Reviewerly/exHarmony huggingface dataset:
A Novel Feature Selection in Vehicle Detection Through the Selection of Dominant Patterns of Histograms of Oriented Gradients (DPHOG)

This paper proposes a novel method that addresses the selection of the dominant patterns of the histograms of oriented gradients (DPHOGs) in vehicle detection. HOG features lead to an expensive classification with high misclassification rates since HOG generates a long vector containing both redundant and ambiguous features (similarities between the vehicle and non-vehicle images). Several modifications of HOG were proposed to resolve these issues such as the vertical histograms of oriented gradient and one that includes position and intensity with HOG; however, these methods still contain some ambiguous features. A feature selection method can exclude these ambiguous features, allowing for better classification rates and a reduction in classification times. The proposed method uses the ideal vectors of the vehicle and non-vehicles images for selecting features in dominant patterns. The segments indicating the differences between the vehicle and non-vehicle classes are the dominant patterns, in which the length of the feature vector is shortened. We performed DPHOG on three standard datasets, in which the kernel extreme learning machine, the support vector machine, K-nearest neighbor, random forest, and deep neural network were used as classifiers. We then compared the performance of the DPHOG with eight well-known feature selection methods and three existing feature extraction methods for vehicle detection. In evaluations with each comparative method concerning the accuracy, true positive, false positive, and F1-score, the DPHOG presented the highest performances with less running time in each dataset.
Complete temporal mode characterization of non-Gaussian states by a dual homodyne measurement

Optical quantum states defined in temporal modes, especially non-Gaussian states like photon-number states, play an important role in quantum computing schemes. In general, the temporal-mode structures of these states are characterized by one or more complex functions called temporal-mode functions (TMFs). Although we can calculate TMF theoretically in some cases, experimental estimation of TMF is more advantageous to utilize the states with high purity. In this paper, we propose a method to estimate complex TMFs. This method can be applied not only to arbitrary single-temporal-mode non-Gaussian states but also to two-temporal-mode states containing two photons. This method is implemented by continuous-wave (CW) dual homodyne measurement and doesn't need prior information of the target states nor state reconstruction procedure. We demonstrate this method by analyzing several experimentally created non-Gaussian states.
Quality Assessment of 3D Synthesized Images via Measuring Local Feature Similarity and Global Sharpness

Depth-image-based rendering (DIBR) techniques can be used to generate virtual views for free-viewpoint video application. However, the DIBR algorithms will introduce geometric distortions that mainly distribute at the disoccluded regions in the synthesized views. It has been demonstrated that conventional 2-D quality metrics are not suitable for the synthesized views. In this paper, we propose a new quality model for 3-D synthesized images by measuring the block-wise texture similarity and color contrast similarity in critical areas, and the global gradient magnitude deviation. A critical area detection module is first employed using a warping method with morphological operation. Then, the critical areas are partitioned into blocks, which are classified as edge blocks, texture blocks, and smooth blocks by computing discrete cosine transform coefficient values. Block-wise texture similarity and color contrast similarity in the corresponding areas are calculated, which are weighted by the size of critical areas. Furthermore, gradient magnitude deviation is measured to quantify global sharpness. Finally, the two scores are pooled to obtain the overall quality. The experimental results on the IRCCyN/IVC, IETR, and MCL-3-D DIBR image databases indicate that our method achieves higher quality prediction accuracy than the state-of-the-art quality metrics.
Precision centric framework for activity recognition using Dempster Shaffer theory and information fusion algorithm in smart environment

Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristi c framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy.
Augmented Reality Based Educational Design for Children

Human-computer interaction (HCI) has developed rapidly in recent years, and more and more researchers are interested in applying HCI techniques into education. Compared with traditional approaches in the real world, gesture recognition is considered as a reasonable alternative since it is vivid and flexible. However, most of educational equipment nowadays achieves the function of augmented reality, without any interaction. This paper implemented a prototype, not only based on augmented reality system, but also especially we think about the interactive design. Accessibility is achieved by mobile devices and the dynamic switch of gesture recognition. By this interactive method, children are able to interact with the virtual objects easily and naturally. Consequently, children can have a profound and deep understanding of what they learn, and the quality of education will be improved.
Application of Blending Learning Based on Network Learning Space in Teaching Design of Digital Art

In traditional face-to-face teaching mode, the teacher plays the leading role in the learning process, while students are in the subordinate status. Such learning mode can be easily interrupted due to the ending of classroom teaching time. To let students keep continuous learning, network learning mode based on information technology emerges. In network learning mode, students have strong learning autonomy, and can arrange learning contents and progress according to their ability and time. However, network learning mode requires students owning strong self-discipline ability to plan and arrange the learning contents and practice. A blending learning mode which can deeply integrate online learning and face-to-face teaching was established on the basis of network learning space, and applied in teaching practice of Digital Art. The results of teaching practice showed that, blending learning has significant effect on improving teaching effect of Digital Art.
Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

