Reviewerly/exHarmony huggingface dataset:
<scp>Context‐aware</scp> resource management and alternative pricing model to improve enterprise cloud adoption

Summary Existing Cloud resource management approaches favor Cloud service providers because of their data‐center origin. The onus of efficiently managing the resources is on the end‐user. Present provisioning/deprovisioning methods and the pricing model could be modified to ensure that the end‐user reaps the benefits. The current study proposes a context‐aware Cloud resource management approach based on a workload‐type provisioning and a just‐in‐time deprovisioning design. Furthermore, it also recommends an alternative pricing model. The design intends to provision/deprovision individual resources like CPU, memory, and so on, according to the workload‐type rather than instance‐type as done in existing Cloud resource management approaches. The proposed approach was commissioned on a private lab infrastructure and several experiments were conducted. It was observed that the proposed approach was able to detect sample workload types with an average sensitivity of 82% and specificity of 76%. The average cost of the proposed approach was found to be 20% less than the present approach.
High Performance Electronic Voting Machine (EVM) Implementation Using ARM Cortex M3

The main building stone in a democratic country are fair elections. In India, the main objective of introducing Electronic voting machine was to reduce mishaps and frauds especially in states which are politically sensitive and are subjected to frequent re-polls due to electoral rigging and imbalance in voting system. The EVM machine currently used by our government requires updated Microcontroller like ARM Cortex M3 which has advanced features like Memory protection, Accurate Time stamping using Ultra Low power RTC, cost sensitive, efficient interrupt controller (NVIC), a RTOS timer (the SysTick). These features make the software on ARM Cortex M3 much more efficient. In this paper using ARM Cortex M3, this research work has replicated features currently available in the EVM and also included some features like RESET and final vote count display as password protected. Designed EVM system will allow a person to cast his/her vote only once, the casted vote being recorded by the ballot unit which is controlled by control unit. In the process of vote, the person casting the vote would able to watch glowing LED near the candidate party symbol. By this candidate conclude themselves that vote has been recorded. The process followed by enabling the ballot on control unit by the PRO.
Elimination of multidimensional outliers for a compression chiller using a support vector data description

Data-driven simulation models can be used for optimal control and decision making in building systems only if they are developed accurately and reliably. An unsolved issue pertains to the data collected from BEMS that generally contain errors owing to sensors, malfunctioning systems, and other unknown reasons. These anomalies generally occur in a multidimensional space and cannot be easily detected. Unfortunately, these anomalies can prevent the simulation model from being accurate, reliable, and scalable. This paper presents an automatic anomaly detection method using a support vector data description (SVDD). The data obtained from a compression chiller in a real office building were classified into raw and filtered datasets based on SVDD. Two artificial neural network (ANN) models were developed (ANNraw and ANNSVDD). Based on the comparison between the two ANNs, the ANNSVDD is found to be better than ANNraw in terms of the model reliability and reproducibility. It is also interesting that the model accuracy differences between the two ANN models are marginal. However, the accuracy of the two ANN models can be improved as long as they are tested against filtered data by SVDD. The improvements in accuracy signify the importance of the elimination of these anomalies.
Attribute Propagation Enhanced Community Detection Model for Bitcoin De-anonymizing

Bitcoin is a kind of decentralized cryptocurrency on a peer-to-peer network. Anonymity makes Bitcoin widely used in online payment but it is a disadvantage for regulatory purposes. We aim to de-anonymize Bitcoin to assist regulation. Many previous studies have used heuristic clustering or machine learning to analyze historical transactions and identify user behaviors. However, the accuracy of user identification is not ideal. Heuristic clustering only uses the topological structure of the transaction graph and ignores many transaction information, and supervised machine learning methods are limited by the size of labeled datasets. To identify user behaviors, we propose a community detection model based on attribute propagation, combining the topological structure of the transaction graph and additional transaction information. We first parse the transaction data of public ledger and construct a bipartite graph to describe correlations between addresses and transactions. We also extract address attributes from historical transactions to construct an attributed graph with the previous bipartite graph. Then, we design an adaptive weighted attribute propagation algorithm named AWAP running on the attributed graph to classify bitcoin addresses, and further identify user behaviors. Extensive experiments highlight that the proposed detection model based on AWAP achieves 5% higher accuracy on average compared to state-of-the-art address classification methods in Bitcoin. AWAP also achieves 25% higher F-score on average compared to previous community detection algorithms on two datasets.
Old Law, New Tech, and Citizen-Created Hashtags: #BlackLivesMatter and the Case for Provisional Hashtag Marks

