Reviewerly/exHarmony huggingface dataset:
Optimization and CMOS design of chaotic oscillators robust to PVT variations: INVITED

Edward Lorenz was an early pioneer of the chaos theory. He discovered that small changes in initial conditions produce large changes in long-term outcome, and introduced a chaotic attractor already known as Lorenz chaotic oscillator, which produces a butterfly-like behavior. This and all kinds of continuous-time chaotic oscillators can be simulated with different numerical methods. However, a bad choice of the step size and/or parameters of the mathematical models can produce errors or even mitigate the chaotic behavior. These issues are related to the main property of chaotic oscillators, the high sensitivity to the initial conditions, which is quantified by evaluating the maximum Lyapunov exponent (MLE). The Lorenz and other representative oscillators like Lü, Chua's circuit and Rössler have been implemented using different discrete electronic devices and few ones with integrated circuits (IC) using CMOS technologies. Designing CMOS chaotic oscillators is challenging because a very small variation in their parameters from their mathematical models or in the sizes of the MOS transistors may suppress the chaotic behavior. This article describes how to perform a successful simulation and optimization, and how to synthesize the mathematical models using CMOS technology. The application of metaheuristics to optimize MLE by varying the parameters of the oscillators, and the optimization of the CMOS IC design to guarantee robustness to process, voltage and temperature (PVT) variations, are discussed. Finally, we discuss issues on the application of chaos generators in random number generators, robotics and chaotic secure communication systems.
Incremental semi-supervised learning on streaming data

In streaming data classification, most of the existing methods assume that all arrived evolving data are completely labeled. One challenge is that some applications where only small amount of labeled examples are available for training. Incremental semi-supervised learning algorithms have been proposed for regularizing neural networks by incorporating various side information, such as pairwise constraints or user-provided labels. However, it is hard to put them into practice, especially for non-stationary environments due to the effectiveness and parameter sensitivity of such algorithms. In this paper, we propose a novel incremental semi-supervised learning framework on streaming data. Each layer of model is comprised of a generative network, a discriminant structure and the bridge. The generative network uses dynamic feature learning based on autoencoders to learn generative features from streaming data which has been demonstrated its potential in learning latent feature representations. In addition, the discriminant structure regularizes the network construction via building pairwise similarity and dissimilarity constraints. It is also used for facilitating the parameter learning of the generative network. The network and structure are integrated into a joint learning framework and bridged by enforcing the correlation of their parameters, which balances the flexible incorporation of supervision information and numerical tractability for non-stationary environments as well as explores the intrinsic data structure. Moreover, an efficient algorithm is designed to solve the proposed optimization problem and we also give an ensemble method. Particularly, when multiple layers of model are stacked, the performance is significantly boosted. Finally, to validate the effectiveness of the proposed method, extensive experiments are conducted on synthetic and real-life datasets. The experimental results demonstrate that the performance of the proposed algorithms is superior to some state-of-the-art approaches.
Human Activity Recognition Based on Single Sensor Square HV Acceleration Images and Convolutional Neural Networks

Human Activity Recognition (HAR) provides the context for many user-centered personal recommender systems in areas such as healthcare, sports, lifelong learning or home automation. Based on different types of sensors (either camera based, environmental sensors or wearable and mobile sensors) user related data provides the basis to extract movement related features from which the activity that the user is performing can be assessed. Among the different types of sensors, wearable sensors provide a user convenient, non-intrusive, always available alternative that has gained special attention for HAR. Wearable sensors will be a relevant part of the Internet of Things. This paper presents a novel mechanism to detect which particular activity a user is performing based on the data from a single tri-axial accelerometer. A Convolutional Neural Network is used in order to automatically extract the most relevant features to characterize acceleration patterns with inter-activity discrimination capacity. The user anchored coordinate system generating the data from the accelerometer sensor is transformed into a georeferenced coordinate system in order to estimate the horizontal and vertical acceleration components. A sliding window with 50% overlap is used to extract 5 seconds of acceleration data from which a square horizontal-vertical acceleration image is computed. Both monochrome and colored images are generated either by adding the influence of the time evolution of the acceleration series or not in the generated image. The results for both p-fold cross-validation and leave on out approaches are presented using a public dataset. The results outperform by around 8% of those obtained by the authors of the dataset in the case of using a p-fold cross-validation.
Identifying and ranking CSFs for KM implementation in urban water and sewage companies using ISM-DEMATEL technique

