Search results for: modulation recognition

Authors: Sham Kidane, Fitsum Gaim, Ibrahim Abdella, Sirak Asmerom, Yoel Ghebrihiwot, Simon Mulugeta, Natnael Ambassager

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The lack of annotated datasets is a bottleneck to the progress of NLP in low-resourced languages. The work presented here consists of large-scale annotated datasets and models for the named entity recognition (NER) system for the Tigrinya language. Our manually constructed corpus comprises over 340K words tagged for NER, with over 118K of the tokens also having parts-of-speech (POS) tags, annotated with 12 distinct classes of entities, represented using several types of tagging schemes. We conducted extensive experiments covering convolutional neural networks and transformer models; the highest performance achieved is 88.8% weighted F1-score. These results are especially noteworthy given the unique challenges posed by Tigrinya’s distinct grammatical structure and complex word morphologies. The system can be an essential building block for the advancement of NLP systems in Tigrinya and other related low-resourced languages and serve as a bridge for cross-referencing against higher-resourced languages.

Keywords: Tigrinya NER corpus, TiBERT, TiRoBERTa, BiLSTM-CRF

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

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Authors: Bongsoo Jeon, Jayoung Kim, Jihong Lee

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Methods for measuring or estimating of ground shape by a laser range finder and a vision sensor (exteroceptive sensors) have critical weakness in terms that these methods need prior database built to distinguish acquired data as unique surface condition for driving. Also, ground information by exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Therefore, this paper proposes a method of recognizing exact and precise ground shape using Inertial Measurement Unit (IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.

Keywords: inertial measurement unit, laser range finder, real-time recognition of the ground shape, proprioceptive sensor

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Authors: L. Hamsaveni, Navya Prakash, Suresha

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Document Image Analysis recognizes text and graphics in documents acquired as images. An approach without Optical Character Recognition (OCR) for degraded document image analysis has been adopted in this paper. The technique involves document imaging methods such as Image Fusing and Speeded Up Robust Features (SURF) Detection to identify and extract the degraded regions from a set of document images to obtain an original document with complete information. In case, degraded document image captured is skewed, it has to be straightened (deskew) to perform further process. A special format of image storing known as YCbCr is used as a tool to convert the Grayscale image to RGB image format. The presented algorithm is tested on various types of degraded documents such as printed documents, handwritten documents, old script documents and handwritten image sketches in documents. The purpose of this research is to obtain an original document for a given set of degraded documents of the same source.

Keywords: grayscale image format, image fusing, RGB image format, SURF detection, YCbCr image format

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Authors: Yori Gidron, Laura Caton, Irit Ben-Aharon

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Background: Many patients and even certain clinicians attribute cancer development to psychosocial factors. Yet, empirical data supports more the prognostic role, rather than the etiological role, of psychosocial factors in cancer. Part of the inconsistency may result from not considering possible moderating factors in the etiological role of psychosocial factors. One important candidate moderating factor is the vagal nerve, whose activity is indexed by heart-rate variability (HRV). The vagal nerve may prevent cancer since it reduces inflammation on the one hand, and since it increases anti-tumor immunity on the other hand. This study examined the moderating role of the vagus in the relation between life threatening events (LTE) and cancer development. Method: We re-analyzed data from the Lifelines Dutch longitudinal cohort study of over 150,000 people. The present study included 82,751 adults, who initially were cancer-free. We extracted information on background factors (e.g., age, gender, fat consumption), whether they ever experienced LTE, HRV and cancer diagnosis as reported by patients in annual clinic visits. HRV was derived from brief ECGs. Results: Of the full sample, 1011 people developed cancer during a follow-up. In the full sample, LTE significantly predicted cancer development (R.R = 1.063 p < .01) and HRV significantly predicted a reduced risk of cancer development (R.R = .506 p <.001). Importantly, LTE significantly predicted cancer only when HRV was low (R.R = 1.056, 95% CI: 1.007 - 1.108, p < .05) but not when HRV was high (R.R = 1.014; 95% CI: 0.916 - 1.122, p > 0.05), independent of confounders. Conclusions: To the best of our knowledge, this is the first study showing in a large sample that LTE predict cancer development, and that this occurs only when vagal nerve activity (HRV) is relatively low. These results could result from lack of vagal modulation of inflammation and also from lack of vagal modulation of stress responses. Results are in line with the cancer-protective role of the vagus. HRV needs to be routinely monitored in the population and future intervention trials need to examine whether vagal nerve activation can prevent cancer in people with LTE and with other cancer risk factors.

