IoT devices. Our main idea is to formulate the problem of encrypted geographic queries as range-based pattern matching and carefully craft security schemes to enable efficient range queries in the ciphertext domain. The proposed range-match scheme is provably secure and can reduce accessible information during distance comparisons. We formally analyze the security strengths and complete the prototype implementation. The comprehensive experimental results demonstrate the practicality of our designs.

In the research of control chart pattern recognition (CCPR), most previous methods used a classifier to label abnormal CCPs. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis. This article presents a novel multiscale control chart pattern recognition scheme, MS-CCPR, which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses a proposed histogram-based data representation in conjunction with time series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time series subsequence matching algorithms.

Traditional techniques for visual question answering ( VQA ) are mostly end-to-end neural network based, which often perform poorly (e.g., low accuracy) due to lack of understanding and reasoning. To overcome the weaknesses, we propose a comprehensive approach with following key features. (1) It represents inputs, i.e., an image I and a natural language question Q nl as an entity-attribute graph and a query graph, respectively, and employs pattern matching to find answers; (2) it leverages reinforcement learning based model to identify a set of policies that are used to guide visual tasks and construct an entity-attribute graph, based on Q nl ; (3) it employs a novel method to parse a question Q nl and generate corresponding query graph Q ( u o ) for pattern matching; and (4) it integrates inference scheme to further improve result accuracy, in particular, it learns a graph-structured classifier for missing value inference and a co-occurrence matrix for candidate selection. With these features, our approach can not only process visual tasks efficiently, but also answer questions with high accuracy. To evaluate the performance of our approach, we conduct empirical studies on Soccer , Visual-Genome and GQA , and show that our approach outperforms the state-of-the-art methods in result accuracy and system efficiency.

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. Existing distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework for distributed subgraph enumeration on static data graphs. Batch-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. To handle large-scale data graphs that may exceed the memory capacity of a single machine, Batch-BENU stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand, shuffling the data graph instead of partial matching results. To support incremental subgraph enumeration on dynamic data graphs, we propose the Streaming-BENU framework. Streaming-BENU turns the problem of enumerating incremental matching results into enumerating all matching results of incremental pattern graphs at each time step. We implement Batch-BENU and Streaming-BENU with the local database cache and the load balance optimization to improve their efficiency. Extensive experiments show that Batch-BENU and Streaming-BENU can scale to big graphs and complex pattern graphs. They outperform the state-of-the-art distributed methods by up to one and two orders of magnitude, respectively.

As there are countless significant works done for handwritten character recognition, very meager effort has been reported for inscription characters especially for Tamil stone inscriptions. The real challenge faced in handling stone inscription is dataset collection and foreground and background discrimination. Till present days, the archeological department follows traditional way of capturing, preserving and deciphering stone inscriptions which is manual, more time consuming and need expert assistance. Hence digitized recognition is essential and efficient pattern matching algorithm is needed to be developed to deal with variations in shape and size of complex structured characters present in Tamil stone inscriptions. In this paper, an automated character recognition by pattern matching approach is developed, where character features were extracted by using pattern matching algorithm that helps achieving good recognition rate. Recognition of ancient Tamil stone inscriptions characters and finding their corresponding contemporary Tamil character is done by Image-based Character Pattern Identification (ICPI) system. Modified Speeded Up Robust Feature with Bag of Grapheme (MSURF-BoG) algorithm is implemented to detect the strongest key points from the input character with different orientations. These key point features were created for training the image as a model called Bag of Grapheme (BoG) with code word creation. Hence unsupervised key point features were extracted and pattern matching is performed. 11th century Tamil stone inscriptions were taken as samples which has 7 vowels and 17 consonants, totally 24 characters were used. Here samples with different orientation from each 24 character were used for training the system. The proposed system is evaluated by recognition accuracy which is reported for character wise at the maximum of 96%.

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

As agriculture seems to be a crucial part in food security as well as economic development of a country, selecting crops for cultivation is a most important aspect in an agricultural planning. It relies on variety of parameters which includes weather condition, soil property and government policies. The suggested system helps the farmers to select suitable crop based on season and region of sowing. It will in-turn help the farmers by improving the net profit to them. By considering different datasets with respect to five parameters such as rainfall, temperature, slope, humidity and soil moisture of horticulture data, the system builds a model or method using which can suggest list of crops which is mainly helpful for the farmers in his/her decision making.

