Reviewerly/exHarmony huggingface dataset:
Energy Aware Cluster Based Optimal Virtual Machine Placement in Cloud Environment

A good virtual machine placement strategy would make better consolidation of virtual machines in a data center that optimizes resource utilization and hence reduces energy consumption. However, it is hard to balance the hosts in DCs due to the workload fluctuation by application and scaling of virtual machines (VMs). The optimal decision on VMs placement and consolidation is the NP-hard problem and many researchers proposed a solution to tackle this problem and lacks in exploiting the mechanisms efficiently. Therefore, this paper proposes a hierarchical cluster-aware approach based on a meta-heuristic crow search algorithm for the optimal selection of hosts to place the VMs and consolidates a maximum number of VMs on a minimum number of hosts. The proposed approach optimizes the energy consumption of data centers while satisfying the QoS requirements of applications to meet SLA. Evaluation of the proposed approach is performed under the cloudSim framework using real workload traces and is compared with classical approaches. Results of the experiment show that proposed work reduces energy consumption and SLA violations while ensuring better resource utilization.
Application of 5G+VR Technology in Landscape Design Teaching

As the VR technology that has been applied in many fields in China, the arrival of 5G era provides a strong impetus for VR technology to take off again. And the in-depth application of 5G + VR technology will further promote higher education. In view of the problems and deficiencies of the current landscape design teaching methods in China's universities, the Introduction to landscape design course and teaching as an example, 5G + VR technology is introduced in this paper to carry out the innovative design and teaching application of the course system. The teaching effect feedback and evaluation were carried out. The research and application results show that the innovation of 5G + VR in the teaching content and form of landscape design makes the course teaching simple and vivid, has strong situational experience and learning atmosphere, and effectively stimulates students' interest in learning.
Rapid Detection of Crowd Abnormal Behavior Based on the Hierarchical Thinking

In view of current algorithms for the crowd abnormal behavior detection are not suitable for different scenarios, a hierarchical detection algorithm is proposed. First, the target area is extracted using the Gaussian mixture model. According to the pixel ratio, decide which type the group belongs to: individualism behaviors, social interaction behaviors or leadership-led behaviors. Then divide the video according to the category of the crowd, calculate HOG-LBP for the crowd with individualism to judge the abnormal appearance. For other categories, calculate the trajectory and entropy in the divided image to obtain the speed, deviation from the trajectory and variance of the trajectory. Then compare with the corresponding thresholds to determine whether an abnormality occurs. The value of the entropy and its first-order function are used to judge the abnormal extent. When the entropy does not exceed 3/2 of the threshold, the optical flow is extracted to calculate CMI, and the peak value is used to detect anomalies. After experiments, our algorithm is verified to be rapid and accurate in different scenarios.
Enhanced Storage optimization System (SoS) for IaaS Cloud Storage

Cloud computing is an inevitable technology that leads to demanding business requirements, the cloud services of IaaS utilization are being expanded in the data storage space, which leads to an increase in the requirements of bandwidth. To reduce the storage space, the De-Duplication Compression (DDC) algorithm has been proposed to improve the storage and utilization of bandwidth in the Storage optimization System (SoS) through the elimination of duplicate files and monitor the IaaS storage utilization. The various types of metadata standards help to identify the respective file objects and the file data elements are linked with corresponding block can be clustering into segmented bins. The DDC algorithm is associated with a linked list structure to access the file on different parameters to classify the future file access patterns against the cloud storage. Based on user access frequency, the ranking has been determined to predict future usage based on file access patterns, the SoS system possesses a dashboard that can help the user to decide on the file operation. This system result has experimented with the real environment with different parameter simulation. The proposed SoS framework will decrease cloud IaaS space size up to 13.80% than the existing cloud storage system.
Time series anomaly detection in medical break-the-glass

The time-critical nature of medical emergencies, the requirements for system availability, and for real-time communication all make it exceedingly challenging to consistently enforce least-privilege access during medical emergencies (Break the Glass situations). Strict access control has to be suspended (must fail-open) when an emergency is declared, and only after the emergency has passed can a post-hoc audit be performed to determine the reasons (legitimacy) for overriding access control - standard operating procedure for healthcare facilities. Unfortunately, this does not proactively protect against misuse, but provides for identification and punishment of culprits. It is therefore essentially impossible to limit clinicians access to bare minimum permissions to perform life-saving activities during emergency access, especially in distributed medical systems. In this work we investigate the effectiveness of anomaly detection to ease the human burden of post-hoc audits in the medical Break-the-Glass (BTG) context. We use two different prediction models to perform real-time and post-BTG statistical analysis on time-series session log data for flagging anomalous user sessions and actions. Our approach combines a real-time fast analysis engine working on a partial feature set, as well as a post-hoc, slower analysis tool which works with the complete times series data of everything which occurred during the entire time of the emergency.
Adversarial Examples for Chinese Text Classification