In daily life, the need of automatically digitizing paper documentations and recognizing textual images is still present with existing and potential upcoming rooms for improvements, especially for languages like Arabic, which is unlike English as an instance, has more complex context and not been extensively supported by research in a such domain.As yet, the available online offline optical character recognition (OCR) systems have utilized functional techniques and achieved high performance mainly on machine printed data images.However, in case of handwritten script, the recognition task becomes highly unconstrained and much more challenging.Amongst a large verity of recognizable multi-lingual characters, handwritten digit recognition is a considerably useful task for different purposes and countless applications.In this research, the focus is on Arabic (known today as Indic or Indian) digit recognition using different proposed Gabor-based approaches in several combinations with different classification methods.The proposed approaches are trained and tested using 91120 digit samples of two independent standard databases (Arabic-Handwritten-Digits and AHDBase), allowing performance variability assessments and comparisons not only between the different combinations of features and classifiers but also between different datasets.The proposed Arabic-Indic digit recognition system achieves high recognition rates reach up to 99.87%.This research practically shows that one of the proposed approaches with significant dimensionality reduced features remains attaining a high recognition rate with low complexity time, which can be hence recommended further for online digit recognition systems.
Strain Gauges Based 3D Shape Monitoring of Beam Structures Using Finite Width Gauge Model

This paper presents a new approach validated experimentally to reconstruct with strain gauges the deformed shape of a straight beam with circular cross section. It is based on a novel beam-specific strain gauge model that improves the strain measurement by taking into account the width of the gauges. These improved strain measurements are used by a 3D finite strain large displacement beam shape reconstruction method to recover the deformed shape iteratively. The whole reconstruction approach has been validated experimentally with 3D deformations of a beam instrumented with strain gauges. Results show that the strain gauge model developed improves reconstruction accuracy and that beam reconstruction can be achieved effectively.
Spatiotemporal Relation Networks for Video Action Recognition

Two-stream convolutional networks have shown strong performance in a video action recognition task for its ability to capture spatial and temporal features simultaneously. However, the calculation of optical flow is time-consuming and it cannot be applied to the real-time processing of video. To address this problem, this paper proposes a new end-to-end architecture called SpatioTemporal Relation Networks (STRN) to extract spatial information and temporal information simultaneously from the video with the only RGB input. STRN consist of two branches, called appearance stream and motion stream, respectively. Appearance stream retains the structure of the original spatial stream in the two-stream architecture with the input of consecutive frames instead of a single frame. Motion stream, which takes relation information between the adjacent features in the appearance stream as an input, can effectively complement appearance stream. A relation block is an extractor which is used to extract relation information from the appearance stream. STRN can learn spatiotemporal information from the video with the only RGB input, which avoids the calculation of optical flow. We validate the STRN on UCF-101 and HMDB-51 and achieve better performance.
Dilated Deeply Supervised Networks for Hippocampus Segmentation in MRI

Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer’s Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks.We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.
Research into the E-Learning Model of Agriculture Technology Companies: Analysis by Deep Learning

With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, and experiences, and also draw on multimedia content and active interactive models. Modern education emphasizes that learning should occur in the process of constructing knowledge scenarios and should proceed through learning scenarios and activities. Compared to traditional “spoon-feeding” education, the model learning scenario is initiated with the learner at the center, allowing the person involved in the learning activity to solve problems and further develop their individual capabilities through exploring, thinking and a series of interactions and feedback. This study examined how students in the agriculture technological industry make use of AR digital learning to develop their industry-related knowledge and techniques to become stronger and more mature so that they unconsciously apply these techniques as employees, as well as encouraging innovative thought and methods to create new value for the enterprise.
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning

Vehicle fine-grained classification is a challenging research problem with little attention in the field. In this paper, we propose a deep network architecture for vehicles fine-grained classification without the need of parts or 3D bounding boxes annotation. Co-occurrence layer (COOC) layer is exploited for unsupervised parts discovery. In addition, a two-step procedure with transfer learning and fine-tuning is utilized. This enables us to better fine-tune models with pre-trained weights on ImageNet in some layers while having random initialization in some others. Our model achieves 86.5% accuracy outperforming the state of the art methods in BoxCars116K by 4%. In addition, we achieve 95.5% and 93.19% on CompCars on both train-test splits, 70-30 and 50-50, outperforming the other methods by 4.5% and 8% respectively.
Anytime Subgroup Discovery in Numerical Domains with Guarantees

Subgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression ("How far are we of an exhaustive search"). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time; (ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns. Code related to this paper is available at: https://github.com/Adnene93/RefineAndMine .
Approaches to the development of digital competencies of law students

Н. Г. Храмцова, Т
Discrete Geometry from Quantum Walks

A particular family of Discrete Time Quantum Walks (DTQWs) simulating fermion propagation in 2D curved space-time is revisited. Usual continuous covariant derivatives and spin-connections are generalized into discrete covariant derivatives along the lattice coordinates and discrete connections. The concepts of metrics and 2-beins are also extended to the discrete realm. Two slightly different Riemann curvatures are then defined on the space-time lattice as the curvatures of the discrete spin connection. These two curvatures are closely related and one of them tends at the continuous limit towards the usual, continuous Riemann curvature. A simple example is also worked out in full.
Distributed Blockchain-based Trusted Control with Multi- Controller Collaboration for Software Defined Data Center Optical Networks in 5G and Beyond

