Search results for: text emotion recognition

Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Andriana Mkrtchyan, Vahe Khlghatyan

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The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

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Authors: Mario Kubek, Herwig Unger

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Centroid terms are single words that semantically and topically characterise text documents and so may serve as their very compact representation in automatic text processing. In the present paper, centroids are used to measure the relevance of text documents with respect to a given search query. Thus, a new graphbased paradigm for searching texts in large corpora is proposed and evaluated against keyword-based methods. The first, promising experimental results demonstrate the usefulness of the centroid-based search procedure. It is shown that especially the routing of search queries in interactive and decentralised search systems can be greatly improved by applying this approach. A detailed discussion on further fields of its application completes this contribution.

Keywords: search algorithm, centroid, query, keyword, co-occurrence, categorisation

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Authors: Insiya Bhalloo

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It is quite common in the Muslim faith for non-Arabic speakers to be able to convert written Arabic, especially Quranic Arabic, into a phonological code without significant semantic or syntactic knowledge. This is due to prior experience learning to read the Quran (a religious text written in Classical Arabic), from a very young age such as via enrolment in Quranic Arabic classes. As compared to native speakers of Arabic, these Arabic readers do not have a comprehensive morpho-syntactic knowledge of the Arabic language, nor can understand, or engage in Arabic conversation. The study seeks to investigate whether mere phonological experience (as indicated by the Arabic readers’ experience with Arabic phonology and the sound-system) is sufficient to cause phonological-interference during word recognition of previously-heard words, despite the participants’ non-native status. Both native speakers of Arabic and non-native speakers of Arabic, i.e., those individuals that learned to read the Quran from a young age, will be recruited. Each experimental session will include two phases: An exposure phase and a test phase. During the exposure phase, participants will be presented with Arabic words (n=40) on a computer screen. Half of these words will be common words found in the Quran while the other half will be words commonly found in Modern Standard Arabic (MSA) but either non-existent or prevalent at a significantly lower frequency within the Quran. During the test phase, participants will then be presented with both familiar (n = 20; i.e., those words presented during the exposure phase) and novel Arabic words (n = 20; i.e., words not presented during the exposure phase. ½ of these presented words will be common Quranic Arabic words and the other ½ will be common MSA words but not Quranic words. Moreover, ½ the Quranic Arabic and MSA words presented will be comprised of nouns, while ½ the Quranic Arabic and MSA will be comprised of verbs, thereby eliminating word-processing issues affected by lexical category. Participants will then determine if they had seen that word during the exposure phase. This study seeks to investigate whether long-term phonological memory, such as via childhood exposure to Quranic Arabic orthography, has a differential effect on the word-recognition capacities of native Arabic speakers and Arabic readers; we seek to compare the effects of long-term phonological memory in comparison to short-term phonological exposure (as indicated by the presentation of familiar words from the exposure phase). The researcher’s hypothesis is that, despite the lack of lexical knowledge, early experience with converting written Quranic Arabic text into a phonological code will help participants recall the familiar Quranic words that appeared during the exposure phase more accurately than those that were not presented during the exposure phase. Moreover, it is anticipated that the non-native Arabic readers will also report more false alarms to the unfamiliar Quranic words, due to early childhood phonological exposure to Quranic Arabic script - thereby causing false phonological facilitatory effects.

Keywords: modern standard arabic, phonological facilitation, phonological memory, Quranic arabic, word recognition

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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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: Michaela Porubanova

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In this exploratory study to examine the effects of emotional significance on change detection using the flicker paradigm, three different categories of scenes were randomly presented (neutral, positive and negative) in three different blocks. We hypothesized that because of the different effects on attention, performance in change detection tasks differs for scenes with different effective values. We found the greatest accuracy of change detection was for changes occurring in positive and negative scenes (compared with neutral scenes). Secondly and most importantly, changes in negative scenes (and also positive scenes, though not with statistical significance) were detected faster than changes in neutral scenes. Interestingly, women were less accurate than men in detecting changes in emotionally significant scenes (both negative and positive), i.e., women detected fewer changes in emotional scenes in the time limit of 40s. But on the other hand, women were quicker to detect changes in positive and negative images than men. The study makes important contributions to the area of the role of emotions on information processing. The role of emotion in attention will be discussed.

