Search results for: scene text recognition

Authors: Kayode A. Olaniyi, Olabanji F. Omotoye, Adeola A. Ogunleye

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The skew detection and correction form an important part of digital document analysis. This is because uncompensated skew can deteriorate document features and can complicate further document image processing steps. Efficient text document analysis and digitization can rarely be achieved when a document is skewed even at a small angle. Once the documents have been digitized through the scanning system and binarization also achieved, document skew correction is required before further image analysis. Research efforts have been put in this area with algorithms developed to eliminate document skew. Skew angle correction algorithms can be compared based on performance criteria. Most important performance criteria are accuracy of skew angle detection, range of skew angle for detection, speed of processing the image, computational complexity and consequently memory space used. The standard Hough Transform has successfully been implemented for text documentation skew angle estimation application. However, the standard Hough Transform algorithm level of accuracy depends largely on how much fine the step size for the angle used. This consequently consumes more time and memory space for increase accuracy and, especially where number of pixels is considerable large. Whenever the Hough transform is used, there is always a tradeoff between accuracy and speed. So a more efficient solution is needed that optimizes space as well as time. In this paper, an improved Hough transform (HT) technique that optimizes space as well as time to robustly detect document skew is presented. The modified algorithm of Hough Transform presents solution to the contradiction between the memory space, running time and accuracy. Our algorithm starts with the first step of angle estimation accurate up to zero decimal place using the standard Hough Transform algorithm achieving minimal running time and space but lacks relative accuracy. Then to increase accuracy, suppose estimated angle found using the basic Hough algorithm is x degree, we then run again basic algorithm from range between ±x degrees with accuracy of one decimal place. Same process is iterated till level of desired accuracy is achieved. The procedure of our skew estimation and correction algorithm of text images is implemented using MATLAB. The memory space estimation and process time are also tabulated with skew angle assumption of within 00 and 450. The simulation results which is demonstrated in Matlab show the high performance of our algorithms with less computational time and memory space used in detecting document skew for a variety of documents with different levels of complexity.

Keywords: hough-transform, skew-detection, skew-angle, skew-correction, text-document

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Ahmed Hammed, Wady Naanaa

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In this paper, we propose a general approach to deal with the problem of a convolutive mixture of images. We use a direct blind source separation method by adding only one non-statistical justified constraint describing the relationships between different mixing matrix at the aim to make its resolution easy. This method can be applied, provided that this constraint is known, to degraded document affected by the overlapping of text-patterns and images. This is due to chemical and physical reactions of the materials (paper, inks,...) occurring during the documents aging, and other unpredictable causes such as humidity, microorganism infestation, human handling, etc. We will demonstrate that this problem corresponds to a convolutive mixture of images. Subsequently, we will show how the validation of our method through numerical examples. We can so obtain clear images from unreadable ones which can be caused by pages superposition, a phenomenon similar to that we find every often in archival documents.

Keywords: blind source separation, convoluted mixture, degraded documents, text-patterns overlapping

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Authors: I. Pina, M. Painho

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Increased knowledge about the nature of place and the conditions under which space becomes place is a key factor for better urban planning and place-making. Although there is a broad consensus on the relevance of this knowledge, difficulties remain in relating the theoretical framework about place and urban management. Issues related to representation of places are among the greatest obstacles to overcome this gap. With this critical discussion, based on literature review, we intended to explore, in a common framework for geographical analysis, the potential of stories to spell out place meanings, bringing together qualitative text analysis and text mining in order to capture and represent the singularity contained in each person's life history, and the patterns of social processes that shape places. The development of this reasoning is based on the extensive geographical thought about place, and in the theoretical advances in the field of Geographic Information Science (GISc).

