Reviewerly/exHarmony huggingface dataset:
Augmented reality application: What are the constraints and perceptions of the students during the covid 19 pendemic’s 3D geometry learning process?

Abstract The Covid 19 epidemic has altered the interplay of the classroom learning process. To facilitate the learning process of 3D geometry, secondary school teachers adopt several technology items. Augmented reality technology is one of those technological goods. This study therefore aims to explain the constraints and perceptions of secondary school students during the COVID-19 pandemic, during the 3D geometry learning process. This study used a qualitative research design descriptive of it. The number of participants to this study was five, namely 3 male students and two female students. Techniques for collecting data using sheets of observations and interviews. Although data analysis by Miles and Huberman [1] adopts data analysis, which consists of data selection, data reduction, data presentation and conclusions or verification processes. Study results show that there are still many challenges faced during the learning process using augmented reality technologies, namely: (a) Constraints when downloading the program on the cellphones of the instructor and the pupil. (b) Marker constraints pertaining to AR identification. (c) Constraints on the student’s smartphone requirements. (d) Limit and network restrictions for accessing the Resources section. (e) Students found too many constraints on appraisal questions on the test checklist. While several challenges do remain, the majority of the students have a good understanding of the learning experience using augmented reality technologies.
Automatic layered RGB‐D scene flow estimation with optical flow field constraint

Scene flow estimation with RGB-D frames is receiving increasing attention in digital video processing and computer vision due to the widespread use of depth sensors. Existing methods based on object segmentation have shown their effectiveness for object occlusion and large displacement. However, improper segmentation often causes incomplete segmented areas or incorrect edges, which will result in inaccurate occlusion inference and scene flow estimation. To this end, an automatic layered RGB-D scene flow estimation method is proposed, which achieves more accurate layering of objects in depth image by exploring motion information. The authors exploit super-pixel segmentation for initial layering, which is beneficial for preserving edges and integrity of the objects. Furthermore, an optical flow which is highly correlated with the scene is also used for automatic layering. Finally, the utilisation of super-pixel segmentation and motion information would ensure the integrity of the object area and improve the accuracy of scene flow. They have validated their approach both qualitatively and quantitatively on several public datasets. Experimental results show that the proposed method is able to preserve the integrity of the object and achieve lower root mean square error and average angular error as compared with the current state-of-the-art algorithms.
Efficient Exploration by Decision Making Considering Curiosity and Episodic Memory in Deep Reinforcement Learning

Reinforcement learning is difficult in sparse environments where rewards are not easy to obtain, such as large scale real space. In recent years, methods to promote exploration by generating intrinsic rewards based on curiosity in such environments have received much attention. However, in large state spaces, efficiency is a problem. In this paper, we propose a DQN-based algorithm that takes into account both episodic memory and intrinsic rewards during making decision in order to improve the efficiency of exploration. We evaluate this method through some tasks.
Efficient Mining of Non-Dominated High Quantity-Utility Patterns

In this paper, we propose a new pattern called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms respectively called SQUM-1 and SQUM-2 are presented to efficiently mine the set of SQUPs. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets respectively utilized in two developed algorithms. Our in-depth experimental results prove that our proposed algorithms achieve good performance in terms of runtime and memory usage.
FlexiPair: An Automated Programmable Framework for Pairing Cryptosystems

Pairing cryptosystems are extremely powerful mathematical tools for developing cryptographic protocols that can provide end-to-end security for applications like Internet-of-Things (IoT), cloud services and cyber-physical systems (CPS). However, these applications require the implementations to be light-weight but still real-time, with the additional feature of being flexible. The flexibility can come from different choices of underlying algorithms along with suitable parameter choices. A software implementation offers better flexibility but lacks in timing performance, whereas custom hardware delivers better performance but has poor flexibility. Furthermore, the designs over small characteristic curves are now insecure against recent attacks. Existing designs do not address the drawback of less flexibility and huge resource consumption collectively. In this article, we present a micro-program controlled hardware design which has the least resource consumption among the similar existing designs on FPGA that offer such programmability and flexibility. This redundant number arithmetic-based architecture consumes only 2506 slices on Xilinx Virtex-7 FPGA. It can be migrated to other device families or updated for different algorithms without data-path or control-path modification. To enhance the flexibility, we developed a custom assembly-like finite state machine (FSM) description, called Prism, and necessary tool to generate the micro-program states. To illustrate the functionality of Prism, we present designs for Tate and Optimal-Ate pairing with the micro-program states generated using this tool.
WITHDRAWN: Secured implementation of steganography in multicloud

