Reviewerly/exHarmony huggingface dataset:
Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression

Abstract Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA.
Robust uncalibrated visual stabilization for nonholonomic mobile robots

Yao Huanga & Jianbo Sua* a Department of Automation, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaYao Huang received the BS degree from Tianjin Polytechnic University, Tianjin, and the MS degree in Instrument Science and Technology from the Shanghai University, Shanghai, China, in 2011 and 2014, respectively. She is currently working toward the PhD degree with the Department of Automation, Shanghai Jiao Tong University, Shanghai. Her research interests are in visual servoing and robotics.Jianbo Su received the BS degree in Control Theory and Control Engineering from Shanghai Jiaotong University, Shanghai, China, in 1989, the MS degree in Pattern Recognition and Intelligent System from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 1992, and PhD in Control Theory and Control Engineering from Southeast University, Nanjing, China, in 1995. From 1995 to 1997, he was a postdoctoral research fellow in Robotics in Chinese Academy of Sciences. He is currently a professor in the Department of Automation, Shanghai Jiao Tong University. His research interests include robotics, pattern recognition and computer vision.
Implementation of High Dimensional-QKD using BB84 Protocol in the Security of Aerospace Industry

Aerospace Industry is making breakthrough development with Quantum System. Along with, security of the system is the most promising issue. Where Quantum Physics play a vital role in the security and challenges of Aircraft, a lot of cryptographic algorithms used for securing the Aerial communication. In this research, we discuss the contribution of Airbus Company in aerospace industry and adaptation of Quantum System for solving future challenges. One of the most awakening issues is the security of Aerospace industry, where Quantum Cryptography algorithms are used. Here, we are trying to replace QKD with HD-QKD, because during data/key transmission photon efficiency rate effects. By using the BB84 Protocol with HDQKD, we tried to enhance photon rate efficiency, and proposed a HDQKD based Model for Aerial and Non Aerial Communication. So, Quantum Cryptography with HDQKD techniques enhances the security of Aerospace industry more firmly.
Comparative analysis of edge detection methods on beaker glass

Abstract Digital image processing in the form of edge detection using the Sobel, Robert, Prewitt, Canny, and LoG algorithms, in addition to the binarization algorithm, can determine the edges of an object so that it is expected to improve accuracy. In this study, edge detection analysis will be performed on RGB (Red, Green, Blue) images from image capture using a Digital Single-lens Reflex Camera (DSLR) camera. Image processing of the DLSR image uses algorithm design that is made systematically, structured, and neat so that the results of the program can be strived to run following what is expected.
Ethereum Based IoT Architecture

Internet of Things (IoT) is expanding exponentially due to the manufacturing of cheaper electronics and high-speed communication. Blockchain technology has paved way for the introduction of digital cryptocurrency called bitcoin. The blockchain is one of the four main technologies that have enabled cryptocurrency. Conceptually blockchain is a distributed digital ledger that stores data in blocks, which are cryptographically linked with the previous blocks. This makes blockchain immutable. Blockchain has gained its popularity in the scientific community with the success of bitcoin and ever since researchers are trying to adopt this technology to other areas in order to solve problems beyond the smart sectors like Internet of Things (IoT), supply chain and so on. In our research work, we adopt blockchain in IoT and implement ethereum based IoT architecture for securing the IoT network. We have implemented a two-node ethereum network where transactions are made at the expense of ether and each transaction is verified by the miner using the smart contract written in the ethereum network.
A New Intelligent Trajectory Planning Algorithm Based on Bug2 Algorithm:Bridge Algorithm

A new algorithm is developed for unmanned omnidirectional robot to establish a feasible path that avoids obstacles and does not have randomness in a given environment : Bridge Algorithm. The implementation of the Bridge Algorithm is similar to the process of finding the shortest feasible path for human beings, which determines the straight line where the Starting point and the end point are located, then determines the obstacles intersecting with the straight line, selects the short edge of the obstacle contour as the obstacle avoidance path, and repeats the process until the end point. The implementation ensures the consistent of the path generated by the algorithm, i.e., for the same Starting point, end point and environment, the path generated each time is consistent. This consistency is crucial for application occasions that emphasize safety and stability. According to the experimental results of the algorithm in the real unmanned omni-directional wheel robot, Bridge Algorithm has good real-time performance, and the actual walking trajectory of the robot is basically the same as the planned trajectory, which proves that the algorithm has good performance.
An End-to-End Future Frame Prediction Method for Vehicle-Centric Driving Videos

