Search results for: distant named entity recognition

Authors: Ali Ganoun, Wesam Algablawi, Wasim BenAnaif

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Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain traffic and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences.

Keywords: license plate recognition, localization, segmentation, recognition

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Authors: Delowar Hossain, Genci Capi

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This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: deep learning, genetic algorithm, object recognition, robot grasping

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Khenfer-Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

Abstract:

this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: voice command, HMM, TIMIT, noise, HTK, Arabic, speech recognition

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

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Authors: G. Tamulevičius, A. Serackis, T. Sledevič, D. Navakauskas

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We consider the biggest challenge in speech recognition – noise reduction. Traditionally detected transient noise pulses are removed with the corrupted speech using pulse models. In this paper we propose to cope with the problem directly in Dynamic Time Warping domain. Bidirectional Dynamic Time Warping algorithm for the recognition of isolated words impacted by transient noise pulses is proposed. It uses simple transient noise pulse detector, employs bidirectional computation of dynamic time warping and directly manipulates with warping results. Experimental investigation with several alternative solutions confirms effectiveness of the proposed algorithm in the reduction of impact of noise on recognition process – 3.9% increase of the noisy speech recognition is achieved.

Keywords: transient noise pulses, noise reduction, dynamic time warping, speech recognition

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

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Authors: Nada Farhani, Naim Terbeh, Mounir Zrigui

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The recognition of the objects contained in images has always presented a challenge in the field of research because of several difficulties that the researcher can envisage because of the variability of shape, position, contrast of objects, etc. In this paper, we will be interested in the recognition of objects. The classical Hough Transform (HT) presented a tool for detecting straight line segments in images. The technique of HT has been generalized (GHT) for the detection of arbitrary forms. With GHT, the forms sought are not necessarily defined analytically but rather by a particular silhouette. For more precision, we proposed to combine the results from the GHT with the results from a calculation of similarity between the histograms and the spatiograms of the images. The main purpose of our work is to use the concepts from recognition to generate sentences in Arabic that summarize the content of the image.

Keywords: recognition of shape, generalized hough transformation, histogram, spatiogram, learning

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Authors: Catarina Cruz, Ana Breda

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Tiling problems have been capturing the attention of many mathematicians due to their real-life applications. In this study, we deal with tilings of Zⁿ by Lee spheres, where n is a positive integer number, being these tilings related with error correcting codes on the transmission of information over a noisy channel. We focus our attention on the question ‘for what values of n and r does the n-dimensional Lee sphere of radius r tile Zⁿ?’. It seems that the n-dimensional Lee sphere of radius r does not tile Zⁿ for n ≥ 3 and r ≥ 2. Here, we prove that is not possible to tile Z⁷ with Lee spheres of radius 2 presenting a proof based on a combinatorial method and faithful to the geometric idea of the problem. The non-existence of such tilings has been studied by several authors being considered the most difficult cases those in which the radius of the Lee spheres is equal to 2. The relation between these tilings and error correcting codes is established considering the center of a Lee sphere as a codeword and the other elements of the sphere as words which are decoded by the central codeword. When the Lee spheres of radius r centered at elements of a set M ⊂ Zⁿ tile Zⁿ, M is a perfect r-error correcting Lee code of word length n over Z, denoted by PL(n, r). Our strategy to prove the non-existence of PL(7, 2) codes are based on the assumption of the existence of such code M. Without loss of generality, we suppose that O ∈ M, where O = (0, ..., 0). In this sense and taking into account that we are dealing with Lee spheres of radius 2, O covers all words which are distant two or fewer units from it. By the definition of PL(7, 2) code, each word which is distant three units from O must be covered by a unique codeword of M. These words have to be covered by codewords which dist five units from O. We prove the non-existence of PL(7, 2) codes showing that it is not possible to cover all the referred words without superposition of Lee spheres whose centers are distant five units from O, contradicting the definition of PL(7, 2) code. We achieve this contradiction by combining the cardinality of particular subsets of codewords which are distant five units from O. There exists an extensive literature on codes in the Lee metric. Here, we present a new approach to prove the non-existence of PL(7, 2) codes.