Zero-shot learning constitutes a variant of the broader category of weakly supervised learning algorithms. Its main asset is the possibility of identifying entities for which no training data are provided in advance. Under this extreme scenario, conventional supervised learning methods cannot operate properly, while consumption of human resources for obtaining even limited instances may be highly restricted, especially when the label space is quite complex because of its cardinality and the underlying semantic dependencies. However, removing the human factor from the learning loop under complicated tasks cannot guarantee robust performance. Thus, semi-automated solutions are widely accepted by both the research and industrial communities, favoring cooperation of human and machine, mainly for alleviating the spent effort of the former, and for acquiring safer predictions. In contrast with the majority of existing Zero-shot learning approaches, we propose a generalized instance-based method oriented towards tackling the Multi-label classification task without performing any transductive operations over the test instances. Instead, we aim to provide a label ranking of the unseen classes exploiting sentence-based semantic embeddings and label similarities, through a dedicated fine-tuned language representational model. We also use a pattern matching rule to further boost the ranking of our method. Some realistic assumptions are made in order for our approach to work correctly and provide said ranking. Results on a biomedical database with a semantically rich fine-grained label space are really promising, rendering its utilization as a helpful and computationally inexpensive tool for facilitating semi-automated indexing.

Static checker frameworks support software developers by automatically discovering bugs that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for such patterns and allow developers to add custom detectors for their own projects. However, existing frameworks generally encode detectors in imperative specifications, with extensive details of not only what to detect but also how . These details complicate detector maintenance and evolution, and also interfere with the framework’s ability to change how detection is done, for instance, to make the detectors incremental. In this paper, we present JavaDL, a Datalog-based declarative specification language for bug pattern detection in Java code. JavaDL seamlessly supports both exhaustive and incremental evaluation from the same detector specification. This specification allows developers to describe local detector components via syntactic pattern matching , and nonlocal (e.g., interprocedural) reasoning via Datalog-style logical rules . We compare our approach against the well-established SpotBugs and Error Prone tools by re-implementing several of their detectors in JavaDL. We find that our implementations are substantially smaller and similarly effective at detecting bugs on the Defects4J benchmark suite, and run with competitive runtime performance. In our experiments, neither incremental nor exhaustive analysis can consistently outperform the other, which highlights the value of our ability to transparently switch execution modes. We argue that our approach showcases the potential of clear-box static checker frameworks that constrain the bug detector specification language to enable the framework to adapt and enhance the detectors.

This paper addresses the problem of multiple graph matching (MGM) by considering both offline batch mode and online setting. We explore the concept of cycle-consistency over pairwise matchings and formulate the problem as finding optimal composition path on the supergraph, whose vertices refer to graphs and edge weights denote score function regarding consistency and affinity. By our theoretical study we show that the offline and online MGM on supergraph can be converted to finding all pairwise shortest paths and single-source shortest paths respectively. We adopt the Floyd algorithm [1] and shortest path faster algorithm (SPFA) [2] , [3] to effectively find the optimal path. Extensive experimental results show our methods surpass state-of-the-art MGM methods, including CAO [4] , MISM [5] , IMGM [6] , and many other recent methods in offline and online settings. Source code will be made publicly available.

Plants are useful to humans by providing food, medicine, fuel, fibre, shelter etc. But it is important to identify the type and uses of a plant to utilize its benefits. So, we have proposed an automated deep learning algorithm to classify plants into appropriate taxonomy using a leaf. Different plant images are captured in a natural environment and created a Leaf dataset containing 12798 leaf images with white background. Preprocessing, segmentation and pattern matching techniques were used to obtain the desired output. Convolutional Neural Network has been used as a pattern matcher to compare the information of an input image with the images in the dataset. Thus, we obtained an accuracy ranging from 95% to 99% by using Convolutional Neural Network algorithm. Hence, this paper will be useful to identify plants using a leaf for Botanists, Industrialists, Food Engineers, Physicians, etc.