Keywords: cancer development, life-events, moderation, vagal nerve

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Authors: Rania Ahmed Kadry Abdel Gawad Birry, Mohamed El-Habrouk

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This paper presents a brief survey of the techniques used for sign language recognition along with the types of sensors used to perform the task. It presents a modified method for identification of an isolated sign language gesture using Microsoft Kinect with the OpenNI framework. It presents the way of extracting robust features from the depth image provided by Microsoft Kinect and the OpenNI interface and to use them in creating a robust and accurate gesture recognition system, for the purpose of ASL translation. The Prime Sense’s Natural Interaction Technology for End-user - NITE™ - was also used in the C++ implementation of the system. The algorithm presents a simple finger counting algorithm for static signs as well as directional Finite State Machine (FSM) description of the hand motion in order to help in translating a sign language gesture. This includes both letters and numbers performed by a user, which in-turn may be used as an input for voice pronunciation systems.

Keywords: American sign language, finger counting, hand tracking, Microsoft Kinect

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Authors: Farshid Hajati, Soheila Gheisari, Ali Cheraghian, Yongsheng Gao

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This paper presents a new Surface Geodesic Derivative Pattern (SGDP) for matching textured deformable 3D surfaces. SGDP encodes micro-pattern features based on local surface higher-order derivative variation. It extracts local information by encoding various distinctive textural relationships contained in a geodesic neighborhood, hence fusing texture and range information of a surface at the data level. Geodesic texture rings are encoded into local patterns for similarity measurement between non-rigid 3D surfaces. The performance of the proposed method is evaluated extensively on the Bosphorus and FRGC v2 face databases. Compared to existing benchmarks, experimental results show the effectiveness and superiority of combining the texture and 3D shape data at the earliest level in recognizing typical deformable faces under expression, illumination, and pose variations.

Keywords: 3D face recognition, pose, expression, surface matching, texture

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Authors: Lamiek Abraham, Xinli Du, Yohan Noh, Polin Hsu, Tingting Wu, Tom Logan, Ifan Yen

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In the field of minimally invasive, smart medical instruments such as catheters and guidewires are typically used at a remote distance to gain access to the diseased artery, often negotiating tortuous, complex, and diseased vessels in the process. Three optical fiber sensors with a diameter of 1.5mm each that are 120° apart from each other is proposed to be mounted into a catheter-based pump device with a diameter of 10mm. These sensors are configured to solve the challenges surgeons face during insertion through curvy major vessels such as the aortic arch. Moreover, these sensors deal with providing information on rubbing the walls and shape sensing. This study presents an experimental and mathematical models of the optical fiber sensors with 2 degrees of freedom. There are two eight gear-shaped tubes made up of 3D printed thermoplastic Polyurethane (TPU) material that are connected. The optical fiber sensors are mounted inside the first tube for protection from external light and used TPU material as a prototype for a catheter. The second tube is used as a flat reflection for the light intensity modulation-based optical fiber sensors. The first tube is attached to the linear guide for insertion and withdrawal purposes and can manually turn it 45° by manipulating the tube gear. A 3D hard material phantom was developed that mimics the aortic arch anatomy structure in which the test was carried out. During the insertion of the sensors into the 3D phantom, datasets are obtained in terms of voltage, distance, and position of the sensors. These datasets reflect the characteristics of light intensity modulation of the optical fiber sensors with a plane project of the aortic arch structure shape. Mathematical modeling of the light intensity was carried out based on the projection plane and experiment set-up. The performance of the system was evaluated in terms of its accuracy in navigating through the curvature and information on the position of the sensors by investigating 40 single insertions of the sensors into the 3D phantom. The experiment demonstrated that the sensors were effectively steered through the 3D phantom curvature and to desired target references in all 2 degrees of freedom. The performance of the sensors echoes the reflectance of light theory, where the smaller the radius of curvature, the more of the shining LED lights are reflected and received by the photodiode. A mathematical model results are in good agreement with the experiment result and the operation principle of the light intensity modulation of the optical fiber sensors. A prototype of a catheter using TPU material with three optical fiber sensors mounted inside has been developed that is capable of navigating through the different radius of curvature with 2 degrees of freedom. The proposed system supports operators with pre-scan data to make maneuverability and bendability through curvy major vessels easier, accurate, and safe. The mathematical modelling accurately fits the experiment result.

