Search results for: Unsupervised feature learning.

Authors: Mousa Mojarrad, Amir Masoud Rahmani, Mehrab Mohebi

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Detection, feature extraction and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes and the high dimensionality of articulated body models and also the important field in Image, Signal and Vision Computing in recent years. In this paper, four types of people in 2D dimension image will be tested and proposed. The system will extract the size and the advantage of them (such as: tall fat, short fat, tall thin and short thin) from image. Fat and thin, according to their result from the human body that has been extract from image, will be obtained. Also the system extract every size of human body such as length, width and shown them in output.

Keywords: Analysis of Image Processing, Canny Edge Detection, Human Body Recognition, Measurement, Pose Estimation, 2D Human Dimension.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: Biometric characters, facial recognition, neural network, OpenCV.

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Authors: Yenni Anggrayni

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The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: Bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement.

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

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5D face recognition, pose.

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Authors: Bhaskar Thakker, Anoop Lal Vyas

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Wrist pulse analysis for identification of health status is found in Ancient Indian as well as Chinese literature. The preprocessing of wrist pulse is necessary to remove outlier pulses and fluctuations prior to the analysis of pulse pressure signal. This paper discusses the identification of irregular pulses present in the pulse series and intricacies associated with the extraction of time domain pulse features. An approach of Dynamic Time Warping (DTW) has been utilized for the identification of outlier pulses in the wrist pulse series. The ambiguity present in the identification of pulse features is resolved with the help of first derivative of Ensemble Average of wrist pulse series. An algorithm for detecting tidal and dicrotic notch in individual wrist pulse segment is proposed.

Keywords: Wrist Pulse Segment, Ensemble Average, Dynamic Time Warping (DTW), Pulse Similarity Vector.

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Authors: Hyun-Woo Cho

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Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: M. Al-Jepoori, D. Bennett

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Teaching Object-Oriented Programming (OOP) as part of a Computing-related university degree is a very difficult task; the road to ensuring that students are actually learning object oriented concepts is unclear, as students often find it difficult to understand the concept of objects and their behavior. This problem is especially obvious in advanced programming modules where Design Pattern and advanced programming features such as Multi-threading and animated GUI are introduced. Looking at the students’ performance at their final year on a university course, it was obvious that the level of students’ understanding of OOP varies to a high degree from one student to another. Students who aim at the production of Games do very well in the advanced programming module. However, the students’ assessment results of the last few years were relatively low; for example, in 2016-2017, the first quartile of marks were as low as 24.5 and the third quartile was 63.5. It is obvious that many students were not confident or competent enough in their programming skills. In this paper, the reasons behind poor performance in Advanced OOP modules are investigated, and a suggested practice for teaching OOP based on a complex case study is described and evaluated.

Keywords: Complex programming case study, design pattern, learning advanced programming, object oriented programming.

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

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

Keywords: Recognition, CNN, convolutional neural network, Yi character, divergence.

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Authors: N. Pritchard

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Within architectural education, students arrive fore-armed with; their life-experience; knowledge gained from subject-based learning; their brains and more specifically their imaginations. The learning-by-doing that they embark on in studio-based/project-based learning calls for supervision that allows the student to proactively undertake research and experimentation with design solution possibilities. The degree to which this supervision includes direction is subject to debate and differing opinion. It can be argued that if the student is to learn-by-doing, then design decision making within the design process needs to be instigated and owned by the student so that they have the ability to personally reflect on and evaluate those decisions. Within this premise lies the problem that the student's endeavours can become unstructured and unfocused as they work their way into a new and complex activity. A resultant weakness can be that the design activity is compartmented and not holistic or comprehensive, and therefore, the student's reflections are consequently impoverished in terms of providing a positive, informative feedback loop. The construct proffered in this paper is that a supportive 'armature' or 'Heuristic-Framework' can be developed that facilitates a holistic approach and reflective learning. The normal explorations of architectural design comprise: Analysing the site and context, reviewing building precedents, assimilating the briefing information. However, the student can still be compromised by 'not knowing what they need to know'. The long-serving triad 'Firmness, Commodity and Delight' provides a broad-brush framework of considerations to explore and integrate into good design. If this were further atomised in subdivision formed from the disparate aspects of architectural design that need to be considered within the design process, then the student could sieve through the facts more methodically and reflectively in terms of considering their interrelationship conflict and alliances. The words facts and sieve hold the acronym of the aspects that form the Heuristic-Framework: Function, Aesthetics, Context, Tectonics, Spatial, Servicing, Infrastructure, Environmental, Value and Ecological issues. The Heuristic could be used as a Hermeneutic Model with each aspect of design being focused on and considered in abstraction and then considered in its relation to other aspect and the design proposal as a whole. Importantly, the heuristic could be used as a method for gathering information and enhancing the design brief. The more poetic, mysterious, intuitive, unconscious processes should still be able to occur for the student. The Heuristic-Framework should not be seen as comprehensive prescriptive formulaic or inhibiting to the wide exploration of possibilities and solutions within the architectural design process.