In this study, a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine environment. The autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging (LiDAR) sensor and performs pattern matching by recognizing the shape of the tunnel wall. The proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy; it is combined with the encoder sensor to estimate the location of the robot. In addition, when the robot is driving, the vertical direction of the underground mine is scanned through the vertical LiDAR sensor and stacked to create a 3D map of the underground mine. The performance of the proposed method was superior to that of previous studies; the mean absolute error achieved was 0.08 m for the X-Y axes. A root mean square error of 0.05 m2 was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

Noninvasive brain–computer interface (BCI)-based electroencephalograms (EEGs) have made great progress in cognitive activities detection. However, the decoding of premovements from EEG signals remains a challenge for noninvasive BCI. This work aims to decode human intention (movement or rest) before movement onset from EEG signals. We propose to decode premovement patterns from movement-related cortical potential activities with task-related component analysis and canonical correlation patterns (TRCA+CCPs). Specifically, we first optimize the MRCP data with the spatial filter TRCA. CCPs are then extracted from the optimized signals. The extracted CCPs are classified with the linear discriminated analysis classifier. We applied the classification in a sliding window, which changes from readiness potential (RP section) to movement-monitoring potential (MMP section). The classification result on event-related desynchronization (ERD) indicates that the motor cortex becomes active as the limbs move. When applying classification between elbow flexion and rest, the proposed TRCA+CCP method achieves an accuracy of 0.9001±0.0997 in the RP section. The previous methods, discriminative canonical pattern matching + common spatial pattern (DCPM+CSP) and the optimized DCPM+CSP method, exhibit accuracy values of 0.7827± 0.1276 and 0.8141±0.1295 for the RP section, respectively. Compared with these methods, the proposed TRCA+CCP method achieves higher average accuracy in the RP section. The proposed TRCA+CCP method can decode the patterns in the RP section efficiently, which indicates that the premovement patterns in EEG signals can be decoded before execution of the movement. The system is expected to assist movement detection in ERD analysis.

Graph mining applications, such as subgraph pattern matching and mining, are widely used in real-world domains such as bioinformatics, social network analysis, and computer vision. Such applications are considered as a new class of data-intensive applications that generate massive irregular computation workloads and memory accesses, which are different from many well-studied graph applications such as BFS and page rank. In this letter, we use the emerging process-in-memory architecture to accelerate data-intensive operations in graph mining tasks. We first identify the code blocks that are best suitable for PIM execution. Then, we observe a significant load imbalance on PIM architecture and analyze the root cause for such imbalance in graph mining applications. Lastly, we evaluate several scheduling schemes that help reduce the load imbalance and discuss potential optimizations to enhance performance further.

Non-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.

Rapid worldwide spread of Corona virus Disease 2019 (COVID 19) has resulted in a global pandemic. In present scenario due to covid-19, the mask has been an important part of our live for our safety as well as for the others safety so there is a need for efficient face mask detection applications in crowded areas like shopping malls, Public transportation etc. To ensure safety of the people in the surroundings. Face Mask Detection using NI LabVIEW. In this project a real-time system is developed to detect whether the person is wearing a mask or not by acquiring a real-time image of him through a Camera. The main challenges in detecting the mask are there are masks with various colors and patterns and secondly the background, light intensity are also the factors that affect the result. So, all these factors should be taken into consideration while developing the system in real-time. This system used for this application consists of vision development module. Vision development module helps to develop applications for machine vision and image processing applications we can use it with LabVIEW for real- time systems. A camera with good pixel quality is used for image acquisition. The captured image is of RGB format, it is difficult to analyze the image in this format, so it undergoes color plane extraction in this only a single plane of the image is considered which separates the mask from surroundings and results in a grey scale image for further processing. The image later is compared to a custom-made template dataset using pattern matching algorithm from vision assistant which helps to detect the mask region. overlaying techniques are used to highlight the mask region which shows that the person is wearing the mask.

Accurate stereo matching requires a large amount of memory at a high bandwidth, which restricts its use in resource-limited systems such as mobile devices. This problem is compounded by the recent trend of applications requiring significantly high pixel resolution and disparity levels. To alleviate this, we present a memory-efficient and robust stereo matching algorithm. For cost aggregation, we employ the semiglobal parametric approach, which significantly reduces the memory bandwidth by representing the costs of all disparities as a Gaussian mixture model. All costs on multiple paths in an image are aggregated by updating the Gaussian parameters. The aggregation is performed during the scanning in the forward and backward directions. To reduce the amount of memory for the intermediate results during the forward scan, we suggest to store only the Gaussian parameters which contribute significantly to the final disparity selection. We also propose a method to enhance the overall procedure through a learning-based confidence measure. The random forest framework is used to train various features which are extracted from the cost and intensity profile. The experimental results on KITTI dataset show that the proposed method reduces the memory requirement to less than 3 percent of that of semiglobal matching (SGM) while providing a robust depth map compared to those of state-of-the-art SGM-based algorithms.