Keywords: discourse analysis, geographic information science place, place-making, stories

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Maria Antonietta Marongiu

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Propositional information in discourse is made coherent, intelligible, and persuasive through metadiscourse. The linguistic and rhetorical choices that writers/speakers make to organize and negotiate content matter are intended to help relate a text to its context. Besides, they help the audience to connect to and interpret a text according to the values of a specific discourse community. Based on these assumptions, this work aims to analyse the use of metadiscourse in the spoken performance of teachers in online EFL, ESP, and subject-teacher courses taught in English to non-native learners in higher education. In point of fact, the global spread of Covid 19 has forced universities to transition their in-class courses to online delivery. This has inevitably placed on the instructor a heavier interactional responsibility compared to in-class courses. Accordingly, online delivery needs greater structuring as regards establishing the reader/listener’s resources for text understanding and negotiating. Indeed, in online as well as in in-class courses, lessons are social acts which take place in contexts where interlocutors, as members of a community, affect the ways ideas are presented and understood. Following Hyland’s Interactional Model of Metadiscourse (2005), this study intends to investigate Teacher Talk in online academic courses during the Covid 19 lock-down in Italy. The selected corpus includes the transcripts of online EFL and ESP courses and subject-teachers online courses taught in English. The objective of the investigation is, firstly, to ascertain the presence of metadiscourse in the form of interactive devices (to guide the listener through the text) and interactional features (to involve the listener in the subject). Previous research on metadiscourse in academic discourse, in college students' presentations in EAP (English for Academic Purposes) lessons, as well as in online teaching methodology courses and MOOC (Massive Open Online Courses) has shown that instructors use a vast array of metadiscoursal features intended to express the speakers’ intentions and standing with respect to discourse. Besides, they tend to use directions to orient their listeners and logical connectors referring to the structure of the text. Accordingly, the purpose of the investigation is also to find out whether metadiscourse is used as a rhetorical strategy by instructors to control, evaluate and negotiate the impact of the ongoing talk, and eventually to signal their attitudes towards the content and the audience. Thus, the use of metadiscourse can contribute to the informative and persuasive impact of discourse, and to the effectiveness of online communication, especially in learning contexts.

Keywords: discourse analysis, metadiscourse, online EFL and ESP teaching, rhetoric

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Vera Lucia Teixeira da Silva, Sandra Regina Buttros Gattolin de Paula

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The present study focuses on academic literacy and addresses the impact of semantic-discursive resources on the constitution of genres that are produced in such context. The research considers the development of writing in the academic context in Portuguese. Researches that address academic literacy and the characteristics of the texts produced in this context are rare, mainly with focus on the development of writing, considering three variables: the constitution of the writer, the perception of the reader/interlocutor and the organization of the informational text flow. The research aims to map the semantic-discursive resources of the written register in texts of several genres and produced by students in the first semester of the undergraduate course in Letters. The hypothesis raised is that writing in the academic environment is not a recurrent literacy practice for these learners and can be explained by the ontogenetic and phylogenetic nature of language development. Qualitative in nature, the present research has as empirical data texts produced in a half-yearly course of Reading and Textual Production; these data result from the proposition of four different writing proposals, in a total of 600 texts. The corpus is analyzed based on semantic-discursive resources, seeking to contemplate relevant aspects of language (grammar, discourse and social context) that reveal the choices made in the reader/writer interrelationship and the organizational flow of the Text. Among the semantic-discursive resources, the analysis includes three resources, including (a) appraisal and negotiation to understand the attitudes negotiated (roles of the participants of the discourse and their relationship with the other); (b) ideation to explain the construction of the experience (activities performed and participants); and (c) periodicity to outline the flow of information in the organization of the text according to the genre it instantiates. The results indicate the organizational difficulties of the flow of the text information. Cartography contributes to the understanding of the way writers use language in an effort to present themselves, evaluate someone else’s work, and communicate with readers.

Keywords: academic writing, Portuguese mother tongue, semantic-discursive resources, academic context

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Authors: Yufen Qin

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Topic-to-Essay generation is a challenging task in Natural language processing, which aims to generate novel, diverse, and topic-related text based on user input. Previous research has overlooked the generation of articles under the constraints of event elements, resulting in issues such as incomplete event elements and logical inconsistencies in the generated results. To fill this gap, this paper proposes an event-constrained approach for a topic-to-essay generation that enforces the completeness of event elements during the generation process. Additionally, a language model is employed to verify the logical consistency of the generated results. Experimental results demonstrate that the proposed model achieves a better BLEU-2 score and performs better than the baseline in terms of subjective evaluation on a real dataset, indicating its capability to generate higher-quality topic-related text.

Keywords: event element, language model, natural language processing, topic-to-essay generation.

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Ingrid Argote, John Archila, Marcelo Becker

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In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

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Authors: Shaza Melies, Saadeya Salem, Seham Kareh

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Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience. Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience.

