Reviewerly/exHarmony huggingface dataset:
Modeling and Analysis Brain Development via Discriminative Dictionary Learning

Research on modeling and exploring of the normal brain maturity, such as in vivo study of the anatomy of the developing brain, can provide references for developmental pathologies. In this paper, we model and explore brain development by learning a discriminative representation of the cortical brain data (T1 MRI) with a class-wise non-negative dictionary learning (NDDL) approach. For each class, the proposed approach performs data modeling by first projecting the data into non-negative low-rank encoding coefficients with an analysis dictionary and then applying the coefficients onto an orthogonal synthesis dictionary to reconstruct the data. It also uses additional regularizers to enforce distal classes to fit into different analysis dictionaries. The learning problem is formulated as a sparse and low rank optimization problem, and solved with an alternating direction method of multipliers(ADMM). The effectiveness of the proposed approach is tested on brain age prediction problems by exploring the cortical status, and the experiments are conducted on the PING dataset. The proposed approach produces competitive results. Further, we were able for the first time to capture the status of brain thickness of specific cortical surface area with aging.
Eye Center Guided Constrained Local Model for Landmark Localization in Facial Image

Landmark localization is a very important step for many face-related computer vision applications. Compare to the holistic approaches (e.g. AAMs), constrained local models (CLMs) shows good performance for landmark localization in non-rigid facial images. But these methods are always limited by the initialization. This paper proposed an eye center guided constrained local model where the initialization is performed by mean face shape taking eyes as references. First, we have adopted a hybrid eye detector method to find both the eye centers and then mean face shape is placed on the basis of orientation and distance of two eye centers. Moreover, we have analyzed our models with some descriptors to find the best descriptor to represent our model. The proposed CLM approach has been tested on AR and Multi-PIE databases with 130 and 68 landmarks respectively. The experimental results suggest that our proposed method has achieved improved performance as compared to existing methods.
Knowledge Graph Completion via Complete Attention between Knowledge Graph and Entity Descriptions

The objective of learning representation of knowledge graph is assumed to encode both entities and relations into a continuous low-dimensional vector space. Previous methods usually represent the same entity in different triples with the same representation. Considering the fact that different entities should have different semantics in different triples, this paper proposes a method to learn entity description information based on the triple, under the complete attention (CATT) mechanism, in the knowledge graphs. By doing so, the entity has different representations of corresponding semantics in different triples. For encoding the information of entity description, we use three deep learning methods including CNN, Bi-LSTM and Transformer. Experimental results show that, with the proposed method, the performance on both entity prediction and relationship prediction improved significantly.
The Implementation of Backpropagation Artificial Neural Network for Recognition of Batik Lampung Motive

Abstract United Nations Educational, Scientific, and Culture Organization (UNESCO) has recognized batik cloth is one of the world cultural heritage that originated from Indonesia, exactly on October 2, 2009. Batik in Indonesia has a motive that many, varied and almost every motive of batik various regions have similar motives, but if viewed in more detail batik cloth from different regions are not the same. Certain people who have expertise and knowledge in the field of batik that can distinguish batik motive from various regions. Lampung is one area in Indonesia that has a cloth motive that characterizes the Lampung area used as batik cloth. This study discusses the backpropagation artificial neural network that will be used for the classification of pattern batik motive Lampung. Batik motive lampung used is sembagi, siger ratu agung, jung agung and siger clove cengkih, while for batik is not a motive Lampung used parang kusumo and broken parang. Stages to be done are scaling, grayscale, tresholding and classification. Comparison of training data and data testing used is 70:30 and 80:30 with the need of backpropagation neural network that is epoch = 2000, learning rate = 0.1 and target error = 0.001. The greatest accuracy value is found in the 70:30 data is 92%.
A Finite State Machine Model to Determine Syllables of Indonesian Text

Syllable segmentation is an important process in the development of realistic visual speech synthesis for the Indonesian language. The process is to determine the syllables in a text by applying certain rules. In this study, we develop a rule-based syllable segmentation to convert Indonesian text into syllables. The rules are represented in the form of a finite state machine (FSM). The FSM model depends on the combination of vowels and consonants. The syllable peak indicated by the sound of a vowel, while the consonant sound represents a syllable valley. In a syllable, there is only a syllable peak, but the syllable valley can be more than one. Therefore, the number of syllables can be determined by looking at the vowels present in a word. The rules can be formulated by looking at the syllable pattern. The pattern can only be a vowel sound, a vowel with a consonant sound, and a vowel with two consonant sounds and others. The evaluation is done by entering Indonesian texts to the FSM model. The result shows that the FSM model could determine the syllables properly.
Towards Personalized Feedback in a Smart Learning Environment For Teaching Conceptual Modelling