Keywords: Intensity modulated optical fiber sensor, mathematical model, plane projection, shape sensing.

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Lisete Barlach, Guilherme Ary Plonski

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Innovative ideas are not always synonymous with business opportunities. Any idea can be creative and not recognized as a potential project in which money and time will be invested, among other resources. Even in firms that promote and enhance innovation, there are two 'check-points', the first corresponding to the acknowledgment of the idea as creative and the second, its consideration as a business opportunity. Both the recognition of new business opportunities or new ideas involve cognitive and psychological frameworks which provide individuals with a basis for noticing connections between seemingly independent events or trends as if they were 'connecting the dots'. It also involves prototypes-representing the most typical member of a certain category–functioning as 'templates' for this recognition. There is a general assumption that these kinds of evaluation processes develop through experience, explaining why expertise plays a central role in this process: the more experienced a professional, the easier for him (her) to identify new opportunities in business. But, paradoxically, an increase in expertise can lead to the inflexibility of thought due to automation of procedures. And, besides this, other cognitive biases can also be present, because new ideas or business opportunities generally depend on heuristics, rather than on established algorithms. The paper presents a literature review about the Einstellung effect by tracking famous cases of rejected ideas, extracted from historical records. It also presents the results of empirical research, with data upon rejected ideas gathered from two different environments: projects rejected during first semester of 2017 at a large incubator center in Sao Paulo and ideas proposed by employees that were rejected by a well-known business company, at its Brazilian headquarter. There is an implicit assumption that Einstellung effect tends to be more and more present in contemporaneity, due to time pressure upon decision-making and idea generation process. The analysis discusses desirability, viability, and feasibility as elements that affect decision-making.

Keywords: cognitive biases, Einstellung effect, recognition of business opportunities, rejected ideas

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Authors: Maria Hopkins, Sarah Koch, Fred Biasini

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Autism is lifelong disorder that affects one out of every 110 Americans. The deficits that accompany Autism Spectrum Disorders (ASD), such as abnormal behaviors and social incompetence, often make it extremely difficult for these individuals to gain functional independence from caregivers. These long-term implications necessitate an immediate effort to improve social skills among children with an ASD. Any technology that could teach individuals with ASD necessary social skills would not only be invaluable for the individuals affected, but could also effect a massive saving to society in treatment programs. The overall purpose of the first study was to develop, implement, and evaluate an avatar tutor for social skills training in children with ASD. “Face Say” was developed as a colorful computer program that contains several different activities designed to teach children specific social skills, such as eye gaze, joint attention, and facial recognition. The children with ASD were asked to attend to FaceSay or a control painting computer game for six weeks. Children with ASD who received the training had an increase in emotion recognition, F(1, 48) = 23.04, p < 0.001 (adjusted Ms 8.70 and 6.79, respectively) compared to the control group. In addition, children who received the FaceSay training had higher post-test scored in facial recognition, F(1, 48) = 5.09, p < 0.05 (adjusted Ms: 38.11 and 33.37, respectively) compared to controls. The findings provide information about the benefits of computer-based training for children with ASD. Recent research suggests the value of also using socially assistive robots with children who have an ASD. Researchers investigating robots as tools for therapy in ASD have reported increased engagement, increased levels of attention, and novel social behaviors when robots are part of the social interaction. The overall goal of the second study was to develop a social robot designed to teach children specific social skills such as emotion recognition. The robot is approachable, with both an animal-like appearance and features of a human face (i.e., eyes, eyebrows, mouth). The feasibility of the robot is being investigated in children ages 7-12 to explore whether the social robot is capable of forming different facial expressions to accurately display emotions similar to those observed in the human face. The findings of this study will be used to create a potentially effective and cost efficient therapy for improving the cognitive-emotional skills of children with autism. Implications and study findings using the robot as an intervention tool will be discussed.