Using #BlackLivesMatter as a case study, this research documents the tensions and harms associated with trademarking online social movement hashtags. Grounded in the work of critical race theory and intellectual property scholars, this study analyzes the inconsistencies in the U.S. Patent and Trademark Office application practice. The contradictions signal a limited or “mis”understanding of the utility of citizen-created hashtags and online social movement slogans. We propose a provisional networked trademark that would grant limited protection to social movements to show that their marks demonstrate the kind of secondary meaning required for a traditional trademark.
Feature Redirection Network for Few-Shot Classification

Few-shot classification aims to learn novel categories by giving few labeled samples. How to make best use of the limited data to obtain a learner with fast learning ability has become a challenging problem. In this paper, we propose a feature redirection network (FRNet) for few-shot classification to make the features more discriminative. The proposed FRNet not only highlights relevant category features of support samples, but also learns how to generate task-relevant features of query samples. Experiments conducted on three datasets have demonstrate its superiority over the state-of-the-art methods.
Fast and Accurate Hand-Raising Gesture Detection in Classroom

This paper proposes a fast and accurate method for hand-raising gesture detection in classrooms. Our method is based on a one-stage detector, CenterNet, which significantly reduces the inference time. Meanwhile, we design three mechanisms to improve the performance. Firstly, we propose a novel suppression loss to prevent easy and hard examples from overwhelming the training process. Secondly, we adopt a deep layer aggregation network to fuse semantic and spatial representation, which is effective for detecting tiny gestures. Thirdly, due to less variation in aspect ratios, we only regress single width property to predict whole bounding box. Thus achieving a more accurate result. Experiments show that our method achieves 91.4% mAP on our hand-raising dataset and runs at 26 FPS, 6.7 $$\times $$ faster than the two-stage ones.
A Factorized Extreme Learning Machine and Its Applications in EEG-Based Emotion Recognition

Extreme learning machine (ELM) is an efficient learning algorithm for single hidden layer feed forward neural networks. Its main feature is the random generation of the hidden layer weights and biases and then we only need to determine the output weights in model learning. However, the random mapping in ELM impairs the discriminative information of data to certain extent, which brings side effects for the output weight matrix to well capture the essential data properties. In this paper, we propose a factorized extreme learning machine (FELM) by incorporating another hidden layer between the ELM hidden layer and the output layer. Mathematically, the original output matrix is factorized so as to effectively explore the structured discriminative information of data. That is, we constrain the group sparsity of data representation in the new hidden layer, which will be further projected to the output layer. An efficient learning algorithm is proposed to optimize the objective of the proposed FELM model. Extensive experiments on EEG-based emotion recognition show the effectiveness of FELM.
Local Equivalence of Multipartite Entanglement

Let R be an invariant polynomial ring of a reductive group acting on a vector space, and let d be the minimum integer such that R is generated by those polynomials in R of degree no more than d. To upper bound such d is a long standing open problem since the very initial study of the invariant theory in the 19th century. Motivated by its significant role in characterizing multipartite entanglement, we study the invariant polynomial rings of local unitary groups - the direct product of unitary groups acting on the tensor product of Hilbert spaces, and local general linear groups - the direct product of general linear groups acting on the tensor product of Hilbert spaces. For these two group actions, we prove explicit upper bounds on the degrees needed to generate the corresponding invariant polynomial rings. On the other hand, systematic methods are provided to construct all homogeneous polynomials that are invariant under these two groups for any fixed degree. Thus, our results can be regarded as a complete characterization of the invariant polynomial rings. As an interesting application, we show that multipartite entanglement is additive in the sense that two multipartite states are local unitary equivalent if and only if r-copies of them are local unitary equivalent for some r.
Evaluation of Partial Discharge Denoising using Power Spectral Subtraction

Partial discharge (PD) measurement is widely adopted to estimate the condition of insulation quality. The main hurdle in the monitoring of online PD is the extraction of PD signal from excessive noise originating from the surrounding environment. There is an active research field to tackle this problem and the trend gravitates towards using wavelet denoising techniques. In this work, the feasibility of power spectral subtraction denoising (PSSD) as a PD denoising tool was investigated. In the performance test, simulated noise was used to contaminate the simulated PD signals to emulate real PD signals measured in the field. The denoising test results showed that PSSD is able to achieve higher signal to noise ratio and lower mean square error compared to several variant of wavelet denoising methods.
Effects of Nano-Protein Complexes on Apoptosis of Myocardial Infarction Cells Based on Complex Curve Analysis