Purpose This paper aims to study the critical success factors (CSF) of knowledge management (KM) in Iranian urban water and sewage companies (IUWSC) using interpretive structural modeling (ISM)-decision-making trial and evaluation laboratory (DEMATEL) method. Design/methodology/approach Through the literature review among many key factors of KM success, nine factors were selected as the main factors. After confirming the related indices by industry experts, a questionnaire was distributed among them for determining the relationship between the relevant components. Using ISM-DEMATEL approach, a structural model was formed and then the causal relationship among factors was visualized through a cause-effect relationship diagram. Findings The analysis of the results showed that among the studied factors, strategies and goals would have the greatest impact on success of KM implementation and senior management support, and teamwork and organizational culture are other CSFs of KM in IUWSC. In the end, the author’s proposed model for implementation of KM was presented based on the results for the status quo of the studied community. Practical implications The set of CSFs can act as a guideline for organizations to ensure that basic factors and issues are covered during implementation KM. Moreover, it provides a clue for effective development of KM in a stepwise approach. Originality/value This survey is probably the first to provide an integrated view of CSFs for implementation KM in IUWSC. It is hoped to give valuable information to help this business sector to accomplish KM effectively.
Classification of colorectal cancer based on the association of multidimensional and multiresolution features

Colorectal cancer is one of the most common types of cancer according to worldwide incidences statistics. The correct diagnosis of this lesion leads to the indication of the most adequate treatments for cancer-affected patients. The diagnosis is made through the visual analysis of tissue samples by pathologists. However, this analysis is susceptible to intra- and inter-pathologists variability in addition to being a complex and time-consuming task. To deal with these challenges, image processing methods are developed for application on histological images obtained through the digitization of the tissue samples. To do so, feature extraction and classification techniques are investigated to aid pathologists and make it possible a faster and more objective diagnosis definition. Therefore, in this work, we propose a method that associates multidimensional fractal techniques, curvelet transforms and Haralick descriptors for the study and pattern recognition of colorectal cancer, which not yet explored in the Literature. The proposed method considered a feature selection approach and different classification techniques for evaluating associations, such as decision tree, random forest, support vector machine, naive Bayes, k*, and a polynomial method. This strategy allowed for more precise interpretations regarding the best associations for the separation of groups concerning histological images of colorectal cancer. The proposal was tested on colorectal images from two distinct datasets commonly investigated in the Literature. The best result was reached with features based mainly on lacunarity and percolation obtained from curvelet sub-images, using a polynomial classifier. The tests were evaluated by applying the 10-fold cross-validation method and the result was 0.994 of AUC, which is a relevant contribution to the Literature of pattern recognition of colorectal cancer. The obtained performance with a detailed analysis involving different types of features and classifiers are important contributions for pathologists, specialists interested in the study of this cancer and histological image processing researchers, which aim to develop the clinically applicable computational techniques.
A novel approach to improve multimedia security utilizing 3D mixed chaotic map

Chaotic maps contribute a vital part in modern cryptography. The algorithms constructed using chaotic maps are considered more effectual and secure. In this work, a novel technique for image encryption utilizing 3D mixed chaotic map is presented. In the proposed scheme, we used 3D mixed chaotic map for the transformation of pixels position by permuting then in row, column-wise and mixing, aim to fulfill the requirements of secure image transfer. We analyzed the proposed scheme with existing cryptanalysis and compare their results with the results of some other algorithms from literature. The proposed scheme is more efficacious than other algorithms and the outcomes of security analysis, statistical analysis and differential analysis validate that the anticipated scheme is exceptional for secure image transmission.
An Intelligent Cloud Workflow Scheduling System With Time Estimation and Adaptive Ant Colony Optimization