In the field of autonomous driving, training an agent to watch and think similar to human drivers is an efficient way to solve self-driving problems. Inspired by NVIDIA's frame-level command generation task [2] and the discovery of humans memory capacity [13], we propose a future frame prediction method for vehicle-centric driving videos. An end-to-end deep learning architecture called future frame prediction (FFPRE) network is proposed, which can generate a future frame following the input video sequence. In particular, we develop a general memory preserving module to extract meaningful history information from input data. This module consists of two parts, namely, memory recall and memory refine. We train this module to generate the short-term spatiotemporal information of a given video batch, which is a concatenation of history appearance and temporal clues. Thereafter, the two history clues will be transformed into future representations by a long-term prediction module. Thus, humans' driving prediction progress is mimicked in a completely modular manner. Given the FFPRE network's effective long-short spatiotemporal feature learning ability, the proposed network can construct an internal representation (content and dynamic) of vehicle-centric driving videos without tracking the trajectory of every pixel. Experimental results on publicly released datasets of NVIDIA and DR(eye)VE indicate that our proposed method is efficient.
An Improved Gaussian Mixture Model Based Hole-filling Algorithm Exploiting Depth Information

Virtual views generation is of great significance in free viewpoint video (FVV) as it can avoid the need to transmit a large volume of video data. An important issue in generating virtual views is how to fill the holes caused by occlusion. Using the Gaussian mixture model (GMM) to generate the background reference image is a commonly used hole-filling method. However, GMM usually has poor performance for sequences with reciprocal motion. In this paper, we propose an improved GMM-based method. To avoid the foreground pixels misclassified as the background pixels, we use depth information to adjust the learning rate in GMM. Foreground pixel is given a smaller learning rate than the background. Further, a refined foreground depth correlation (FDC) algorithm is proposed, which generates the background frame by tracking the change of the foreground depth in the temporal direction. In contrast to existing algorithms, we use a sliding window to obtain multiple background reference frames. These reference frames are then fused together to generate a more accurate background frame. Finally, we adaptively choose the background pixel from the GMM and FDC for hole filling. The experimental results show that subjective gain can be achieved, and significant objective gain can be observed in reciprocal motion sequences.
User Experience over Time with Personal Assistants of Mobile Banking Application in Turkey

Being able to chat with artificial intelligence-based applications has been changing user interaction and has opened doors for a various number of new user experiences. The new apps that emerged lately with new technologies now launched in mobile banking applications as the form of personal assistants. This paper aims to research what features of the assistants affect the overall user experience. At the end of the study, a possible design guideline for designing these assistants in Turkey discussed, since most of the existing guidelines are for personal assistants for everyday life. Twelve users of Maxi, one of the most used personal mobile banking assistant, participated in the research and evaluated their experience after 15 days of usage. The research showed that their impression was positive during the first interview but during the second interview, there was a decline for all values of the impressions of user experience. In the end, the results showed that the original application of the bank was more preferable than the personal assistant.
Applying effective cloud computing maturity model (CCMM)

Abstract Cloud computing has evolved become important computing paradigm for all organizations around the world. Cloud computing emerges to address the limitations of organizational resources in deploying IT services. Due to its advantages, there are still many debates about how to apply cloud computing effectively. Many organisations have reported that embracing the cloud computing is not an easy task and surrounded with many complexities. The use of cloud computing depends heavily on the ability of the organization to adapt with cloud computing paradigm. In order to maximize the benefits of cloud application, each organisation needs to develop a systematic, broad, holistic, and pragmatic approach. Cloud computing maturity model (CCMM) is initated to provide easy tool for assessing current conditions and improve service capabilities related to cloud computing. Measurements the implementation of cloud computing however, is challenging task since it needs to address certain factors in both business and technical perspectives. The article applies the popular framework initiated by the Open Data Center Alliances (ODCA) cloud computing maturity model (CCMM) as essential reference to guide applying cloud environment.
Robust Collaborative Filtering Based on Multiple Clustering

Collaborative filtering is widely used at online vendors and review sites in order to recommend items based on the ratings of many users. However, there are several problems with this method, and one of them is the existence of attacks that intend to distort the predicted ratings of specific items. This paper proposes a collaborative filtering method that reduces the impact of attacks while maintaining or improving the prediction accuracy by applying clustering to the target data multiple times and predicting the ratings for unrated items inside each cluster. In addition, the usefulness of the method is investigated using an evaluation method that measures the error between actual user ratings and predicted ratings. Furthermore, the robustness against attacks is investigated by comparing the prediction errors before and after attacks.
On Lower Semicontinuity of the Quantum Conditional Mutual Information and Its Corollaries

It is shown that the recently established lower semicontinuity of the quantum conditional mutual information implies (in fact, is equivalent to) the lower semicontinuity of the loss of the quantum (conditional) mutual information under local channels considered as a function on the Cartesian product of the set of all states of a composite system and the sets of all local channels (equipped with the strong convergence topology). Some applications of this property are considered. New continuity conditions for the quantum mutual information and for the squashed entanglement in both bipartite and multipartite infinite-dimensional systems are obtained. It is proved, in particular, that the multipartite squashed entanglement of any countably indecomposable separable state with finite marginal entropies is equal to zero. Special continuity properties of the information gain of a quantum measurement with and without quantum side information are established that can be treated as robustness (stability) of these quantities with respect to perturbation of the measurement and the measured state.
Research on Evaluation of Flipped Classroom Based on Big Data

Class teaching evaluation based on data analysis technology has been applied in traditional classes. As a new teaching evaluation method, the teaching quantitative behavior implemented in flipped classroom provides a good data base for the construction of big data analysis model of teaching evaluation, it is much important to the teachers. At present, the assessment model of flipped classroom based on big data analysis is rare. Based on collecting large data, this paper builds or applies relevant big data analysis models and then implements them in the flipped classroom evaluation of the students. It pays close attention to data connotation information and reflects reasonable evaluation. We have achieved good data analysis results after experimental verification. The evaluation of flipped classroom based on big data is feasible.
LAQF: Lightweight Document Oriented, Reusable Agile Quality Framework

Agile software development is taking over traditional methodologies. Agile methodologies provide more benefits than traditional methodologies that's the reason why more software organizations, teams are making their transitions towards agile era. There are many flavors of agile methodologies available today. Organizations can choose them according to their needs. These methodologies include Scrum, eXtreme Programming (XP), Dynamic Systems Development Method (DSDM) and others. Agile methods especially target functional aspects of the software to be built, quality is their second priority. Software Quality is one of the major concerns for software organizations now a days. Reusability, on the other hand, is also important in terms of reducing cost to market, less time consumption in development and increased software productivity. Agile methods put a very little emphasis on these two highlighted points. In the paper, authors have proposed an effective, more reactive framework to increase the capabilities of agile to address reusability, documentation and ultimately quality concerns. Authors evaluated the proposed framework with the existing agile methodologies and the results show that the proposed framework improves quality better than existing agile methodologies.
Slope-to-optimal-solution-based evaluation of the hardness of travelling salesman problem instances

Abstract The travelling salesman problem is one of the most popular problems in combinatorial optimization. It has been frequently used as a benchmark of the performance of evolutionary algorithms. For this reason, nowadays practitioners request new and more difficult instances of this problem. This leads to investigate how to evaluate the intrinsic difficulty of the instances and how to separate ease and difficult instances. By developing methodologies for separating easy- from difficult-to-solve instances, researchers can fairly test the performance of their combinatorial optimizers. In this work, a methodology for evaluating the difficulty of instances of the travelling salesman problem near the optimal solution is proposed. The question is if the fitness landscape near the optimal solution encodes enough information to separate instances in function of their intrinsic difficulty. This methodology is based on the use of a random walk to explore the closeness of the optimal solution. The optimal solution is modified by altering one connection between two cities at each step, at the same time that the fitness of the altered solution is evaluated. This permits evaluating the slope of the fitness landscape. Later, and using the previous information, the difficulty of the instance is evaluated with random forests and artificial neural networks. In this work, this methodology is confronted with a wide set of instances. As a consequence, a methodology to separate the instances of the travelling salesman problem by their degree of difficulty is proposed and evaluated.
Improved Grey Wolf Optimizer and Their Applications

Grey Wolf Optimizer is a kind of artificial intelligence optimization algorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initial population to increase population diversity; and uses simulated annealing algorithm's strong ability to jump out of local optimum solution to make up for the shortcoming that GWO is easy to fall into local optimum; finally, the first three individuals of population fitness are mutated to improve the improvement of the algorithm. The speed and accuracy of the algorithm are improved to avoid falling into local optimum. The superiority of the improved algorithm is verified by simulation experiments.
Obstacle Avoidance with Reinforcement Learning and Adaptive Resonance Theory

The reinforcement learning (RL) of the autonomous mobile agent is one of the actual research topics. It permits mobile agents to interact constantly with their environment and to avoid obstacles. First, this paper presents an algorithm which integrates Deep Deterministic Policy Gradient (DDPG) algorithm and Fuzzy Adaptive Resonance Theory (ART) in order to improve generalization performance of RL. Then the curiosity is introduced to integrate with the first proposed algorithm to solve the problem of slow convergence caused by Fuzzy ART. Results of the simulation experiments demonstrate the effectiveness of the proposed algorithm. It shows that the algorithms perform well in both low-dimension and high-dimension state space.
The Effect of Different Types of Internal Rewards in Distributed Multi-Agent Deep Reinforcement Learning

Distributed multiagent reinforcement learning in the same environment is prohibitively hard, due to the difficulty of assigning credit for the individual actions of the agent, especially when the agent is a member of a team. Meanwhile, the sparse delayed reward about the team from the environment such as winning makes the learning progress more challenging. To solve the credit assignment and sparse delayed reward problems which are common in multiagent reinforcement learning, researchers usually construct or learn an internal reward signal that acts as a proxy for winning and provides denser rewards for individual agent. To improve the learning effect of a typical multiagent learning task, we conducted three types of internal rewards for multiagent team members and evaluated the effect of these rewards. The results show that not all internal reward can improve the learning effect of multiagent reinforcement learning, it seems that when the learning task is not very complex and the time of finishing the task for the team is not very long, the sparse reward such as winning have the best learning effect, and the learning effect of the other two forms of reward is not as good as that of the simple sparse reward. To some extent, our research results can provide a reference for the design of reward function in the application of distributed multi-agent reinforcement learning.
Design Trade-offs in Threshold Implementations

With the expansion of the IoT and the emergence of vast amounts of remote hardware devices side-channel attacks pose a serious threat, requiring adequate protection. Among the hardware countermeasures, Threshold Implementations (TI) is one of the most used ones. It is designed to avoid the leakage of sensitive data from hardware designs. In this work, we examine the cost of applying TI to the block cipher PRINCE with respect to the latency, power/energy consumption, occupied area. We explore different versions of TI with respect to the security order and TI variations, and compare the strengths and weaknesses of each approach.
A lightweight inductive method for process assessment based on frequent feedback: A study in a micro software company

Software process assessment is the most important phase in process improvement projects since it enables identification of all process issues that need to be improved.Small and micro software companies suffer from several restrictions that do not allow them to apply standards and best practice guidelines for process assessment.These companies rather implement lightweight assessment methods that can be easily tailored to their needs.This article presents a lightweight inductive process assessment method that can be adapted to specific needs of small companies.The method is based on active participation of company employees and assumes frequent exchange of information during feedback oriented working sessions in the assessed company.The method is implemented in an indigenous micro software company in Serbia for assessing software maintenance process.Four potential improvements were identified, while the best ranked one was implemented.Benefits for the company, as well as implications for practitioners from industry and researchers from academia are discussed.