Keywords: attention, emotion, flicker task, IAPS

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Authors: Kayode A. Olaniyi, Tola. M. Osifeko, Adeola A. Ogunleye

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Text line segmentation is an important step in document image processing. It represents a labeling process that assigns the same label using distance metric probability to spatially aligned units. Text line detection techniques have successfully been implemented mainly in printed documents. However, processing of the handwritten texts especially unconstrained documents has remained a key problem. This is because the unconstrained hand-written text lines are often not uniformly skewed. The spaces between text lines may not be obvious, complicated by the nature of handwriting and, overlapping ascenders and/or descenders of some characters. Hence, text lines detection and segmentation represents a leading challenge in handwritten document image processing. Text line detection methods that rely on the traditional global projection profile of the text document cannot efficiently confront with the problem of variable skew angles between different text lines. Hence, the formulation of a horizontal line as a separator is often not efficient. This paper presents a technique to segment a handwritten document into distinct lines of text. The proposed algorithm starts, by partitioning the initial text image into columns, across its width into chunks of about 5% each. At each vertical strip of 5%, the histogram of horizontal runs is projected. We have worked with the assumption that text appearing in a single strip is almost parallel to each other. The algorithm developed provides a sliding window through the first vertical strip on the left side of the page. It runs through to identify the new minimum corresponding to a valley in the projection profile. Each valley would represent the starting point of the orientation line and the ending point is the minimum point on the projection profile of the next vertical strip. The derived text-lines traverse around any obstructing handwritten vertical strips of connected component by associating it to either the line above or below. A decision of associating such connected component is made by the probability obtained from a distance metric decision. The technique outperforms the global projection profile for text line segmentation and it is robust to handle skewed documents and those with lines running into each other.

Keywords: connected-component, projection-profile, segmentation, text-line

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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: Abdelhadi Nadjer

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The structure of the text to the systemic school -(glossématique-Helmslev). At the beginning of the note we have a cursory look around the concepts of general linguistics The science that studies scientific study of human language based on the description and preview the facts away from the trend of education than we gave a detailed overview the founder of systemic school and most important customers and more methods and curriculum theory and analysis they extend to all humanities, practical action each offset by a theoretical and the procedure can be analyzed through the elements that pose as another method we talked to its links with other language schools where they are based on the sharp criticism of the language before and deflected into consideration for the field of language and its erection has outside or language network and its participation in the actions (non-linguistic) and after that we started our Valglosamatik analytical structure of the text is ejected text terminal or all of the words to was put for expression. This text Negotiable divided into types in turn are divided into classes and class should not be carrying a contradiction and be inclusive. It is on the same materials as described relationships that combine language and seeks to describe their relations and identified.

Keywords: text, language schools, linguistics, human language

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Authors: Tina Ternes, Florian Kleinau

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The study investigates the content and triggers of mind wandering (MW) in readers of fictional texts. It asks whether readers’ MW is productive (text-related) or unproductive (text-unrelated). Methodologically, it bridges the gap between narratological and data-driven approaches by utilizing a sentence-by-sentence self-paced reading paradigm combined with thought probes in the reading of an excerpt of A. L. Kennedy’s “Baby Blue”. Results show that the contents of MW can be linked to text properties. We validated the role of self-reference in MW and found prediction errors to be triggers of MW. Results also indicate that the content of MW often travels along the lines of the text at hand and can thus be viewed as productive and integral to interpretation.

Keywords: narratology, mind wandering, reading fiction, meta cognition

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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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: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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

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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: 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: Hyojung Shin, Munhee Kweon

