Reviewerly/exHarmony huggingface dataset:
Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

This article presents the evaluation of multicriteria planning heuristics for demand response in datacenters and supercomputing facilities. This is a relevant problem for science nowadays, when the growing application of cutting-edge technologies (numerical methods, big data processing, artificial intelligence, smart systems, etc.) has raised the energy demands in datacenters. The proposed approach involves a negotiation mechanism for colocation datacenters, where the datacenter operator agrees prices and quality of service with a group of tenants. Twelve different multicriteria heuristics are proposed for planning using both local and global information at tenants and datacenter operator levels. The proposed approach is evaluated applying simulations over realistic scenarios considering different tenant sizes and heterogeneity levels that model different business models for datacenters. Several metrics are computed and Pareto analysis is provided. The main results indicate that accurate trade-off values between the problem objectives are obtained, offering different options for decision making. The proposed approach provides a useful and applicable method for demand response planning in modern datacenters.
Design Space Exploration for Secure IoT Devices and Cyber-Physical Systems

With the advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), embedded devices have been gaining importance in our daily lives, as well as industrial processes. Independent of their usage, be it within an IoT system or a CPS, embedded devices are always an attractive target for security attacks, mainly due to their continuous network availability and the importance of the data they handle. Thus, the design of such systems requires a thorough consideration of the various security constraints they are liable to. Introducing these security constraints, next to other requirements, such as power consumption, and performance increases the number of design choices a system designer must consider. As the various constraints are often conflicting with each other, designers face the complex task of balancing them. System designers facilitate Design Space Exploration (DSE) tools to support a system designer in this job. However, available DSE tools only offer a limited way of considering security constraints during the design process. In this article, we introduce a novel DSE framework, which allows the consideration of security constraints, in the form of attack scenarios, and attack mitigations in the form of security tasks. Based on the descriptions of the system’s functionality and architecture, possible attacks, and known mitigation techniques, the framework finds the optimal design for a secure IoT device or CPS. Our framework’s functionality and its benefits are shown based on the design of a secure sensor system.
A small UAV tracking algorithm based on AIMM-UKF

Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.
A Weighted Bit Flipping Decoder for QC-MDPC-based Cryptosystems

A new “Weighted Bit-flipping” (WBF) iterative decoder is presented and analyzed with respect to its Decoding Failure Rate (DFR). We show that the DFR is indeed lower than that of the BGF decoder as suggested by the BIKE third round submission to the NIST PQC standardization process. The WBF decoder requires more iterations to complete than BGF, but by creating a hybrid decoder we show that a lower DFR compared to that of the BGF decoder can still be achieved while keeping the computational tradeoff to a minimum.
Mine Consortium Blockchain: The Application Research of Coal Mine Safety Production Based on Blockchain

To solve the problem that the safety data in the process of coal mine production are easy to be maliciously tampered with and deleted, a mine consortium blockchain data security monitoring system is proposed. The coal mine consortium blockchain includes supervision department, builds favourable centralized and decentralized production mode, and improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism to implement practical coal mine safety production. The evaluation shows that the architecture we proposed is more appropriate and efficient for the mine Internet of Things than the traditional blockchain architecture. The Hyperledger Fabric platform is used to build the mine consortium blockchain system to achieve the sensor data reliability, node consensus, safe operation automation management, and major equipment traceability.
Using Emojis and drawings in surveys to measure children’s attitudes to mathematics

This article considers the implementation of Emojis as responses within survey research, measuring attitudes towards mathematics in children aged eight and nine years old. Participants answered two multi-item scales. The first required them to provide an Emoji to provide their responses to statements, whilst the second additionally required them to draw the Emoji they wished to use. The rationale was to allow children to feel more familiar with the common means of communication used in a ‘digital era’ in order to aid reliability and validity of thge measures. Evidence suggests that future research be carried out to measure and assess children’s attitudes with techniques from the current study to help them understand the nature of what is being researched. This article concludes that children as young as eight years old can be deemed reliable respondents for survey methods and that more research should be carried out to capture children’s attitudes to concepts.
LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Classifying Web services plays a critical role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. However, traditional Web service classification approaches usually difficult to dispose unstructured sparse documents and underutilize the rich network relations. The consideration of multiple document representation schemes can ameliorate the former problem, whereas an appropriate network representation method could be a positive solution to the latter problem. In this paper, we propose a general Web service classification framework via deep fusion of structured and unstructured features, named LDNM. Firstly, we transform each service document into feature vectors by using two document representation methods: topic distribution based on LDA, and neural-network-based document embedding model known as Doc2vec. Then we obtain structured representation vectors which stem from service invoking and tagging graphs by applying Node2vec. Finally, we fuse these features and train a service classifier by using an MLP neural network. Comprehensive experiments are conducted on real-world datasets to demonstrate the effectiveness of the proposed approach.
Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm’s convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm’s update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.
Privacy Preserving Association Rules based on Compression and Cryptography (PPAR-CC)