Myocardial infarction is one of the common types of coronary heart disease in the clinic. Its morbidity, lethality and disability are high, and it has become a serious threat to human health. At present, it is shown that in the early stage of acute myocardial infarction, myocardial cells are mainly apoptotic, suggesting that effectively blocking myocardial apoptosis in the early stage of myocardial infarction is of great significance for reducing tissue necrosis in the infarcted area. Recent studies have shown that NG nano-protein complexes have a better therapeutic effect on acute myocardial infarction and can inhibit left ventricular remodeling in patients with acute myocardial infarction. However, there are few studies on the effect of NG nano-protein complexes on myocardial cell apoptosis after ischemia. This study used a rat model of acute myocardial infarction to analyze its effect on apoptotic proteins of myocardial cells in rats with acute myocardial infarction in order to provide a certain theoretical basis for its clinical application. In this study, 45 SD rats were randomly divided into a sham operation group, a myocardial infarction group, and a NG nano-protein complex group, with 15 in each group. The sham operation group only underwent thoracotomy, and received normal saline gavage postoperatively; the myocardial infarction group and the NG nano-protein complex group were ligated to the left anterior descending coronary artery of the rat to establish an acute myocardial infarction model, and were performed separately treatment with saline and NG nanoprotein complexes. Finally, we conclude that this nano-protein complex can significantly reduce the expression level of myocardial apoptosis-related proteins in rats with acute myocardial infarction, and is of great significance in inhibiting the apoptosis of acute myocardial infarction cells.
A Study on Cryptographic Techniques

In current scenario, to store data securely on online is a tedious task as a result proper security over the network. Cryptography is a technique that masks the data over the channel of communication. Hiding the data to outsiders is an art. As the technology grows day by day, the need for data security over the channel of communication is greatly increased. Several cryptographic schemes are used for secure communication. The cryptographic technique and various algorithms are used to ensure the application has the necessary security. Cryptography is the science of both encryption and decryption. This paper describes all of the cryptographic techniques in detail.
Pose transfer based on generative adversarial networks

The person-related vision tasks face many challenges, such as insufficient or lack of diversity of datasets and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. To address this issue, this paper proposes a human posture transfer method based on improving CycleGAN. Transferring a given person’s posture to the target one, while keep the character's identity unchanged, thereby augment the diversity of datasets. The generator of the network contains a sequence of transfer blocks which have similar structures, each transfer block is utilized to transform different part of body as a local transfer. Therefore, avoiding learning the complex structure of the global manifold and address the large spatial misalignment issues induced by transformations of target pose. The discriminator of the network comprised of two convolutional neural networks, to judge the appearance and shape. Quantitative comparisons with state-of-the-art, the proposed method can generate images with the highest score on the metrics, and get performance boost of Re-ID on account of data augmentation.
Accuracy of Model-based and Learning-based Approaches for Image Noise Variance Estimation

This paper deals with the problem of noise parameters' estimation from noisy image patches. We consider two estimators - model- and learning-based ones - with ability to estimate noise standard deviation (SD) and predict confidence of this estimate individually for each image patch. The model-based approach represents maximum likelihood estimator (MLE) of fractional Brownian motion (fBm) field parameters, and learning-based is NoiseNet convolutional neural network (CNN) trained on real-life images. We compare efficiency and ability to predict noise SD estimates confidence in two domains: pure fBm data and real-life images. We demonstrate that the learning-based approach is less effective on pure fBm data due to bias, and the model-based approach fails for complex image patches from real-life images. Based on analysis of this paper, usage of synthetic fBm data could be suggested as additional source of training data for learning-based methods of noise parameters estimation.
Hidden Markov Pólya Trees for High-Dimensional Distributions

The Pólya tree (PT) process is a general-purpose Bayesian nonparametric model that has found wide application in a range of inference problems. It has a simple analytic form and the posterior computation boils down to beta-binomial conjugate updates along a partition tree over the sample space. Recent development in PT models shows that performance of these models can be substantially improved by (i) allowing the partition tree to adapt to the structure of the underlying distributions and (ii) incorporating latent state variables that characterize local features of the underlying distributions. However, important limitations of the PT remain, including (i) the sensitivity in the posterior inference with respect to the choice of the partition tree, and (ii) the lack of scalability with respect to dimensionality of the sample space. We consider a modeling strategy for PT models that incorporates a flexible prior on the partition tree along with latent states with Markov dependency. We introduce a hybrid algorithm combining sequential Monte Carlo (SMC) and recursive message passing for posterior sampling that can scale up to 100 dimensions. While our description of the algorithm assumes a single computer environment, it has the potential to be implemented on distributed systems to further enhance the scalability. Moreover, we investigate the large sample properties of the tree structures and latent states under the posterior model. We carry out extensive numerical experiments in density estimation and two-group comparison, which show that flexible partitioning can substantially improve the performance of PT models in both inference tasks. We demonstrate an application to a mass cytometry dataset with 19 dimensions and over 200,000 observations. Supplementary Materials for this article are available online.
The recipes of Philosophy of Science: Characterizing the semantic structure of corpora by means of topic associative rules

