Reviewerly/exHarmony huggingface dataset:
A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges

Cloud Computing is a promising paradigm in comparison with traditional information technology approaches, in which organizations and enterprises adopt elastic cloud services on a pay-per-use model in order to reduce costs. Virtualization is a technique pervasively applied in modern datacenters (DCs) to maximize resource utilization, reduce greenhouse gas emissions, and lower overall cost. Virtual machine (VM) migration is widely exploited within and across DCs to meet a variety of needs of the virtualized cloud environment. For instance, server consolidation needs VM migration for power management. Also, load balancing, fault tolerance, system maintenance, and minimizing the service-level agreement (SLA) violation rate require live VM migration. The VM migration process is very resource intensive and requires intelligent approaches to avoid saturating network bandwidth and to keep server downtime to a minimum. In addition, in Dynamic Voltage Frequency Scaling (DVFS) in cluster servers in intrinsically heterogeneous DCs the voltage-frequency is varied based on workload to reduce power consumption, which is a large part of the overall cost. Although miscellaneous cloud computing studies have been presented in the literature concerning economic, privacy, and security issues, etc., there is a clear lack of survey studies on VM migration, server consolidation, and DVFS techniques. To fill this gap, we present different schemes to classify commonalities and discrepancies between the perspectives of researchers, based on metrics derived from the literature. Finally, open issues, challenges, and future directions are discussed for improving existing schemes and approaches.
Semi-Supervised Domain Adaptation by Covariance Matching

Transferring knowledge from a source domain to a target domain by domain adaptation has been an interesting and challenging problem in many machine learning applications. The key problem is how to match the data distributions of the two heterogeneous domains in a proper way such that they can be treated indifferently for learning. We propose a covariance matching approach DACoM for semi-supervised domain adaptation. The DACoM embeds the original samples into a common latent space linearly such that the covariance mismatch of the two mapped distributions is minimized, and the local geometric structure and discriminative information are preserved simultaneously. The KKT conditions of DACoM model are given as a nonlinear eigenvalue equation. We show that the KKT conditions could at least ensure local optimality. An efficient eigen-updating algorithm is then given for solving the nonlinear eigenvalue problem, whose convergence is guaranteed conditionally. To deal with the case when homogeneous information could only be matched nonlinearly, a kernel version of DACoM is further considered. We also analyze the generalization bound for our domain adaptation approaches. Numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness and efficiency of the proposed approach. The experiments show that our method outperforms other existing methods for both homogeneous and heterogeneous domain adaptation.
Predicting ICU readmission using grouped physiological and medication trends

Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Intuitively, snapshot measurements, especially the last measurements, are effective predictors that are widely used by researchers. However, methods that only use snapshot measurements neglect predictive information contained in the trends of physiological and medication variables. Mean, maximum or minimum values take multiple time points into account and capture their summary statistics, however, these statistics are not able to catch the detailed picture of temporal trends. In this study, we find strong predictors with ability of capturing detailed temporal trends of variables for 30-day readmission risk and build prediction models with high accuracy.We study physiological measurements and medications from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) clinical dataset. Time series of each variable are converted into trend graphs with nodes being discretized measurements of each variable. Then we extract important temporal trends by applying frequent subgraph mining on the trend graphs. The frequency of a subgraph is a good cue to find important temporal trends since similar patients often share similar trends regarding their pathophysiological evolution under medical interventions. Important temporal trends are then grouped automatically by non-negative matrix factorization. The grouped trends could be considered as an approximate representation of patients' pathophysiological states and medication profiles. We train a logistic regression model to predict 30-day ICU readmission risk based on snapshot measurements, grouped physiological trends and medication trends.Our dataset consists of 1170 patients who are alive 30 days after discharge from ICU and have at least 12 h of data. In the dataset, 860 patients were not readmitted and 310 were readmitted, within 30 days after discharge. Our model outperforms all comparison models, and shows an improvement in the area under the receiver operating characteristic curve (AUC) of almost 4% from the best comparison model.Grouped physiological and medication trends carry predictive information for ICU readmission risk. In order to build predictive models with higher accuracy, we should add grouped physiological and medication trends as complementary features to snapshot measurements.
Divide and Count: Generic Object Counting by Image Divisions