Keywords: domestication, dubbing, Mulan, translation theories

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Fariba Ebrahimi, Mehdi Ghorbani, Ali Salajegheh

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Comprehensively water management considers economic, environmental, technical and social and also sustainability of water resources for future generations. Grassland management implies cooperative approach and involves all stakeholders and also introduces issues to managers, decision and policy makers. Solving these issues needs integrated and system approach. According to the recognition of actors or key persons in necessary to apply cooperative management of Water. Therefore, based on stakeholder analysis and social network analysis can be used to demonstrate the most effective actors for environmental decisions. In this research, social powers according are specified to social network approach at Water utilizers’ level of Natural in Jirood catchment of Latian basin. In this paper, utilizers of water resources were recognized using field trips and then, trust and collaboration matrix produced using questionnaires. In the next step, degree centrality index were Examined. Finally, geometric position of each actor was illustrated in the network. The results of the research based on centrality index have a key role in recognition of cooperative management of Water in Jirood and also will help managers and planners of water in the case of recognition of social powers in order to organization and implementation of sustainable management of Water.

Keywords: social network analysis, water co-management, social power, centrality index, local stakeholders network, Jirood catchment

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Authors: Priyanka Bharti

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Decades ago designs were based on common sense and tradition, but after an enhancement in visualization technology and research, we are now able to comprehend the cognitive ability involved in the decoding of the visual information. However, many fields in visuals need intense research to deliver an efficient explanation for the events. Visuals are an information representation mode through images, symbols and graphics. It plays an impactful role in decision making by facilitating quick recognition, comprehension, and analysis of a situation. They enhance problem-solving capabilities by enabling the processing of more data without overloading the decision maker. As research proves that, visuals offer an improved learning environment by a factor of 400 compared to textual information. Visual information engages learners at a cognitive level and triggers the imagination, which enables the user to process the information faster (visuals are processed 60,000 times faster in the brain than text). Appropriate information, visualization, and its presentation are known to aid and intensify the decision-making process for the users. However, most literature discusses the role of visual aids in comprehension and decision making during normal conditions alone. Unlike emergencies, in a normal situation (e.g. our day to day life) users are neither exposed to stringent time constraints nor face the anxiety of survival and have sufficient time to evaluate various alternatives before making any decision. An emergency is an unexpected probably fatal real-life situation which may inflict serious ramifications on both human life and material possessions unless corrective measures are taken instantly. The situation demands the exposed user to negotiate in a dynamic and unstable scenario in the absence or lack of any preparation, but still, take swift and appropriate decisions to save life/lives or possessions. But the resulting stress and anxiety restricts cue sampling, decreases vigilance, reduces the capacity of working memory, causes premature closure in evaluating alternative options, and results in task shedding. Limited time, uncertainty, high stakes and vague goals negatively affect cognitive abilities to take appropriate decisions. More so, theory of natural decision making by experts has been understood with far more depth than that of an ordinary user. Therefore, in this study, the author aims to understand the role of visual aids in supporting rapid comprehension to take appropriate decisions during an emergency situation.

Keywords: cognition, visual, decision making, graphics, recognition

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Authors: Mehrnoosh Pirhayati

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In this study, the researcher tried to propose a model of Translation Criticism (TC) regarding the phenomenon of Translation Quality Assessment (TQA). With changing the general view on re/writing as an illegal act, the researcher defined a scale for the act of translation and determined the redline of translation with other products. This research attempts to show TC as a related phenomenon to TQA. This study shows that TQA with using the rules and factors of TC as depicted in both product-oriented analysis and process-oriented analysis, determines the orientation or the level of the quality of translation. This study also depicts that TC, regarding TQA’s perspective, reveals the aim of the translation of original text and the root of ideological manipulation and re/writing. On the other hand, this study stresses the existence of a direct relationship between the linguistic materials and semiotic codes of a text or book. This study can be fruitful for translators, scholars, translation criticizers, and translation quality assessors, and also it is applicable in the area of pedagogy.