Privacy-Preserving Data Mining (PPDM) is a modern technique through which data is mined while maintaining the confidentiality and privacy of sensitive information from unauthorized persons. The Privacy-Preserving Association Rules Mining (PPARM) is the most important technique for privacy-preserving data mining. PPARM means the mining of association rules with preserving the non-disclosure of sensitive correlations among items or features for competitors or the public, especially data of sensitive organizations such as financial organizations and others. In this paper, we propose an approach to hiding association rules after performing the mining process and obtaining knowledge through vertical and horizontal compressing then encoded the compressing form by using cryptography methods. The proposed approach is resistant to many known attacks and is undetectable because it includes three stages of compression and encryption in which the basic representation and size of the data change dramatically. The proposed approach significantly reduces storage space, maintains knowledge security, reduces transmission time, and facilitates the transmission of knowledge over any network.
A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.
Development of a Real-Time Surface Solar Radiation Measurement System Based on the Internet of Things (IoT)

The determination of the levels of solar radiation incident on the terrestrial surface (W·m−2) is essential for several areas such as architecture, agriculture, health, power generation, telecommunications, and climate forecasting models. The high cost of acquiring and maintaining radiometric equipment makes it difficult to create and expand monitoring networks. It contributes to the limited Brazilian radiometric network and affects the understanding and availability of this variable. This paper presents the development of a new surface solar radiation measurement system based on silicon photodiodes (Si) with a spectral range between 300 nm and 1400 nm incorporating Internet of Things (IoT) technology with an estimated cost of USD 200. The proposed system can provide instantaneous surface solar radiation levels, connectivity to wireless networks and an exclusive web system for monitoring data. For the sake of comparison, the results were compared with those provided by a government meteorology station (INMet). The prototype validation resulted in determination coefficients (R2) greater than 0.95 while the statistical analysis referred to the results and uncertainties for the range of ±500 kJ·m−2, less than 4.0% for the developed prototypes. The proposed system operates similarly to pyranometers based on thermopiles providing reliable readings, a low acquisition and maintenance cost, autonomous operation, and applicability in the most varied climatological and energy research types. The developed system is pending a patent at the National Institute of Industrial Property under registration BR1020200199846.
Psycho-acoustics inspired automatic speech recognition

Understanding the human spoken language recognition process is still a far scientific goal. Nowadays, commercial automatic speech recognisers (ASRs) achieve high performance at recognising clean speech, but their approaches are poorly related to human speech recognition. They commonly process the phonetic structure of speech while neglecting supra-segmental and syllabic tracts integral to human speech recognition. As a result, these ASRs achieve low performance on spontaneous speech and require enormous costs to build up phonetic and pronunciation models and catch the large variability of human speech. This paper presents a novel ASR that addresses these issues and questions conventional ASR approaches. It uses alternative acoustic models and an exhaustive decoding algorithm to process speech at a syllabic temporal scale (100–250 ms) through a multi-temporal approach inspired by psycho-acoustic studies. Performance comparison on the recognition of spoken Italian numbers (from 0 to 1 million) demonstrates that our approach is cost-effective, outperforms standard phonetic models, and reaches state-of-the-art performance.
Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.
A KNN Classifier for Face Recognition