We propose a general object counting method that does not use any prior category information. We learn from local image divisions to predict global image-level counts without using any form of local annotations. Our method separates the input image into a set of image divisions-each fully covering the image. Each image division is composed of a set of region proposals or uniform grid cells. Our approach learns in an end-to-end deep learning architecture to predict global image-level counts from local image divisions. The method incorporates a counting layer which predicts object counts in the complete image, by enforcing consistency in counts when dealing with overlapping image regions. Our counting layer is based on the inclusion-exclusion principle from set theory. We analyze the individual building blocks of our proposed approach on Pascal-VOC2007 and evaluate our method on the MS-COCO large scale generic object data set as well as on three class-specific counting data sets: UCSD pedestrian data set, and CARPK, and PUCPR+ car data sets.
CNN-VWII: An efficient approach for large-scale video retrieval by image queries

This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we propose a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.
Evaluating the explanatory power of theoretical frameworks on intention to comply with information security policies in higher education

Higher education institutions have invested heavily in their high-tech infrastructure to ensure the security and integrity of their information. Incompliance with information technology policies has shown to lead to mass information leaks, reputational damage and potential litigation. Little research has been conducted on the subject of employees' compliance with such sensitive protocols. This paper presents a comprehensive theoretical model based on Theory of Planned Behavior, Protection Motivation Theory, General Deterrence Theory and Organizational Theory for predicting intentions of higher education employees' compliance with information security policies. Utilizing a survey instrument and using Structural Equation Modeling-Partial Least Squares method, this study found that perceived vulnerability, response efficacy and response cost to be the most predictive indicators that are positively associated with intentions of information security compliance among university staff and faculty. But, little support was found for the General Deterrence Theory, Theory of Planned Behavior and Organizational Theory in explaining the variance of higher education staff intentions to comply with information security policies. Results indicated that the Protection Motivation Theory provides the best theoretical framework to understand higher education employees' behavior with respect to compliance with information security. Such results confirmed earlier empirical investigations attempting to understand the basic question of why do employees differ with respect to compliance with information security. Consistent with the prior research, severe sanctions, close management supervision, peers' pressure and attitudes towards information security do not matter as much as perceived vulnerability and response efficacy in ensuring higher levels of intentions to comply with ISPs in organizations. The study recommends universities and colleges to invest in applied information security training for their staff, as well as for the university overall community.
Data Augmentation-Based Joint Learning for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) is the process of matching face images captured from different sources. HFR plays an important role in security scenarios. However, HFR remains a challenging problem due to the considerable discrepancies (i.e., shape, style, and color) between cross-modality images. Conventional HFR methods utilize only the information involved in heterogeneous face images, which is not effective because of the substantial differences between heterogeneous face images. To better address this issue, this paper proposes a data augmentation-based joint learning (DA-JL) approach. The proposed method mutually transforms the cross-modality differences by incorporating synthesized images into the learning process. The aggregated data augments the intraclass scale, which provides more discriminative information. However, this method also reduces the interclass diversity (i.e., discriminative information). We develop the DA-JL model to balance this dilemma. Finally, we obtain the similarity score between heterogeneous face image pairs through the log-likelihood ratio. Extensive experiments on a viewed sketch database, forensic sketch database, near-infrared image database, thermal-infrared image database, low-resolution photo database, and image with occlusion database illustrate that the proposed method achieves superior performance in comparison with the state-of-the-art methods.
GA-SURF: A new Speeded-Up robust feature extraction algorithm for multispectral images based on geometric algebra