Keywords: Atomistic, hermeneutic, holistic, approach architectural design studio education.

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Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: Antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition.

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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Mentoring is provided by professionals with a higher level of experience and competence as part of the professional development of a university faculty. This paper explores the characteristics of the mentoring provided by those teachers participating in the development of an active methodology program run at the University of the Basque Country: to examine and to analyze mentors’ performance with the aim of providing empirical evidence regarding its value as a lifelong learning strategy for teaching staff. A total of 183 teachers were trained during the first three programs. The analysis method uses a coding technique and is based on flexible, systematic guidelines for gathering and analyzing qualitative data. The results have confirmed the conception of mentoring as a methodological innovation in higher education. In short, university teachers in general assessed the mentoring they received positively, considering it to be a valid, useful strategy in their professional development. They highlighted the methodological expertise of their mentor and underscored how they monitored the learning process of the active method and provided guidance and advice when necessary. Finally, they also drew attention to traits such as availability, personal commitment and flexibility in. However, a minority critique is pointed to some aspects of the performance of some mentors.

Keywords: Higher education, Mentoring, Professional development, University teachers.

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Authors: Hiroyuki Ohsaki, Makoto Imase

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In Grid computing, a data transfer protocol called GridFTP has been widely used for efficiently transferring a large volume of data. Currently, two versions of GridFTP protocols, GridFTP version 1 (GridFTP v1) and GridFTP version 2 (GridFTP v2), have been proposed in the GGF. GridFTP v2 supports several advanced features such as data streaming, dynamic resource allocation, and checksum transfer, by defining a transfer mode called X-block mode. However, in the literature, effectiveness of GridFTP v2 has not been fully investigated. In this paper, we therefore quantitatively evaluate performance of GridFTP v1 and GridFTP v2 using mathematical analysis and simulation experiments. We reveal the performance limitation of GridFTP v1, and quantitatively show effectiveness of GridFTP v2. Through several numerical examples, we show that by utilizing the data streaming feature, the average file transfer time of GridFTP v2 is significantly smaller than that of GridFTP v1.

Keywords: Grid Computing, GridFTP, Performance Evaluation, Queuing Theory.

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Authors: Thaweesak Yingthawornsuk, Chusak Thanawattano

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Acoustical properties of speech have been shown to be related to mental states of speaker with symptoms: depression and remission. This paper describes way to address the issue of distinguishing depressed patients from remitted subjects based on measureable acoustics change of their spoken sound. The vocal-tract related frequency characteristics of speech samples from female remitted and depressed patients were analyzed via speech processing techniques and consequently, evaluated statistically by cross-validation with Support Vector Machine. Our results comparatively show the classifier's performance with effectively correct separation of 93% determined from testing with the subjectbased feature model and 88% from the frame-based model based on the same speech samples collected from hospital visiting interview sessions between patients and psychiatrists.

Keywords: Depression, SVM, Vocal Extract, Vocal Tract

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Authors: Shih-Chieh Liao, Cheng-Yan Shuai

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The feature of a side-scrolling shooting game is facing the surrounding enemy and barraging in an entire screen. The player will be in trouble when they are trying to do complicated operations because of the physical and system limitations of the joystick in the games. This study designed the prototype of a type of arcade stick by focus group and assessed by the expert. We selected the most representative joystick prototype and built the control system for the joystick. We conducted two experimental tests using time and bullet consumption as objective indicators, aiming to demonstrate its efficiency in the game. Finally, the prototype of L-1 solves the dilemma of scroll shooting games when the player uses the arcade stick and improves the function of the arcade stick.

Keywords: Joystick, user interface, side-scrolling shooting game, improved user experience.

