Reviewerly/exHarmony huggingface dataset:
Activation Ensembles for Deep Neural Networks

Many activation functions have been proposed in the past, but selecting an adequate one requires trial and error. We propose a new methodology of designing activation functions within a neural network at each layer. We call this technique an “activation ensemble” because it allows the use of multiple activation functions at each layer. This is done by introducing additional variables, α, at each activation layer of a network to allow for multiple activation functions to be active at each neuron. By design, activations with larger α values at a neuron is equivalent to being “chosen” by the network. We implement the activation ensembles on a variety of datasets using an array of FFNs and CNNs. By using the activation ensemble, we achieve superior results compared to traditional techniques. In addition, because of the flexibility of this methodology, we more deeply explore activation functions and the features that they capture.
Descriptor Generation and Optimization for a Specific Outdoor Environment

Modern outdoor self-localizing computer vision applications require descriptors more than repeatability.The descriptors need to be invariant to light conditions and transformation changes to give support for efficient classification.This paper investigates a new framework based on the genetic algorithm to create and optimize extensible modular descriptors for specific outdoor environments.The algorithm returns descriptors with improved efficiency and classification performance.It controls the image processing and machine learning parameters and optimizes the descriptor size by activating the necessary modules.To show the strength of the descriptor, we compared it with the most commonly used standard descriptors on speed, accuracy, and invariance to light conditions, image resolution changes, scale, affine transformation, rotation, and classification.The results show that it has an average result in transformation invariance, and its description ability in sparse areas is significantly better than that of the most-used descriptors.The descriptor was also integrated into an augmented reality algorithm to create a self-regulating segmentation application.
An Estimation of Distribution Algorithm With Multi-Leader Search

The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.
Dependability‐based cluster weighting in clustering ensemble

Abstract After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.
Equilibrium: an elasticity controller for parallel tree search in the cloud

Abstract Elasticity is considered to be the most beneficial characteristic of cloud environments, which distinguishes the cloud from clusters and grids. Whereas elasticity has become mainstream for web-based, interactive applications, it is still a major research challenge how to leverage elasticity for applications from the high-performance computing (HPC) domain, which heavily rely on efficient parallel processing techniques. In this work, we specifically address the challenges of elasticity for parallel tree search applications. Well-known meta-algorithms based on this parallel processing technique include branch-and-bound and backtracking search. We show that their characteristics render static resource provisioning inappropriate and the capability of elastic scaling desirable. Moreover, we discuss how to construct an elasticity controller that reasons about the scaling behavior of a parallel system at runtime and dynamically adapts the number of processing units according to user-defined cost and efficiency thresholds. We evaluate a prototypical elasticity controller based on our findings by employing several benchmarks for parallel tree search and discuss the applicability of the proposed approach. Our experimental results show that, by means of elastic scaling, the performance can be controlled according to user-defined thresholds, which cannot be achieved with static resource provisioning.
Modeling and Forecasting Armed Conflict: AutoML with Human-Guided Machine Learning

Machine learning has made slow inroads into quantitative social science due to both a mismatch of machine learning's strengths to the causal and inferential tasks domain researchers pursue [1] and also a lack of algorithmic training among many domain experts [2]. However, conflict research- the empirical examination of political unrest, violence and civil war-has seen a growing emphasis on prediction and forecasting models. We describe automated machine learning (AutoML) to identify models, and human-guided machine learning (HGML), and show how these can incorporate domain knowledge and research requirements into model selection and assessment, and provide high quality machine learning pipelines to domain experts comparable to state-of-the-literature solutions. We examine three peer-reviewed papers with predictive models of conflict [3, 4, 5] and run their data through our HGML system using multiple AutoML engines and find this system produces slightly elevated performance on each paper's model, without any ML expertise required of the user. Our research has three takeaways for computational social science. First, predictive models of conflict would benefit from even minimal applications of AutoML; Secondly, human-guided machine learning offers the attractive option of constraining AutoML systems to address the kinds of questions conflict researchers assess with predictive models; Finally, current existing AutoML implementations produce divergent solutions and so can be productively harnessed in parallel.
Learning Curve Estimation with Large Imbalanced Datasets

Datasets for machine learning are constantly increasing in size, along with computational requirements for processing the data. A useful exercise for machine learning experiments is to approximate model performance as dataset size increases. This can inform application building and data collection efforts as well as improve computational efficiency by using subsets of the data. In this paper, we evaluate a learning curve estimation method on three large imbalanced datasets. Estimation is performed by fitting an inverse power law model to a learning curve created on a small amount of data. We then explore how well this estimated curve fits to the full learning curve of each dataset. The method has been previously evaluated for small datasets (hundreds or thousands of instances), and in this study we show that the method is indeed effective for larger datasets with millions of instances. This is beneficial because only a few thousand instances are required to accurately estimate the performance of models using millions of instances. To the best of our knowledge, this is the first study to systematically explore the use of an inverse power law curve fitting method for big data.
mBSO: A Multi-Population Brain Storm Optimization for Multimodal Dynamic Optimization Problems