Through the web, various services have been delivered by Cloud Computing. Applications like databases, data storage, servers, networking, and software are carried out. They can be saved in a foreign database with the help of Cloud-based storage. It can access the software program for running it or data, only when an electronic device has web access. One of the popular approaches for accessing dynamically and shared configurable resources through the network is Cloud Computing. The important requirement of that application is secured data storage. Here between the servers the data is being transmitted or transferred for the users. Steganography in cloud is one of the effective techniques for secured communication. Data hidden within digital files is Steganography. Cryptography is coupled along with Steganography which empowers the security of the file which is stored in the cloud. Steganography is used in a video file, an audio file, or a picture file. In this method, the person who sends and receives files can only understand the communication because here we use the tactic of writing hidden messages known as Steganography which cannot be understood by others. It helps in defending from being pirated for copyrights, and also stops the unauthorized viewing. To provide assurance of security of the data in cloud computing, this paper provides a substitute text steganography approach for concealing a bunch of secret English documents during a canopy. This approach enhances time efficiency, data security, and data hiding capacity.
Cleaning Robot Operation Decision Based on Causal Reasoning and Attribute Learning

In order to improve the operation ability of cleaning robots, this paper proposes a decision method for cleaning robot's operation mode. Firstly, we use the hierarchical expression ability of deep network to obtain the attributes of garbage such as state, shape, distribution, size and so on. Then the causal relationship between the attributes and the operation modes can be built by using joint learning of association attributes with depth network model and causal inference. Based on this, a fuzzy inference decision network is designed. With the help of causal analysis, the structure of the decision model is greatly simplified. Compared with conventional fuzzy neural networks, the total parameters of the model are reduced by 2 / 3. The method proposed in this paper imitates the way that human dispose of different types of garbage and has good interpretability. The experimental results verify the effectiveness of the proposed method.
On the x-coordinates of Pell equations that are sums of two Padovan numbers

Abstract Let $$ (P_{n})_{n\ge 0} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:math> be the sequence of Padovan numbers defined by $$ P_0=0 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> , $$ P_1 = P_2=1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , and $$ P_{n+3}= P_{n+1} +P_n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:mrow> </mml:math> for all $$ n\ge 0 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> . In this paper, we find all positive square-free integers d such that the Pell equations $$ x^2-dy^2 = N $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>x</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>-</mml:mo> <mml:mi>d</mml:mi> <mml:msup> <mml:mi>y</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>=</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> with $$ N\in \{\pm 1, \pm 4\} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> , have at least two positive integer solutions ( x , y ) and $$(x^{\prime }, y^{\prime })$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>x</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>,</mml:mo> <mml:msup> <mml:mi>y</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> such that both x and $$x^{\prime }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>x</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> are sums of two Padovan numbers.
Program Slice Based Vulnerable Code Clone Detection

Vulnerabilities in software will not only lead to security problems of the software itself, but also cause the spread of vulnerabilities through code clones. It is important to detect and locate vulnerabilities among the source code to facilitate the fix. Although many methods are proposed to detect code clones in source code, most of them fail to detect code clones that involve statement addition and deletion effectively or are not suitable for vulnerability detection. In this paper, we propose a method that can detect vulnerabilities caused by code clones. Program slices are used to filter statements that are not related to vulnerabilities and extract important vulnerable statements in function. Hash function and bitvector are applied to improve efficiency during the detection. The results are displayed in html, among which the vulnerable statements are highlighted to help subsequent patching work. Our method is evaluated on open source software (Openssl, Linux Kernel, FFmpeg and QEMU). The results of experiments show that our method detects 12.72% more vulnerable clones in acceptable time compared with Vuddy, proving the effectiveness of our method.
Robust Estimation Approach for Plane Fitting in 3D Laser Scanning Data