Deep neural networks (DNNs) have been widely adopted in various areas such as image recognition and natural language processing. However, many works show that DNNs for image classification are vulnerable to adversarial examples, which are generated by adding small-magnitude perturbations to the original inputs. In this paper, we show that DNNs for Chinese text classification are also vulnerable to adversarial examples. We propose a marginal attack method to generate adversarial examples that could fool the DNNs. This method adopts the Naïve Bayes principle to filter sensitive words and it only adds a small number of sensitive words at the end of the original text. The generated adversarial example could fool a variety of Chinese text classification DNNs, such that the text would be classified to incorrect category with high probability. We conduct extensive experiments to evaluate the attack performance and the results show that the success ratio of the attacks could reach almost 100% by adding only five sensitive words.
Comments on “Critical assessment of geochronological data from the Carajás mineral Province, Brazil: Implications for metallogeny and tectonic evolution”

Despite representing one of the largest cratons on Earth, the early geological evolution of the Amazonia Craton remains poorly known due to relatively poor exposure and because younger metamorphic and tectonic events have obscured initial information. In this study, we investigated the sedimentary archives of the Carajás Basin to unravel the early geological evolution of the southeastern Amazonia Craton. The Carajás Basin contains sedimentary rocks that were deposited throughout a long period spanning more than one billion years from the Mesoarchean to the Paleoproterozoic. The oldest archives preserved in this basin consist of a few ca. 3.6 Ga detrital zircon grains showing that the geological roots of the Amazonia Craton were already formed by the Eoarchean. During the Paleoarchean or the early Mesoarchean (<3.1 Ga), the Carajás Basin was large and rigid enough to sustain the formation and preservation of the Rio Novo Group greenstone belt. Later, during the Neoarchean, at ca. 2.7 Ga, the southeastern Amazonia Craton witnessed the emplacement of the Parauapebas Large Igneous Province (LIP) that probably covered a large part of the craton and was associated with the deposition of some of the world largest iron formations. The emplacement of this LIP immediately preceded a period of continental extension that formed a rift infilled first by iron formations followed by terrigenous sediments. This major change of sedimentary regime might have been controlled by the regional tectonic evolution of the Amazonia Craton and its emergence above sea-level. During the Paleoproterozoic, at ca. 2.1 Ga, the Rio Fresco Group, consisting of terrigenous sediments from the interior of the Amazonia Craton, was deposited in the Carajás Basin. At that time, the Amazonian lithosphere could have either underwent thermal subsidence forming a large intracratonic basin or could have been deformed by long wavelength flexures that induced the formation of basins and swells throughout the craton under the influence of the growing Transamazonian mountain belt.
Application and Research of Spam Classification Based on Cluster Intelligence Algorithm to Optimize SVM

Abstract With the development of science and technology, the increasing popularity of the Internet, email has been widely used. Because of its convenience and low cost, e-mail has been used by more and more criminals to maliciously spread commercial advertisements, computer viruses and bad information. In order to resist spam, this paper proposes a spam classification method based on swarm intelligence algorithm optimized SVM. It classifies English spam and extracts features with special sensitive word coding. The model and method in this paper have certain reference value for spam classification research.
Fourier optics modelling of instrument response for interference microscopy

Interferometers for the measurement of topography rely on imaging systems to map surface points to a camera. The response of an interferometer to variations in surface height depends on the filtering properties of the imaging system. Here we provide a simple and practical model of an imaging interferometer using classical Fourier optics, including the effects of partial coherence. The model is useful for understanding basic properties such as lateral resolution and error sources related to measurement principles that make assumptions regarding the fidelity of the instrument response over a range of surface spatial frequencies and light source wavelengths.
Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue

In this paper we examine how people assign, interpret, negotiate and repair the frame of reference (FoR) in online text-based dialogues discussing spatial scenes in English and Swedish. We describe our corpus and data collection which involves a coordination experiment in which dyadic dialogue participants have to identify differences in their picture of a visual scene. As their perspectives of the scene are different, they must coordinate their FoRs in order to complete the task. Results show that participants do not align on a global FoR, but tend to align locally, for sub-portions (or particular conversational games) in the dialogue. This has implications for how dialogue systems should approach problems of FoR assignment – and what strategies for clarification they should implement.
Artificial Intelligence

Virtually everyone has to make hundreds of decisions every day in his day-to-day life. “Good decision making” means we are informed and have relevant and appropriate information on which to base our choices among alternatives. Decision support systems are emerging as a very powerful tool for making rational decision based on various sources of information. In this chapter, the authors attempt to understand how the intelligent decisions are required for any successful endeavour. In this complex world where information explosion has good and bad news, both finding analysing the adequate information is a tedious task that always requires expert advice. In today's digitalised world, various programs are designed especially for the education sector, which helps both the learner and tutor. Technology can reduce time to make decisions for the fussy or confused learner, thereby providing assistance.
Contour analysis of external images

Image recognition occupies a special place in the science of computer vision. Contour analysis particular important in pattern recognition. In this paper, we consider the structure of the contour analysis of external images. Active contour method shows the possibilities of using the minimum energy curve. The energy function that defines boundaries without first isolating the boundaries of an image object. Therefore, the Canny Boundary Detector algorithm is used to detect the contours of an object in an image. This algorithm smooths out image blur and eliminates noise, eliminates errors or interference in the picture. The path tracking method crosses out the boundaries between the subject and the background. Algorithms such as machine learning are needed in order to reflect its performance and the need for recognition. Clustering is used as a machine learning method to find the nearest neighbor criteria. The mathematical model of clustering has its own uniqueness and application for identifying borders or contours in the image. The contour detection and linking approach uses graph analysis, which works and does not lose efficiency in the presence of noise. The convexity defects contour analysis algorithm is able to determine the size of borders, and also uses the search for contour recesses and the introduction of key features when calculating object parameters.
Optimized Clustering based on Semantic Similarity of Components for Short Text

Short text is usually made up of several words and one sentence at most. Considering sparse features and complicated expressions, the similarity measurement and the clustering of the texts may not work well. The semantic clustering for short text is studied. Firstly, according to the analysis of the dependency syntax structure, the event component and modified components are extracted. Then, the semantic similarity of the texts is analyzed with the component as the basic unit. The strategy is that the text would be similar when not only the event component but also the modified components are similar. Further, considering the proposed semantic similarity may lead to increased topics, which means various cluster shape, indefinite cluster number, and increased noise point for the cluster, the density peak clustering is selected, and a regression parameter is designed to improve the cluster number and the noise. Based on the public data set, the proposed semantic clustering is tested: purity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> is 96% and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> measure is 71.97%. The proposed method has been used in the electrical power industry and is worth promoting.
Learning Additive Noise Channels: Generalization Bounds and Algorithms

An additive noise channel is considered, in which the noise distribution is unknown and does not known to belong to any parametric family. The problem of designing a codebook and a generalized minimal distance decoder (which is parameterized by a covariance matrix) based on samples of the noise is considered. High probability generalization bounds for the error probability loss function, as well as for a hinge-type surrogate loss function are provided. A stochastic-gradient based alternating-minimization algorithm for the latter loss function is presented. Bounds on the average empirical error and generalization error are provided for a Gibbs based algorithm that gradually expurgates codewords from a large initial codebook to obtain a smaller codebook with improved error probability.
Classification and Identification of Micro-Doppler Signatures of Pedestrian and Cyclists Using Simulated Data and ELM

There are great interests to study detection, and classification of pedestrian and cyclists using low-cost frequency modulated continuous wave (FMCW) radar sensors for intelligent transport systems (ITS). This paper presents a study about detection and classification of pedestrian and/or cyclists based on realistically simulated FMCW radar sensor echoes. From radar echoes, the micro-Doppler signatures of different targets can be observed through time-velocity diagrams and then extracted. Using a relatively small number of data samples for training, an Extreme Learning Machine (ELM) can be trained to detect the number of targets and classify their types. Numerical experimental results show the effectiveness of the proposed approach and the potential of low-cost radar sensors in ITS applications.
Vulnerability trends in web servers and browsers

