Reviewerly/exHarmony huggingface dataset:
Towards Multi-Agent Control in Energy-Efficient Data Centres

Modern data centres consume electricity at the industrial scale; at the same time, most of them demonstrate redundancy in energy consumption. The two most significant energy consumers in a data centre are its computational system and cooling system. This work focuses on techniques, which adapt the system energy use based on resource requirements at run-time. Actually, this work is an inception phase, which determines the main requirements to control the energy-efficient data centre and develops its general project. For that aim, the general design of the multi-agent control is proposed. The different types of agents are identified and their objectives are determined. Based on agent types the architecture of the multi-agent control is developed and agents' interactions are considered. The paper presents an example of an agent controlling a server fan. The agent is examined using closed-loop co-simulation with a server model.
Optimal skeleton and reduced Huffman trees

A skeleton Huffman tree is a Huffman tree from which all full subtrees of depth h≥1 have been pruned. Skeleton Huffman trees are used to save storage and enhance processing time in several applications such as decoding, compressed pattern matching and wavelet trees for random access. A reduced skeleton tree prunes the skeleton Huffman tree further to an even smaller tree. The resulting more compact trees can be used to further enhance the time and space complexities of the corresponding algorithms. However, it is shown that the straightforward ways of basing the constructions of a skeleton tree as well as that of a reduced skeleton tree on a canonical Huffman tree do not necessarily yield the least number of nodes. New algorithms for achieving such trees are given.
An Efficient 3D Watermarking algorithm for 3D Mesh Models

Building efficient data masking of encrypted 3D (Three dimensional) mesh models is considered to be an efficient solution for masking information of 3Dmesh models. First, using the secret key, the original 3D mesh models are encrypted. Second, without procuring any prior information of original 3D mesh models it is conceivable mask information on encrypted 3D mesh models. Third, the original 3D mesh models are reconstructed by extracting masked information. Recently, Data masking for 3D mesh models has attained wide attention across the various research community. This is because of the adoption of cloud computing (CC) platform for storing customer information. Thus, preserving the privacy of customer information is a major research problem. Nonetheless, no prior work permit masking with high capacity in reversible nature. For addressing the research problem, this paper presents an efficient 3D data masking (DM) method that is reversible in nature with a very high masking capacity. Experiment outcome shows the proposed high capacity data masking (HCDM) for 3D mesh model attain better performance in terms of PSNR and RMSE over existing data masking methods.
An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products

Considerable growth of computer science has created novel solutions for variable problem fields and has increased the efficiency of available solutions. Evolutionary algorithms are quite successful in dealing with real-world problems that require optimization. In this article, we implemented a Genetic Algorithm that is well known evolutionary algorithm in order to provide an efficient solution for the Distribution of Pharmaceutical Products, which is a vital optimization problem, especially in situations such as a pandemic. The Multiple Traveling Salesman Problem approach was used to distribute pharmaceutical products as soon as possible. Moreover, we strengthened our proposal algorithm with 2-Opt Algorithm to get optimal results in earlier iterations. Different datasets from a library were applied to measure the quality of solutions and computation time. At the end of the work, we observed that our proposed algorithm generates successful solutions in an acceptable running time. This study will be extended with a new mutation concept as future work.
Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data

Matrix factorization is a popular technique used in recommender systems based on collaborative filtering. Given a matrix that represents ratings of items by users, one can obtain latent feature vectors of the users and the items by applying one of the existing matrix factorization algorithms. In this paper, we focus our attention on matrices obtained from categorical ratings using one-hot encoding, and propose an element-wise alternating least squares algorithm to obtain latent feature vectors from such matrices. We next show that the proposed algorithm has the global convergence property in the sense of Zangwill. We also show through experiments using a benchmark dataset that the proposed algorithm is effective for prediction of unknown ratings.
UniquID: A Quest to Reconcile Identity Access Management and the IoT

The Internet of Things (IoT) has caused a revolutionary paradigm shift in computer networking. After decades of human-centered routines, where devices were merely tools that enabled human beings to authenticate themselves and perform activities, we are now dealing with a device-centered paradigm: the devices themselves are actors, not just tools for people. Conventional identity access management (IAM) frameworks were not designed to handle the challenges of IoT. Trying to use traditional IAM systems to reconcile heterogeneous devices and complex federations of online services (e.g., IoT sensors and cloud computing solutions) adds a cumbersome architectural layer that can become hard to maintain and act as a single point of failure. In this paper, we propose UniquID, a blockchain-based solution that overcomes the need for centralized IAM architectures while providing scalability and robustness. We also present the experimental results of a proof-of-concept UniquID enrolment network, and we discuss two different use-cases that show the considerable value of a blockchain-based IAM.
SecRand: A Secure Distributed Randomness Generation Protocol With High Practicality and Scalability