Introduction. An important part of an automotive unmanned vehicle (UV) control system is the environment analysis module. This module is based on various types of sensors, e.g. video cameras, lidars and radars. The development of computer and video technologies makes it possible to implement an environment analysis module using a single video camera as a sensor. This approach is expected to reduce the cost of the entire module. The main task in video image processing is to analyse the environment as a 3D scene. The 3D trajectory of an object, which takes into account its dimensions, angle of view and movement vector, as well as the vehicle pose in a video image, provides sufficient information for assessing the real interaction of objects. A basis for constructing a 3D trajectory is vehicle pose estimation.Aim. To develop an automatic method for estimating vehicle pose based on video data analysis from a single video camera.Materials and methods. An automatic method for vehicle pose estimation from a video image was proposed based on a cascade approach. The method includes vehicle detection, key points determination, segmentation and vehicle pose estimation. Vehicle detection and determination of its key points were resolved via a neural network. The segmentation of a vehicle video image and its mask preparation were implemented by transforming it into a polar coordinate system and searching for the outer contour using graph theory.Results. The estimation of vehicle pose was implemented by matching the Fourier image of vehicle mask signatures and the templates obtained based on 3D models. The correctness of the obtained vehicle pose and angle of view estimation was confirmed by experiments based on the proposed method. The vehicle pose estimation had an accuracy of 89 % on an open Carvana image dataset.Conclusion. A new approach for vehicle pose estimation was proposed, involving the transition from end-to-end learning of neural networks to resolve several problems at once, e.g., localization, classification, segmentation, and angle of view, towards cascade analysis of information. The accuracy level of end-to-end learning requires large sets of representative data, which complicates the scalability of solutions for road environments in Russia. The proposed method makes it possible to estimate the vehicle pose with a high accuracy level, at the same time as involving no large costs for manual data annotation and training.

Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.

Accuracy and real-timeliness are the top concerns in vehicle plate recognition. Several factors put restrictions on plate recognition system, including illumination, vehicle high speed, camera angle, and bad weather condition. Damaged and pale plates also lead to incorrect recognition in the present approaches. In this regard, this paper proposes an efficient robust method for vehicle plate recognition, which consists of four steps: (i) vehicle detection, (ii) plate detection, (iii) character segmentation, and (iv) character recognition. In the first step, the vehicle image is detected using background emission. Plates are localized by means of character recognition and pattern matching approaches in the second step, where the contours are recognized and extracted using connected component analysis, and then, low-density areas are emitted using density criterion and vehicle plate is extracted. In the third step, statistical feature, filtering methods, and morphology operators are employed for segmentation and extraction of plate characters. After plate segmentation, statistical and global features and local pattern are extracted from each segment image for segment classification in the final step, where features are ranked using F-Score, and then, classification of each section to one of 37 classes is performed using random forest. The proposed method is evaluated using several databases in both left to right and right to left languages; English for the former and Persian for the latter. In the first part of the evaluation, the proposed approach is evaluated in terms of robustness and recognition speed. The proposed method has the accuracy of 99.2% for plate recognition, 100% for plate segmentation, and 98.41% for character recognition. In this part, the dataset of Iranian plates is collected by the authors of this paper. However, character recognition rate is 100% in other Persian databases. Moreover, the experimental evaluations witness that the proposed method can process at least 8 frames per second, that means it is fast enough to be adopted for real-time applications. In the second phase, the proposed method is evaluated on an English plate dataset. In this dataset, the proposed method shows an accuracy of 100% for plate detection and 97.5% for character recognition. The experimental results show that the proposed method outperforms methods proposed in recent years in terms of time and accuracy that is also independent of plate language.

When automated pattern matching tools are used to execute user-knowledge pattern matching (UKPM) in the expert knowledge recommendation system (EKRS), user-knowledge matching is uncertain and the matching efficiency is low. To solve the above problems, the dynamic UKPM mathematical model is established and the “Entropy-Beta” method of crowdsourcing task assignment is designed to solve the model in the study. Firstly, the concept of Entropy is combined with crowdsourcing. The uncertainty of user-knowledge matching results is measured and the magnitude of the uncertainty is calculated. Secondly, based on the Beta distribution function, the accuracy of matching results is measured. The optimal matching results are selected and the matching results were sent to EKRS according to the matching probability. Thirdly, the knowledge recommendation process of UKPM is dynamically adjusted according to the matching probability. Finally, the comparison results of several algorithms showed that the Entropy-Beta algorithm could largely improve the accuracy, efficiency, dynamic regulation, and other performances of EKRS.