Conceptual modelling is one of the most challenging areas of learning in software or enterprise engineering education. Building this complex skill requires tremendous efforts and amounts of elaborative feedback from the teacher, especially in the case of novice modelers. However, the growing numbers of students wishing to pursue degrees requiring conceptual modelling as an integral skill make sufficient personalized feedback an unreachable goal for educators. The situation is aggravated by the lack of a universal, commonly accepted educational framework for conceptual modelling, which could serve as a basis for learning material and feedback development. In this paper, the steps towards the development of feedback architecture of a smart learning environment for conceptual modelling are presented, along with the intermediate results of the research project. The steps include the development of learning ontology for conceptual data modelling, implementation of CaMeLOT educational framework and the learning ontology for providing personalized feedback and finding the appropriate feedback timing.
Neural Networks and Collaborative Filtering

This paper discusses several methods and experiments in Collaborative filtering. Every set of methods has its own advantages and disadvantages and provides better results under different circumstances, which is why these methods are being combined, in the recent days. Each of these methods has been analyzed to see if they are appropriate for a particular phase of the customer decision process. We have discussed the conventional Collaborative filtering (CF) based methods that use factor models, matrix factorization as well as the newly proposed methods like content aware CF, Collaborative deep learning (CDL), recurrent Neural Networks (RNNs), session-based recommendation, neural CF (using a multi-layer perceptron) and Collaborative topic regression (CTR). We have also discussed how the well-known cold-start problem is dealt with in the recent methods.
Low Rank Poisson Denoising (LRPD): A Low Rank Approach Using Split Bregman Algorithm for Poisson Noise Removal From Images

Occurrence of Poisson noise in captured observations is inevitable in various real imaging applications ranging from medical imaging to night vision imaging. Restoration of fine details of an image is difficult when it is corrupted by Poisson noise. Recently, low rank approaches outperformed several state-of-the-art techniques for image denoising, deblurring, image completion, super-resolution, etc. The ability of low rank techniques to preserve fine details, even though the image is corrupted by severe noise, motivated us to develop an optimization framework wherein, we propose to use a low rank prior for Poisson noise removal. In the proposed low rank Poisson denoising (LRPD) algorithm, we resort to split Bregman technique to solve an appropriate objective function. We incorporate the forward-backward splitting scheme to minimize the first subproblem and the weighted nuclear norm minimization (WNNM) for the second subproblem of split Bregman algorithm to arrive at the final solution. We conduct several experiments on both simulated and real-world Poisson noisy data and show the superiority of the proposed method over other state-of-the-art Poisson denoising techniques.
OCR Perspectives in Mobile Teaching and Learning for Early School Years in Basic Education

In these days, teaching and learning systems in schools with the use of mobile or portable devices such as tablets, e-readers, smartphones are becoming keen interests of educators as well as parents and teachers in worldwide. In this aspect, the early years of school children in basic education are the most challenging and important in developing effective and quality education for life. Since they are quite young and unable to dedicate time the need for easy to use and effective learning aids has become vital. Especially their writing skills are somehow needed to be improved with encouragements. In order to do so, the handwritten of characters and numerals performed by the children especially those living in less developing countries should be correctly recognized so that means and ways for remedies and solutions for improvements could be found. Thus the optical character recognition techniques for handwritten alphabets and numerals are moving into front especially the handwritten of children of early years in schools. In this paper, we introduce a Mobile Tutor-an effective and correct way of character segmentation and recognition of messy and unclear handwritten characters to help children learn and practice handwriting and early numeric operations such as addition and subtraction as well as help teachers monitor and review children's progress. Some experiments are performed by providing tablets to the users and collecting handwritten characters from the users for recognition and analysis.
Scaling Longitudinal Functional Health Assessment in Multi-Inhabitant Smarthome