The ever-increasing pervasiveness of decentralized applications, such as blockchain, is creating challenges for sources of randomness, which play an integral part in decentralized settings. Distributed randomness generation (DRG) protocols, aiming at producing high-quality randomness without a central party, have drawn increased attention from academia as well as industry. Previous approaches lacked security proofs, and their dependence on secure messaging channels reduced their practicality. In this work, we first formalize the desired properties of a secure DRG protocol and build a security model using these formal definitions. To the best of our knowledge, this is the first work to build a security model for DRG protocols, which can be used as a general framework for security analysis of DRG protocols. We then present SecRand, a secure DRG protocol with high practicality and scalability. We improve upon previous approaches by modifying the secret generation method in the reconstruction phase, which ensures the same scalability but achieves resistance against an adversary's malicious behavior of withholding its secret shares. We also provide strict proofs under our security model, showing that SecRand achieves the desired properties and is secure enough to be used in decentralized applications. Furthermore, we present a detailed performance evaluation of SecRand by deploying it on a laptop with a Windows 10 environment in the C language. The experimental data showed that SecRand achieved a better performance compared with previous approaches in the presence of corrupted participants, and this performance advantage grew linearly with the number of corrupted participants.
WITHDRAWN: Evaluation model of soil heavy metal pollution index based on machine learning and particle image recognition

This is a big challenge to identify a number of different sources of soil contamination by heavy metals. In this examination, the geographic information that can be gotten gratis from the web index utilized in blend with Artificial Intelligence (AI) strategies, the recognized and order potential pollution organizations. Information from five distinctive Machine Learning (ML) strategies has isolated the sort of free four-in-one industry. Past work, arrange the wellspring of pollution of a current key, utilize a profound learning technique. Soil weighty metal contamination tries, the precision of the video tally shows that it is superior to other video preparing arrangements. At the point when the framework is inactivity, is inside the worthy reach, it has been proposed answers to meet the ongoing prerequisites. Wellsprings of tainting can be isolated into characteristic contamination sources and fake wellsprings of pollution. Characteristic wellsprings of contamination allude to the climate cause results, for example, common to deliver or a functioning spring of gushing lava hurtful substances. Man-made wellspring of contamination alludes to the contamination source, which is shaped by human exercises, it is additionally the fundamental motivation behind the examination and control of ecological security.
Functional clustering methods for longitudinal data with application to electronic health records

We develop a method to estimate subject-level trajectory functions from longitudinal data. The approach can be used for patient phenotyping, feature extraction, or, as in our motivating example, outcome identification, which refers to the process of identifying disease status through patient laboratory tests rather than through diagnosis codes or prescription information. We model the joint distribution of a continuous longitudinal outcome and baseline covariates using an enriched Dirichlet process prior. This joint model decomposes into (local) semiparametric linear mixed models for the outcome given the covariates and simple (local) marginals for the covariates. The nonparametric enriched Dirichlet process prior is placed on the regression and spline coefficients, the error variance, and the parameters governing the predictor space. This leads to clustering of patients based on their outcomes and covariates. We predict the outcome at unobserved time points for subjects with data at other time points as well as for new subjects with only baseline covariates. We find improved prediction over mixed models with Dirichlet process priors when there are a large number of covariates. Our method is demonstrated with electronic health records consisting of initiators of second-generation antipsychotic medications, which are known to increase the risk of diabetes. We use our model to predict laboratory values indicative of diabetes for each individual and assess incidence of suspected diabetes from the predicted dataset.
Access Structure Hiding Secret Sharing from Novel Set Systems and Vector Families

Secret sharing provides a means to distribute shares of a secret such that any authorized subset of shares, specified by an access structure, can be pooled together to recompute the secret. The standard secret sharing model requires public access structures, which violates privacy and facilitates the adversary by revealing the high-value targets. In this paper, we address this shortcoming by introducing hidden access structures, which remain secret until some authorized subset of parties collaborate. The central piece of this work is the construction of a set-system $$\mathcal {H}$$ with strictly greater than $$\exp \left( c \dfrac{1.5 (\log h)^2}{\log \log h}\right) $$ subsets of a set of h elements. Our set-system $$\mathcal {H}$$ is defined over $$\mathbb {Z}_m$$ , where m is a non-prime-power, such that the size of each set in $$\mathcal {H}$$ is divisible by m but the sizes of their pairwise intersections are not divisible by m, unless one set is a subset of another. We derive a vector family $$\mathcal {V}$$ from $$\mathcal {H}$$ such that superset-subset relationships in $$\mathcal {H}$$ are represented by inner products in $$\mathcal {V}$$ . We use $$\mathcal {V}$$ to “encode” any access structure and thereby develop the first access structure hiding secret sharing scheme. The information rate (secret-size/maximum-share-size) of our scheme is 1/2. For a setting with $$\ell $$ parties, our scheme supports $$2^{\ell }$$ out of the $$2^{2^{\ell - O(\log \ell )}}$$ possible access structures. The scheme assumes semi-honest polynomial-time parties, and its security relies on the Generalized Diffie-Hellman assumption.
Missing Values Imputation Using Fuzzy C Means Based On Correlation of Variable