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

This paper describes how a simple novel Galois Power-of-Two (GPOW2) real-time embedding scheme is used to improve the performance and accuracy of downstream NLP tasks. GPOW2 computes embeddings live on the fly (real time) in the context of target NLP tasks without the need for tabulated pre-embeddings. One excellent feature of the method is the ability to capture multilevel embeddings in the same pass. It simultaneously computes character, word and sentence embeddings on the fly. GPOW2 has been derived in the context of attempts to improve the performance of the SWAM Arabic morphological engine, which is a multipurpose tool that supports segmentation, classification, POS tagging, spell checking, word embeddings, sematic search, among other tasks. SWAM is a pattern-oriented algorithm that relies on morphological patterns and POS tagging to perform NLP tasks. The paper demonstrates how GPOW2 led to improvements in the accuracy of POS tagging and pattern matching, and accordingly the performance of the whole engine. The accuracy for pattern prediction is 99.47% and is 98.80% for POS tagging.

Traditional fluorescence-based tags, used for anticounterfeiting, rely on primitive pattern matching and visual identification; additional covert security features such as fluorescent lifetime or pattern masking are advantageous if fraud is to be deterred. Herein, we present an electrohydrodynamically printed unicolour multi-fluorescent-lifetime security tag system composed of lifetime-tunable lead-halide perovskite nanocrystals that can be deciphered with both existing time-correlated single-photon counting fluorescence-lifetime imaging microscopy and a novel time-of-flight prototype. We find that unicolour or matching emission wavelength materials can be prepared through cation-engineering with the partial substitution of formamidinium for ethylenediammonium to generate “hollow” formamidinium lead bromide perovskite nanocrystals; these materials can be successfully printed into fluorescence-lifetime-encoded-quick-read tags that are protected from conventional readers. Furthermore, we also demonstrate that a portable, cost-effective time-of-flight fluorescence-lifetime imaging prototype can also decipher these codes. A single comprehensive approach combining these innovations may be eventually deployed to protect both producers and consumers., Nature Communications, 12 (1), ISSN:2041-1723

The flexible pattern matching approach has witnessed increasing popularity. By combining deduction with induction in logic, flexible pattern matching is well suited for exploration and theory development. The paper discusses its logic, advantages and process of this approach while offering a review of research adopting this approach. We also compare and contrast it with another popular qualitative data analysis technique, the grounded theory approach, to further ground the method on the established knowledge and elaborate its strength and fitting context. This paper advances the flexible pattern matching approach by suggesting a five-step roadmap to conduct qualitative research with the approach.

In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet , a sub-sequence wherein the frames share strong similarities and are thus grouped together. The optimal seqlets and matching between them are learned jointly, without any supervision from users. The learned seqlets preserve the locality information at the scale of interest and resolve the ambiguities during matching, which are omitted by frame-based matching methods. We show that our proposed approach outperforms the state-of-the-art ones on datasets of different domains including human actions, facial expressions, speech, and character strokes.

In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.

Active stereo vision has been used for real-time measurement in both academic and industrial fields. Currently, it is still very challenging for active stereo vision to achieve the continuous and robust measurement of a dynamic object due to the lack of a robust pattern extraction and matching method. In this article, we introduce a novel red green blue (RGB) line pattern with coarse-to-fine features in which the intervals between the adjacent green lines are largest and the intervals between the adjacent red lines are smallest. The large interval represents the coarse feature of the pattern and the small interval represents the fine feature of the pattern. Accordingly, we propose a matched pixel difference modeling (MPDM) method to model the matching relationship between two camera views based on the designed pattern. The line pattern is extracted in the hue saturation value (HSV) color space by slope difference distribution (SDD)-based threshold selection. The coarse green lines in two camera views are matched according to the minimum distance principle after the centroid-based alignment. The matching relationship is then modeled based on the matched green lines by least squares (LS) method. With the matching model, the fine blue lines and the fine red lines are matched successively. After stereo matching, the 3-D point on the measured object is computed by the ray intersection method. Experimental results showed that the proposed method is robust in measuring the dynamic objects by single-shot.