Keywords: Golomb-Welch conjecture, Lee metric, perfect Lee codes, tilings

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Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: Ravindra Dastikop, G. S. Thyagaraju, U. P. Kulkarni

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Even though the number of context aware applications is increasing day by day along with the users, till today there is no generic programming paradigm for context aware applications. This situation could be remedied by design and developing the appropriate context modeling and programming paradigm for context aware applications. In this paper, we are proposing the static context model and metrics for validating the expressiveness and understandability of the model. The proposed context modeling is a way of describing a situation of user using context entities , attributes and relationships .The model which is an extended and hybrid version of ER model, ontology model and Graphical model is specifically meant for expressing and understanding the user situation in context aware environment. The model is useful for understanding context aware problems, preparing documentation and designing programs and databases. The model makes use of context entity attributes relationship (CEAR) diagram for representation of association between the context entities and attributes. We have identified a new set of graphical notations for improving the expressiveness and understandability of context from the end user perspective .

Keywords: user context, context entity, context entity attributes, situation, sensors, devices, relationships, actors, expressiveness, understandability

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Authors: Bilgehan Gultekin, Tuba Gultekin

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Diplomatic public relations gives an ideal concept for recognition of palestine state in all over the europe. The first step of official recognition is approval of palestine state in international political organisations such as United Nations and Nato. So, diplomatic public relations provides a recognition process in communication scale. One of the aims of the study titled “Diplomatic Public Relations Techniques for Recognition of Palestine State in Europe” is to present some communication projects on diplomatic way. The study also aims at showing communication process at diplomatic level. The most important level of such kind of diplomacy is society based diplomacy. Moreover,The study provides a wider perspective that gives some creative diplomatic communication strategies for attracting society. To persuade the public for official recognition also is key element of this process. The study also finds new communication routes including persuasion techniques for society. All creative projects are supporting parts in original persuasive process of official recognition of Palestine.

Keywords: diplomatic public relations, diplomatic communication strategies, diplomatic communication, public relations

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Authors: Hiroshi Katanoda, Mohd Hazwan bin Yusof

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A theoretical investigation from the viewpoint of gas-dynamics and thermodynamics was carried out, in order to clarify the energy separation mechanism in a viscous compressible vortex, as a primary flow element in a uni-flow vortex tube. The mathematical solutions of tangential velocity, density and temperature in a viscous compressible vortical flow were used in this study. It is clear that a total temperature in the vortex core falls well below that distant from the vortex core in the radial direction, causing a region with higher total temperature, compared to the distant region, peripheral to the vortex core.

Keywords: energy separation mechanism, theoretical analysis, vortex tube, vortical flow

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Authors: Zhifei Hu, Feng Xia

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In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model.

Keywords: graph attention network, knowledge graph, recommendation, information propagation

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Authors: Elyes Zarrouk, Yassine Benayed, Faiez Gargouri

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This paper presents a new hybrid model for isolated Arabic words recognition. To do this, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities within the Dynamic Bayesian networks (DBN). This paper deals a comparative study between DBN and SVM/DBN systems for multi-dialect isolated Arabic words. Performance using SVM/DBN is found to exceed that of DBNs trained on an identical task, giving higher recognition accuracy for four different Arabic dialects. In fact, the average of recognition rates for the four dialects with SVM/DBN was 87.67% while 83.01% with DBN.

Keywords: dynamic Bayesian networks, hybrid models, supports vectors machine, Arabic isolated words

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Authors: Almagul Assainova , Dariya Abykenova, Liudmila Goncharenko, Sergey Sybachin, Saule Rakhimova, Abay Aman

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The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text.

Keywords: handwriting recognition system, image recognition, Kazakh font, machine learning, neural networks

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Authors: Mohammed Al-Zubaidi, Katherine Ong, Pravin Viswambaram, Steve McCombie, Oliver Oey, Jeremy Ong, Richard Gauci, Ronny Low, Dickon Hayne