Keywords: a model of translation criticism, a model of translation quality assessment, critical discourse analysis (CDA), re/writing, translation criticism (TC), translation quality assessment (TQA)

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Asmaa Fathy Mohamed Desoky

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The subject of this research is the inference of intentionality and context from the verses of the Meccan surahs, which include the paradox of reward and punishment, applied to the duality of disbelief and faith; The Holy Quran is the most important sacred linguistic reference in the Arabic language because it is rich in all the rules of the language in addition to the linguistic miracle. the Quranic text is a first-class intentional text, sent down to convey something to the recipient (Muhammad first and then communicates it to Muslims) and influence and convince him, which opens the door to many Ijtihad; a desire to reach the will of Allah and his intention from his words Almighty. Intentionality as a term is one of the most important deliberative terms, but it will be modified to suit the Quranic discourse, especially since intentionality is related to intention-as it turned out earlier - that is, it turns the reader or recipient into a predictor of the unseen, and this does not correspond to the Quranic discourse. Hence, in this research, a set of dualities will be identified that will be studied in order to clarify the meaning of them according to the opinions of previous interpreters in accordance with the sanctity of the Quranic discourse, which is intentionally related to the dualities of reward and punishment, such as: the duality of disbelief and faith, noting that it is a duality that combines opposites and Paradox on one level, because it may be an external paradox between action and reaction, and may be an internal paradox in matters related to faith, and may be a situational paradox in a specific event or a certain fact. It should be noted that the intention of the Qur'anic text is fully realized in form and content, in whole and in part, and this research includes a presentation of some applied models of the issues of intention and context that appear in the verses of the paradox of reward and punishment in the Meccan surahs in Quraan.

Keywords: intentionality, context, the paradox, reward, punishment, Meccan surahs

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Authors: Martha Serrano-Arias, Martha Frías-Armenta

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Significant results have been found both supporting universality of emotion recognition and cultural background influence. Thus, the aim of this research was to test a Mexican version of the MTSI in different cultures to find differences in their performance. The MTSI-Mx assesses through a scenario approach were subjects must evaluate real persons. Two target persons were used for the construction, a man (FS) and a woman (AD). The items were grouped in four variables: Picture, Video, and FS and AD scenarios. The test was applied to 201 students from Mexico and Germany. T-test for picture and FS scenario show no significance. Video and AD had a significance at the 5% level. Results show slight differences between cultures, although a more comprehensive research is needed to conclude which culture can perform better in this kind of assessments.

Keywords: emotion recognition, MTSI, social intelligence, transcultural study

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, unsupervised training, text retrieval

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Authors: Moshe Wasserblat, Jonathan Mamou, Oren Pereg

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Natural Language Processing (NLP) has made a major leap in the last few years, in practical integration into medical solutions; for example, extracting clinical concepts from medical texts such as medical condition, medication, treatment, and symptoms. However, training and deploying those models in real environments still demands a large amount of annotated data and NLP/Machine Learning (ML) expertise, which makes this process costly and time-consuming. We present a practical and efficient method for clinical concept extraction that does not require costly labeled data nor ML expertise. The method includes three steps: Step 1- the user injects a large in-domain text corpus (e.g., PubMed). Then, the system builds a contextual model containing vector representations of concepts in the corpus, in an unsupervised manner (e.g., Phrase2Vec). Step 2- the user provides a seed set of terms representing a specific medical concept (e.g., for the concept of the symptoms, the user may provide: ‘dry mouth,’ ‘itchy skin,’ and ‘blurred vision’). Then, the system matches the seed set against the contextual model and extracts the most semantically similar terms (e.g., additional symptoms). The result is a complete set of terms related to the medical concept. Step 3 –in production, there is a need to extract medical concepts from the unseen medical text. The system extracts key-phrases from the new text, then matches them against the complete set of terms from step 2, and the most semantically similar will be annotated with the same medical concept category. As an example, the seed symptom concepts would result in the following annotation: “The patient complaints on fatigue [symptom], dry skin [symptom], and Weight loss [symptom], which can be an early sign for Diabetes.” Our evaluations show promising results for extracting concepts from medical corpora. The method allows medical analysts to easily and efficiently build taxonomies (in step 2) representing their domain-specific concepts, and automatically annotate a large number of texts (in step 3) for classification/summarization of medical reports.

