Reviewerly/exHarmony huggingface dataset:
Effective Similarity Search on Heterogeneous Networks: A Meta-path Free Approach

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. In contrast, graphs that have a single type of nodes and edges, are often called homogeneous graphs. Measuring similarities among objects is an important task in data mining applications, such as web search, link prediction, and clustering. Currently, several similarity measures are defined for HINs. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of similarity scores. This makes using existing similarity measures in real applications difficult. To address this problem, we extend SimRank, a well-known similarity measure on homogeneous graphs, to HINs, by introducing the concept of the decay graph. The newly proposed similarity measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, and the efficiency of our proposed algorithms for computing HowSim scores, are demonstrated by extensive experiments.
An Augmented Reality-based Software Architecture to Support Military Situational Awareness

Situational Awareness (SA) is the perception of our surrounding, comprehension of its meaning and projection of its status. The lack of SA is a causal factor in many military accidents. In this paper, Augmented Reality (AR) techniques are used to support SA and interaction in time-pressuring crisis situations. Firstly, existing military projects are reviewed. Then, the design of a military AR-based architecture as support to SA, called RAIOM (Augmented Reality for the identification of Military Objectives), is introduced and validated with real users considering traditional military scenarios. A distributed processing in a client-server architecture was implemented using an optical see-through AR glasses by the client side and a mini board by the server side. An AR application using the proposed AR architecture was deployed as an integrated proof of concept. The deployment was evaluated to measure the effectiveness, efficiency and usability of the AR software architecture in terms of three levels of SA such as perception, comprehension and projection. Different techniques were used in the evaluation such as User Testing, Thinking Aloud Protocol and SAGAT / SART. Also the User Experience (UX) was evaluated using UMUX questionnaire. The results were optimistic according to the degree of compliance of the SA got by the participants in the experiment.
Classification of quadratic APN functions with coefficients in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> for dimensions up to 9

Almost perfect nonlinear (APN) and almost bent (AB) functions are integral components of modern block ciphers and play a fundamental role in symmetric cryptography. In this paper, we describe a procedure for searching for quadratic APN functions with coefficients in F2 over the finite field F2n and apply this procedure to classify all such functions over F2n with n≤9. We discover two new APN functions (which are also AB) over F29 that are CCZ-inequivalent to any known APN function over this field. We also verify that there are no quadratic APN functions with coefficients in F2 over F2n with 6≤n≤8 other than the currently known ones.
Learning Contour-Based Mid-Level Representation for Shape Classification

This article proposes a novel contour-based mid-level shape description method for shape classification. This method resolves the shortcomings of low-level shape descriptors in dealing with the shapes of objects with large intra-class changes and non-linear deformation (articulation, occlusion and noise), thus improving the accuracy of shape classification. First, we extract the outer contour of an object and sample it. We next describe each sampling point on the shape contour with a triangular feature and regard it as a local feature. Then, a shape codebook is learned, and the Fisher vector encoding method is used to produce a compact mid-level shape feature. Finally, the learned mid-level shape features are sent to the linear support vector machine (SVM) classifier for shape recognition. The proposed method has been extensively tested on several standard shape datasets, and the experimental results show that our approach attains high accuracy of shape classification. Comparisons to other state-of-the-art shape classification approaches further prove the superiority and effectiveness of our method.
Virtual Learning Objects’ of Math Educative Process

Nowadays, we are facing rapid educational and technological era, therefore there is increased need to highlight the connection for teachers to rediscover improved teaching-learning processes. The actual educative processes are being developed in virtual learning environments, offering flexible, far reaching distance learning, and better tools for teachers and students in the 2.0 web, this has allowed discovering countless opportunities to improve learning environments, offering students an education that responds to the reality where they can flourish, innovate and develop. The present research brings up as its objective the analysis of the implementation of virtual learning objects in learning math, and as a result, the creation of an innovator technological proposal in the process of teaching-learning.
AI-FML Agent for Robotic Game of Go and AIoT Real-World Co-Learning Applications