Autonomous longitudinal functional health assessment is critically important to support the rehabilitation of older adults in Skilled Nursing Facilities (SNFs). Although the wide availability of commodity smarthome sensors and internet-of-things (IoT) is facilitating continual monitoring of individuals' health-related vital signs and behaviors, missing values, presence of multi-inhabitants and diversity of smarthomes interfere with successful longitudinal assessment and impact the scalability of autonomous health assessments. In this paper, we propose a novel scalable framework to provide health assessments of older adults living in varied smarthome environments. As a critical first step, we propose a novel algorithm to track individuals in a multi-inhabitant smarthome environment. We then propose a novel data curation technique to address missing sensor signals in a multi-modal ambient sensor-assisted environment. Finally, we propose a novel trajectory featurization method inspired Deep Convolutional Neural Network TDCNN, leveraging appropriate samples from a well-labeled source smarthome, to transfer functional health assessment knowledge to unlabeled diverse smarthomes, boosting the scalability of autonomous health assessment. Our evaluation on real SNFs data, collected over 5 months from 95 individuals residing in 9 diverse sensored SNFs environments shows promising results (93% accuracy) with respect to the scalability of our framework.
Human Behavior Learning in Joint Space Using Dynamic Time Warping and Neural Networks

The usual human behavior learning methods are in the robot task space, i.e., three-dimension Cartesian space. After learning, the desired trajectories have to be transformed into the joint space by the inverse kinematics of the robot. However, for most robots, the analytical solutions of the inverse kinematics cannot be obtained. In this paper, we learn human behavior directly in the joint space. There are some problems to learn the demonstrations in the joint space, such as the demonstration trajectories depending on different velocities and tremors in Cartesian space produced by natural human behavior. We use dynamic time warping and neural networks to solve these problems. More importantly, we avoid calculating the inverse kinematics. Experiment results have shown this method to be effective.
Phase estimation of a 2D fringe pattern using a monogenic-based multiscale analysis

In this paper, a multiscale monogenic analysis is applied to 2D interference fringe patterns. The monogenic signal was originally developed as a 2D generalization of the well-known analytic signal in the 1D case. The analytic and monogenic tools are both useful to extract phase information, which can then be directly linked with physical quantities. Previous studies have already shown the interest in the monogenic signal in the field of interferometry. This paper presents theoretical and numerical illustrations of the connection between the physical phase information and the phase estimated with the monogenic tool. More specifically, the ideal case of pure cosine waves is deeply studied, and then the complexity of the fringe patterns is progressively increased. One important weakness of the monogenic transform is its singularity at the null frequency, which makes the phase estimations of low-frequency fringes diverge. Moreover, the monogenic transform is originally designed for narrowband signals, and encounters difficulties when dealing with noised signals. These problems can be bypassed by performing a multiscale analysis based on the monogenic wavelet transform. Moreover, this paper proposes a simple strategy to combine the information extracted at different scales in order to get a better estimation of the phase. The numerical tests (synthetic and real signals) show how this approach provides a finer extraction of the geometrical structure of the fringe patterns.
Structured Domain Adaptation for 3D Keypoint Estimation

Motivated by recent advances in deep domain adaptation, this paper introduces a deep architecture for estimating 3D keypoints when the training (source) and the test (target) images greatly differ in terms of visual appearance (domain shift). Our approach operates by promoting domain distribution alignment in the feature space adopting batch normalization-based techniques. Furthermore, we propose to collect statistics about 3D keypoints positions of the source training data and to use this prior information to constrain predictions on the target domain introducing a loss derived from Multidimensional Scaling. We conduct an extensive experimental evaluation considering three publicly available benchmarks and show that our approach out-performs state-of-the-art domain adaptation methods for 3D keypoints predictions.
New Insights into Road Accident Analysis through the Use of Text Mining Methods

Traffic safety is one of the main goals when designing infrastructure or vehicles. Many entities are interested in accident data in order to identify current problems. The accurate recording and analysis of accident data, though, is a time-consuming task. New data mining methods allow for a more efficient analysis of large amounts of (unstructured) data. In this paper, accident data provided by the police department of the city of Munich is analyzed both by classical methods considering only classified accidents, as well as with the help of text mining methods, taking into account all descriptions of the accidents. The results indicate that text mining methods can give quick results identifying main problems at the given locations. Using statistical and machine learning methods for the analysis shows promising results. The results of the automatic classification of accidents into accident types based on a learned classification model using the textual descriptions is highly accurate. Retrieving structured information from the descriptions, though, requires a more concise writing of the accidents reports. We conclude that in future, data mining methods could be used to reduce the workload for police officers for both the reporting work as well as the analysis of road accidents.
Fusing Handcrafted and Contextual Features for Human Activity Recognition

In this paper we present an approach for the recognition of human activity that combines handcrafted features from 3D skeletal data and contextual features learnt by a trained deep Convolutional Neural Network (CNN). Our approach is based on the idea that contextual features, i.e., features learnt in a similar problem are able to provide a diverse representation, which, when combined with the handcrafted features is able to boost performance. To validate our idea, we train a CNN using a dataset for action recognition and use the output of the last fully-connected layer as a contextual feature representation. Then, a Support Vector Machine is trained upon an early fusion step of both representations. Experimental results prove that the proposed method significantly improves the recognition accuracy in an arm gesture recognition problem, compared to the use of handcrafted features only.
Secure Chaotic Maps-based Authentication Scheme for Real-Time Data Access In Internet of Things