Keywords: clinical concepts, concept expansion, medical records annotation, medical records summarization

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Authors: Darshna Sharma, Suban K. Sahoo

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The detection of intracellular fluoride in human cancer cell HeLa was achieved by chemosensors derived from vitamin B6 cofactors using fluorescence imaging technique. These sensors were first synthesized by condensation of pyridoxal/pyridoxal phosphate with 2-amino(thio)phenol. The anion recognition ability was explored by experimental (UV-VIS, fluorescence and 1H NMR) and theoretical DFT [(B3LYP/6-31G(d,p)] methods in DMSO and mixed DMSO-H2O system. All the developed sensors showed both naked-eye detectable color change and remarkable fluorescence enhancement in the presence of F- and AcO-. The anion recognition was occurred through the formation of hydrogen bonded complexes between these anions and sensor, followed by the partial deprotonation of sensor. The detection limit of these sensors were down to micro(nano) molar level of F- and AcO-.

Keywords: chemosensors, fluoride, acetate, turn-on, live cells imaging, DFT

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Authors: Kristine D. de Leon, Junifer A. Abatayo, Jose Cristina M. Pariña

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The current study is a comparative analysis of the use of shell nouns as a cohesive device (CD) in an English for Second Language (ESL) setting in order to identify their use and relationship in the quality of second language (L2) writing. As these nouns were established to anticipate the meaning within, across or outside the text, their use has fascinated writing researchers. The corpus of the study included published articles from reputable journals and graduate students’ papers in order to analyze the frequency of shell nouns using “highly prevalent” nouns in the academic community, to identify the different lexicogrammatical patterns where these nouns occur and to the functions connected with these patterns. The result of the study implies that published authors used more shell nouns in their paper than graduate students. However, the functions of the different lexicogrammatical patterns for the frequently occurring shell nouns are somewhat similar. These results could help students in enhancing the cohesion of their text and in comprehending it.

Keywords: anaphoric, cataphoric, lexico-grammatical, shell nouns

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Authors: Ajay Kadam, Ramesh Kagalkar

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The proposed research objective is to add to a framework for programmed recognition of sound. In this framework the real errand is to distinguish any information sound stream investigate it & anticipate the likelihood of diverse sounds show up in it. To create and industrially conveyed an adaptable sound web crawler a flexible sound search engine. The calculation is clamor and contortion safe, computationally productive, and hugely adaptable, equipped for rapidly recognizing a short portion of sound stream caught through a phone microphone in the presence of frontal area voices and other predominant commotion, and through voice codec pressure, out of a database of over accessible tracks. The algorithm utilizes a combinatorial hashed time-recurrence group of stars examination of the sound, yielding ordinary properties, for example, transparency, in which numerous tracks combined may each be distinguished.

Keywords: fingerprinting, pure tone, white noise, hash function

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Authors: Somayeh Abedian, Pirhossein Kolivand, Hamid Reza Lornejad, Amin Karampour, Ebrahim Keshavarz Safari

Abstract:

For emergency and relief service providers such as pre-hospital emergencies, quick arrival at the scene of an accident or any EMS mission is one of the most important requirements of effective service delivery. Response time (the interval between the time of the call and the time of arrival on scene) is a critical factor in determining the quality of pre-hospital Emergency Medical Services (EMS). This is especially important for heart attack, stroke, or accident patients. Location-based e-services can be broadly defined as any service that provides information pertinent to the current location of an active mobile handset or precise address of landline phone call at a specific time window, regardless of the underlying delivery technology used to convey the information. According to research, one of the effective methods of meeting this goal is determining the location of the caller via the cooperation of landline and mobile phone operators in the country. The follow-up of the Communications Regulatory Authority (CRA) organization has resulted in the receipt of two separate secured electronic web services. Thus, to ensure human privacy, a secure technical architecture was required for launching the services in the pre-hospital EMS information management system. In addition, to quicken medics’ arrival at the patient's bedside, rescue vehicles should make use of an intelligent transportation system to estimate road traffic using a GPS-based mobile navigation system independent of the Internet. This paper seeks to illustrate the architecture of the practical national model used by the Iranian EMS organization.

Keywords: response time, geographic location inquiry service (GLIS), location-based service (LBS), emergency medical services information system (EMSIS)

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Authors: T. Hari Prasath, P. Ithaya Rani

Abstract:

In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

Abstract:

In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Hee-Choul Kwon, Heeyong Kwon

Abstract:

Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

Procedia PDF Downloads 250