Reviewerly/exHarmony huggingface dataset:
Recurrent Binary Patterns and CNNs for Offline Signature Verification

Signature representations that are extracted by convolutional neural networks (CNN) can achieve low error rates. However, a trade-off exists between such models’ complexities and hand-crafted features’ slightly higher error rates. A novel writer-dependent (WD) recurrent binary pattern (RBP) network, and a novel signer identification CNN is proposed. RBP network is a recurrent neural network (RNN) to learn the sequential relation between binary pattern histograms over image windows. A novel histogram selection method is introduced to remove the stop-word codes. Dimensionality is reduced by more than 25% while improving the results. This work is the first to combine binary patterns and RNNs for static signature verification. Several test sets, derived from large-scale and popular databases (GPDS-960 and GPDS-Synthetic-10000) are used. Without training any global classifier, RBP network provides competitive equal error rates (EER). The proposed architectures are compared and integrated with other recent CNN models. Score-level integration of WD classifiers trained with different representations are investigated. Cross-validation tests demonstrate the EERs reduced compared to the best single classifier. A state-of-the-art EER of 1.11% is reported with a global decision threshold (0.57% EER with user-based thresholds) on GPDS-160 database.
Integrating Activity Recognition and Nursing Care Records: the System, Experiment, and the Dataset

In this paper, we introduce a system that integrates activity recognition, which collects various activities of the elderly people of a nursing care facility in Japan. This system incorporates various activity levels, sensor data through smartphones, and records by nurses for a period of 4 months. There are 28 activity labels, recorded by a number of staffs/nurses. The system architecture has mobile application to encode data and a networked system in the 6-floored facility, covering a number of residents. The system is designed in such a manner that even a non-expert nurse or user can manage this system. After introducing the entire system and data collection procedure, we recognized the acitivities by exploiting some statistical features and Extremely Randomized Tree. We noticed that there are discrepancies between start and end recording time-stamps. Therefore, we explored a time correction technique. The dataset is an extensive one and it can be explored for various activity recognition in smart-homes or elder care facilities. The dataset includes the sensor data from staffs' smartphones, activity labels, and care details input using the system.
Interval Type-2 Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

Deep learning (DL) has played a crucial role in many domains of image and pattern recognition, extraction of features from video and text processing etc. One of the quintessential elements of deep learning is Restricted Boltzmann Machines (RBM). RBMs are capable of extracting the high-level features from raw data very efficiently. Nevertheless, feature extraction process is prone to external and unwanted noises, which introduces uncertainty in the decision making process. Moreover, existing RBM-based DL methods are not robust enough to handle such noises in the data samples while training within layers. To tackle these drawbacks, Fuzzy Restricted Boltzmann Machine (FRBM) had been available in the literature. FRBM utilizes Type-1 Fuzzy Sets (T1FS) to handle such uncertainties in governing parameters of the system. However, membership values of membership functions used in T1FSs are also crisp. Thus, in this state-of-art paper, we propose the use Interval Type-2 Fuzzy Sets (IT2FSs) to model parameters in RBM for training, as they are efficient in handling higher level of uncertainty. Experiments performed for MNIST digits show more generative and discriminative capabilities of IT2FRBM over RBM and FRBM.
Supervised I-vector modeling for language and accent recognition

The conventional i-vector approach to speaker and language recognition constitutes an unsupervised learning paradigm where a variable length speech utterance is converted into a fixed dimensional feature vector (termed as i-vector). The i-vector approach belongs to the broader family of factor analysis models where the utterance level adapted means of a Gaussian Mixture Model - Universal Background Model (GMM-UBM) are assumed to lie in a low rank subspace. The latent variables in the low rank model are assumed to have a standard Gaussian prior distribution. In this paper, we rework the theory of i-vector modeling in a supervised framework where the class labels (like language or accent) of the speech recordings are introduced directly into the i-vector model using a mixture Gaussian prior where each mixture component is associated with a class label. We provide the mathematical formulation for minimum mean squared error estimate (MMSE) of the supervised i-vector (s-vector) model. A detailed analysis of the s-vector model is given and this is contrasted with the traditional i-vector framework. The proposed model is used for language recognition tasks using the NIST Language Recognition Evaluation (LRE) 2017 dataset as well as an accent recognition task using the Mozilla common voices dataset. In these experiments, the s-vector model provides significant improvements over the conventional i-vector model (relative improvements of up to 24% for LRE task in terms of primary detection cost metric).
Propagation Mechanism for Deep and Wide Neural Networks