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Non-suicide Self Injuri (NSSI) can be defined as the act of an individual who does not intend to die directly and intentionally damaging his or her body tissues. According to a study conducted by the Korean Ministry of Education in 2018, the NSSI is widely spreading among teenagers, with 7.9 percent of all middle school students and 6.4 percent of high school students reporting experience in NSSI. As such, it is understood that the first time of the NSSI is in adolescence. However, the NSSI may not start and stop at a certain time, but may last longer. However, despite the widespread prevalence of NSSI among teenagers, little is known about the process and maintenance of NSSI college students on a continuous development basis. Korea's NSSI research trends are mainly focused on individual internal vulnerabilities (high levels of painful emotions/awareness, lack of pain tolerance) and interpersonal vulnerabilities (poor communication skills and social problem solving), and little studies have been done on individuals' unique characteristics and environmental factors such as substrate or environmental vulnerability factors. In particular, environmental factors are associated with the occurrence of NSSI by acting as a vulnerability factor that can interfere with the emotional control of individuals, whereas individual factors play a more direct role by contributing to the maintenance of NSSI, so it is more important to consider this for personal environmental involvement in NSSI. This study focused on the Fight-Flight-Freeze System as a factor in the defensive avoidance system of Reward Sensitivity in individual factors. Also, Environmental factors include the level of expressed emotion in family. Wedig and Nock (2007) said that if parents with a self-critical cognitive style take the form of criticizing their children, the experience of NSSI increases. The high level of parental criticism is related to the increasing frequency of NSSI acts as well as to serious levels of NSSI. If the normal coping mechanism fails to control emotions, people want to overcome emotional difficulties even through NSSI, and emotional disturbances experienced by individuals within an unsupported social relationship increase vulnerability to NSSI. Based on these theories, this study is to find ways to prevent NSSI and intervene in counseling effectively by verifying the differences between the characteristics experienced NSSI persons and non-experienced NSSI persons. Therefore, the purpose of this research was to examine the relationship of Fight-Flight-Freeze System (FFFS), level of expressed emotion in family and emotion regulation difficulties, comparing those who experienced Non-Suicidal Self-Injury (NSSI) with those who did not experienced Non-Suicidal Self-Injury (NSSI). The data were collected from university students in Seoul Korea and Gyeonggi-do province. 99 subjects were experienced student of NSSI, while 375 were non- experienced student of NSSI. The results of this study are as follows. First, the result of t-test indicated that NSSI attempters showed a significant difference in fight-flight-freeze system, level of expressed emotion and emotion regulation difficulties, compared with non-attempters. Second, fight-flight-freeze system, level of expressed emotion in family and emotion regulation difficulties of NSSI attempters showed a significant difference in correlation. The correlation was significant only freeze system of fight-flight-freeze system, Level of expressed emotion in family and emotion regulation difficulties. Third, freeze system and level of expressed emotion in family predicted emotion regulation difficulties of NSSI attempters. Fight-freeze system and level of expressed emotion in family predicted emotion regulation difficulties of non-NSSI attempters. Lastly, Practical implications for counselors and limitations of this study are discussed.

Keywords: fight-flight-freeze system, level of expressed emotion in family, emotion regulation difficulty, non-suicidal self injury

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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: Amber Hatch, Eric Wright, Feihong Wang

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Research suggests that attachment in close relationships affects one’s emotional processes, mindfulness, conflict-management behaviors, and interpersonal interactions. Attachment insecurity is often associated with maladaptive social interactions and suboptimal relationship qualities. Past studies have considered how the nature of emotion regulation and mindfulness in teachers may be related to student or classroom outcomes. Still, no research has examined how the relationship between such internal experiences and classroom management outcomes may also be related to teachers’ attachment insecurity. This study examined the interrelationships between teachers’ attachment insecurity, mindfulness tendencies, emotion regulation abilities, and classroom management efficacy as indexed by students’ classroom behavior and teachers’ response effectiveness. Teachers’ attachment insecurity was evaluated using the global ECRS-SF, which measures both attachment anxiety and avoidance. The present study includes a convenient sample of 357 American elementary school teachers who responded to a survey regarding their classroom management efficacy, attachment in/security, dispositional mindfulness, emotion regulation strategies, and difficulties in emotion regulation, primarily assessed via pre-existing instruments. Good construct validity was demonstrated for all scales used in the survey. Sample demographics, including gender (94% female), race (92% White), age (M = 41.9 yrs.), years of teaching experience (M = 15.2 yrs.), and education level were similar to the population from which it was drawn, (i.e., American elementary school teachers). However, white women were slightly overrepresented in our sample. Correlational results suggest that teacher attachment insecurity is associated with poorer classroom management efficacy as indexed by students’ disruptive behavior and teachers’ response effectiveness. Attachment anxiety was a much stronger predictor of adverse student behaviors and ineffective teacher responses to adverse behaviors than attachment avoidance. Mindfulness, emotion regulation abilities, and years of teaching experience predicted positive classroom management outcomes. Attachment insecurity and mindfulness were more strongly related to frequent adverse student behaviors, while emotion regulation abilities were more strongly related to teachers’ response effectiveness. The teaching experience was negatively related to attachment insecurity and positively related to mindfulness and emotion regulation abilities. Although the data were cross-sectional, path analyses revealed that attachment insecurity is directly related to classroom management efficacy. Through two routes, this relationship is further mediated by emotion regulation and mindfulness in teachers. The first route of indirect effect suggests double mediation by teacher’s emotion regulation and then teacher mindfulness in the relationship between teacher attachment insecurity and classroom management efficacy. The second indirect effect suggests mindfulness directly mediated the relationship between attachment insecurity and classroom management efficacy, resulting in improved model fit statistics. However, this indirect effect is much smaller than the double mediation route through emotion regulation and mindfulness in teachers. Given the significant predication of teacher attachment insecurity, mindfulness, and emotion regulation on teachers’ classroom management efficacy both directly and indirectly, the authors recommend improving teachers’ classroom management efficacy via a three-pronged approach aiming at enhancing teachers’ secure attachment and supporting their learning adaptive emotion regulation strategies and mindfulness techniques.