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Authors: K. Muthukannan, P. Latha

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In addition to environmental parameters like rain, temperature diseases on crop is a major factor which affects production quality & quantity of crop yield. Hence disease management is a key issue in agriculture. For the management of disease, it needs to be detected at early stage. So, treat it properly & control spread of the disease. Now a day, it is possible to use the images of diseased leaf to detect the type of disease by using image processing techniques. This can be achieved by extracting features from the images which can be further used with classification algorithms or content based image retrieval systems. In this paper, color image is used to extract the features such as mean and standard deviation after the process of region cropping. The selected features are taken from the cropped image with different image size samples. Then, the extracted features are taken in to the account for classification using Fuzzy Inference System (FIS).

Keywords: Image Cropping, Classification, Color, Fuzzy Rule, Feature Extraction.

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: Accelerate MRI scans, image reconstruction, pyramid network, deep learning.

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Authors: Z. Baimagambetova, Zh. Sairambaeva

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This article discusses the prospects of participation of the Republic of Kazakhstan in Hague Conference on Private International Law on the unification of collision law in the international trade. The article analyzes some conventions on international trade. The appropriate conclusions based on the opinions of scientists and experts in this field have been made. First, all issues presented in the form of gaps or spaces in conventions should be the subject to direct negotiations in the course of the activities of Hague Conference, and have a comprehensive feature, be transparent and taken under simplified procedure. Secondly, one should not underestimate the value of conventions that do not become active due to various reasons and having a positive impact on the development and improvement of national legislation and practice in the field of private international law. Thirdly, Kazakhstan has to reconsider its attitude to Hague Conference, having become its full member and aiming at providing constructive and fruitful cooperation with both the organization itself and its member states.

Keywords: Hague Conference on Private International Law, Hague Conventions, unification, collision norms, international trade, international private law, integration.

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Authors: Pei-Ju Chao, Tsair-Fwu Lee, Wei-Luen Huang, Long-Chang Chen, Te-Jen Su, Wen-Ping Chen

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The main goal in this paper is to quantify the quality of different techniques for radiation treatment plans, a back-propagation artificial neural network (ANN) combined with biomedicine theory was used to model thirteen dosimetric parameters and to calculate two dosimetric indices. The correlations between dosimetric indices and quality of life were extracted as the features and used in the ANN model to make decisions in the clinic. The simulation results show that a trained multilayer back-propagation neural network model can help a doctor accept or reject a plan efficiently. In addition, the models are flexible and whenever a new treatment technique enters the market, the feature variables simply need to be imported and the model re-trained for it to be ready for use.

Keywords: neural network, dosimetric index, radiation treatment, tumor

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Authors: Nermine Ahmed Hendy, Hania Farag

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Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time

Keywords: Classification, emotion recognition, features extraction, feature selection, neural network

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: Aikaterini Fountoulaki, Nikos Karacapilidis, Manolis Manatakis

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This paper proposes an innovative methodology for Acceptance Sampling by Variables, which is a particular category of Statistical Quality Control dealing with the assurance of products quality. Our contribution lies in the exploitation of machine learning techniques to address the complexity and remedy the drawbacks of existing approaches. More specifically, the proposed methodology exploits Artificial Neural Networks (ANNs) to aid decision making about the acceptance or rejection of an inspected sample. For any type of inspection, ANNs are trained by data from corresponding tables of a standard-s sampling plan schemes. Once trained, ANNs can give closed-form solutions for any acceptance quality level and sample size, thus leading to an automation of the reading of the sampling plan tables, without any need of compromise with the values of the specific standard chosen each time. The proposed methodology provides enough flexibility to quality control engineers during the inspection of their samples, allowing the consideration of specific needs, while it also reduces the time and the cost required for these inspections. Its applicability and advantages are demonstrated through two numerical examples.

Keywords: Acceptance Sampling, Neural Networks, Statistical Quality Control.

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Authors: Morteza Pourazar, Fatemeh Mirakhori, Fazlolah Bagherzadeh, Rasool Hemayattalab

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The purpose of study was to examine the effects of external and internal focus of attention in the motor learning of children with cerebral palsy. The study involved 30 boys (7 to 12 years old) with CP type 1 who practiced throwing beanbags. The participants were randomly assigned to the internal focus, external focus, and control groups, and performed six blocks of 10-trial with attentional focus reminders during a practice phase and no reminders during retention and transfer tests. Analysis of variance (ANOVA) with repeated measures on the last factor was used. The results show that significant main effects were found for time and group. However, the interaction of time and group was not significant. Retention scores were significantly higher for the external focus group. The external focus group performed better than other groups; however, the internal focus and control groups’ performance did not differ. The study concluded that motor skills in Spastic Hemiparetic Cerebral Palsy (SHCP) children could be enhanced by external attention.