Recent deep neural networks (DNN) utilize identity mappings involving either element-wise addition or channel-wise concatenation for the propagation of these identity mappings. In this paper, we propose a new propagation mechanism called channel-wise addition (cAdd) to deal with the vanishing gradients problem without sacrificing the complexity of the learned features. Unlike channel-wise concatenation, cAdd is able to eliminate the need to store feature maps thus reducing the memory requirement. The proposed cAdd mechanism can deepen and widen existing neural architectures with fewer parameters compared to channel-wise concatenation and element-wise addition. We incorporate cAdd into state-of-the-art architectures such as ResNet, WideResNet, and CondenseNet and carry out extensive experiments on CIFAR10, CIFAR100, SVHN and ImageNet to demonstrate that cAdd-based architectures are able to achieve much higher accuracy with fewer parameters compared to their corresponding base architectures.
Generative Adversarial Irregularity Detection in Mammography Images

This paper presents a novel method for detection of irregular tissues in mammography images. Previous works solved such irregularity detection tasks with binary classifiers. Here, we propose to detect irregularities by only observing the healthy samples and describe anything largely different from them as irregularity (i.e., unhealthy or cancerous tissues in terms of demographic breast images). This is particularly of great interest as it is very complicated to acquire datasets with all types of cancer cell shapes and tissues for building binary classifiers. Our modeling allows for learning an irregularity detector without any supervising signal from the irregular class. To this end, we propose an architecture with two deep convolutional networks ($$\mathbf {R}$$ and $$\mathbf {M}$$) that are trained adversarially. $$\mathbf {R}$$ learns to Reconstruct regular mammography images by only observing healthy tissues and $$\mathbf {M}$$ (a Matching network) to detect if its input is healthy or not. The experimental results confirm the reliability and superior performance of our methods for detecting cancer tissues in mammography images in comparison with state-of-the-art irregularity detection methods. The code is available at https://github.com/milad-ahmadi/GAID .
Robotic manipulator path-planning: Cost-function approximation with fuzzy inference system

This paper presents an offline path-planning method for robotic manipulators in static environment. The framework is based on the Transition-based Rapidly-exploring Random Tree (T-RRT) algorithm that requires a cost for each configurations. In this work, the calculation of this cost-function is based on the distance between the position and configurations that cause collisions. This function is evaluated with fuzzy function-approximation which lead to an efficient way to determine the cost all over the configuration space. The method is general, the only restriction is that the segments of the robot and the obstacles are modelled as convex polyhedrons. The approach is validated through simulations in MATLAB Simulink environment with Mitsubishi RV-2F-Q manipulator.
A new fuzzy clustering algorithm for interval-valued data based on City-Block distance

Interval-valued data are needed, for example, when an object represents a group of individuals and the variables used to describe it need to assume a value which expresses the variability inherent to the description of a group. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. In this paper is proposed a robust partitioning fuzzy clustering algorithm for interval-valued data based on adaptive City-Block distance that takes into account the relevance of the variables according to the boundaries. This distance changes at each iteration of the algorithm and is different from one cluster to another. The method optimizes an objective function by alternating three steps to compute the representatives of each group, the fuzzy partition, as well as relevance weights for the interval-valued variables for each boundary. Experiments on synthetic and real interval-valued datasets corroborate the usefulness and robustness of the proposed algorithm.
Automatic Classification of Discourse in Chinese Classroom Based on Multi-feature Fusion

In the analysis of classroom teaching behaviors, the current systems of quantitative analysis mainly segment, identify, classify and code classroom teaching behaviors manually, including language behaviors, without using automatic methods. Therefore, this paper applied text classification method to classroom discourse analysis to optimize and improve the classification and coding process. In this paper, a feature extraction method fusing TF-IDF feature with word2vec word vector feature was proposed based on the chi-square test for feature selection of the three types of teacher discourse, namely lecture, instruction and question in class. SVM was used as a classifier to realize the automatic classification of teacher discourse in class. The classification accuracy was up to 86.07%.
A Comparative Study of Different Features for Vehicle Classification