Speeded-Up Robust Features (SURF) can be used for tasks such as object recognition, image registration, classification or 3D reconstruction. However, existing SURF algorithm cannot be directly applied to deal with multispectral images. In this paper, based on SURF and the theory of Geometric Algebra (GA), a novel feature extraction algorithm named GA-SURF is proposed for multispectral images. First, a Hessian matrix based on GA is calculated for locating interest points in spatial and spectral space. The box filters based on GA are used to simplify the calculation of Hessian matrix and generate image pyramids. Then, following the procedures of SURF, interest points are located by the image pyramids and described in GA space. Experimental results demonstrate that, compared with other feature extraction algorithms for multispectral images, GA-SURF can be computed much faster and are more robust and distinctive.
Improved Power/EM Side-Channel Attack Resistance of 128-Bit AES Engines With Random Fast Voltage Dithering

This paper demonstrates the improved power and electromagnetic (EM) side-channel attack (SCA) resistance of 128-bit Advanced Encryption Standard (AES) engines in 130-nm CMOS using random fast voltage dithering (RFVD) enabled by integrated voltage regulator (IVR) with the bond-wire inductors and an on-chip all-digital clock modulation (ADCM) circuit. RFVD scheme transforms the current signatures with random variations in AES input supply while adding random shifts in the clock edges in the presence of global and local supply noises. The measured power signatures at the supply node of the AES engines show upto 37× reduction in peak for higher order test vector leakage assessment (TVLA) metric and upto 692× increase in minimum traces required to disclose (MTD) the secret encryption key with correlation power analysis (CPA). Similarly, SCA on the measured EM signatures from the chip demonstrates a reduction of upto 11.3× in TVLA peak and upto 37× increase in correlation EM analysis (CEMA) MTD.
New set of multi-channel orthogonal moments for color image representation and recognition

Orthogonal moments and their invariants to similarity transformations for monochrome and gray-scale images are widely used in many pattern recognition and image processing applications. Quaternion orthogonal moments are used with color images. Recently, the multi-channel framework is proposed as a successful alternative of the quaternion orthogonal moments in representation and recognition of the color images. In this paper, a new set of multi-channel orthogonal moments and their invariants to rotation, scaling and translation (RST) is proposed for color image representation and recognition. The proposed multi-channel moments are based on the orthogonal radial substituted Chebyshev functions. The multi-channel orthogonal radial substituted Chebyshev moments (MORSCMs) are defined in polar coordinates over a unit circle. An accurate kernel-based method is utilized for accurate computation of the MORSCMs. A series of experiments is performed to validate this new set of multi-channel moments and compare its performance with the existing quaternion and multi-channel orthogonal moments. The obtained results ensure the superiority of the proposed MORSCMs over all existing moments in representation and recognition of the color images.
Adaptive genetic algorithms used to analyze behavior of complex system

In the present study, we consider a complex system whose behavior is characterized by set of various time-dependent factors. Some of these factors can characterize the external influences on the system, whereas other factors contain information generated by system. We demonstrate that time-dependence of these factors can be reproduced by the nonlinear regression model. The concrete form of this regression model is constructed on the basis of the genetic algorithms technique. This allows us to predict a possible behavior of the system and to identify the so-called significant factors that have a significant impact on the behavior of the system. To demonstrate validity of the method, we apply it to analyze the data characterizing a manufacturing company and the meteorological data.
Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT

IoT leads to abrupt variations producing an immense number of data streams for storage, which is a considerable task in the heterogeneous cloud computing environment. Extant techniques consider task deadlines for virtual machine (VM) allocation and migration. This creates a resource famine leading to haphazard and numerous VM migrations, high energy consumption and unbalanced resource utilization. To solve this issue, an energy-efficient resource ranking and utilization factor-based virtual machine selection (ERVS) approach is proposed. ERVS encompasses the resource requirement rate for task classification, comprehensive resource balance ranking, processing element cost and the resource utilization square model for migration. It evaluates overloaded and underloaded hosts and types of VM by predicting CPU utilization rate and energy consumption. Based on this, tasks are sorted and VMs are optimally assigned, which enhances the resource utilization rate, reducing the number of live VM migrations. The experiments evaluate the ability of the proposed approach to diminish energy consumption without violation of service level agreements.
Augmented reality via expert demonstration authoring (AREDA)