Face Recognition has always been a hot topic, especially when the prevalence of Covid-19 calls for ways involving less physical contact in places where personnel identification is critical. However, although there are various algorithms for face recognition, there is limited research in determining the performance of these algorithms using scientific evidence. To address this issue, this paper evaluates the performance of K-Nearest Neighbors (KNN) for face recognition under different situations. To make the result of this study more applicable, this paper aims to train and test the model using photographs taken in profile and partially covered faces to simulate the situation in which the object needs to be identified does not face the camera at a right angle or wears masks. The experimental results demonstrate that K-Nearest Neighbors (KNN) achieved superior performance in recognizing uncovered frontal faces, with a success probability of 95.0%. Nevertheless, the model has a less satisfactory result when classifying profile or masked faces, and the corresponding success probability for the former is 22.2%, the latter 2.22%. It is worth remarking that the accuracy of KNN classifier when used in face recognition is 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for uncovered frontal faces and 74.7% for covered ones.
Two Dimensional Position-Based Visual Servoing for Soft Tissue Endomicroscopy

Probe-based confocal laser endomicroscopy (pCLE) is a vital technology to realize <i>in-vivo</i> imaging of the organ surface in real-time. However, the field of view (FOV) of pCLE is too small to get a full picture of targeted tissues for diagnosing. It is recognized that collecting the image stream and assembling the images in a large mosaic can overcome FOV limitations. Many studies have been carried out to develop robust and fast methods for image processing to get a clear mosaic. However, it is still difficult to obtain high-quality images consistently due to the presence of tissue deformations on soft tissue surfaces. The control of probe movement is still a key research challenge. In this work, a position-based visual servoing scheme is proposed to achieve high accuracy and gap-less mosaics for soft tissue endomicroscopy. Experimental results show that the proposed control scheme with visual servoing can obtain a large and accurate spiral mosaic (approximately 7 <i>mm</i><sup>2</sup>) without gaps. Compared to open-loop control, the obtained trajectory better matches with the target one, with the error of circular trajectory reduced by 31.79% and the error of spiral trajectory reduced by 46.86% on average. The proposed control scheme has a high potential to achieve a large and accurate mosaic for soft tissue endomicroscopy.
A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms.In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast.This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions.The proposed method provides fully-automatic segmentation of the suspicious regions.This method is based on determining an adaptive multi-threshold value by using three different techniques together.These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications.In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss.The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature.The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient.According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.
Solar air heaters performance prediction using multi-layer perceptron neural network– A systematic review

In solar thermal systems, solar air heater (SAH) is an important device for heating air using energy of the sun. The solar collector is the most important part of SAH which collects solar radiations as thermal energy (heat) and transmits it to the air. For efficient utilization of solar energy, the system performance needs to be optimized. There are many techniques that are used for optimization but Artificial Intelligence (AI) technique is very effective. Artificial neural networks (ANN) are coming up as valuable options in place of traditional statistical modeling techniques in many scientific disciplines. ANN technique is the most used one for modeling, prediction, and control. This technique is computes faster results and it can solve the complex and nonlinear problems which cannot be solved by other conventional approaches. Also this technique requires large data sets but not separate programming for solving the problems. In this paper, all the published research works from year 2009 to 2021 related to MLP model for performance prediction has been reviewed, which are applied on solar air heating systems. The main aim of current study is to review research work related to multi-layer perceptron (MLP) model applied in SAH and to find a gap for future research. This paper encompasses many research articles related to neural networks which focus particularly on MLP model. Review of some research papers has been included in detail for the proposed work. Also the present review article reported that MLP structure of neural model is the most commonly applicable model used for prediction of SAHs performances with satisfactory results.
Multi-keyword Fuzzy Search over Encrypted Cloud Storage Data

With the rapid development of the cloud computing, more and more users choose to store their encrypted data on the cloud server. Searchable encryption (SE) schemes capacitate legal users to retrieve encrypted data in the cloud server and the cloud server does not leak sensitive information about the encrypted data. In this paper, we use searchable symmetric encryption (SSE) to construct our scheme. SSE can be divided into exact keyword searchable encryption and fuzzy keyword searchable encryption. Fuzzy keyword searchable encryption scheme takes into account the minor clerical errors of users. When the query keyword does not match the index stored in the cloud server, the fuzzy keyword searchable encryption schemes can find the closest keyword and return the matching encrypted documents, which improves the availability of the cloud server. In this paper, we propose a multi-keyword fuzzy searchable encryption scheme by using locality sensitive hashing (LSH) and bloom filter which has high search accuracy.