This paper presents a comparative study of different features for vehicle classification. Real-time vehicle classification system using computer vision is relatively cheaper and easy to install. As traffic is heterogeneous in India, road planning and traffic management is challenging. So an automated vehicle detection and classification system is useful for traffic survey, planning, signal time optimization and surveillance. In this work, traffic video data is collected using a camera placed on the top of a vehicle parking on the side of a road at an angle of approximately 45°. Both audio and video are used for vehicle detection. The presence of a vehicle is detected from frames corresponding to the peaks in the short time energy of audio. The process of adaptive background subtraction is performed on the selected frames to separate the vehicle from the background. After background subtraction, morphological processes such as erosion, dilation and closing are applied to get the region of interest. There may be mulitiple frames with the same vehicle are detected at this stage. To reduce the multiple occurrences of the same vehicle in selected frames, Speeded-Up Robust Feature (SURF) matching algorithm is used. Different features like Histogram Oriented Gradient (HOG), Local Binary Pattern (LBP), KAZE, Binary Robust Invariant Scale Keypoint (BRISK) features of selected frames are extracted and Support Vector Machine (SVM) models are developed. Vehicle classification accuracy of various features are compared using a 20 minutes traffic video. It is observed that HOG gives the best result compared to KAZE, LBP and BRISK, with an accuracy of 85.50%.
A Comparative Study of Some Central Notions of <i>ASPIC</i><sup>+</sup> and <i>DeLP</i>

Abstract This paper formally compares some central notions from two well-known formalisms for rule-based argumentation, DeLP and ASPIC + . The comparisons especially focus on intuitive adequacy and inter-translatability, consistency, and closure properties. As for differences in the definitions of arguments and attack, it turns out that DeLP ’s definitions are intuitively appealing but that they may not fully comply with Caminada and Amgoud’s rationality postulates of strict closure and indirect consistency. For some special cases, the DeLP definitions are shown to fare better than ASPIC + . Next, it is argued that there are reasons to consider a variant of DeLP with grounded semantics, since in some examples its current notion of warrant arguably has counterintuitive consequences and may lead to sets of warranted arguments that are not admissible. Finally, under some minimality and consistency assumptions on ASPIC + arguments, a one-to-many correspondence between ASPIC + arguments and DeLP arguments is identified in such a way that if the DeLP warranting procedure is changed to grounded semantics, then ’s DeLP notion of warrant and ASPIC + ’s notion of justification are equivalent. This result is proven for three alternative definitions of attack.
Challenges of using machine learning algorithms for cybersecurity

This chapter is aimed to introduce how researchers and security experts are using forums and social media posts as source for predicting security-related events against computational assets. First, we present an overview of the data-driven approaches and methods for processing the natural language communication extracted from the mentioned sources. Afterwards, we present an overview of common activities that take place on these channels; for instance, the trade of hacking tools and the disclosure of software flaws (vulnerabilities) on social media online communities. In the end, the chapter concludes with a discussion regarding the challenges of using learning-based techniques in cybersecurity. Moreover, we highlight some unsolved problems regarding the ethical considerations of using the content of hacker online communities for cybersecurity research.
Application of Building Information Modelling, Extended tracking technique and Augmented Reality in Building Operating Management

This paper presents a combination of Augmented Reality (AR), Extended Tracking Technique (ETT) and Building Information Modelling (BIM) to interact with building information in real-time on construction site. Using this method, we have programmed an application running in mobile device to help manage and operate the building facility systems. In this application, the building model and its information are stacked and augmented on the mobile device camera when projecting on the target image mounted on the building. ETT allows extending the object displayed on the screen, even when the mobile device camera no longer captures the target image. Based on this process, engineers can interact with the software to add, change the information needed of facilities on construction site, such as size, manufacturer, date of maintenance, date of replacement, device life cycle, etc.
Creative Innovation of Augmented Reality for Promote Sustainable Tourism of Chiang Mai Moat

Abstract Currently, augmented reality technology has been broadly applied to serve numerous purposes. There is a potential that the application will grow even more rapidly in the future. Augmented reality technology has been raised to assist tourism industry. Thus, this research implemented augmented reality technology to advocate Chiang Mai moat areas by presenting information on nine major temples located around the city’s moat in the form of augmented reality demonstration. The assessment result of system efficiency and application satisfaction was in a very good level. Therefore, it can be concluded that the developed application is considered a technological innovation that benefited Chiang Mai moat area’s tourism. It provided information on the local tourist attractions in an easy to access augmented reality presentation which prompts more tourists by increasing informative channel that can boost tourism value.
Ocular Indicators of Mental Workload: A Comparison of Scanpath Entropy and Fixations Clustering

Among the different eye-tracking metrics, both the entropy-based analysis of the scanpath and the spatial distribution of fixations points have been suggested as indices of mental workload. However, they have never been directly compared so far. In this study, eye movements were recorded from fourteen subjects while performing a visuo-spatial task (the "spot the differences" puzzle game). Subjective workload ratings were also collected throughout the session along with performance data. Entropy Rate and Nearest Neighbor Index show matching patterns both indicating high mental workload after two minutes of visual scanning. As predicted elsewhere, the distribution of fixations becomes more clustered under increases of the visuo-spatial demand and showing the same pattern of the Entropy rate that becomes more stereotyped. Temporal demand, instead, has been reported to produce spreading of the fixation pattern. Interestingly, both ocular indices seem to anticipate the drop in performance that is visible after the sixth minute of visual scanning.
Do we still need fuzzy classifiers for Small Data in the Era of Big Data?