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Objective: Lymph node involvement along with distant metastasis in a patient with invasive bladder cancer determines the disease survival, therefeor, it is an essential determinant of the therapeutic management and outcome. This retrospective study aims to determine the accuracy of FDG PET scan in detecting lymphatic involvement and distant metastatic urothelial cancer compared to conventional CT staging. Method: A retrospective review of 76 patients with UC or BC who underwent surgery or confirmatory biopsy that was staged with both CT and 18F-FDG-PET (up to 8 weeks apart) between 2015 and 2020. Fifty-sevenpatients (75%) had formal pelvic LN dissection or biopsy of suspicious metastasis. 18F-FDG-PET reports for positive sites were qualitative depending on SUV Max. On the other hand, enlarged LN by RECIST criteria 1.1 (>10 mm) and other qualitative findings suggesting metastasis were considered positive in CT scan. Histopathological findings from surgical specimens or image-guided biopsies were considered the gold standard in comparison to imaging reports. 18F-FDG-avid or enlarged pelvic LNs with surgically proven nodal metastasis were considered true positives. Performance characteristics of 18F-FDG-PET and CT, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (PPV), were calculated. Results: Pelvic LN involvement was confirmed histologically in 10/57 (17.5%) patients. Sensitivity, specificity, PPV and NPV of CT for detecting pelvic LN metastases were 41.17% (95% CI:18-67%), 100% (95% CI:90-100%) 100% (95% CI:59-100%) and 78.26% (95% CI:64-89%) respectively. Sensitivity, specificity, PPV and NPV of 18F-FDG-PET for detecting pelvic LN metastases were 62.5% (95% CI:35-85%), 83.78% (95% CI:68-94%), 62.5% (95% CI:35-85%), and 83.78% (95% CI:68-94%) respectively. Pre-operative staging with 18F-FDG-PET identified the distant metastatic disease in 9/76 (11.8%) patients who were occult on CT. This retrospective study suggested that 18F-FDG-PET may be more sensitive than CT for detecting pelvic LN metastases. 7/76 (9.2%) patients avoided cystectomy due to 18F-FDG-PET diagnosed metastases that were not reported on CT. Conclusion: 18F-FDG-PET is more sensitive than CT for pelvic LN metastases, which can be used as the standard modality of bladder cancer staging, as it may change the treatment by detecting lymph node metastasis that was occult in CT. Further research involving randomised controlled trials comparing the diagnostic yield of 18F-FDG-PET and CT in detecting nodal and distant metastasis in UC or BC is warranted to confirm our findings.

Keywords: FDG PET, CT scan, urothelial cancer, bladder cancer

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Jinsiang Shaw, Pik-Hoe Chen

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This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection.

Keywords: Automatic Tracking, Back Projection, Motion Recognition, Shoplifting

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Authors: Hasan Sungur, Evrim Baran, Benjamin Ahn, Aliye Karabulut Ilgu, Chris Rehmann, Cassandra Rutherford

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Engineering identity contributes to the professional and educational persistence of women engineers. A crucial factor contributing to the development of the engineering identity is recognition. Those without adequate recognition often do not succeed in positively building their identities. This research draws on Honneth’s recognition theory to identify factors impacting women civil engineers’ feelings of recognition as civil engineers. A survey was composed and distributed to 330 female alumni who graduated from the Department of Civil, Construction, and Environmental Engineering at Iowa State University in the last ten years. The survey items include demographics, perceptions of the identity of civil engineering, and factors that influence the recognition of civil engineering identities, such as views of society and family. Descriptive analysis of the survey responses revealed that the perceptions of civil engineering varied widely. Participants’ definitions of civil engineering included the terms: construction, design, and infrastructure. Almost half of the participants reported that the major reason to study civil engineering was their interest in the subject matter, and most reported that they were proud to be civil engineers. Many study participants reported that their parents see them as civil engineers. Treatment of institutions and the workplace were also considered as having a significant impact on the recognition of women civil engineers. Almost half of the participants reported that they felt isolated or ignored at work because of their gender. This research emphasizes the importance of recognition for the development of the civil engineering identity of women

Keywords: civil engineering, gender, identity, recognition

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Authors: Khenfer Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

Abstract:

This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: Arabic speech recognition, Hidden Markov Model (HMM), HTK, noise, TIMIT, voice command

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Authors: João Rato, Nuno Costa

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The human verbal communication is a two-way process which requires a mutual understanding that will result in some considerations. This kind of communication, also called dialogue, besides the supposed human agents it can also be performed between human agents and machines. The interaction between Men and Machines, by means of a natural language, has an important role concerning the improvement of the communication between each other. Aiming at knowing the performance of some speech recognition systems, this document shows the results of the accomplished tests according to the Word Error Rate evaluation method. Besides that, it is also given a set of information linked to the systems of Man-Machine communication. After this work has been made, conclusions were drawn regarding the Speech Recognition Systems, among which it can be mentioned their poor performance concerning the voice interpretation in noisy environments.

Keywords: automatic speech recognition, man-machine conversation, speech recognition, spoken dialogue systems, word error rate

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

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Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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