In previous work we have looked at trends in vulnerabilities due to ordinary programming errors [2, 3]. This analysis focuses on two of the most widely used types of software in today's internet, web browsers and web servers. In addition to reports of vulnerabilities, we were able to consider market share to infer some information about the impact of vulnerabilities. The key questions we sought to address are:
Upper Arm Action Recognition for Self Training with a Smartphone

Abstract The action recognition for upper arm training, in low-cost and effective way, has great application in both sport training and rehabilitation training. However,they almost require extra and expensive equipments. This paper proposes an approach for real-time recognition of upper arm actions based on Hidden Markov Model (HMM) only one sensor and one smartphoneare needed. Data collected by a sensor and Action Pattern Sets are established with HMM training. Empirical Results with the smartphone, one for self-coaching for badminton technique improvement and another for arm injuries rehabilitation, validating the effectiveness and usability. Some problems found on site show that the next step is to further optimize the Action Pattern Sets to improve the overall accuracy and users’subjective satisfaction.
Marriage Recommendation Algorithm Based on KD-KNN-LR Model

With the rapid development of machine learning, recommendation algorithms have been gradually applied in the matrimonial service. In this paper, we focus on the application of machine learning techniques in marriage recommendation. Based on the fusion model of KD-KNN (K-Dimensional tree and k nearest neighbors) and LR (Logistic Regression), we propose a marriage recommendation algorithm, which is called KD-KNN-LR in this paper. Given a set of male and female users, KD-KNN-LR aims to recommend a set of best marriage candidates to each user. It takes bidirectional matching into account. Experimental results show that KD-KNN_LR has a better performance with an accuracy rate of 86%.
JPEG-XR-GCP: Promoting JPEG-XR Compression by Gradient-Based Coefficient Prediction

JPEG-XR is a still-image compression standard with high compression ratio and low computation cost. It supports lossy and lossless coding for still images. To further improve the compression performance of JPEG-XR in both lossy and lossless modes, in this paper, we propose an improved JPEGXR by using gradient-based coefficient prediction (JPEG-XRGCP). The proposed JPEG-XR-GCP predicts the coefficients based on the estimating of local gradients, and predicts the value of coefficients adaptively by using its neighboring and secondorder neighboring coefficients in gradients. The experimental evaluations demonstrate that our proposed coding scheme has a better compression performance in both lossy and lossless modes compared with the original JPEG-XR.
Detection and Prediction of Spam Emails Using Machine Learning Models

One of today's important means of communication is email. The extensive use of email for communication has led to many problems. Spam emails being the most crucial among them. It is one the major issues in today's internet world. Spam emails contain mostly advertisements and offensive content, which are often sent without the recipient's request and are generally annoying, time consuming, and wasting space on the communication media's resources. It creates inconveniences and financial loss to the recipients. Hence, there is always the need to filter the spam emails and separate them from the legitimate emails. There are a lot of content-based machine learning techniques that have proven to be effective in detecting and filtering spam emails. Due to a large increase in email spamming, the emails are studied and classified as spam or not spam. In this chapter, three machine learning models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM), are used classify the emails as spam and benign.
Connections Between Fuzzy Inference Systems and Kernel Machines

This paper explores the connection between fuzzy inference systems and kernel methods. Particularly, we explore the connection between the fuzzy-basis-function expansion of non-singleton fuzzy systems and regularized kernel methods with positive-definite kernels. We show that positive-definite kernel on fuzzy sets, i.e., a real-valued function defined on the set of fuzzy sets, is the core concept enabling such a connection. Furthermore, we rewrite the fuzzy-basis-function (FBF) expansion of non-singleton fuzzy systems in terms of a kernel-basis-function expansion with positive-definite kernels on fuzzy sets. The consequence of doing this is that fuzzy systems can be trained by regularized kernel methods, and the advantages of doing this are: 1) fuzzy systems can have more resistance to the curse of dimensionality, 2) they can explicitly regularize the function being approximated, 3) they can satisfy the Representer Theorem of kernel methods, which bounds the number of learned rules to the number of training observations, and, 4) they can learn functions in the functional space induced by the kernel on fuzzy sets. Consequently, from this perspective fuzzy systems can be regarded as kernel methods. Different from previous works, this connection relates the FBF-expansion directly to kernel methods without dropping the denominator of the expansion; also, thanks to the use of kernels on fuzzy sets, we provide an algorithm that makes it possible to drop the restriction of having fuzzy sets that share some of their antecedent parameters, e.g., Gaussian membership functions with the same variance in rule antecedents as is usually done in related works.