Keywords: autism, intervention, technology, emotions

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Authors: E. Calil, L. A. Pereira

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The acquisition of orthography is a complex process, involving both lexical and grammatical questions. This learning occurs simultaneously with the domain of multiple textual aspects (e.g.: graphs, punctuation, etc.). However, most of the research on orthographic acquisition focus on this acquisition from an autonomous point of view, separated from the process of textual production. This means that their object of analysis is the production of words selected by the researcher or the requested sentences in an experimental and controlled setting. In addition, the analysis of the Spelling Problems (SP) are identified by the researcher on the sheet of paper. Considering the perspective of Textual Genetics, from an enunciative approach, this study will discuss the SPs recognized by dyads of newly literate students, while they are writing a text collaboratively. Six proposals of textual production were registered, requested by a 2nd year teacher of a Portuguese Primary School between January and March 2015. In our case study we discuss the SPs recognized by the dyad B and L (7 years old). We adopted as a methodological tool the Ramos System audiovisual record. This system allows real-time capture of the text in process and of the face-to-face dialogue between both students and their teacher, and also captures the body movements and facial expressions of the participants during textual production proposals in the classroom. In these ecological conditions of multimodal registration of collaborative writing, we could identify the emergence of SP in two dimensions: i. In the product (finished text): SP identification without recursive graphic marks (without erasures) and the identification of SPs with erasures, indicating the recognition of SP by the student; ii. In the process (text in progress): identification of comments made by students about recognized SPs. Given this, we’ve analyzed the comments on identified SPs during the text in progress. These comments characterize a type of reformulation referred to as Commented Oral Erasure (COE). The COE has two enunciative forms: Simple Comment (SC) such as ' 'X' is written with 'Y' '; or Unfolded Comment (UC), such as ' 'X' is written with 'Y' because...'. The spelling COE may also occur before or during the SP (Early Spelling Recognition - ESR) or after the SP has been entered (Later Spelling Recognition - LSR). There were 631 words entered in the 6 stories written by the B-L dyad, 145 of them containing some type of SP. During the text in progress, the students recognized orally 174 SP, 46 of which were identified in advance (ESRs) and 128 were identified later (LSPs). If we consider that the 88 erasure SPs in the product indicate some form of SP recognition, we can observe that there were twice as many SPs recognized orally. The ESR was characterized by SC when students asked their colleague or teacher how to spell a given word. The LSR presented predominantly UC, verbalizing meta-orthographic arguments, mostly made by L. These results indicate that writing in dyad is an important didactic strategy for the promotion of metalinguistic reflection, favoring the learning of spelling.

Keywords: collaborative writing, erasure, learning, metalinguistic awareness, spelling, text production

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Authors: Reem Alkadeem

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This study aims at evaluating the overall satisfaction of faculty members and relating it to organizational and personal aspects in Egyptian public and private universities. These aspects are identified through an extensive study of all factors that might affect job satisfaction. The most influencing parameters selected are academics’ demographics, human resource management, organizational profile, workload, teamwork skills, recognition, autonomy, teaching activity, research activity, and motivation. A questionnaire of 94 questions was used to assess job satisfaction and the previously mentioned parameters. It was distributed among seven hundred members of different universities in Egypt. Two hundred and twenty-seven faculty members responded. This sample was gathered from twelve universities and The Supreme Council of Universities. The ANOVA showed a significant relationship (p < 0.05) between eight of the selected parameters and job satisfaction. These parameters are age, rank, human resource management, profile of organizational characteristics, workload, recognition, teaching activity, and motivation.

Keywords: job satisfaction, higher education, organizational profile, Egyptian universities

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Authors: Jiaoya Huang, Jianghong Liu

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This paper endeavors to comprehend and scrutinize the entrepreneurial development process within Chinese cultural and creative small and medium-sized enterprises (SMEs), as well as the factors that impinge on entrepreneurs' recognition and exploitation of entrepreneurial opportunities from the vantage point of institutional theory. The study is centered around three key research questions: namely, the drivers and impediments for entrepreneurs to identify opportunities within three prominent Chinese cultural and creative regions and the influence of institutional facets on the exploitation and recognition of opportunities within the cultural industry. Adopting a qualitative interpretivist research paradigm, a comparative multiple case study design is utilized. Semi-structured interviews will be carried out with founders and mid-level professionals of SMEs in Beijing, Shanghai, and Guangzhou, which are chosen in accordance with specific criteria. The data will be analyzed through an inductive thematic approach. Anticipatedly, this research will contribute to bridging the research gap in the nexus between institutional theory and entrepreneurial opportunities within the context of cultural and creative industries.