A pattern matching method (signature-based) is widely used in basic network intrusion detection systems (IDS). A more robust method is to use a machine learning classifier to detect anomalies and unseen attacks. However, a single machine learning classifier is unlikely to be able to accurately detect all types of attacks, especially uncommon attacks e.g., Remote2Local (R2L) and User2Root (U2R) due to a large difference in the patterns of attacks. Thus, a hybrid approach offers more promising performance. In this paper, we proposed a Double-Layered Hybrid Approach (DLHA) designed specifically to address the aforementioned problem. We studied common characteristics of different attack categories by creating Principal Component Analysis (PCA) variables that maximize variance from each attack type, and found that R2L and U2R attacks have similar behaviour to normal users. DLHA deploys Naive Bayes classifier as Layer 1 to detect DoS and Probe, and adopts SVM as Layer 2 to distinguish R2L and U2R from normal instances. We compared our work with other published research articles using the NSL-KDD data set. The experimental results suggest that DLHA outperforms several existing state-of-the-art IDS techniques, and is significantly better than any single machine learning classifier by large margins. DLHA also displays an outstanding performance in detecting rare attacks by obtaining a detection rate of 96.67% and 100% from R2L and U2R respectively.

A single core SRAM memory with a unified memory controller is proposed for regex patterns which contains complex signatures. Many existing NIDS systems are based on token stream with dedicated hardware units to accommodate byte oriented matching with moderate network throughput rate. The overall NIDS performance metrics is degraded when the number of bytes in the database increased. It will lead the demands of coordinated payload monitoring scheme in any memory based digital NIDS system. In this work, a bit based pattern matching algorithm with FSM state transition is proposed which gives both parallel processing and network assertion payload validity check during the conversion of segments into tokens. The Finite State Machine state controller is used in early misdetection to optimize overall matching time in case of segments with multiple non-trivial tokens where the regex patterns are divided into sub-patterns and the concurrent matches are halted in a parallel manner. Finally synchronization problem during hierarchical state transition and vector matching over multiple pages unique SRAM controllers are used which is driven with fully down converted sub-groups. The efficiency of proposed NIDS system over moderate intrusion database process is proved in terms of classification rate. Hence, proposed NIDS system consumes lesser memory resources and it is verified through comparison with state-of-the-art methods.

We propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

The Video game-Crowdsourcing model to recollect data motivates people to participate by entertaining them. Research showed that the solutions players make in this model are competitive against experts in the area. Yet, the studies in the area focus on mimicking people’s behavior, including their mistakes. Therefore, we use a Video game-Crowdsourcing to model a problem of interest to find strategies for it. To describe matches from the video game we created, we designed a representation that simplifies the discovery of strategies. Our experimentation compares high score matches against low score ones to find the best behaviors. We played 13 matches employing a known strategy for the problem to validate the methodology. Then, we applied the methodology to matches from players. The results suggest that extracting sub-sequences is a process to find strategies and that we can use them to design algorithms to improve current algorithmic solutions for that problem.

Pattern matching has been widely adopted in functional programming languages, and is gradually getting popular in OO languages, from Scala to Python. The structural pattern matching currently in use has its foundation on algebraic data types from functional languages. To better reflect the pointer structures of OO programs, we propose a pattern matching extension to general statically typed OO languages based on object graphs. By this extension, we support patterns having aliasing and circular referencing, that are typically found in pointer structures. With the requirement of only an abstract subtyping preorder on types, our extension is not restricted to a particular hierarchical class model. We give the formal base of the graph model, that is able to handle aliases and cycles in patterns, together with the abstract syntax to construct the object graphs. More complex cases of conjunction and disjunction of multiple patterns are explored with resolution. We present the type checking rules and operational semantics to reason about the soundness by proving the type safety. We also discuss the design decisions, applicability and limitation of our pattern matching extension.

The local feature-based method is the most popular choice for froth velocity measurement. However, due to similar patterns or objects in the froth image pair, current local feature-based methods only consider local intensity character (LIC) and cannot extract the matches well. Therefore, we propose a new feature descriptor named relative position information (RPI)-SURF that integrates the LIC and the RPI from the nearest reference point. First, we use the SURF descriptor with a distance constraint to extract the LIC. Next, we calculate the RPI by the direction and distance to the nearest reference point pair. After that, we integrate the LIC and RPI to demonstrate the new feature descriptor. Extensive experiments verify the effectiveness of the proposed feature descriptor. Compared with the SURF descriptor, the recall and $F_{1}$ -score of the proposed RPI-SURF descriptor have been obviously increased, and the number of detected accurate matches on real froth image pairs by the RPI-SURF descriptor has been increased by at least nearly 60%.