Keywords: Cerebral Palsy, external attention, internal attention, throwing task.

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Authors: Yan Ma, ShunBao Li

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A new approach is adopted in this paper based on Turk and Pentland-s eigenface method. It was found that the probability density function of the distance between the projection vector of the input face image and the average projection vector of the subject in the face database, follows Rayleigh distribution. In order to decrease the false acceptance rate and increase the recognition rate, the input face image has been recognized using two thresholds including the acceptance threshold and the rejection threshold. We also find out that the value of two thresholds will be close to each other as number of trials increases. During the training, in order to reduce the number of trials, the projection vectors for each subject has been averaged. The recognition experiments using the proposed algorithm show that the recognition rate achieves to 92.875% whilst the average number of judgment is only 2.56 times.

Keywords: Eigenface, Face Recognition, Threshold, Rayleigh Distribution, Feature Extraction

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Authors: Yan Xu, Toshihiro Nishimura

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Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.

Keywords: fuzzy c-means, spatial information, structure tensor, ultrasound image segmentation

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Authors: A. Borji, M. Hamidi

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A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise

Keywords: Comprehensive learning Particle Swarmoptimization, fuzzy classification.

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Authors: C. Benjbara, A. Habbani

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Wireless networks are getting more and more used in every new technology or feature, especially those without infrastructure (Ad hoc mode) which provide a low cost alternative to the infrastructure mode wireless networks and a great flexibility for application domains such as environmental monitoring, smart cities, precision agriculture, and so on. These application domains present a common characteristic which is the need of coexistence and intercommunication between modules belonging to different types of ad hoc networks like wireless sensor networks, mesh networks, mobile ad hoc networks, vehicular ad hoc networks, etc. This vision to bring to life such heterogeneous networks will make humanity duties easier but its development path is full of challenges. One of these challenges is the communication complexity between its components due to the lack of common or compatible protocols standard. This article proposes a new patented routing protocol based on the OLSR standard in order to resolve the heterogeneous ad hoc networks communication issue. This new protocol is applied on a specific network architecture composed of MANET, VANET, and FANET.

Keywords: Ad hoc, heterogeneous, ID-Node, OLSR.

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Authors: Viet Dzung Nguyen, Duc Thuan Nguyen, Tien Dzung Nguyen, Van Thanh Pham

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Mammography is the most effective procedure for an early diagnosis of the breast cancer. Nowadays, people are trying to find a way or method to support as much as possible to the radiologists in diagnosis process. The most popular way is now being developed is using Computer-Aided Detection (CAD) system to process the digital mammograms and prompt the suspicious region to radiologist. In this paper, an automated CAD system for detection and classification of massive lesions in mammographic images is presented. The system consists of three processing steps: Regions-Of- Interest detection, feature extraction and classification. Our CAD system was evaluated on Mini-MIAS database consisting 322 digitalized mammograms. The CAD system-s performance is evaluated using Receiver Operating Characteristics (ROC) and Freeresponse ROC (FROC) curves. The archived results are 3.47 false positives per image (FPpI) and sensitivity of 85%.

Keywords: classification, computer-aided detection, featureextraction, mass detection.

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Authors: F. Saptouw

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This paper briefly reviews the range of curatorial interventions made to preserve and display the Timbuktu Manuscripts. The government of South Africa and Mali collaborated to preserve the manuscripts, and brief notes will be presented about the value of archives in those specific spaces. The research initiatives of the Tombouctou Manuscripts Project, based at the University of Cape Town, feature prominently in the text. A brief overview of the history of the archive will be presented and its preservation as a key turning point in curating the intellectual history of the continent. ­­­The strategies of preservation, curation, publication and digitization are presented as complimentary interventions. Each materialization of the manuscripts contributes something significant; the complexity of the contribution is dependent primarily on the format of presentation. This integrated reading of the manuscripts is presented as a means to gain a more nuanced understanding of the past, which greatly surpasses how much information would be gleaned from relying on a single media format.

Keywords: Archive, curatorship, cultural heritage, museum practice, Timbuktu manuscripts.

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Authors: Christina Van Wingerden

Abstract:

This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, learning management systems, sense of belonging, third space.

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