User authentication plays an important role in generic IoT networks that prevents malicious parties from gaining access to various services offered by remote servers. As user-end IoT devices are typically resource-constrained, how to design secure and efficient multi-factor authentication schemes remains hard to tackle. Very recently, instead of using traditional asymmetric encryption algorithms such as RSA, EIGamal encryption, a number of attempts have been made to employ chaotic maps as building blocks to design multi-factor authentication schemes for IoT environments. In this paper, we first revisit two foremost chaotic maps based multi-factor user authentication schemes presented by Roy et al. and Truong et al., and show that, despite being armed with a formal security proof, none of them can achieve the goal of “truly multi-factor security”. Besides, we find Roy et al.'s scheme fails to achieve the claimed feature of forward secrecy, while Truong et al.'s scheme suffers from stolen verifier attack and violation of user anonymity. Further, we indicate how to mend these weaknesses and propose an enhanced protocol with high efficiency. Security and efficiency analysis suggest that our scheme outperforms existing schemes and is practical for real applications of IoT environments.
Video-based Fall Risk Detection System for the Elderly

The use of high frequencies is essential in detecting deterioration in a physical function, but expert evaluation is expensive, and there are various other problems such as the need for many measurement devices. The aim of this study was to automate the evaluation of fall risk by video recording the gaits of elderly people using a general camera. We took videos of 12 subjects performing a 5-m walking test in a nursing home. In this paper, we evaluate (1) the use of the feature extraction method on walking behavior obtained from video recordings and (2) the accuracy of fall prediction using machine learning.
A Novel Method to Prevent Phishing by using OCR Technology

Phishing is one of the most common attacks in the world, especially with the increasing usage of mobile platforms and e-commerce. Although many users are weary of phishing attacks from suspicious paths in the URL address, phishing still accounts for a large proportion of all of malicious attacks as it is easy to deploy. Most browser vendors mainly adopt two approaches against phishing; the blacklist and the heuristic-based. However, both have related limitations. In this paper, a novel method was proposed to protect against phishing attacks. By using image recognition (OCR) technology, phishing attacks can be distinguished from the actual website by reading the logos on the website and comparing with the site URL. An easy to implement prototype demonstrated a high accuracy of detection in the experimental trials.
“Offensiphobia” is a Red Herring: On the Problem of Censorship and Academic Freedom

Abstract In a recent article, J. Angelo Corlett criticises what he takes to be the ‘offensiphobic’ practices characteristic of many universities. The ‘offensiphobe’, according to Corlett, believes that offensive speech ought to be censured precisely because it offends. We argue that there are three serious problems with Corlett’s discussion. First, his criticism of ‘offensiphobia’ misrepresents the kinds of censorship practiced by universities; many universities may in some way censure speech which they regard as offensive, but this is seldom if ever a manifestation of ‘offensiphobia’. Second, we attempt to reconstruct Corlett’s criticism of ‘offensiphobia’ as a criticism of the practice of censuring hate speech, and show that this argument is unsuccessful. Third, we offer some brief reflections on how labelling universities as ‘offensiphobic’ is especially problematic in light of the current climate of political interference in university research and teaching.
Y-Position based Myanmar Touching Character Segmentation and Sub-components based Character Classification

This paper publicizes the touching character segmentation and recognition for the Myanmar warning text signboards. In the touching character segmentation step of this system, pixel connected component characters are extracted using connected component (CC) labeling with bounding box process. This system firstly search location zone of bounding box characters using minimum y-position and high features. This system develops a touching character segmentation technique that segment the Myanmar touching characters based on zone location. This segmentation technique uses the existing features such as number of holes, end points, horizontal black stroke count, vertical black stroke count, pixel count and the new features such as upper and lower sub-components in two horizontal zones, left and right sub-components in two vertical zones, end point existed zones. These features are further used in classification of segmented Myanmar characters. The system investigated on three types of datasets. First dataset includes 101 printed warning sign images. Second dataset includes 45 handwritten warning sign images that manually resized with various ranges based on visual font size, font style and number of text. The remaining dataset includes 152 real worlds Warning Sign Images (WSI) that automatically resized into 480×640 from 3120×4160 resolution and 640×480 form 4160×3120 resolution.