We present a distributed blockchain-based trusted control (BlockTC) architecture with multi-controller credible routing for software defined data center optical network in 5G and beyond. The feasibility and efficiency of architecture are verified on our testbed.
Multispectral Heterogeneity Detection Based on Frame Accumulation and Deep Learning

Hyperspectral transmission imaging may provide a means for rapid screening of breast tumors, but tissue has a strong nature of scattering, thus causing a great difficulty in identifying heterogeneity. In this paper, a combination of frame accumulation and deep learning was proposed to detect heterogeneity, and we designed the simulation experiment of collecting phantom images. On the basis of frame accumulation preprocessing, the heterogeneous detection is performed on multispectral images by using faster regions with convolutional neural networks (R-CNN) features and a single shot multibox detector (SSD), two typical detection frameworks of deep learning. The results show that the mean average precision (mAP) of faster R-CNN and SSD reach 90.8% and 95.1%, respectively, when three classes (including background) are detected with the help of the dataset provided in this paper, and the mAP of two frameworks both reach 99.9% when two classes (including background) are detected. The detection efficiency of the SSD is higher than faster, SSD's detection speed can reach 50 fps, and the detection accuracy of the images after frame accumulation preprocessing is higher than that without frame accumulation processing. In summary, we validate the possibility of employing faster R-CNN and SSD to detect heterogeneity in multispectral images based on frame accumulation that improves image grayscale resolution, and it has a certain degree of reference significance for the application of deep learning in multispectral image detection.
Studying the Social Media Adoption by university students in the United Arab Emirates

Lately, instructors, as well as learners, have been integrating social networking media in the context of higher education systems. Social networking media has also become the subject of interest for researchers to explore its impacts in the process of imparting and acquiring higher education. Various platforms have been introduced by the social networking media to facilitate the process of communication between the teachers and students while it eases creating and sharing for its users. Through conducting research on the dynamics of students and instructors, their acceptance regarding the various innovations and their attitude towards these platforms, useful modifications of the current approaches can be done accordingly in a way that ensures a wide adaptation of the software. The current study is built on the ground of established research termed the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings of this research are employed to confirm the factors of utilizing social networking media for e-learning in the United Arab Emirates higher education context. The quantitative data has been acquired based on the survey method. The sample of this current investigation is students and instructors from two different universities in the higher education sector of the United Arab Emirates. The predictive behavior of the anticipated stimulants for the research model was analyzed using the Structural Equation Modelling. The findings of this study are useful to comprehend the factors that drive the Emirati students to embrace certain perceptions of social media technology in the higher education context.
Forensic Analysis of Docker Swarm Cluster using Grr Rapid Response Framework

An attack on Internet network does not only happened in the web applications that are running natively by a web server under operating system, but also web applications that are running inside container.The currently popular container machines such as Docker is not always secure from Internet attacks which result in disabling servers that are attacked using DoS/DDoS.Therefore, to improve server performance running this web application and provides the application log, DevOps engineer builds advance method by transforming the system into a cluster computers.Currently this method can be easily implemented using Docker Swarm.This research has successfully investigated digital evidence on the log file of containerized web application running on cluster system built by Docker Swarm.This investigation was carried out by using the Grr Rapid Response (GRR) framework.
Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity

&#x0D; &#x0D; &#x0D; In multi-agent path finding (MAPF), it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication. Furthermore, we will analyze what the computational costs associated with such a coordination regime are. As it turns out, guarantees can be given assuming that the agents are of a certain type. However, the implied computational costs are quite severe. In the distributed setting, we either have to solve a sequence of NP-complete problems or have to tolerate exponentially longer executions. In the setting with destination uncertainty, bounded plan existence becomes PSPACE-complete. This clearly demonstrates the value of communicating about plans before execution starts.&#x0D; &#x0D; &#x0D;
Pairwise Markov Chain: A Task Scheduling Strategy for Privacy-Preserving SIFT on Edge

In this paper, we propose a task scheduling strategy, which can achieve image feature extraction on edge while ensuring privacy. Our task scheduling strategy applies to a fairly popular privacy-preserving Scale-Invariant Feature Transform SIFT scheme, where images to be processed are firstly randomly split into two portions for encryption and transmitted to two different edge nodes for feature extraction. Then, in the edge, our task scheduling strategy will re-assign these two portions to proper edge nodes for processing. During the whole process, two portions of the same image should not be assigned to the same edge node in order to preserve privacy. We show that this privacy constraint can be enforced through constructing a pairwise Markov chain, and carefully designing system states and transition probabilities. We further formulate the whole task scheduling problem as a stochastic latency minimization problem and solve it by converting it into a linear programming problem. Simulation results show that our proposed task scheduling strategy can achieve lower latency than baseline strategies while satisfying the privacy constraint.