Conventional computer vision algorithms, including stereo matching algorithms, take finely rendered color images as input. However, existing image signal processing (ISP) pipelines for color image generation are designed for photography with a goal of generating pleasing images for human eyes. This paper describes a new end-to-end pipeline for stereo matching from raw Bayer pattern images to disparity maps with customized ISP. Unlike conventional stereo matching systems which need a complete ISP module to render full-size standard RGB (sRGB) images, a subsampling-based demosaicing-downsampling (SDD) operation is introduced in the proposed pipeline to demosaic and downsample the Bayer pattern images. The resultant half-size color image pairs are processed with simple denoising and tone mapping algorithms to generate the final input images of stereo matching algorithms. It is found that the simple nearest neighbor upsampling method is good enough to generate the final full-size disparity maps. Experimental results show that the proposed pipeline is capable of generating comparable or even better stereo matching results than the conventional pipeline. By skipping most of the unnecessary ISP steps and reducing the size of input images, the computational complexity of the end-to-end stereo matching pipeline is significantly reduced.

Proliferation of IoT devices has caused an increase in malware. Much IoT malware includes static linking of library functions, and their symbols such as function names and addresses are stripped hindering function-level analysis. We previously showed that pattern matching could identify all library functions statically linked to IoT malware for Intel 80386 and all toolchains used to build them, but it remained unclear how our method identified toolchains used to build IoT malware for other architectures. In this paper, we extend our previous method to identify toolchains used to build IoT malware not only for Intel 80386 but for other architectures: ARC, ARM 32-bit, MIPS 32-bit, MIPS 64-bit, mk68k, PowerPC, sh4, SPARC, and x86-64. Evaluation of toolchain identification of 3,991 malware samples showed our method identified all toolchains used to build them. Only 14 toolchains had been used to build the samples, and are all available on the Web. We found 91.4% of samples other than Intel 80386 were built with the toolchain described in the installation guide of Mirai, which was similar to that of Intel 80386. To share the results of this study with the antimalware community, we have published a list of the toolchain names of each sample and the names and addresses of the library functions linked to each sample on GitHub.

Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (Re-ID) algorithms. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose to establish the patch-wise correspondences of positive training pairs via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences can be obtained. To circumvent the cumbersome \emph{on-line} graph matching in testing phase, we propose to transfer the \emph{off-line} learned patch-wise correspondences from the positive training pairs to test pairs. In detail, for each test pair, the training pairs with similar pose-pair configurations are selected as references. The matching patterns (i.e., the correspondences) of the selected references are then utilized to calculate the patch-wise feature distances of this test pair. To enhance the robustness of correspondence transfer, we design a novel pose context descriptor to accurately model human body configurations, and present an approach to measure the similarity between a pair of pose context descriptors. Meanwhile, to improve testing efficiency, we propose a correspondence template ensemble method using the voting mechanism, which significantly reduces the amount of patch-wise matchings involved in distance calculation. With aforementioned strategies, the REGCT model can effectively and efficiently handle the spatial misalignment problem in Re-ID. Extensive experiments on five challenging benchmarks, including VIPeR, Road, PRID450S, 3DPES and CUHK01, evidence the superior performance of REGCT over other state-of-the-art approaches., Tech. Report. The source code is available at http://www.dabi.temple.edu/~hbling/code/gct.htm. arXiv admin note: text overlap with arXiv:1804.00242

Recently, the image visibility graphs (IVG) had introduced as simple algorithms by which images map into complex networks. However, current methods based on IVG use global statistical behaviors of the resulting graph to extract image features, which leads to loss of the local structural information of the image. To extract more informative image features by using the concept of IVG, we propose a new concept called matching intensity for image visibility graphs (MIIVG). The key idea of MIIVG is to separate the image into segments and represent the structural behavior of each with reference patterns and corresponding matching intensity. Theoretical analysis shows that the operation of MIIVG can be simplified to convolution operation and provides 256 convolution kernels with clear and apparent physical meaning, through which we can extract image features from multi-viewpoints and obtained more informative image features. Theoretical analysis and experiments demonstrate that MIIVG has a remarkable computing speed and is sufficiently stable against noise. Its high performance in image feature extraction we confirmed by two experiments. In keypoint matching experiments, MIIVG achieves a competitive result compared with SIFT. In texture classification experiments, compared with LBP, MIIVG is superior to LBP in calculation speed and classification effect. Compared with several current deep learning models, they all have the best feature extraction effect and very fast, but the features extracted by MIIVG are more concise. Also, MIIVG hardware requirements are lower, so it is easier to deploy. It is worth mentioning that MIIVG achieved 99.7% classification accuracy on the Multiband datasets, which is a state of the art performance on texture classification task of Multiband datasets and fully demonstrates the effectiveness of MIIVG.

We propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes (i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.

ObjectiveBecause of the lack of highly skilled experts, automated technologies that support electroencephalogram (EEG)-based in epilepsy diagnosis are advancing. Deep convolutional neural network-based models have been used successfully for detecting epileptic spikes, one of the biomarkers, from EEG. However, a sizeable number of supervised EEG records are required for training.ApproachThis study introduces the Satelight model, which uses the self-attention (SA) mechanism. The model was trained using a clinical EEG dataset labeled by five specialists, including 16,008 epileptic spikes and 15,478 artifacts from 50 children. The SA mechanism is expected to reduce the number of parameters and efficiently extract features from a small amount of EEG data. To validate the effectiveness, we compared various spike detection approaches with the clinical EEG data.Main resultsThe experimental results showed that the proposed method detected epileptic spikes more effectively than other models (accuracy = 0.876 and false positive rate = 0.133).SignificanceThe proposed model had only one-tenth the number of parameters as the other effective model, despite having such a high detection performance. Further exploration of the hidden parameters revealed that the model automatically attended to the EEG’s characteristic waveform locations of interest.

Purpose To develop a deep learning method for rapidly reconstructing T1 and T2 maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images. Methods A neural network was developed that outputs T1 and T2 values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based pattern matching in 58 healthy subjects at 1.5T. Results In simulations, the normalized root-mean-square error (nRMSE) for T1 was below 1% in myocardium, blood, and liver for all tested heart rates. For T2 , the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T1 or T2 observed in vivo between dictionary matching and deep learning was 3.6 ms for T1 and -0.2 ms for T2 . Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms. Conclusion A neural network is introduced for reconstructing cMRF T1 and T2 maps directly from undersampled spiral images in under 400 ms and is robust to arbitrary cardiac rhythms, which paves the way for rapid online display of cMRF maps.

Cross-lingual information retrieval (CLIR) allows user query in a different language from the language of target resources. Thus, translation is the key element in the query processing. There are three translation approaches: query, document, or hybrid query-document. However, query translation is very challenging due to the polysemy problem. Different linguistic nature of the languages will lead to ambiguity of meaning subsequently user’s true intention could be misinterpreted. This paper presents a semantic technique on query translation for a multilingual knowledge repository to improve the query processing. Offline translated documents or parallel corpora in English, Arabic, and Malay language including Jawi text was used as the data. Set of keywords were constructed preidentified by expert related to prophetic food. These keywords were annotated with the relevant Quranic verses, Hadith texts, Manuscript text images and scientific article determined by expert. The synonym and context-based translation was annotated together with the specific keyword. A query will do a three-way pattern match based on the keyword indexing list that link to the relevant documents. A one-stop knowledge repository on prophetic food was developed as a proof of concept using sources are from al-Quran, Hadith, classical manuscript, and scientific articles verified by experts to ensure the content authenticity and integrity.

Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph-driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. A CEP-based state optimization VEKG-Time Aggregated Graph (VEKG-TAG) is proposed over VEKG representation for faster event detection. VEKG-TAG is a spatiotemporal graph aggregation method that provides a summarized view of the VEKG graph over a given time length. We defined a set of nine event pattern rules for two domains (Activity Recognition and Traffic Management), which act as a query and applied over VEKG graphs to discover complex event patterns. To show the efficacy of our approach, we performed extensive experiments over 801 video clips across 10 datasets. The proposed VEKG approach was compared with other state-of-the-art methods and was able to detect complex event patterns over videos with F-Score ranging from 0.44 to 0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99% and 93% of VEKG nodes and edges, respectively, with 5.19X faster search time, achieving sub-second median latency of 4 20ms. This work was supported with the financial support of the Science Foundation Ireland grant SFI/13/RC/2094 and SFI/12/RC/2289 P2. peer-reviewed 2021-09-30

This letter proposes a novel partial pattern classification system that uses local binary patterns as a classifier and a content-addressable memory, which has the parallel search capability, to perform classification at a higher speed. The proposed pattern classification system uses Manhattan distance for class assignment and further uses logical resources on the Xilinx Virtex-7 field-programmable gate array to perform classification. Our proposed system assigns a class to a pattern of any size and shape in as less as $1.12~\mu \text{s}$ , which is 33% faster than the state-of-the-art pattern classification system.