Miracle-dz/UniFAR-FTUs huggingface dataset:
In virtuo modelling for biologists: How to design graphical interface for the RéISCOP simulation platform?

This article presents a future component of the ReISCOP simulation platform. ReISCOP provides an interaction-based meta-model to build models for simulations. Currently, model construction needs to be done by a programmer. This is a problem for the biologist who cannot construct models by himself. We propose to include a graphical interactive interface to allow biologists to build models in ReISCOP with help of in virtuo experiments.
The Operationalization of Competitive Strategy: Choosing an Appropriate Method of Measurement

The purpose of this article is to describe and analyze those methods which are commonly applied to operationalize the competitive strategy variable. Those which have been most often applied in the scientific strategic management and strategic human resource management literature are specifically identified. The most appropriate alternative, which makes valid, reliable operationalization of the strategic competitive variable possible, is established via analysis of the identified methods. This occurs by way of the understanding that competitive strategy is a multidimensional phenomenon which must reflect consistency in its formulation and implementation processes. Identified alternative facilitates appropriate investigative decisions related to operationalization of the competitive strategy variable, based on appropriate methodological arguments. Additionally, it resolves the problem of a lack of homogenization in competitive strategy measurements, a situation that exists in the literature, and which hinders the comparison of results between studies.
SmartPI: Understanding Permission Implications of Android Apps from User Reviews

With the unprecedented convenience brought by Apps on mobile devices, we are facing severe security attacks and privacy leakage caused by them since they may stealthily access unclaimed or unneeded permissions for some purposes. Many works strive to discover these malicious apps using program analysis techniques, however, they fail to tell users why an app needs to request the permission from users’ perspective. In this paper, we leverage the power of the crowdsourced user reviews to understand why an app requests a permission. We propose a framework, called SmartPI, that automatically identifies functionality-relevant user reviews and infers the permission implication of them, bridging the gap between the functionalities and the actual behaviors of an app. In particular, we extract features from the platform documents to identify functionality-relevant user reviews from noisy crowdsourced user reviews with Natural Language Processing (NLP) techniques. The topic model is further adopted to infer the permission implications of apps from the functionality-relevant user reviews. More than 20,000 apps, 2,653,159 users, and 4,247,769 user reviews are crawled from Google Play as a real-world dataset to evaluate the performance of SmartPI. The experiments results show that the permission usage of apps can be better reflected by user reviews than the claimed descriptions of apps.
An improved KCF tracking algorithm based on multi-feature and multi-scale

The purpose of visual tracking is to associate the target object in a continuous video frame. In recent years, the method based on the kernel correlation filter has become the research hotspot. However, the algorithm still has some problems such as video capture equipment fast jitter, tracking scale transformation. In order to improve the ability of scale transformation and feature description, this paper has carried an innovative algorithm based on the multi feature fusion and multi-scale transform. The experimental results show that our method solves the problem that the target model update when is blocked or its scale transforms. The accuracy of the evaluation (OPE) is 77.0%, 75.4% and the success rate is 69.7%, 66.4% on the VOT and OTB datasets. Compared with the optimal one of the existing target-based tracking algorithms, the accuracy of the algorithm is improved by 6.7% and 6.3% respectively. The success rates are improved by 13.7% and 14.2% respectively.
A New External Force for Snake Algorithm Based on Energy Diffusion

 Abstract —Object shape detection and localization techniques that utilize snake deformable models are one of the most promising image detection techniques. The binary edge maps, derived from the original image, are basically the class acted upon by the snake to extract the desired features. As a result, high and low energy content pixels are obtained. The high energy pixels are the pixels that reflect the object borders of a given image. This paper addresses a new external force that is calculated from the energy diffusion of high content pixels and is used to balance the internal forces of the snake. The proposed scheme showed better results in terms of computation speed and capture range than standard snake models. On the basis of the concavity convergence, analogous results are achieved in the proposed scheme compared with standard models.
CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN

Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.
Scaling Up Large-scale Sparse Learning and Its Application to Medical Imaging

Large-scale `1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. In many applications, it remains challenging to apply the sparse learning model to large-scale problems that have massive data samples with high-dimensional features. One popular and promising strategy is to scaling up the optimization problem in parallel. Parallel solvers run multiple cores on a shared memory system or a distributed environment to speed up the computation, while the practical usage is limited by the huge dimension in the feature space and synchronization problems. In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer’s Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
Book Recommendation using Cosine Similarity

Recommendations have been a driving force for the sale of products. For years, recommendations have always been based upon the product review of customer which in a later stage, was upgraded to personalization, whereby customers having similar purchasing patterns are clubbed together and their preferences are interchanged. This paper takes on a different approach when it comes to recommendation of books. Books are usually referred based on the author, genre and book ratings. Each book has its own plot summary which is different from the description that gives only a gist of the book. These plot summaries can be used to find how two books are very much alike. The paper explains how this is achieved by the calculation of the degree of likeness between the two plots based on the terms that are used. It involved the application of text mining to find the significant terms that will help to contribute to finding the angle of closeness using the mathematical calculation of cosine similarity. Keywords: Recommendation system, Information retrieval, TF-IDF score, Cosine similarity.
Mobile Cloud Application Framework and Offloading Strategies

Mobile Cloud computing has shown its capability to support mobile devices for provisioning computing, storage and communication resources. A distributed mobile cloud service system called “POEM” is presented to manage the mobile cloud resource and compose mobile cloud applications. POEM considers resource management not only between mobile devices and clouds, but also among mobile devices. It implements both computation offloading and service composition features. The proposed POEM solution is demonstrated by using OSGi and XMPP techniques. Offloading is one major type of collaborations between mobile device and cloud to achieve less execution time and less energy consumption. Offloading decisions for mobile cloud collaboration involve many decision factors. One of important decision factors is the network unavailability. This report presents an offloading decision model that takes network unavailability into consideration. The application execution time and energy consumption in both ideal network and network with some unavailability are analyzed. Based on the presented theoretical model, an application partition algorithm and a decision module are presented to produce an offloading decision that is resistant to network unavailability. Existing offloading models mainly focus on the one-to-one offloading relation. To address the multi-factor and multi-site offloading mobile cloud application scenarios, a multi-factor multi-site risk-based offloading model is presented, which abstracts the offloading impact factors as for offloading benefit and offloading risk. The offloading decision is made based on a comprehensive offloading risk evaluation. This presented model is generic and expendable. Four offloading impact factors are presented to show the construction and operation of the presented offloading model, which can be easily extended to incorporate more factors to make offloading decision more comprehensive. The overall offloading benefits and risks are aggregated based on the mobile cloud
Manipulator Meta-Imitation Learning Algorithm with Memory Weight Integration

Versatility is one of the key characteristics of general agent. In order to enable the manipulator to quickly and effectively acquire the ability to perform multiple tasks in an unknown environment, a large capacity model is essential. In this paper, the memory weight integration term adapted to meta-learning algorithm is proposed. By adjusting the plasticity of neurons, the manipulator can learn to learn more effectively in the process of learning multi-task and improve the forgetting problem of multi-task learning. Then, this paper combines the memory weight integration with meta-imitation learning, so that the manipulator can acquire new skills from a single demonstration task. Finally, a 7-DoF manipulator in PusherEnv experiment is used to explore the influence of different integration coefficients on the algorithm. The results show that the memory weight integration can effectively improve the success rate of tasks.
Prediction of the Effectiveness of Rolling Dynamic Compaction Using Artificial Intelligence Techniques and In Situ Soil Test Data