In view of the traditional least square algorithm for plane fitting has the disadvantage of poor resistance, a robust plane parameters estimation method is proposed. The key point is that p-norm ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&lt; \mathrm{p}\leq 1$</tex> ) minimum was used. The experiments show that the method has good robustness to fit plane with outliers up to 50% using simulated data and real scanning data.
Developing Multisensory Augmented Reality As A Medium For Computational Artists

This paper resituates multisensory augmented reality (MSAR) as an artistic medium for the creation of interactive and expressive works by computational artists. If an AR system can be thought of as one that combines real and virtual processes, is interactive in real-time, and is registered in three dimensions; why do we witness the majority of AR applications utilising primarily visual displays of information? In this paper, I propose a practice-led compositional approach for developing 'MSAR Experiences', arguing that, as an medium that combines real and virtual multisensory processes, it must be explored with a multisensory approach. The paper further outlines the study methods that I will use to evaluate the developed experiences. The outcome of this project is the practice-led method as well as MSAR hardware, software and experiences that are developed and evaluated.
Frontal Face Generation Based Multi-angle Face Identification System

Precise identity recognition is a pre-condition for robots to enter the human living environment. Most of the existed face identification methods cannot work on the non-frontal face since the severe texture loss. In this paper, we propose a novel system to deal with multi-angle face identification in video sequence based on frontal face generation, which replaces the process of detection, alignment in the typical face identification system. To solve the problem of face texture loss in large pose variation, we creatively combine generative adversarial networks (GAN) with the state-of-the-art facial landmark localization method. The proposed system was tested on video database containing multi-angle faces, and the experimental results indicate that our system can recognize more faces in the frames, and improve the accuracy of identification for multi-angle face by 130%.
User Persona in Personalized Wireless Networks: A Big Data-Driven Prediction Framework

Wireless network personalization is an emerging technology that has considerable potential to achieve the ultimate balance between resource allocation and user satisfaction. One of the main enablers of personalized networks is the continuous monitoring and prediction of dynamic user satisfaction levels in various contexts. Accurate satisfaction prediction requires a lot of data, and unfortunately, data and the process of acquiring it are expensive. A closer look at user behavior and satisfaction levels reveal that certain users share certain behavioral similarities. A group of users who share similar user behavior and satisfaction patterns is referred to as a persona. Associating users with preexisting user personas will enable networks to provide highly personalized services with a minimal amount of data, thereby improving the efficiency of personalized networks. In this paper, we propose a novel big data-driven framework to predict user personas in personalized wireless networks. Also, we conduct a comprehensive study to investigate the impact of different amounts of data and confidence levels on the performance of the overall framework. Finally, using a simulated personalized wireless network, we compare the behavior of different personas in terms of the amount of saved resources and achieved satisfaction levels.
WITHDRAWN: Machine learning approach for classification from imbalanced software defect data using PCA &amp; CSANFIS

Identifying Defective prone modules in a software is one of the important smart topics in the field of Software Engineering and Soft Computing. Many researchers applied various machine learning algorithms on identifying defective prone modules in software. In this proposed system, we are applying a Machine learning approach for Prediction of Software Defects to overcome from class imbalance problem. We applied principle component analysis as an attribute selection algorithm to identify most relevant attributes in identifying defective prone modules. Later, the data to be classified is imbalanced in nature. To overcome from imbalanced learning we are proposing Cost Sensitive ANFIS to derive a Sugeno Fuzzy Inference scheme for forecasting of Software defects. The performance of derived Fuzzy implication scheme was compared with the classifier used in literature survey. We observed Area under ROC curve has been improved by 5% with Sugeno Fuzzy Inference classier compared to the classifiers Neural Networks and Support Vector machines etc.
Research on Landscape Design Based on Ice and Snow Environment with the Computer aided Technology