The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory requirements. As a result, Big Data classifiers might be expected to be inferior to those designed for standard classification tasks (Small Data) in terms of accuracy and model complexity. To our knowledge, however, there is no empirical evidence to confirm such a conjecture yet. Here, we investigate the extent to which state-of-the-art fuzzy classifiers for Big Data sacrifice performance in favor of scalability. To this end, we carry out an empirical study that compares these classifiers with some of the best performing algorithms for Small Data. Assuming the latter were generally designed for maximizing performance without considering scalability issues, the results of this study provide some intuition around the tradeoff between performance and scalability achieved by current Big Data solutions. Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible.
Interpretable Machine Learning with Boosting by Boolean Algorithm

Much research has been conducted for developing efficient, automated, interpretable, and highly accurate Machine Learning algorithms. However, the primary challenge remains - developing algorithms successful in all of these aspects simultaneously. For applications, where human lives are at stake, having to choose between accuracy and interpretability is not acceptable. To address this challenge, previously the Dominance Classifier and Predictor (DCP) algorithm, capable of discovering of the human-understandable models in simple and visualizable terms was proposed. On the benchmark Wisconsin Breast Cancer (WBC) dataset, it achieved greater accuracy than other interpretable algorithms, reducing the gap between the prediction accuracy of interpretable and non-interpretable algorithms on these data. This paper proposes a new interpretable algorithm RPPR, to bridge this gap more by boosting DCP via discovering properties of misclassified cases. Experiments with RPPR on WBC and two other benchmark datasets using 10-fold cross validation, achieved accuracies greater than 99%. Thus, the accuracy of non-interpretable algorithms is reachable without sacrificing interpretability.
Understanding Compatibility-based Classifier Personalization in Activity Recognition

Personalization related research has been conducted for decades with the objective of investigating personal-level effectiveness in irregular or abnormal cases among many types of domains. However, the activity recognition community generally investigates one-fits-all model, i.e., a single recognition model for all people and suffers from poor performance generalization or a huge amount of training data for high generalization. In this paper, we propose Compatibility-based classifier personalization (CbCP) as a subject-dependent activity recognition method that uses the classifier with the highest compatibility (similarity) with a particular user. Furthermore, we explore the effectiveness of classifier personalization through a particular group of activities, which results in a hierarchical recognition model. We present a comparative evaluation of 1) the traditional one-fits-all classifier vs. CbCP models and 2) compatibility evaluation through all activities (non-hierarchical classification) vs. a group of activities (hierarchical classification). The results of the two public datasets imply the effectiveness of compatibility-based approach for a hierarchical classifier formation.
Image Inpainting Method Based on Mixed Median

The image inpainting is an interesting problem of image processing that has wide range of application in science, engineering, medicine and other fields of life. The inpainting is a process to fill the corrupted or missing parts of images. So, the image inpainting can be used in various cases, such as restoration of corrupted images, removal of unnecessary objects on images to improve quality of postprocessing tasks, image zooming etc. In this paper, we propose an image inpainting method based on mixed median. The mixed median proposed here is a combination of two kinds of median of pixels intensity: median of uncorrupted pixels in a clipping window and median of the maximum repetitive pixel values. In the experiment, we handle the test on the open dataset with given masks to generate the corrupted regions. This method is helpful to assess the inpainting quality based on the peak signal-to-noise ratio and the structure similarity metrics. We also compare the proposed method with the harmonic inpainting method to prove its own effectiveness.
Cylindric Kleene Lattices for Program Construction

Cylindric algebras have been developed as an algebraisation of equational first order logic. We adapt them to cylindric Kleene lattices and their variants and present relational and relational fault models for these. This allows us to encode frames and local variable blocks, and to derive Morgan’s refinement calculus as well as an algebraic Hoare logic for while programs with assignment laws. Our approach thus opens the door for algebraic calculations with program and logical variables instead of domain-specific reasoning over concrete models of the program store. A refinement proof for a small program is presented as an example.