Augmented Reality (AR) has in the past decade shown great potential in manufacturing, specifically AR guided assembly. This is the use of AR to provide product assembly instructions to factory workers. AR guided assembly has shown to outperform conventional (paper based) and virtual based methods of instruction delivery in terms of accuracy and time. However, authoring the AR content displayed to the worker is difficult and time consuming. The main challenge for an AR authoring tool is to allow the author the ability to register the content intuitively without having to understand complicated AR concepts. Many authoring tools have tried to overcome this challenge but have been unable to sufficiently create a good working abstraction for the novice author. The work presented in this paper focused on creating AR work instructions for bench top assemblies using a novel form of authoring termed expert demonstration. The working concept is that an expert is recorded performing the assembly steps by a computer vision system. The recording is then automatically processed to generate AR work instructions. Augmented Reality via Expert Demonstration Authoring (AREDA) is the name of the representative software developed in this research. AREDA is divided into two phases: the demonstration phase and refinement phase. Using computer vision algorithms along with a novel skin detection algorithm known as Particle Swarm Optimized Gaussian Mixture Model, the demonstration phase was created. This phase takes 3D point clouds of assembly sequences and converts them into ordered 3D parts along with their transformations. The refinement phase then allows a user to modify the automatically generated AR work instructions to fix errors and add useful virtual content (textual instructions). Finally, three use cases are presented using AREDA to generate the work instructions. Through these use cases, this paper shows the significance of the expert demonstration authoring approach.
Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images

Observing and identifying blood cells is a direct way for early diagnosis of blood diseases. Traditional blood cell recognition methods are usually time-consuming and laborious tasks for medical staff. This paper proposed an efficient leukocyte recognition method based on microscopic hyperspectral imaging technology. In order to achieve better segmentation performance and further improve the representativeness of features, the sequential maximum angle convex cone algorithm and iterative self-organizing data analysis technique algorithm are combined to segment the leukocytes from microscopic hyperspectral images. In addition, the uniform and rotation invariant local binary pattern is adopted as a textural measurement of the leukocytes. Combined the texture features with shape and spectral features, support vector machine is used to classify the leukocytes into different types. Experimental results show that the proposed method provides higher segmentation and recognition accuracy compared with the existing method. Moreover, the addition of spectral features improves the recognition performance shows the potential diagnosis capacity of microscopic hyperspectral imaging technology.
Taking a Deeper Look at the Inverse Compositional Algorithm

In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax these assumptions by incorporating data-driven priors into this model. More specifically, we unroll a robust version of the inverse compositional algorithm and replace multiple components of this algorithm using more expressive models whose parameters we train in an end-to-end fashion from data. Our experiments on several challenging 3D rigid motion estimation tasks demonstrate the advantages of combining optimization with learning-based techniques, outperforming the classic inverse compositional algorithm as well as data-driven image-to-pose regression approaches.
A Thermal Infrared Face Database With Facial Landmarks and Emotion Labels

Thermal infrared imaging is an emerging modality that has gained increasing interest in recent years, mostly due to technical advances resulting in the availability of affordable microbolometer-based IR imaging sensors. However, while sensors are widely available, algorithms for thermal image processing still lack robustness and accuracy when compared to their RGB counterparts. Current methods developed for RGB data make use of machine learning algorithms that require large amounts of labeled images which are currently not available for the thermal domain. In this paper, we address the question whether providing a large number of labeled images would allow the application of current image processing methods on the example of solving challenging face analysis tasks. We introduce a high-resolution thermal facial image database with extensive manual annotations and explore how it can be used to adapt methods from the visual domain for infrared images. In addition, we extend existing approaches for infrared landmark detection with a head pose estimation for improved robustness and analyze the performance of a deep learning method on this task. An evaluation of algorithm performance shows that learning algorithms either outperform available solutions or allow completely new applications that could previously not be addressed. As a conclusion, we prove that investing the effort into acquiring appropriate training data and adapting competitive algorithms is not only a viable approach in analysing thermal infrared images but can also allow outperforming specific task-designed solutions. The database is freely available for academic use at https://github.com/marcinkopaczka/thermalfaceproject.