Keywords: Classroom management efficacy, student behavior, teacher attachment, teacher emotion regulation, teacher mindfulness

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Authors: Merav Meiron-Goren, Orna Braun-Lewensohn, Tal Litvak-Hirsh

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In recent years, there has been an increasing tendency towards separation and divorce in relationships. Nevertheless, many couples in a first marriage do not anticipate this as a probable possibility and do not make any preparation for it. Same sex couples establishing a family encounter a much more complicated situation than do heterosexual couples. Although there is a trend towards legal recognition of same sex marriage, many countries, including Israel, do not recognize it. The absence of legal recognition or the existence of partial recognition creates complexity for these couples. They have to fight for their right to establish a family, like the recognition of the biological child of a woman, as a child of her woman spouse too, or the option of surrogacy for a male couple who want children, and more. The lack of legal recognition is burden on the lives of these couples. In the absence of clear norms regarding the conduct of the family unit, the couples must define for themselves the family structure, and deal with everyday dilemmas that lack institutional solutions. This may increase the friction between the two couple members, and it is one of the factors that make it difficult for them to maintain the relationship. This complexity exists, perhaps even more so, in separation. The end of relationship is often accompanied by a deep crisis, causing pain and stress. In most cases, there are also other conflicts that must be settled. These are more complicated when rights are in doubt or do not exist at all. Complex issues for separating same sex couples may include matters of property, recognition of parenthood, and care and support for the children. The significance of the study is based on the fact that same sex relationships are becoming more and more widespread, and are an integral part of the society. Even so, there is still an absence of research focusing on such relationships and their ending. The objective of the study is to research the perceptions of same sex couples regarding the possibility of separation, preparing for it, conflict management and resolving disputes through the separation process. It is also important to understand the point of view of couples that have gone through separation, how they coped with the emotional and practical difficulties involved in the separation process. The doctoral research will use a qualitative research method in a phenomenological approach, based on semi-structured in-depth interviews. The interviewees will be divided into three groups- at the beginning of a relationship, during the separation crisis and after separation, with a time perspective, with about 10 couples from each group. The main theoretical model serving as the basis of the study will be the Lazarus and Folkman theory of coping with stress. This model deals with the coping process, including cognitive appraisal of an experience as stressful, appraisal of the coping resources, and using strategies of coping. The strategies are divided into two main groups, emotion-focused forms of coping and problem-focused forms of coping.

Keywords: conflict management, coping, legal recognition, same-sex relationship, separation

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Authors: Norzaliza Md. Nor, Sh-Hussain Salleh, Mahyar Hamedi, Hadrina Hussain, Wahab Abdul Rahman

Abstract:

Motivational factor is mainly the students’ desire to involve in learning process. However, it also depends on the goal towards their involvement or non-involvement in academic activity. Even though, the students’ motivation might be in the same level, but the basis of their motivation may differ. In this study, it focuses on the intrinsic motivational factor which student enjoy learning or feeling of accomplishment the activity or study for its own sake. The intrinsic motivational factor of students in learning mathematics and science has found as difficult to be achieved because it depends on students’ interest. In the Program for International Student Assessment (PISA) for mathematics and science, Malaysia is ranked as third lowest. The main problem in Malaysian educational system, students tend to have extrinsic motivation which they have to score in exam in order to achieve a good result and enrolled as university students. The use of electroencephalogram (EEG) signals has found to be scarce especially to identify the students’ intrinsic motivational factor in learning science and mathematics. In this research study, we are identifying the correlation between precursor emotion and its dynamic emotion to verify the intrinsic motivational factor of students in learning mathematics and science. The 2-D Affective Space Model (ASM) was used in this research in order to identify the relationship of precursor emotion and its dynamic emotion based on the four basic emotions, happy, calm, fear and sad. These four basic emotions are required to be used as reference stimuli. Then, in order to capture the brain waves, EEG device was used, while Mel Frequency Cepstral Coefficient (MFCC) was adopted to be used for extracting the features before it will be feed to Multilayer Perceptron (MLP) to classify the valence and arousal axes for the ASM. The results show that the precursor emotion had an influence the dynamic emotions and it identifies that most students have no interest in mathematics and science according to the negative emotion (sad and fear) appear in the EEG signals. We hope that these results can help us further relate the behavior and intrinsic motivational factor of students towards learning of mathematics and science.

Keywords: EEG, MLP, MFCC, intrinsic motivational factor

Procedia PDF Downloads 334

Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

Procedia PDF Downloads 202

Authors: Bilgehan Gultekin, Tuba Gultekin

Abstract:

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

Procedia PDF Downloads 409