In this paper, we propose an AI-FML agent for robotic game of Go and AIoT real-world co-learning applications. The fuzzy machine learning mechanisms are adopted in the proposed model, including fuzzy markup language (FML)-based genetic learning (GFML), eXtreme Gradient Boost (XGBoost), and a seven-layered deep fuzzy neural network (DFNN) with backpropagation learning, to predict the win rate of the game of Go as Black or White. This paper uses Google AlphaGo Master sixty games as the dataset to evaluate the performance of the fuzzy machine learning, and the desired output dataset were predicted by Facebook AI Research (FAIR) ELF Open Go AI bot. In addition, we use IEEE 1855 standard for FML to describe the knowledge base and rule base of the Open Go Darkforest (OGD) prediction platform in order to infer the win rate of the game. Next, the proposed AI-FML agent publishes the inferred result to communicate with the robot Kebbi Air based on MQTT protocol to achieve the goal of human and smart machine co-learning. From Sept. 2019 to Jan. 2020, we introduced the AI-FML agent into the teaching and learning fields in Taiwan. The experimental results show the robots and students can co-learn AI tools and FML applications effectively. In addition, XGBoost outperforms the other machine learning methods but DFNN has the most obvious progress after learning. In the future, we hope to deploy the AI-FML agent to more available robot and human co-learning platforms through the established AI-FML International Academy in the world.
Deep Learning-based Automatic Morphological Classification of Leukocytes using Blood Smears

Classification of leukocytes into morphological classes is one of the main steps in the effective recognition of cancerous white blood cells and the diagnosis of malignancies. Before the evolution of computer-aided system designs, the process of morphological classification and inspection of microscopic blood smears is carried out by qualified and proficient human examiners which makes the procedure inefficient and slow. In this paper, a computer-aided classification technique is presented which is based on a combination of image processing and deep learning approaches to classify blood smears into its discrete morphological classes. The proposed methodology includes the conversion of blood images to L*a*b color space from RGB, K-mean clustering for segmentation, and deep convolutional networks for classification. Transfer learning is employed to shift the learned knowledge of pre-trained networks including AlexNet and ResNet18. Statistical evaluation parameters are used to validate the performance of the system on the Munich AML Morphological dataset. The results show reliable classification accuracy of 93.30% and 93.85% for AlexNet and ResNet18 respectively. The classification approach discussed in this paper can be used in real-time clinical environments as it is trained on a large dataset it allows detection in an earlier stage of the disease.
Technologies to Support Tourism Innovation and Cultural Heritage: Development of an Immersive Virtual Reality Application

Recent advances in technology and its applications have impacted fundamentally the tourism industry. Technological progress and innovations have for many years worked in facilitating the development of tourism [23, 31]. More recently, the emergence of new advanced technologies such as multimedia technologies, virtual reality (VR) and augmented reality (AR) have become key tools for tourism promotion and development. The use of these tools enhance travelers' richness and interaction and provide them a simulated real visit and virtual experience before visiting the destination or the attraction [20]. Especially, VR technologies offer to travelers the possibility to get immersed in the digital world, and to navigate the real situations through a virtual environment [20]. As such these technologies have also created new opportunities for promotion and marketing of different tourism products such as destination cultural heritage, museums, parks etc [5, 11, 21, 28]. Following these developments in this paper we aim to present a technological solution that could be an innovative and valuable tool for tourism promotion of the area. In particular, the technological tool consists in the development of an app that uses Erasmus students as ambassadors for the promotion of the destination by providing tourists with a real immersive virtual reality experience.
EXTRA: An Extended Radial Mean Response Pattern for Hand Gesture Recognition

Hand gesture recognition (HGR) has gained significant attention in recent year due to its varied applicability and ability to interact with machines efficiently. Hand gestures provide a way of communication for hearing-impaired persons. The HGR is a quite challenging task as its performance is influenced by various aspects such as illumination variations, cluttered backgrounds, spontaneous capture, multi-view etc. Thus, to resolve these issues in this paper, we propose an extended radial mean response (EXTRA) pattern for hand gesture recognition. The EXTRA pattern encodes the intensity variations by establishing a reconciled relationship between local neighboring pixels located at two radials r1 and r2. The gradient information between radials preserves the transitional texture that enhances the robustness to deal with illuminations changes. Moreover, the EXTRA pattern holds extensive radial information, thus it can conserve both high level and micro level edge variations that filter hand posture texture from the cluttered background. Furthermore, the mean responsive relationship between adjacency radial pixels improves robustness to noise conditions. The proposed technique is evaluated on three standard datasets viz NUS hand posture dataset-I, MUGD and Finger Spelling dataset. The experimental results and visual representations show that the proposed technique performs better than the existing algorithms for the purpose intended.
Supervised discriminative dimensionality reduction by learning multiple transformation operators

