Reviewerly/exHarmony huggingface dataset:
AREd: Anatomy Learning Using Augmented Reality Application

With the advancements in technology, innovative teaching methodologies are being utilized in the educational environment. Among these, the most promising methodology is Augmented Reality which makes it possible for students to fully understand an abstract concept by representing a visualized and interactive 3D model. This paper gives review of recent studies on AR in different fields of education as well as an overview of some applications that use AR in the field of education to highlight the benefits of utilization of augmented reality. This paper presents an augmented reality application which generates 3D models of anatomy which aims to help students and instructors alike. A survey was conducted with the purpose of exploring acceptance and benefits of this application. The outcomes of survey suggested that students retain what they learned from this application longer compared to traditional teaching methods, students retain concentration from proposed method.
Multi-parameter safe sample elimination rule for accelerating nonlinear multi-class support vector machines

Abstract K-SVCR and Twin-KSVC are two novel algorithms to deal with multi-class problems. They have achieved good performance since they evaluate all training samples into a “1-versus-1-versus-rest” structure. But they are extremely time consuming, so it remains challenging to apply them into large-scale problems directly. Inspired by the sparse solution of SVMs, in this paper, we propose a safe sample elimination rule (SSE) for multi-class classifiers K-SVCR and Twin-KSVC, termed as SSE-K-SVCR and SSE-T-KSVC, to reduce computation time. With our rule, many redundant samples of all classes can be identified and deleted before actually solving the problem, so the scale of dual problems can be reduced a lot. And our methods are safe, i.e., they can derive identical optimal solutions as K-SVCR and Twin-KSVC, respectively. So the testing accuracy keeps unchanged. Besides, the methods can be embedded into grid search method to accelerate the whole training process, and they are effective both for penalty parameter and kernel parameter. Finally, a fast algorithm clipDCD is introduced to reduce the computation time for larger datatset. Experimental results on one artificial dataset and seventeen benchmark datasets demonstrate the effectiveness and safety of our proposed methods.
Row and column iteration methods to solve linear systems on a quantum computer

We consider the quantum implementations of two classical iterative solvers for a system of linear equations, including the row (Kaczmarz) method which utilizes a row of the coefficient matrix in each iteration step, and the column (coordinate descent) method which uses a column instead. These two methods are widely applied in big data science due to their simple iteration schemes. We propose fast quantum algorithms for these two approaches by constructing efficient unitary operators in each iteration step based on the block-encoding technique. The construction is based on the unitaries to prepare the quantum states of rows or columns of the coefficient matrix. If the quantum states are efficiently prepared, for example, by qRAM, then the quantum iterative methods achieve an exponential speedup in the problem size $n$ over the classical versions. Meanwhile the dependence on the number of steps is linear, which is the same as the classical counterparts. The complexity of the quantum iterative linear solvers is $O({\ensuremath{\kappa}}_{s}^{2}(logn)\left(log\frac{1}{\ensuremath{\epsilon}}\right))$, where ${\ensuremath{\kappa}}_{s}$ is the scaled condition number, and $\ensuremath{\epsilon}$ is the error. The quantum iterative linear solvers do not depend on Hamiltonian simulations and quantum phase estimation, and also work for the dense case.
Graph Coloring on the GPU

We design and implement parallel graph coloring algorithms on the GPU using two different abstractions—one data-centric (Gunrock), the other linear-algebra-based (GraphBLAS). We analyze the impact of variations of a baseline independent-set algorithm on quality and runtime. We study how optimizations such as hashing, avoiding atomics, and a max-min heuristic affect performance. Our Gunrock graph coloring implementation has a peak 2x speed-up, a geomean speed-up of 1.3x and produces 1.6x more colors over previous hardwired state-of-the-art implementations on real-world datasets. Our GraphBLAS implementation of Luby's algorithm produces 1.9x fewer colors than the previous state-of-the-art parallel implementation at the cost of 3x extra runtime, and 1.014x fewer colors than a greedy, sequential algorithm with a geomean speed-up of 2.6x.
Statistical Approach to Compare Image Denoising Techniques in Medical MR Images