Keywords: entrepreneurial opportunities, cultural and creative industries, institutional theory, Chinese SMEs

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Authors: Amita Barik, Ranjit Prasad Bahadur

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We investigate the role of water molecules in 89 protein-RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein-RNA interfaces are hydrated less than protein-DNA interfaces, but more than protein-protein interfaces. Majority of the waters at protein-RNA interfaces makes multiple H-bonds; however, a fraction does not make any. Those making Hbonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein-DNA interfaces, mainly due to the presence of the 2’OH, the ribose in protein-RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein-RNA interfaces is hydrated more than the major groove, while in protein-DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein-RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein-RNA recognition and should be carefully treated while engineering protein-RNA interfaces.

Keywords: h-bonds, minor-major grooves, preserved water, protein-RNA interfaces

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Authors: S. Biondi, V. Catania, R. Di Natale, A. R. Intilisano, D. Panno

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One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network.

Keywords: spoken dialog system, spoken language understanding, web semantic, name entity recognition

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Authors: Salma A. El-Marasy, Reham M. Abd-Elsalam, Omar A. Ahmed-Farid

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Dementia is characterized by impairments in memory and other cognitive abilities. This study aims to elucidate the possible ameliorative effect of silymarin on scopolamine-induced dementia using the object recognition test (ORT). The study was extended to demonstrate the role of cholinergic activity, oxidative stress, neuroinflammation, brain neurotransmitters and histopathological changes in the anti-amnestic effect of silymarin in demented rats. Wistar rats were pretreated with silymarin (200, 400, 800 mg/kg) or donepezil (10 mg/kg) orally for 14 consecutive days. Dementia was induced after the last drug administration by a single intraperitoneal dose of scopolamine (16 mg/kg). Then behavioral, biochemical, histopathological, and immunohistochemical analyses were then performed. Rats pretreated with silymarin counteracted scopolamine-induced non-spatial working memory impairment in the ORT and decreased acetylcholinesterase (AChE) activity, reduced malondialdehyde (MDA), elevated reduced glutathione (GSH), restored gamma-aminobutyric acid (GABA) and dopamine (DA) contents in the cortical and hippocampal brain homogenates. Silymarin dose-dependently reversed scopolamine-induced histopathological changes. Immunohistochemical analysis showed that silymarin dose-dependently mitigated protein expression of a glial fibrillary acidic protein (GFAP) and nuclear factor kappa-B (NF-κB) in the brain cortex and hippocampus. All these effects of silymarin were similar to that of the standard anti-amnestic drug, donepezil. This study reveals that the ameliorative effect of silymarin on scopolamine-induced dementia in rats using the ORT maybe in part mediated by, enhancement of cholinergic activity, anti-oxidant and anti-inflammatory activities as well as mitigation in brain neurotransmitters and histopathological changes.

Keywords: dementia, donepezil, object recognition test, rats, silymarin, scopolamine

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Authors: Semyachkina-Glushkovskaya Oxana, Fedosov Ivan, Blokhina Inna, Terskov Andrey, Evsukova Arina, Elovenko Daria, Adushkina Viktoria, Dubrovsky Alexander, Jürgen Kurths

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In modern neurobiology, sleep is considered a novel biomarker and a promising therapeutic target for brain diseases. This is due to recent discoveries of the nighttime activation of the brain lymphatic system (BLS), playing an important role in the removal of wastes and toxins from the brain and contributes neuroprotection of the central nervous system (CNS). In our review, we discuss that night stimulation of BLS might be a breakthrough strategy in a new treatment of Alzheimer’s and Parkinson’s disease, stroke, brain trauma, and oncology. Although this research is in its infancy, however, there are pioneering and promising results suggesting that night transcranial photostimulation (tPBM) stimulates more effectively lymphatic removal of amyloid-beta from mouse brain than daily tPBM that is associated with a greater improvement of the neurological status and recognition memory of animals. In our previous study, we discovered that tPBM modulates the tone and permeability of the lymphatic endothelium by stimulating NO formation, promoting lymphatic clearance of wastes and toxins from the brain tissues. We also demonstrate that tPBM can also lead to angio- and lymphangiogenesis, which is another mechanism underlying tPBM-mediated stimulation of BLS. Thus, photo-augmentation of BLS might be a promising therapeutic target for preventing or delaying brain diseases associated with BLS dysfunction. Here we present pioneering technology for simultaneous tPBM in humans and sleep monitoring for stimulation of BLS to remove toxins from CNS and modulation of brain immunity. The wireless-controlled gadget includes a flexible organic light-emitting diode (LED) source that is controlled directly by a sleep-tracking device via a mobile application. The designed autonomous LED source is capable of providing the required therapeutic dose of light radiation at a certain region of the patient’s head without disturbing of sleeping patient. To minimize patients' discomfort, advanced materials like flexible organic LEDs were used. Acknowledgment: This study was supported by RSF project No. 23-75-30001.