Missing values is one of the problems in real-world data and an unavoidable one. It should be handled carefully in a pre-processing technique before being processed in a data mining technique. This paper proposes an imputation technique of Fuzzy C Mean (FCM) with the improved version. The aim is to reduce errors and increase the accuracy of the processing technique. In this paper, the correlation technique was applied before the process of FCM to choose the variables with a certain criterion to be processed in FCM imputation. The result shows that the proposed technique outperforms the conventional technique and useful to overcome the disadvantages of the FCM technique.
Mean hitting times of quantum Markov chains in terms of generalized inverses

We study quantum Markov chains on graphs, described by completely positive maps, following the model due to S. Gudder (J. Math. Phys. 49, 072105, 2008) and which includes the dynamics given by open quantum random walks as defined by S. Attal et al. (J. Stat. Phys. 147:832-852, 2012). After reviewing such structures we examine a quantum notion of mean time of first visit to a chosen vertex. However, instead of making direct use of the definition as it is usually done, we focus on expressions for such quantity in terms of generalized inverses associated with the walk and most particularly the so-called fundamental matrix. Such objects are in close analogy with the theory of Markov chains and the methods described here allow us to calculate examples that illustrate similarities and differences between the quantum and classical settings.
Research on Voice Cloning with a Few Samples

With the rapid development of computer technology, voice technology has become a research hotspot in the field of deep learning. Allowing computers to listen, see, speak and feel is the future development direction of human-computer interaction. Among them, voice becomes the most promising human-computer interaction method in the future which has more advantages than other interaction methods. Voice cloning is one of the branches of voice technology, which can imitate the voice of a specific person. In order to solve the problem of the need to provide a large number of samples and long waiting time for voice cloning in the past, a method to complete real time voice cloning with only a few samples is proposed. This method is different from the traditional model. Three models are used for joint modeling, and different databases are used for independent training. The vocoder uses a new type of LPCNET, which can achieve good results on some samples and low-performance devices.
Answering Controlled Natural Language Questions over RDF Clinical Data

Clinical trial data requires a lot of processing before it can be submitted in accordance with its standardization requirements. After its processing, data has to be stored carefully, often in different systems and formats. Integrating this data without information loss and enabling easy retrieval for later analysis is a highly challenging task. In this demo, we present our system for answering controlled Natural Language questions over RDF clinical data. Questions entered by a user through the proposed interface are annotated on the fly and suggestions are displayed based on an ontology driven auto-completion system. This approach assures a high level of usability and readability while preserving semantic correctness and accuracy of entered questions.
Error Bounds of Imitating Policies and Environments

Imitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding errors compared to behavioral cloning, and thus has a better sample complexity. Noticed that by considering the environment transition model as a dual agent, imitation learning can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than behavioral cloning, suggesting a novel application of adversarial imitation for model-based reinforcement learning. We hope these results could inspire future advances in imitation learning and model-based reinforcement learning.
Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning

Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.
To the Moon: A History of Bitcoin Price Manipulation

Bitcoin’s price volatility is often attributed to speculative mania. Unmolested prices have been shown to exhibit an expected, natural distribution characterized by Benford’s law. Deviations from this distribution indicate an anomaly, and typically that anomaly is caused by some type of fraud. For bitcoin, the entire period of daily closing prices from July 2010 through May 2020 was analyzed. Analyses for calendar years 2011-2019 was also conducted. We can say with near 100% confidence that bitcoin’s price has been fraudulently manipulated at some point in its lifespan since 2010. We can say with 95% confidence that bitcoin was manipulated in 2013; 95% confidence that bitcoin was manipulated in 2018; and 98% confidence that bitcoin was manipulated in 2019. This article also documents a narrative history of the events preceding and surrounding each suspected manipulation. The aim is to raise the level of awareness such that future illicit behavior in the bitcoin marketplace is more easily identified and mitigated, either through market forces or regulatory oversight. This is the first application of Benford’s law to bitcoin, and serves as empirical proof that speculative mania is a poor explanation for bitcoin’s volatility. Substantial mitigation of bitcoin price manipulation, and hence volatility, could increase bitcoin’s value by about 40%. Lastly, these findings imply that both technical and fundamental approaches to value bitcoin over the suspect periods are likely meaningless because bitcoin’s price did not reflect equally motivated buyers and sellers.
WITHDRAWN: Symmetric cryptography for secure communication in IoT

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
WITHDRAWN: Design of enterprise accounting system based on FPGA and machine learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
The Research of Implementation Method of Canny Edge Detection of Video on FPGA

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time; then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection.