Reviewerly/exHarmony huggingface dataset:
Efficient Online Virtual Machines Migration for Alert-Based Disaster Resilience

Several recent weather-based disasters had very negative impacts on cloud networks, causing Data Center (DC) shutdown, consequent data-loss and intolerable downtime of cloud services. This has put the reactive disaster-resilient design of cloud networks on top the agenda of several cloud DC operators. DC operators are investigating approaches to avoid downtime of cloud services in case a DC is affected by a disaster. Thanks to virtualization most cloud services run on Virtual Machines (VMs) hosted by DCs, so it is possible to keep these services alive if the VMs are evacuated (namely, migrated) before the disaster from a DC affected by the disaster to a DC in a safe location, in an online technique. This technique is known as online “VM migration”, which results without or with a minimal service downtime. In this paper, we present an Integer Linear Programming (ILP) model for efficient online VMs migration in case of an alerted disaster (e.g., most weather-based disasters, as hurricanes) such as to avoid service downtime. The ILP performs scheduling and assigns route and bandwidth to the migration of VMs towards a safe DC within an alert time, with the objective of maximizing the number of VMs migrated and minimizing service downtime, network resource occupation and migration duration. We present a comparative analysis of offline and online migration strategies such as to quantify the trade-off between downtime, network resource utilization and migration duration. Moreover, we investigate the impact of the memory dirtying rate on the online migration process, i.e., the number of VMs evacuated and network resource occupation.
DecidArch V2: An Improved Game to Teach Architecture Design Decision Making

We report on the use of our DecidArch game to teach software architecture design decision making in two consecutive years.We compare the support of three learning goals for the first version of the game with the second, revised version.Results show how the game has clearly improved.For the remaining issues, we suggest final improvements.
A Quaternion-Based Robust Adaptive Spherical Simplex Unscented Particle Filter for MINS/VNS/GNS Integrated Navigation System

An improved filtering algorithm-robust adaptive spherical simplex unscented particle filter (RASSUPF) is proposed to achieve high accuracy, induce the amount of computation, and resist the influence of abnormal interference for the MINS/VNS/GNS integrated navigation system. This algorithm adopts spherical simplex unscented transformation (SSUT) to approximate the probability distribution, employs the spherical simplex unscented Kalman filter (SSUKF) to generate the importance sampling density of particle filter, and applies robust and adaptive estimation to control the influence of the abnormal information on the state model and the observation model. Simulation results demonstrate the proposed algorithm can effectively reduce the navigation error, improve the navigation positioning precision, and decrease the computation cost.
Privacy Enhanced Healthcare Information Sharing System for Home-Based Care Environments

Home-based nursing care services have increased over the past decade. However, accountability and privacy issues as well as security concerns become more challenging during care provider visits. Because of the heterogeneous combination of mobile and stationary assistive medical care devices, conventional systems lack architectural consistency, which leads to inherent time delays and inaccuracies in sharing information. The goal of our study is to develop an architecture that meets the competing goals of accountability and privacy and enhances security in distributed home-based care systems.We realized this by using a context-aware approach to manage access to remote data. Our architecture uses a public certification service for individuals, the Japanese Public Key Infrastructure and Health Informatics-PKI to identify and validate the attributes of medical personnel. Both PKI mechanisms are provided by using separate smart cards issued by the government.Context-awareness enables users to have appropriate data access in home-based nursing environments. Our architecture ensures that healthcare providers perform the needed home care services by accessing patient data online and recording transactions.The proposed method aims to enhance healthcare data access and secure information delivery to preserve user's privacy. We implemented a prototype system and confirmed its feasibility by experimental evaluation. Our research can contribute to reducing patient neglect and wrongful treatment, and thus reduce health insurance costs by ensuring correct insurance claims. Our study can provide a baseline towards building distinctive intelligent treatment options to clinicians and serve as a model for home-based nursing care.
T-SR: A Location Privacy Protection Algorithm Based on POI Query

With the popularity of location-based services in the field of mobile network applications, users enjoy the convenience on one side, they may face the risk of privacy disclosure on the other side. Attackers can easily dig out the user's home address, occupation, and other personal privacy from the data of location-based services. For the problem of user location privacy disclosure based on semantic query of point of interest (POI) in the road network environment, the previous research seldom paid attention to the temporal association relationship between the query semantic and the user's location, as well, seldom considered whether the constructed anonymity sets of fake-locations can fulfill the property of reciprocity. Therefore, in this paper, we propose a location privacy protection algorithm T-SR based on POI query. The algorithm can be divided into three steps. First, the whole road network is divided into multiple areas based on the Voronoi structure, so that the areas are independent and non-overlapping, and the road grid that contains user's real location will be located according to the level of privacy protection H. Second, the temporal association between the POI semantics are mined from the real check-in data based on Gaussian distribution σ rule, and the POIs which have weak association with the POI of user's location will be filtered to resist the temporal association attack. Third, the POI semantics within the grid will be split into several buckets according to query times record, anonymity degree k, and semantic degree l, then the dummy locations will be selected from various kinds of POIs of the bucket where the user's location is. The resulted anonymity set can defend against replay attack, inference attack, and temporal association attack. The theoretical analysis and experimental evaluation prove that the proposed solution can protect user's location privacy efficiently and effectively.
A pipeline for automated face dataset creation from unlabeled images

Computer vision tasks are very dataset dependent and with the emerging growth of image processing tasks and their applications, there is a huge demand for more datasets in terms of variety or size. Considering many face datasets are assembled largely manually, the process of manual dataset construction could be very time-consuming and labor intensive. In addition, some face datasets are constructed automatically over the past few years, but they are all constructed with previous knowledge of labels to some extent.
Practical Resource Usage Prediction Method for Large Memory Jobs in HPC Clusters

Users in high performance computing (HPC) clusters normally face challenges to specify accurate resource estimates for running their applications as batch jobs. Prediction is a common way to alleviate this complexity by using historical job records of previous runs to estimate resource usage for new coming jobs. Most of existing resource prediction methods directly build a single model to consider all of the jobs in clusters. However, people in production usage tend to only focus on the resource usage of jobs with certain patterns, e.g. jobs with large memory consumption. This paper proposes a practical resource prediction method for large memory jobs. The proposed method first tries to predict whether a job tends to use large memory size, and then predicts the final memory usage using a model which is trained by only historical large memory jobs. Using several real-world job traces collected from large production clusters of IBM Spectrum LSF customer sites, the evaluation results show that the average prediction errors can be reduced up to 40% for nearly 90% of large memory jobs. Meanwhile, the model training cost can be reduced over 30% for the evaluated job traces.
Geomeasure: GIS and Scripting for Measuring Morphometric Variability

This paper presents Geomeasure, a methodological tool developed to recover typometric information with a twofold objective. First, to speed up the process of gathering data by automatizing the way in which it is recovered. Second, it adds higher accuracy and the possibility of re-measuring archeological items without further directly interacting with the piece. Based on a combination of R scripting with GIS features, Geomeasure is at the time able to automatically gather 125–130 typometric variables per archaeological item, with the only input of vectorized photographs. It can be used as a reliable methodological aid to extract detailed information on patterns and trends of shape variability. The system has currently been applied over a sample of 2,000+ geometric microliths in the Eastern Iberian Peninsula. The performance results of its application are on display, and the system is compared to other methodological approaches currently in use for capturing shape variability.
An Anti-phishing Training System for Security Awareness and Education Considering Prevention of Information Leakage

Phishing is one of the dangerous threats to organisations. A sender of a phishing e-mail pretends to be a trusted person or a system in order to steal valuable information including personal identity data and credentials. In order to deal with this problem, many organisations have implemented an anti-phishing training. However, the outsourcing of an anti-phishing training requires a high cost. Additionally, many anti-phishing training systems provided by vendors save sensitive data such as e-mail addresses and names of trainees to public servers for an anti-phishing training. This architecture has a problem that attacking these public servers increases for the risk of information leakage about trainees. Therefore, this paper proposes an anti-phishing training system which does not save sensitive data such as an e-mail address and a name of trainees to public servers, and it is implementable at a low cost. This proposed system saves sensitive data to a trainer's local computer instead of public servers. A sensitive data saved on a trainer's local computer and trainees' access log data on public servers are associated with a pseudonym generated via pseudonymisation technique. Thus, if attackers try to steal trainees' sensitive data via the Internet, it becomes difficult for attackers by deleting sensitive data on a trainer's local computer.
A New Data Hiding Method Using Adaptive Quantization &amp; Dynamic Bit Plane Based AMBTC

This paper proposes a novel data hiding method using adaptive quantization & dynamic bit plane based AMBTC technique. In the proposed method, initially, a cover image is partitioned into a set of non-overlapping blocks, then adaptive number of quantization levels and size of the bit-plane for each block is determined, using the complexity level. The secret data is concealed into the bit-planes of the blocks according to their tolerance capability. The secret data is embedded via simple bit replacement strategy. The dynamic categorization of image blocks and determination of number of quantization levels along with the size of bit-planes have significantly contributed in providing high quality marked-image while maintain the same bit-rate. Further, the proposed scheme provides high data hiding capacity as almost every block is being used for hiding the secret data. It is clearly evident from the experimental results that proposed method performs significantly better than the conventional AMBTC based data hiding methods for the hiding capacity and image quality trade-off, without any change in required bit-rate.
Global as-Conformal-as-Possible Non-Rigid Registration of Multi-view Scans

In this paper, we present a novel framework for global non-rigid registration of multi-view scans captured using consumer-level depth cameras. In our method, all scans from different viewpoints are allowed to undergo large non-rigid deformations and finally fused into a complete high quality model. To avoid the well-known loop closure problem, we simultaneously optimize a global alignment problem instead of pairwise non-rigid registration in succession. We employ a joint point-to-point and point-to-plane positional constraint to reduce the influence of wrong correspondences, and incorporate an as-conformal-as-possible constraint to avoid mesh distortions during deformation. We also design a reweighting scheme on position and transformation to reduce registration errors. Experimental results on both public datasets and real scanned datasets demonstrate that our approach outperforms state-of-the-art methods through extensive quantitative and qualitative evaluations.
Analysis of Automatic through Autonomous - Unmanned Ground Vehicles (A-UGVs) Towards Performance Standards

Automatic through Autonomous - Unmanned Ground Vehicles (A-UGVs), as termed by ASTM Standards Committee F45 for Driverless Industrial Vehicles, have much potential for use in manufacturing operations thanks to their versatility and flexibility. To utilize A-UGVs efficiently and effectively, it is needed to specify how the vehicle will be used, in what environment, and how best to control it. By understanding the detailed performance of the A-UGV and the facility environment, the vehicle can potentially operate with maximized productivity. In this paper, various parameters of the A-UGV are analyzed to measure navigation and obstacle avoidance performance. A-UGV aspects related to various facility environments are defined in structural form with organic relations. Performance test methods were developed and verified in a mock facility environment combining ramps and obstacles to measure navigation and obstacle avoidance performance.
Quantum and classical query complexities for generalized Simon's problem

Simon's problem is an essential example demonstrating the faster speed of quantum computers than classical computers for solving some problems. The optimal separation between exact quantum and classical query complexities for Simon's problem has been proved by Cai & Qiu. Generalized Simon's problem can be described as follows. Given a function f:{0,1}n→{0,1}m, with the property that there is some unknown hidden subgroup S such that f(x)=f(y) iff x⊕y∈S, for any x,y∈{0,1}n, where |S|=2k for some 0≤k≤n(m≥n−k). The goal of generalized Simon's problem is to find S. For the case of k=1, it is Simon's problem exactly. In this paper, we propose an exact quantum algorithm with O(n−k) queries and an non-adaptive deterministic classical algorithm with O(k2n−k) queries for solving the generalized Simon's problem. Also, we prove that their lower bounds are Ω(n−k) and Ω(k2n−k), respectively. Therefore, we obtain a tight exact quantum query complexity Θ(n−k) and an almost tight non-adaptive classical deterministic query complexities Ω(k2n−k)∼O(k2n−k) for this problem.
FENet: Roles Classification of IP Addresses Using Connection Patterns

It is valuable to classify IP address roles based on network traffic behavior for network security analysis. Many previous studies have focused on coarse-grained classification (e.g., servers, clients and P2P, and so on.), these do not meet the increasingly diverse needs of applications. In this paper, we propose a novel approach for learning the continuous feature representation of connection patterns that we call FENet, which focuses on the low-dimensional embedding of IP address connection features. Thus, we trained two-tier neural networks that classified IP address roles in the given network dataset. Our approach can achieve more fine granularity representation and classification of IP address roles. Experimental results demonstrate the effectiveness of FENet over existing state-of-the-art techniques. In several real-world networks from active IP addresses, we have achieved very high classification accuracy and stability.
LSTM-Based Recommendation Approach for Interaction Records

Interactive platforms such as Spotify and Steam currently play an increasingly important role on the Internet. Users continuously use the content on these platforms. Therefore, the most important data in interactive platforms are interaction records, which contain an enormous amount of information regarding user interests at any given time. However, previous recommendation approaches have been unable to process such records satisfactorily. Therefore, we propose an LSTM-based recommendation approach for interaction records. In our approach, we used a recurrent neural network (RNN) based on LSTM to make recommendations by learning user interests and their changing trend. We propose a pretreatment called serial filling at equal ratio to apply LSTM. Further, we used a dimensionality reduction technique based on matrix factorization to improve the system efficiency. Finally, we evaluated our approach using Steam datasets. As indicated by the results, our approach performs better than other conventional approaches in three aspects: Accuracy, efficiency, and diversity.
Empirical study on rotation and information exchange in particle swarm optimization

This paper investigates whether rotational variance and information exchange affect the performance of Particle Swarm Optimization (PSO) algorithms. Four PSO versions which include the presence or absence of rotational variance, and the fast or late information exchange among particles are under evaluation. The goal is to highlight the best approach and principal benefits of each PSO version based on numerical simulations. To accomplish the aforesaid, the algorithms were evaluated on CEC 2017 benchmark optimization problems. Additionally, a method to estimate a reliable number of algorithms executions was also proposed. Statistical measurements based on Clerc's rules were used to strengthen the analyses. The results indicated the rotationally variant PSO as the overall winner, and the fast information exchange was statistically significant better than the late one, according to Friedman's and Wilcoxon's tests.
Construction of URDF model based on open source robot dog using Gazebo and ROS

In this paper, the mechanical design and electronics used in the OpenDog are explained along with the process of creating a custom URDF file. The custom URDF based on the available OpenDog model is needed to port it to ROS and perform path planning algorithms on the same. This is carried out by further creating separate packages and launch files for visualization and simulation of the model. The simulations of the movements are performed in Gazebo and visualized in Rviz.
Object-Based Approach for Adaptive Source Coding of Surveillance Video

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.
Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images

In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task. The maximum a posteriori probability (MAP) estimation principle is explored to solve this problem. A fuzzy set associated Gibbs' Markov random field (MRF) is considered to model the spatio-contextual information of an MRI. It is observed that the MAP estimate of the MRF model does not yield good results with any local searching strategy, as it gets trapped to local optimum. Hence, we have exploited the advantage of variable neighborhood searching (VNS)-based iterative global convergence criterion for MRF-MAP estimation. The effectiveness of the proposed scheme is established by testing it on different MRIs. Three performance evaluation measures are considered to evaluate the performance of the proposed scheme against existing state-of-the-art techniques. The simulation results establish the effectiveness of the proposed technique.
Digital Color Documents Authentication Using QR Code Based on Digital Watermarking

With the rapid development of the Internet, many problems regarding the processing of electronic documents have emerged, such as content filtering, information retrieval, security and searching. The security problem of electronic documents mainly focuses on copyright protection, content integrity authentication, monitoring, and other. The digital watermarking as an effective technique for copyright protection, content integrity, and originality authentication has been widely paid attention. The digital documents are different from the printed document where the carrier of it is computer disk, hard disk, or CD magnetic materials, so it is easy to tamper, damaged, deleted and copied. Therefore, there is an urgent need for an effective tool which can verify whether the content of the document has tampered or not. This is because the secure authentication of the document is very important. In this paper, the color document authentication based on QR code and digital watermarking has been implemented with a novel technique. Experimental results show that the designed structure has a good invisibility and ideal capacity. Furthermore, the tampered regions can be precisely detected.