Keywords: brain diseases, brain lymphatic system, phototherapy, sleep

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Authors: Crisanto H. Ecaldre

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Gender perception represents how an individual perceives the gender identity of a person. Since this is a subjective assessment, it paves the way to various social reactions, either in the form of acceptance or discrimination. Reports across the world show that lesbian, gay, bisexual, or transgender (LGBT) people often face discrimination, stigmatization, and targeted violence because of their sexual orientation or gender identity. However, the challenges faced by those who belong to both a sexual minority and a marginalized ethnic, religious, linguistic, or indigenous community are even more complex. Specifically, in Palaw’an community, members own those who identify themselves as gays or lesbians and use “bantut” to identify them. There was also the introduction of various scholarly works to facilitate dialogues that promote visibility and inclusivity across sectors in terms of gender preferences; however, there are still gaps that need to be addressed in terms of recognition and visibility. Though local research initiatives are slowly increasing in terms of numbers, culturally situating gender studies appropriately within the context of indigenous cultural communities is still lacking. Indigenous community-based discourses on gender or indigenizing gender discourses remain a challenge; hence, this study aimed to contribute to addressing these identified gaps. These research objectives were realized through a qualitative approach following an exploratory design. Findings revealed that the Palaw’an indigenous cultural community has an existing concept of homosexuality, which they termed “bantut.” This notion was culturally defined by the participants as (a) kaloob ng diwata; (b) a manifestation of physical inferiority; (c) hindi nakapag-asawa or hindi nagka-anak; and (d) based on the ascribed roles by the community. These were recognized and valued by the community. However, despite the recognition and visibility within the community, the outside people view them otherwise. The challenges experienced by the Palaw’an homosexuals are imposed by the people outside their community, and these include prejudice, discrimination, and double marginalization. Because of these struggles, they are forced to cope. They deal with these imposed limitations, biases, and burdens by non-Palaw’an through self-acceptance, strong self-perception, and the option to leave the community to seek a more open and progressive environment for LGBTs. While these are indications of their ‘resilience’ amidst difficult situations, this reality poses an important concern -how the recognition and visibility of indigenous homosexuals from the mainstream perspective can be attained.

Keywords: gender preference, acceptability, challenge, recognition, visibility, coping

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Authors: Seane Mabitsela

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The Territory of Namibia was entrusted to South Africa as a Mandate under the League of Nations Covenant. After the dissolution of the League of Nations and the commencement of United Nations operations, South Africa's conception of its legal obligations under the mandate varied from those of other members of the United Nations. Because of that, the General Assembly requested the International Court of Justice for an Advisory Opinion on the international obligations of South Africa arising therefrom. The International Court of Justice declared that South West Africa was still a mandatory territory under the Covenant of the League of Nations. It also held that South Africa continued to transmit petitions from inhabitants of the territory, the supervisory functions to be exercised by the United Nations, to which the annual reports and the petitions were to be submitted. Subject to this judgement, the question of South West Africa remained a dispute relating to the mandate brought before the International Court of Justice against South Africa. The International Court of Justice and South Africa dispute reflected the nature of the Namibian inhabitants’ appeal for recognition at the United Nations.

Keywords: International Court of Justice, Namibia, petitions, United Nations

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Rakesh Krishna, Abhishek Poddar

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The paper discusses the work carried out on the implementation of control techniques like Digital Pulse Width Modulation (PWM) and Digital Pulse Fired control(PFC). These techniques are often used in devices like inverters, battery chargers, DC-to-DC converters can also be implemented on household devices like heaters. The advantage being the control and improved life span of device. In case of batteries using these techniques are known to increase the life span of battery in mobiles and other hand-held devices. 8051 microcontroller is used to implement these methods.Thyristors are used for switching operations.