The introduction of workflow in cloud computing has afforded a new and efficient way to tackle large-scale applications. As an NP-hard problem, how to schedule cloud workflows effectively and economically with deadline constraints and different kinds of tasks and resources is extraordinarily challenging. To solve this constrained problem, this paper intends to develop an intelligent scheduling system from the perspective of users to reduce expenditure of workflow, subject to the deadline and other execution constraints. A new estimation model of the task execution time is designed according to virtual machine settings in real public clouds and execution data from practical workflows. Based on the new model, an adaptive ant colony optimization algorithm is proposed to meet the quality of service and orchestrate tasks. The adaptiveness of the algorithm is embodied in two aspects. First, an adaptive solution construction method is designed that each solution is built with a dynamically changing resource pool, thus the search space of the algorithm is narrowed down and the execution time is decreased. Second, two heuristics with self-adaptive weight are introduced to adaptively meet different deadline settings. Simulating results on four types of workflows show that the proposed approach is effective and competitive.
A multi-method approach for the assessment of natural background levels in groundwater

The assessment of geochemical Natural Background Levels (NBLs) in groundwater, aims at distinguishing the naturally high levels of geogenic compounds from anthropogenic pollution. This is a fundamental issue in groundwater management, in particular when the concentration of inorganic compounds exceeds the threshold values set for the evaluation of the groundwater chemical status, as requested by environmental regulations. In this paper, we describe a new procedure that integrates the pre-selection method and statistical techniques, using the example of two case studies. The pre-selection aims to identify suitable groundwater samples for the NBLs assessment. The nitrate concentration threshold, for the removal of the groundwater samples affected by human activities, is established locally through different graphical and statistical approaches. Then, the statistical distribution of each compound is analyzed and the outliers are identified. Normality tests on the datasets allow one to select the most appropriate value, e.g. one percentile, to be adopted as NBL within the data distribution. In the selected case studies, we have defined the NBLs for As, F, Mn, Fe and SO4. The two sites are part of a volcanic-sedimentary aquifer in central Italy, where the geochemical background is frequently well above the standards for human consumption. The results of the simple and easily reproducible pre-selection method are strengthened by integration with statistical techniques, notably in selecting the appropriate percentile. New criteria are suggested for the choice of the nitrate threshold to be used for the pre-selection of uncontaminated samples.
The Harmony Search algorithm with additional improvement of harmony memory for Asymmetric Traveling Salesman Problem

This article constitutes the continuation of the work on adapting the Harmony Search algorithm to effectively solve the Asymmetric Traveling Salesman Problem (ATSP) instances. The author’s modification suggested in this work enables the improvement of harmony memory during the time of running the technique, making it possible to increase the effectiveness of the technique by almost 59% (the summary average error was reduced from 13.42 to 5.54%) and to eliminate the imperfectness revealed in the previous research. The article includes a description of the approach and a comparative study spanning various variants of implementing the improvement, conducted on 19 instances of ATSP, described by means of the occurrence of 17 to 443 nodes. The achieved results were also compared with the results found in reference articles, showing a significant effectiveness of the modification.
Code authorship identification using convolutional neural networks

Although source code authorship identification creates a privacy threat for many open source contributors, it is an important topic for the forensics field and enables many successful forensic applications, including ghostwriting detection, copyright dispute settlements, and other code analysis applications. This work proposes a convolutional neural network (CNN) based code authorship identification system. Our proposed system exploits term frequency-inverse document frequency, word embedding modeling, and feature learning techniques for code representation. This representation is then fed into a CNN-based code authorship identification model to identify the code’s author. Evaluation results from using our approach on data from Google Code Jam demonstrate an identification accuracy of up to 99.4% with 150 candidate programmers, and 96.2% with 1,600 programmers. The evaluation of our approach also shows high accuracy for programmers identification over real-world code samples from 1987 public repositories on GitHub with 95% accuracy for 745 C programmers and 97% for the C++ programmers. These results indicate that the proposed approaches are not language-specific techniques and can identify programmers of different programming languages.
Generating random weights and biases in feedforward neural networks with random hidden nodes

Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is proposed. This method generates the parameters of the hidden nodes in such a way that nonlinear fragments of the activation functions are located in the input space regions with data and can be used to construct the surface approximating a nonlinear target function. The weights and biases are dependent on the input data range and activation function type. The proposed methods allows us to control the generalization degree of the model. These all lead to improvement in approximation performance of the network. Several experiments show very promising results.
Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems are presented. A thorough evaluation of the performance in a simulated environment shows that the proposed STGP approach outperforms the state-of-the-art GP extended Kalman filter approach [N. Wahlstrom and E. Ozkan, “Extended target tracking using Gaussian processes, IEEE Transactions on Signal Processing,” vol. 63, no. 16, pp. 4165-4178, Aug. 2015] with up to 90% improvement in the accuracy in position, 95% in velocity and 7% in the shape, while tracking a simulated asymmetric non-rigid object. The tracking performance improvement for a non-rigid irregular real object is up to 43% in position, 68% in velocity, 10% in the recall, and 115% in the precision measures.
Multi-label classification via label correlation and first order feature dependance in a data stream

Many batch learning algorithms have been introduced for offline multi-label classification (MLC) over the years. However, the increasing data volume in many applications such as social networks, sensor networks, and traffic monitoring has posed many challenges to batch MLC learning. For example, it is often expensive to re-train the model with the newly arrived samples, or it is impractical to learn on the large volume of data at once. The research on incremental learning is therefore applicable to a large volume of data and especially for data stream. In this study, we develop a Bayesian-based method for learning from multi-label data streams by taking into consideration the correlation between pairs of labels and the relationship between label and feature. In our model, not only the label correlation is learned with each arrived sample with ground truth labels but also the number of predicted labels are adjusted based on Hoeffding inequality and the label cardinality. We also extend the model to handle missing values, a problem common in many real-world data. To handle concept drift, we propose a decay mechanism focusing on the age of the arrived samples to incrementally adapt to the change of data. The experimental results show that our method is highly competitive compared to several well-known benchmark algorithms under both the stationary and concept drift settings.
Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks

In this study, for determining the best-input scenarios of the used parameters in predicting the Daily Global Solar Radiation (DGSR), a new approach based on Artificial Neural Networks (ANNs) was presented. The proposed approach is based on comparisons between all possible input combinations for determining the best scenarios that can give perfect correlations and approximations with DGSR. Recorded data from 35 stations belonging to different climatic zones (27 in Morocco and 8 in neighboring countries) were reported for training and testing the obtained results. The used input parameters include geographical coordinates, sun declination, day length, day number, clearness index (KI), Top Of Atmosphere (TOA), average ambient temperature (Ta), maximum temperature (Tmax), minimum temperature (Tmin), difference temperature (ΔT), temperature ratio (TR), relative humidity (Rh) and wind speed (Ws). The results revealed 128 best-input scenarios, where the first relevant input combination was found for KI, Ta, ΔT, TR and TOA. This result indicated that the best-input scenario for predicting DGSR is based only on three climatological parameters: KI, function of Ta f(Ta) and TOA. In addition, based on these found best-input scenarios and on the least square regression (LSR) technique, 128 new linear relationships between DGSR and the found best-input combinations were developed. The statistical analysis expressed through statistical criteria indicated perfect correlations and approximations between the predicted and measured values of DGSR.
Robust heterogeneous discriminative analysis for face recognition with single sample per person

Single sample per person face recognition is one of the most challenging problems in face recognition (FR), where only single sample per person (SSPP) is enrolled in the gallery set for training. Although the existing patch-based methods have achieved great success in FR with SSPP, they still have limitations in feature extraction and identification stages when handling complex facial variations. In this work, we propose a new patch-based method called Robust Heterogeneous Discriminative Analysis (RHDA), for FR with SSPP. To enhance the robustness against complex facial variations, we first present a new graph-based Fisher-like criterion, which incorporates two manifold embeddings, to learn heterogeneous discriminative representations of image patches. Specifically, for each patch, the Fisher-like criterion is able to preserve the reconstruction relationship of neighboring patches from the same person, while suppressing the similarities between neighboring patches from the different persons. Then, we introduce two distance metrics, i.e., patch-to-patch distance and patch-to-manifold distance, and develop a fusion strategy to combine the recognition outputs of above two distance metrics via a joint majority voting for identification. Experimental results on various benchmark datasets demonstrate the effectiveness of the proposed method.