Abstract With the rapid development of socialist market economy, people’s material life is increasingly rich, which will increase people’s demand for spiritual culture. Ice sculpture and snow sculpture are important landscapes in Northeast China, which have formed their own unique culture. Therefore, we need to study the ice and snow (hereinafter referred to as IAS) landscape design, which will get the attention of relevant units. With the rapid development of landscape design industry, landscape design based on IAS environment has become an important human custom to highlight the simplicity and enthusiasm of northern people. Firstly, this paper analyzes the origin of IAS landscape. Then, this paper analyzes the basic characteristics of IAS landscape. Finally, some suggestions are put forward.
Development of an Augmented Reality System Based on Marker Tracking for Robotic Assisted Minimally Invasive Spine Surgery

Spine surgery is nowadays performed for a great number of spine pathologies; it is estimated that 4.83 million surgeries are carried out globally each year. This prevalence led to an evolution of spine surgery into an extremely specialized field, so that traditional open interventions to the spine were integrated and often replaced by minimally invasive approaches. Despite the several benefits associated to robotic minimally invasive surgeries (RMIS), loss of depth perception, reduced field of view and consequent difficulty in intraoperative identification of relevant anatomical structures are still unsolved issues. For these reasons, Augmented Reality (AR) was introduced to support the surgeon in surgical applications. However, even though the irruption of AR has promised breakthrough changes in surgery, its adoption was slower than expected as there are still usability hurdles. The objective of this work is to introduce a client software with marker-based optical tracking capabilities, included into a client-server architecture that uses protocols to enable real-time streaming over the network, providing desktop rendering power to the head mounted display (HMD). Results relative to the tracking are promising (Specificity = 0.98 ± 0.03; Precision = 0.94 ± 0.04; Dice = 0.80 ± 0.07) as well as real-time communication, which was successfully set.
Bearicade: secure access gateway to High Performance Computing systems

Cyber security is becoming a vital part of many information technologies and computing systems. Increasingly, High-Performance Computing systems are used in scientific research, academia and industry. High-Performance Computing applications are specifically designed to take advantage of the parallel nature of High-Performance Computing systems. Current research into High-Performance Computing systems focuses on the improvements in software development, parallel algorithms and computer systems architecture. However, there are no significant efforts in developing common High-Performance Computing security standards. Security of the High-Performance Computing resources is often an add-on to existing varied institutional policies that do not take into account additional requirements for High-Performance Computing security. Also, the users' terminals or portals used to access the High-Performance Computing resources are frequently insecure or they are being used in unprotected networks. In this paper we present Bearicade - a Data-driven Security Orchestration Automation and Response system. Bearicade collects data from the HPC systems and its users, enabling the use of Machine Learning based solutions to address current security issues in the High-Performance Computing systems. The system security is achieved through monitoring, analysis and interpretation of data such as users' activity, server requests, devices used and geographic locations. Any anomaly in users' behaviour is detected using machine learning algorithms, and would be visible to system administrators to help mediate the threats. The system was tested on a university campus grid system by administrators and users. Two case studies, Anomaly detection of user behaviour and Classification of Malicious Linux Terminal Command, have demonstrated machine learning approaches in identifying potential security threats. Bearicade's data was used in the experiments. The results demonstrated that detailed information is provided to the HPC administrators to detect possible security attacks and to act promptly.
Einstein–Podolsky–Rosen steering under asymmetry noise channels

Abstract Symmetric quantum correlations and properties have been often investigated earlier in the process of quantum decoherence. However, the asymmetric quantum features, such as steering, have been rarely considered in this context. In this work, we study Einstein–Podolsky–Rosen steering, measured by steering robustness R , for two-qubit systems under asymmetric noise channels. For the one-sided asymmetric noise channels, we explore the influence of several typical channels on the asymmetric decay of steering. Our results show that the asymmetric steering is directly linked to the initial state as well as the type of noises. We prove that asymmetric decay can be used to characterize the properties of the channels in some certain. For the two-sided asymmetric noise channels, the results show that the behavior of steering are also both initial states and combination types depended. Moreover, we show that there always exist asymmetric steering even the initial state is high symmetry for the depolarizing-amplitude damping combination channel.