In medical image processing, magnetic resonance (MR) imaging techniques play an important role. The images acquired are usually affected from various noise such as gaussian noise, salt and pepper noise, speckle noise, periodic noise etc. Therefore, acquisition of images without noise is nearly impossible task. Various filtering techniques are used to reduce the noise for further analysis of medical images. In this paper, numerous digital filters such as mean filter, median filter, wiener filter, and adaptive filter are applied for the removal of different level of Gaussian noise, salt and pepper noise and speckle noise that are added separately in MR images. The performance of all the filtering techniques are compared on the basis of the statistical parameters such as Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).
A probabilistic topic model for event-based image classification and multi-label annotation

We propose an enhanced latent topic model based on latent Dirichlet allocation and convolutional neural nets for event classification and annotation in images. Our model builds on the semantic structure relating events, objects and scenes in images. Based on initial labels extracted from convolution neural networks (CNNs), and possibly user-defined tags, we estimate the event category and final annotation of an image through a refinement process based on the expectation–maximization (EM) algorithm. The EM steps allow to progressively ascertain the class category and refine the final annotation of the image. Our model can be thought of as a two-level annotation system, where the first level derives the image event from CNN labels and image tags and the second level derives the final annotation consisting of event-related objects/scenes. Experimental results show that the proposed model yields better classification and annotation performance in the two standard datasets: UIUC-Sports and WIDER.
Deep Learning-Based Modulation Classification Using Time and Stockwell Domain Channeling

Deep learning techniques have recently exhibited unprecedented success in classification problems with ill-defined mathematical models. In this paper, we apply deep learning for RF data analysis and classification. We present a novel method of using I-Q time samples to form images with ‘Time and Discrete Orthonormal Stockwell Transform Domain Channels’ which are used for training a convolutional neural network (CNN) for radio modulation classification. Also, a concept inspired from transfer learning is used in extending the number of output classes of the CNN, which helps the network to estimate the approximate SNR of the input signal as well and further improve the classification accuracy. Such a network trained on Time and Stockwell Channeled Images performs superior to similar networks that are trained on just raw I-Q time series samples or time-frequency images, especially when training samples are less. The network achieved an overall classification accuracy of 97.3% at 8 dB SNR over a class of 10 radio modulation schemes (for both digital and analog systems). The study shows that such a trained network can be well applied to achieve high classification accuracies at low and moderate SNR scenarios.
Assessing the Reliability of Automated Knowledge Control Results

Recently the concepts of smart systems, smart education, smart engineering, smart medicine, smart city, smart universities, smart technology and smart communication were introduced in scientific methodology and practice for a modern society. All of these entities are the components of a new system - smart society system. These components must be equipped with corresponding connections that ensure their reliability, or, in other words, to have an appropriate smart infrastructure. One of the most important issues in this regard is a feedback and a control in smart system. This paper describes our approach to an application of statistical methods to automated knowledge control. The obtained research outcomes clearly show that if testing is used to test knowledge level, then a) it gives adequate outcomes of knowledge assessment only under repeated regular use of testing procedure and b) the final assessment should be obtained using the averaging procedure.
A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.
Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning

Mining high utility patterns in dynamic databases is an important data mining task. While a naive approach is to mine a newly updated database in its entirety, the state-of-the-art mining algorithms all take an incremental approach. However, the existing incremental algorithms either take a two-phase paradigm that generates a large number of candidates that causes scalability issues or employ a vertical data structure that incurs a large number of join operations that leads to efficiency issues. To address the challenges with the existing incremental algorithms, this paper proposes a new algorithm incremental direct discovery of high utility patterns (Id <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HUP+). Id <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HUP+ adapts a one-phase paradigm by improving the relevance-based pruning and upper-bound-based pruning proposes a novel data structure for a quick update of dynamic databases and proposes the absence-based pruning and legacy-based pruning dedicated to incremental mining. The extensive experiments show that our algorithm is up to 1-3 orders of magnitude more efficient than the state-of-the-art algorithms, and is the most scalable algorithm.
Crowdsourcing-Based Learning: the Effective Smart Pedagogy for STEM Education