Analyzing and learning from high dimensional data have always been challenging in machine learning tasks, causing serious computational complexities and poor learning performances. Supervised dimensionality reduction is a popular technique to address such challenges in supervised learning tasks, where data are accompanied with labels. Traditionally, such techniques mostly learn one single transformation to project data into a low-dimensional discriminative subspace. However, learning only one transformation for the whole data could be dominated by one or several classes, and the rest of classes receive less discrimination in the reduced space. That is to say, learning one transformation is insufficient to properly discriminate classes of data in the reduced space because they may have complex and completely dissimilar distributions. This insufficiency becomes even more serious if the number of classes increases, leading to poor discrimination and lessening the learning performance in the reduced space. To overcome this limitation, we propose a novel supervised dimensionality reduction method, which learns per-class transformations by optimizing a newly designed and efficient objective function. The proposed method captures more discriminative information from each single class of data compared to the case of one single transformation. Moreover, the proposed objective function enjoys several desirable properties: (1) maximizing margins between the transformed classes of data, (2) having a closed-form solution, (3) being easily kernelized in the case of nonlinear data,(4) preventing overfitting, and (5) ensuring the transformations are sparse in rows so that discriminative features are learned in the reduced space. Experimental results verify that the proposed method is superior to the related state-of-the-art methods and promising in generating discriminative embeddings.
Key Algorithm for Human Motion Recognition in Virtual Reality Video Sequences Based on Hidden Markov Model

This paper provides an in-depth discussion of human motion recognition in Virtual Reality (VR) video sequences through hidden Markov models, which are four steps from VR video acquisition and pre-processing, foreground detection, extraction of human feature parameters, and hidden Markov model human motion recognition.A hybrid Gaussian model was used to build a background model in real-time based on changes in VR video information, and the image was subtracted by the background differential method.The optical flow method was used for foreground detection of the target, and the effects of sparse and dense optical flow were compared to obtain the motion characteristics and optical flow information of the target human body, respectively.Features were extracted for human motion, in terms of common geometric features of the body and optical flow information, respectively.In terms of common geometric information, the width-to-height ratio, perimeter-to-area ratio, center of mass, eccentricity, and feature angle were extracted, respectively.For the optical flow information, optical flow descriptors were constructed using a grid-based approach.And feature fusion was performed for the above two parameters by the k-means method to construct the word pocket model.The hidden Markov model parameters were obtained by using the hidden Markov model for the recognition of human motion and training the human feature parameters for each of the four motions.The recognition of the four common human body movements was realized by the forward-backward algorithm.The test results show that the motion recognition method in this paper has high recognition performance and good anti-interference performance.The time-sequence pooling is used to sort the effective video frame feature sequences to obtain the feature vectors that can represent the dynamic changes of video time sequence; finally, the time-sequence feature vectors are used to train the support vector machine for classification recognition.The recognition accuracy is 65.2% and 89.4% for the HMDB51 and UCF101 datasets, respectively.
Accurate MR image super-resolution via lightweight lateral inhibition network

In recent years, convolutional neural networks (CNNs) have shown their advantages on MR image super-resolution (SR) tasks. Many current SR models, however, have heavy demands on computation and memory, which are not friendly to magnetic resonance imaging (MRI) where computing resource is usually constrained. On the other hand, a basic consideration in most MRI experiments is how to reduce scanning time to improve patient comfort and reduce motion artifacts. In this work, we ease the problem by presenting an effective and lightweight model that supports fast training and accurate SR inference. The proposed network is inspired by the lateral inhibition mechanism, which assumes that there exist inhibitory effects between adjacent neurons. The backbone of our network consists of several lateral inhibition blocks, where the inhibitory effect is explicitly implemented by a battery of cascaded local inhibition units. When model scale is small, explicitly inhibiting feature activations is expected to further explore model representational capacity. For more effective feature extraction, several parallel dilated convolutions are also used to extract shallow features directly from the input image. Extensive experiments on typical MR images demonstrate that our lateral inhibition network (LIN) achieves better SR performance than other lightweight models with similar model scale.
Design of Low-Cost Fuzzy Controllers with Reduced Parametric Sensitivity Based on Whale Optimization Algorithm

The aim of this paper is to demonstrate the feasibility of Whale Optimization Algorithm (WOA) in solving complex control design and tuning problems of fuzzy control systems (FCSs) with a reduced parametric sensitivity. The sensitivity analysis of these FCSs implies the use of sensitivity models defined with respect to the parametric variations of the processes. The main goal is solving the optimization problems defined for servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs), through minimization of objective functions that include the output sensitivity functions. A design method is proposed in this regard, and it is validated through experimental results using a laboratory nonlinear servo system.
Automated Attacker Synthesis for Distributed Protocols