v aspects, are compared with each other in order to assess the suitability and shortcomings of each. Consequently, a recommendation has been made of the most feasible approach for predicting the effectiveness of RDC in different ground conditions with respect to CPT and DCP test data. The models have also been disseminated via a series of mathematical formulae and/or programming code to facilitate their application in practice. It is demonstrated that the developed optimal models are accurate and reliable over a range of soil types, and thus, have been recommended with confidence. As such, the developed models provide preliminary estimates of the density improvement in the ground based on the subsurface conditions and the number of roller passes. Therefore, it is considered that the models are beneficial during the pre-planning stages, and may replace, or at the very least augment, the necessity for RDC field trials prior to fullscale construction. In addition, the analyses demonstrate that the AI techniques provide a feasible approach for non-linear modelling involving many parameters, which in turn, further encourages future applications in the broader geotechnical engineering context. Finally, a comprehensive set of guidelines for each of the AI techniques employed in this research, i.e. ANN and LGP, is provided, with the intention of assisting potential and current users of these techniques.
Development of Information and Technology Platform for Optimal Design of Heating Systems

The paper presents a new methodological approach to development of information and technology platform for optimal design of heating systems. The methodological approach consists of the following components: basic principles of platform development; an architecture of the information and technology platform; a technique of automated software construction on the basis of the Model-Driven Engineering conception and modern metaprogramming technologies. The knowledge about heating systems, applied problems, and the applied software is stored in ontologies. The automated construction of the software system is performed on the basis of a heating system model, ontologies and modern metaprogramming technologies. The proposed approach allows us to successfully solve the problem of separation of methods (algorithms) for solving applied problems and mathematical models of heating system elements. To this end, the methods are implemented in the form of software components that are not related to properties and mathematical models of specific equipment. And the models of heating system elements are automatically integrated into the software system. As a result, the software system oriented to solving a specific applied problem is created in an automated mode. The developed approach has been used for the software implementation to design heating systems. Keywords—methodological approach; Model-Driven Engineering; ontology; metaprogramming; heating system design
A new fast corner detection method based on template matching

Corner detection is the basic link of camera calibration, and its detection accuracy will directly affect the accuracy of camera calibration. In order to improve the extraction precision of the corners of the board, this paper put forward a new checkerboard corner detection method. Two kinds of corner point prototype templates are constructed by using the characteristics of the checkerboard. The similarity between the pixel points and the corner points is calculated by convolution of the convolution kernel and the image. The degree of similarity is used to distinguish the corner points from the non-corner points. The non-maximum suppression algorithm and the gradient statistical algorithm further screen out the target corner points. The experimental results show that the method used in this paper can accurately and quickly detect the corner position and has strong anti-interference ability.
Information security policy noncompliance: An integrative social influence model

Despite the significant advancements made in understanding the factors that drive employees' compliance and noncompliance behaviours with information security policy (ISP), less is known about how different factors interact to impact such behaviours. Having been drawn on the social information processing theory, this research develops an integrative model that investigates how ethical work climate, beliefs, and neutralization interact to jointly explain ISP noncompliance. The model is tested via a survey of a broad cross section of employees. Neutralization, perceived cost of compliance, and perceived cost of noncompliance are found to significantly impact ISP noncompliance. Egoistic, benevolent, and principled climates are found to differentially influence neutralization and individuals' cognitive beliefs about the cost and benefit of ISP compliance versus noncompliance. Neutralization appears to be a more important moderator of the belief‐noncompliance relationship than the principled climate.
Relative attitude estimation for small satellite based on stellar image matching

Small satellites have a bright prospect in space exploration. One of the bottlenecks in developing small satellites is precise attitude estimation. Therefore, a relative attitude estimation method for small satellite is proposed using the stellar images captured by stellar camera. Essentially, the proposed method determines satellites relative attitude variation by infinite homography, which provides constraints on attitude variation of satellite camera. Homography between stellar images induced by stars is used to approximate the infinite homography which is obtained by matching two stellar images captured by satellite camera at different time. The specific procedure of the proposed method is as follows: First, star points features are extracted for matching by segmentation and non-maxima suppression. Second, star descriptors are constructed in a far larger local region for star points based on Speeded Up Robust Features (SURF) descriptors. Third, nearest neighbor method is used to get candidate correspondent star points and then outliers are rejected with RANSAC based strategy. Finally, relative attitude variation between two stellar images is solved with matched star points using regularized least square method. In order to verify the feasibility of the proposed method, experiments are carried out with simulated stellar images and digital simulation. Simulated stellar image matching experiment shows stars points matching method can get abundant of correspondent stars and the errors are below 1 pixel. Digital simulation shows that the accuracy of relative attitude estimation reaches 0.01° with about 0.5 pixels star location errors. Experiment results indicate that the proposed method is feasible and can precisely determine satellite relative attitude change.
Citywide Traffic Volume Inference with Surveillance Camera Records

Real-time traffic monitoring becomes an essential part of an intelligent city. In recent years, the adoption of surveillance cameras is rapidly growing because they are helpful to manage and control the traffic. However, it is impossible to install cameras on every road in a city due to the high costs of deployment and maintenance. Given the information from limited surveillance cameras, can we infer the citywide traffic volume accurately? This is a challenging question because we have no historical data on the roads without cameras. It requires us to design a method that goes beyond the inference using nearby traffic data. Moreover, a nice property of surveillance camera data is that these AI-equipped cameras can recognize individual vehicles. So we can recover incomplete trajectories for vehicles using plate numbers in surveillance camera records. However, for road segments without cameras, we do not know whether those vehicles pass through them or not. How can such incomplete trajectories be effectively used to help citywide traffic inference? In this paper, we propose a framework named CityVolInf to infer citywide traffic volume based on surveillance camera records. Our framework combines a semi-supervised learning-based similarity module with a novel simulation module to address the above challenges. While the similarity module focuses on spatiotemporal correlations of traffic volume between road segments, the simulation module utilizes incomplete trajectories to model transitions of traffic volume between adjacent road segments. Our framework bridges the conventional data-driven approach and transportation domain knowledge from the simulator. We conduct extensive experiments on a real-world dataset, containing 405,370,631 camera records collected from 1,704 surveillance cameras over a period of 31 days in Jinan, China. The experimental results demonstrate the effectiveness of CityVolInf compared with existing methods.
Buyer-Optimal Information with Nonlinear Technology

This paper explores the buyer-optimal information structures in a monopolistic screening context with nonlinear production technology. It shows that the buyer's optimal surplus may increase even when the production cost becomes more uncertain or when the efficient surplus decreases. Under a binary prior, this paper further shows that the buyer-optimal information structures must lie in a family described by truncated Pareto distributions. Such characterization effectively reduces the surplus maximization problem to a monopsony's pricing problem, which further implies that the buyer-optimal surplus is quasi-convex in technologies that are ranked by the rotational order.
Fast image stitching of unmanned aerial vehicle remote sensing image based on SURF algorithm

Fast acquisition and processing of effective data sources are a heated topic in remote sensing image processing research. Unmanned aerial vehicle (UAV) remote sensing system has the advantages of maneuverability, rapidity and economical, it has become a hot topic in the world. The study analyzes the characteristics of remote sensing image and the characteristics of UAV remote sensing system, and refers a variety of images fast processing algorithms to explore the rapid remote sensing images stitching and rapid information extraction methods. Based on the analysis of the relevant research at home and abroad, this paper draws lessons from some image processing ideas of modern photogrammetry, and proposes a fast image stitching method of UAV remote sensing images based on SURF (Speed Up Robust Features) feature description. This method is applied to UAV remote sensing fast image stitching to achieve high-quality UAV remote sensing images for fast and automatic splicing. The stitching speed of this method is much faster than that of SIFT (Scale-invariant feature transform) algorithm. And the splicing effect of this method is satisfactory
Design of head-pursuit guidance law based on sliding mode control

This paper proposes a new head-pursuit guidance law based on sliding mode control to intercept hypersonic vehicle in terminal phase. In order to alleviate chattering phenomenon caused by sliding mode control and ensure the convergence speed, the combination of linear sliding variable and fast power reaching law is adopted. Furthermore, an adaptive sliding mode guidance law is designed for solving the problem of unknown upper bound of the disturbance. Also, these algorithms are proved by the theoretical perspective based on finite-time convergence theory. Finally, numerical simulations are presented to illustrate the correctness and effectiveness of the methods.