Multiple innovative teaching and learning strategies, that strongly support the advanced technology-based education, emerged in recent years. However, there are no published data analytics and validated outcomes regarding how good those innovative teaching approaches support the main concepts of Smart Pedagogy. This paper presents the outcomes of the ongoing research, design and development project at the InterLabs Research Institute, Bradley University (Peoria, IL, USA) aimed to identify, analyze, test, implement and recommend various components of Smart Pedagogy for STEM education. Those components may include but are not limited to 1) learning- by-doing, active learning or experiential learning, 2) games-based learning and gamification of learning, 3) collaborative learning, 4) analytics-based learning, 5) flipped classroom, 6) crowdsourcing-based learning, 7) badging-based learning, and 8) productive failure-based learning. The obtained research outcomes about application of crowdsourcing-based learning in several STEM undergraduate and graduate courses are presented in this paper. Project outcomes undoubtedly demonstrate students' keen interest in active use of crowdsourcing-based learning in Sciences courses, in particular, and in highly technological Smart Pedagogy in STEM education, in general.
Enabling Secure Intelligent Network with Cloud-Assisted Privacy-Preserving Machine Learning

Intelligent networks are regarded as existing networks incorporating some intelligent mechanisms such as cognitive and cooperative approaches to improve network performance. Security is highly essential in intelligent networks but has received less attention so far. In this article, we propose a framework that enables a secure intelligent network with the assistance of cloud-assisted privacy-preserving machine learning. In the framework, the cloud server can first generate a model using outsourced machine learning algorithms and then process testing data from the network with the generated model in real time, which reflects to the network and makes it more intelligent. At the same time, the proposal guarantees the security and privacy of both the training data and the testing data in the sense that the proposed framework takes advantage of differential privacy to perform privacy-preserving data analysis and homomorphic encryption to conduct valid operations over encrypted data. The performance evaluations of the core primitives employed in the framework including differential privacy and homomorphic encryption algorithms demonstrate the practicability of our proposal.
Drivability evaluation model using principal component analysis and optimized extreme learning machine

The accuracy of the evaluation method is essential to optimize the control system and improve a vehicle’s drivability quality. This study aimed at exploring a more effective drivability evaluation method and a drivability evaluation model was proposed on the basis of principal component analysis and optimization of an extreme learning machine. The drivability evaluation model was built using an extreme learning machine. The input of the model was determined by the principal component analysis method, and the optimal number of neurons in the hidden layer of the drivability evaluation model was obtained by a particle swarm optimization algorithm. The experimental results show that considering the evaluation index coupling factors can improve the prediction accuracy of the evaluation model. The R correlation between the score predicted by the drivability evaluation model proposed in this paper and the actual score reached 0.979, and the predicted pass rate also reached 95%, which indicate the model was more accurate and stable than others. The evaluation model can be extended to fuel economy and handling stability. It also has theoretical guidance and application value in practical problems.
Analysis and Prediction of Student Emotions While Doing Programming Exercises

The modeling of student emotions has recently considerable interest in the field of intelligent tutoring systems. However, most approaches are applied in typical interaction models characterized by frequent communication or dialogue between the student and the tutoring model. In this paper, we analyze emotions while students are writing computer programs without any human or agent communication to induce displays of affect. We use a combination of features derived from typing logs, compilation logs, and a video of the students' face while solving coding exercises and determine how they can be used to predict affect. We find that combining pose-based, face-based, and log-based features can train models that predict affect with good accuracy above chance levels and that certain features are discriminative in this task.
An Extended Reality Approach for Creating Immersive Software Project Workspaces

Software project workspaces are areas of constant project activities across different phases of the software development lifecycle. Besides being physical spaces to house the team members these are also areas of teamwork, creativity and collaboration. It is well established that there are several information and insight needs of software teams (both individual and collective) during the course of any project. Interestingly, the use of the project workspaces themselves for assisting in getting those insights has been almost non-existent and there is little technological support for doing so. Here we present our approach that utilizes advances in extended reality to augment the software project bay with important insights which are anchored on and around real objects in the workspace. The approach is based upon mining the data exhaust (disparate sources of data and logs) of the software project and development environment, for data important to construct insights relevant to an XR headset wearer and the team. The wearer's location and parts of the workspace (floors, furniture, ceilings) are detected in the wearer's field of view and thereon spatial mapping is utilized to overlay important representations of the relevant insights onto/around them. The immersive insights are contextual, can be continuously updated and leverage affordances of human perception, for better comprehension, enhanced creativity as well as recall. Along with our approach, we also present an early Microsoft Hololens based implementation which allows for different categories of live augmentations for creating a smart immersive workspace.
Near-term quantum-classical associative adversarial networks