Keywords: PWM, SVM, PFC, bidirectional inverters, snubber

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, Rebecca K. Rossi, Yanmin Yuan, Xianpei Li

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The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction.

Keywords: color fusion, geomorphology, fluvial processes, multispectral images, pattern recognition

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (PBW250) were constructed for a phoneme-level ASR system for the Filipino language. The Entropy Maximization is used to obtain phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word’s phonological distribution using the Add-Delete Method (PBW algorithm) and is compared to the modified PBW algorithm implemented in a dynamic algorithm approach to obtain optimization. The gained entropy score of 4.2791 and 4.2902 for the PBW and modified algorithm respectively. The PBW250 was recorded by 40 respondents, each with 2 sets data. Recordings from 30 respondents were trained to produce an acoustic model that were tested using recordings from 10 respondents using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.

Keywords: entropy maximization, Filipino language, Hidden Markov Model, phonetically balanced words, speech recognition

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Authors: Cenza Rhoda, Falone Sunda, Elvis Kidzeru, Nonhlanhla P. Khumalo, Afolake Arowolo

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Introduction: The human FAM111B gene mutations are associated with POIKTMP, a rare multi-organ fibrosing disease. Recent studies also reported the overexpression of FAM111B in specific cancers. However, the role of FAM111B in these pathologies, particularly fibrosarcoma, remains unknown. Materials and Methods: FAM111B RNA expression in some cancer cell lines was assessed in silico and validated in vitro in these cell lines and skin fibroblasts derived from the South African family member affected by POIKTMP with the heterozygous FAM111B gene mutation: NM_198947.4: c.1861T>G (p. Tyr621Asp or Y621D) by qPCR and western blot. The cellular function of FAM111B was also studied in HT1080 using various cell-based functional assays and a simple and cost-effective PCR-RFLP method for genotyping/screening FAM111B gene mutations described. Results: Expression studies showed upregulated FAM111B mRNA and protein in the cancer cells. High FAM111B expression with robust nuclear localization occurred in HT1080. Additionally, expression data and cell-based assays indicated that FAM111B led to the upregulation of cell migration and decreased cell apoptosis and cell proliferation modulation. FAM111B Y621D mutation showed similar effects on cell migration but minimal impact on cell apoptosis. FAM111B mRNA and protein expression were markedly downregulated (p ≤ 0.05) in the patient's skin-derived fibroblasts. Lastly, the PCR-RFLP method successfully genotyped FAM111B Y621D gene mutation. Discussion: FAM111B is a cancer-associated nuclear protein: Its modulation by mutations may enhance cell migration and proliferation and decrease apoptosis, as seen in cancers and POIKTMP/fibrosis, thus representing a viable therapeutic target in these disorders. Furthermore, the PCR-RFLP method could prove a valuable tool for FAM111B mutation validation or screening in resource-constrained laboratories.

Keywords: FAM111B, POIKTMP, cancer, fibrosis, PCR-RFLP

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Authors: B. G. Abiona, O. E. Fapojuwo, T. Akinlawon

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This study assessed the influence of human resource management practices on agricultural employees’ behavior. Data were collected from 75 randomly selected respondents using a well-structured questionnaire. The mean age of the employees’ was 43.2 years. Major human resource management practices that influence employees behaviors were: In-service training are given to employees on a regular basis (average value of x=3.44), management reward employees who are committed to their job (average value of x =3.41) and reward are designed to encourage wide participation and activity (average value of x=3.41). Also, major employees’ behavior include: Managers and employees’ wants to create better job performance (average value of x=3.13) and administrator provides praise and recognition for effective performance and show appreciation for special effort (average value of x=3.05). Major factors affecting employees’ behavior were: inadequate training (average value of x=2.93), inadequate local and international training (average value of x=2.87), inadequate grants for training programmes (average value of x= 2.81). A significant relationship was found between gender (χ2 = 37.204, P<0.05), educational qualification (χ2 = 59.093, P<0.05), income (r =0.122, P<0.05) and human resource management practices (r = 0.573, P< 0.05) of the respondents and employees’ behavior. Management should encourage employees who are committed to their job through awards and recognition.

Keywords: human resources management, agricultural employees, behaviour research institutes, Nigeria

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