Distributed protocols should be robust to both benign malfunction (e.g. packet loss or delay) and attacks (e.g. message replay). In this paper we take a formal approach to the automated synthesis of attackers, i.e. adversarial processes that can cause the protocol to malfunction. Specifically, given a formal threat model capturing the distributed protocol model and network topology, as well as the placement, goals, and interface of potential attackers, we automatically synthesize an attacker. We formalize four attacker synthesis problems - across attackers that always succeed versus those that sometimes fail, and attackers that may attack forever versus those that may not - and we propose algorithmic solutions to two of them. We report on a prototype implementation called Korg and its application to TCP as a case-study. Our experiments show that Korg can automatically generate well-known attacks for TCP within seconds or minutes.
Smart and Secure Certificate Validation System through Blockchain

In this paper, the problems that arise due to counterfeiting the Certificates and a platform to overcome the mentioned problem are stated. To implement the platform the concept of Blockchain issued. An incontestable advantage of Blockchain is it will make the platform decentralized. The platform is designed to store the Certificates in the block and generate a hash on the request of the student. Once the Certificates are stored in the block they become impossible to fix or to modify by anybody. Putting the information in the block will also remove doubt about the knowledge of the employee. An overview of our platform and its functionalities is presented.
Evaluation of Convolution Operation Based on the Interpretation of Deep Learning on 3-D Point Cloud

The interpretation of deep learning network is an important part in understanding the convolutional neural networks (CNNs). As an exploratory research, this article explored the interpretation method in 3-D point cloud deep learning networks, for the purpose of evaluating the performance of convolution functions in 3-D point cloud CNNs. Specifically, a 3-D point cloud classification network with two branches is used as the interpretation network in two aspects; 1) information entropy is introduced to diagnose the internal representation in the middle layer of CNN; and 2) the external consistency of convolution function is measured by per-point classification accuracy with class activation mapping technique. Four typical convolution functions are tested by the interpretation network on ModelNet40 dataset and the experimental results demonstrate that the proposed evaluation method is reliable. Feature transformation ability and feature recognition ability of convolution functions are extracted by visualization evaluation and proposed measurable metrics evaluation.
Solar Irradiance Forecasting using Decision Tree and Ensemble Models

Sun's radiation is the pivotal driving force of the Earth and its prediction is quite significant for conducting numerous research projects in Renewable Energy Sources (RESs). The solar resource being an intermittent one, improvement in solar radiation prediction accuracy is strived for, to reduce uncertainty in RESs and enhance economical profits derived from them. This paper gives solar irradiance forecasting approach based on Decision Trees (DTs) and their ensemble models. Input data is comprised of 9 daily averaged meteorological parameters and 3 calendar variables for Chandigarh over 2 years (2017 & 2018). The implementation of forecasting models have been analyzed and compared based on Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Correlation Coefficient (R-value). Pearson coefficient technique has also been used to assess the correlation between input features and solar irradiance. The model with least error metrics and highest R-value is considered to be optimal and is utilized to predict daily solar irradiance of Chandigarh for the year 2019.
Neural Network-Based Video Compression Artifact Reduction Using Temporal Correlation and Sparsity Prior Predictions

Quantization in lossy video compression may incur severe quality degradation, especially at low bit-rates. Developing post-processing methods that improve visual quality of decoded images is of great importance, as they can be directly incorporated in any existing compression standard or paradigm. We propose in this article a two-stage method, a texture detail restoration stage followed by a deep convolutional neural network (CNN) fusion stage, for video compression artifact reduction. The first stage performs in a patch-by-patch manner. For each patch in the current decoded frame, one prediction is formed based on the sparsity prior assuming that natural image patches can be represented by sparse activation of dictionary atoms. Under the temporal correlation hypothesis, we search the best matching patch in each reference frame, and select several matches with more texture details to tile motion compensated predictions. The second stage stacks the predictions obtained in the preceding stage along with the decoded frame itself to form a tensor, and proposes a deep CNN to learn the mapping between the tensor as input and the original uncompressed image as output. Experimental results demonstrate that the proposed two-stage method can remarkably improve, both subjectively and objectively, the quality of the compressed video sequence.