We introduce a hybrid quantum-classical adversarial machine learning architecture called a quantum-classical associative adversarial network (QAAN). This architecture consists of a classical generative adversarial network with a small auxiliary quantum Boltzmann machine that is simultaneously trained on an intermediate layer of the discriminator of the generative network. We numerically study the performance of QAANs compared to their classical counterparts on the MNIST and CIFAR-10 data sets, and show that QAANs attain a higher quality of learning when evaluated using the Inception score and the Fr\'echet Inception distance. As the QAAN architecture only relies on sampling simple local observables of a small quantum Boltzmann machine, this model is particularly amenable for implementation on the current and next generations of quantum devices.
Robust joint representation with triple local feature for face recognition with single sample per person

Face recognition (FR) with a single training sample per person (SSPP) is a representative small-sample-size classification problem and occurs in many practical scenarios such as law enforcement, surveillance, identity card, e-passport, etc. By using intra-class variations extracted from an additional training set (i.e., a set excluding gallery subjects) as a generic intra-class variation dictionary, sparse representation based classification (SRC) has been extended to FR with SSPP. However, for FR with SSPP, how to achieve high robustness to gross facial variations (e.g., complex facial lighting, expression and pose variations and various outliers of corruption, occlusion and disguise) is still an open issue. In this paper, we propose a novel model, named robust joint representation with triple local feature (RJR-TLF), to address this issue from the viewpoints of feature extraction and classifier design. In feature extraction, we design robust triple local features, i.e., Gabor facial features with multiple scales and multiple orientations extracted in different facial local regions (e.g., local patches centered around dense regularly sampled points and detected particular points including nose tip, eye centers, etc.), to naturally encode the local scale, local orientation and local space information of a face image. For face images, the densely and regularly sampled facial regions can provide a comprehensive description; the sparsely and particularly detected facial regions can exploit a discriminative description because they cover the most informative facial regions and can be detected robustly. In classifier design, we propose a robust joint representation framework to exploit the distinctiveness and similarity of different local information by requiring triple local features from the same type of Gabor feature (i.e., with the same scale and orientation) to have similar representation coefficients. With the coefficient-similarity constraint and the robust representation fidelity term representing the query image on the single-sample gallery set and the generic intra-class variation dictionary, the local features with large-representation residuals actually indicate corrupted regions with gross facial variations and will be assigned low weights adaptively to reduce their effects on the representation and classification, which further strengthens the robustness of RJR-TLF. The proposed RJR-TLF is evaluated extensively on popular databases, including the AR, the large-scale CMU Multi-PIE, and the LFW databases. Experimental results demonstrate that RJR-TLF is much more robust to various facial variations than the recent FR with SSPP methods.
Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

An autonomous robot performing tasks in a human environment needs to recognize semantic information about places. Semantic mapping is a task in which suitable semantic information is assigned to an environmental map so that a robot can communicate with people and appropriately perform tasks requested by its users. We propose a novel statistical semantic mapping method called {SpCoMapping}, which integrates probabilistic spatial concept acquisition based on multimodal sensor information and a Markov random field applied for learning the arbitrary shape of a place on a map. SpCoMapping can connect multiple words to a place in a semantic mapping process using user utterances without pre-setting the list of place names. We also develop a nonparametric Bayesian extension of SpCoMapping that can automatically estimate an adequate number of categories. In the experiment in the simulation environments, we showed that the proposed method generated better semantic maps than previous semantic mapping methods; our semantic maps have categories and shapes similar to the ground truth provided by the user. In addition, we showed that SpCoMapping could generate appropriate semantic maps in a real-world environment.