Scientific articles have semantic contents that are usually quite specific to their disciplinary origins. To characterize such semantic contents, topic-modeling algorithms make it possible to identify topics that run throughout corpora. However, they remain limited when it comes to investigating the extent to which topics are jointly used together in specific documents and form particular associative patterns. Here, we propose to characterize such patterns through the identification of “topic associative rules” that describe how topics are associated within given sets of documents. As a case study, we use a corpus from a subfield of the humanities—the philosophy of science—consisting of the complete full-text content of one of its main journals: Philosophy of Science . On the basis of a pre-existing topic modeling, we develop a methodology with which we infer a set of 96 topic associative rules that characterize specific types of articles depending on how these articles combine topics in peculiar patterns. Such rules offer a finer-grained window onto the semantic content of the corpus and can be interpreted as “topical recipes” for distinct types of philosophy of science articles. Examining rule networks and rule predictive success for different article types, we find a positive correlation between topological features of rule networks (connectivity) and the reliability of rule predictions (as summarized by the F-measure). Topic associative rules thereby not only contribute to characterizing the semantic contents of corpora at a finer granularity than topic modeling, but may also help to classify documents or identify document types, for instance to improve natural language generation processes.
Comparative Analysis of Load Balancing Dynamic Ratio and Server Ratio Algorithms

Load balancing is a technique for dividing traffic loads and reading the availability of resources on the server. Dividing the traffic load use server hardware such as CPU, memory, and disk. This research designed load balancing using dynamic ratio and ratio algorithms on three types of services namely web server, FTP server, and VoIP server, FTP server, and VoIP server. The highest throughput average value is found in the 3:2 of dynamic ratio algorithm, i.e. 114.18 KB/s of web server and 118.9 KB/s of FTP server. The fastest response time average value is the 3:2 ratio algorithm for 8.7 seconds of web server and 32.3 seconds of FTP server. The average results for the least number of request losses are the 1:1 of ratio algorithm, i.e. web server for 17 request failures, FTP server for 1 file transfer failure, and VoIP server for 0.18% call failure.
A Reduction of Integer Factorization to Modular Tetration

Let [Formula: see text]. By [Formula: see text] and [Formula: see text], we denote the [Formula: see text] th iterate of the exponential function [Formula: see text] evaluated at [Formula: see text], also known as tetration. We demonstrate how an algorithm for evaluating tetration modulo natural numbers [Formula: see text] could be used to compute the prime factorization of [Formula: see text] and provide heuristic arguments for the efficiency of this reduction. Additionally, we prove that the problem of computing the squarefree part of integers is deterministically polynomial-time reducible to modular tetration.
Assessing Suitable Word Embedding Model for Malay Language through Intrinsic Evaluation

Word embeddings were created to form meaningful representation for words in an efficient manner. This is an essential step in most of the Natural Language Processing tasks. In this paper, different Malay language word embedding models were trained on Malay text corpus. These models were trained using Word2Vec and fastText using both CBOW and Skip-gram architectures, and GloVe. These trained models were tested on intrinsic evaluation for semantic similarity and word analogies. In the experiment, the custom-trained fastText Skip-gram model achieved 0.5509 for Pearson correlation coefficient at word similarity evaluation, and 36.80% for accuracy at word analogies evaluation. The result outperformed the fastText pre-trained models which only achieved 0.477 and 22.96% for word similarity evaluation and word analogies evaluation, respectively. The result shows that there is still room for improvement in both pre-processing tasks and datasets for evaluation.
Predetermined thermal conductivity porous medium generated by conditional generation adversarial network

Porous media are extensively used in the engineering field. The effective thermal conductivity and porosity are very important properties of porous medium materials. It is of great significance to obtain a porous medium material that meets the needs of effective thermal conductivity and porosity. In this paper, a four-parameter random generation method is used to produce a training data set, a conditional generation adversarial network (CGAN) is built, and a predetermined effective thermal conductivity and porosity are used as inputs to generate a porous medium structure that meets the input conditions. In particular, since the pore structure distribution of porous medium has a great influence on the effective thermal conductivity of the material, a local structure loss function is proposed to participate in the network training, so that the network can better learn the relationship between the pore distribution and the thermal conductivity. By using the lattice Boltzmann method to verify the effective thermal conductivity of the porous medium structure generated by the neural network, the results show that the method can quickly and accurately generate the porous medium structure with predetermined parameters.