Brain Storm Optimization (BSO), which is an effective swarm intelligence method inspired by the human brainstorming process, has shown promising results in solving static optimization problems. However, The search spaces of many real-world problems change over time, in which the original BSO and its variants are not able to cope with. This paper extends BSO as an adaptive multi-population based algorithm, i.e., mBSO, to solve dynamic optimization problems (DOPs). Firstly, a modified BSO, which uses new update mechanisms independent from the maximum number of iterations and objective space grouping method, is proposed. Then, the modified BSO is embedded in a multi-population framework. Several mechanisms such as convergence detection, exclusion, and re-diversification are employed to address the challenging issues of DOPs. The moving peaks benchmark (MPB) is used to evaluate the performance of mBSO along with comparison with other state-of-the-art methods. The outcome indicates the efficiency of the proposed mBSO in locating optima and tracking them after environmental changes.
Multi-view semi-supervised learning for classification on dynamic networks

In recent decades, the task of graph-based multi-view learning has become a fundamental research problem, which could integrate data from multiple sources to improve performance. The dynamic networks could be treated as one kind of multi-view network, but it is continually evolving and leads to entirely different observations at multiple epochs. In this paper, we treat these observations as multiple views and seek a semi-supervised multi-view approach to address the classification problem. Therefore, we propose Multi-view Semi-supervised learning for Classification on Dynamic networks (MSCD). With the aid of total variation regularization, MSCD can obtain a sparse and smooth combination of the views and a better classification result. From the theoretical point of view, the MSCD model is decomposed into simpler sub-problems, which can be effectively solved under the Alternating Direction Method of Multipliers (ADMM) framework. Extensive experiments on both synthetic and real-world datasets show that our model can outperform the state-of-the-art approaches.
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning

Increasing numbers of MRI brain scans, improvements in image resolution, and advancements in MRI acquisition technology are causing significant increases in the demand for and burden on radiologists' efforts in terms of reading and interpreting brain MRIs. Content-based image retrieval (CBIR) is an emerging technology for reducing this burden by supporting the reading of medical images. High dimensionality is a major challenge in developing a CBIR system that is applicable for 3D brain MRIs. In this study, we propose a system called disease-oriented data concentration with metric learning (DDCML). In DDCML, we introduce deep metric learning to a 3D convolutional autoencoder (CAE). Our proposed DDCML scheme achieves a high dimensional compression rate (4096:1) while preserving the disease-related anatomical features that are important for medical image classification. The low-dimensional representation obtained by DDCML improved the clustering performance by 29.1% compared to plain 3D-CAE in terms of discriminating Alzheimer's disease patients from healthy subjects, and successfully reproduced the relationships of the severity of disease categories that were not included in the training.
Moving Cast Shadow Detection in Video Based on New Chromatic Criteria and Statistical Modeling

A novel moving cast shadow detection method is presented in this paper to detect and remove the cast shadows from the foreground. First, the foreground is detected using the global foreground modeling (GFM) method. Second, the moving cast shadow is detected and removed from the foreground using a new moving cast shadow detection method that contains four hierarchical steps. In the first step, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. In the second step, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. In the third step, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. In the last step, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data 'Highway-3' and the real traffic data from the New Jersey Department of Transportation (NJDOT) show the feasibility of the proposed method.
RecCite: A Hybrid Approach to Recommend Potential Papers

A systematic review of literature is a crucial aspect in pursuing any research problem. With the galloping growth in the number of researchers and scientific publications available online in digital libraries, there is information overload which makes the search of relevant papers cumbersome. An efficient academic paper recommendation process could be a promising maneuver for this purpose. The state-of-the-art methods in this domain primarily employ link-based approaches on citation network and do not consider the semantics (context) of papers being recommended, although the later one largely accounts for the relevance of recommendations. Also achieving online response time (scalability) is a perennial desire for any recommendation system. In this context we propose a hybrid approach “RecCite” that blends the popularity of papers with semantic similarity to acquire relevance. Further, the approach follows a top-down methodology for filtering papers from a more generic and larger network to a community pertaining to a SIG (Special Interest Group) that inherently addresses the scalability aspect. The proposed approach has been systematically evaluated with state-of-the-art baseline method using the publicly available Arnet-Miner data set. Satisfyingly, “RecCite” outperforms the baseline method with up to 37.66%, 20.14%, and 97.24% improvement in precision, recall, and response time, respectively.
Deep Recurrent Neural Networks for Nonlinear System Identification

Deep recurrent neural networks are used as a means for nonlinear system identification. It is shown that state space models can be transformed into recurrent neural networks and vice versa. This transformation and the understanding of the long short-term memory cell in terms of common system identification nomenclature makes the advances in deep learning more accessible to the controls and system identification communities. A systematic study of deep recurrent neural networks is carried out on a state-of-the-art system identification benchmark. The results indicate that if high amounts of data are available, standard recurrent neural networks achieve comparable performance to state-of-the-art system identification methods.
Learning variable-length representation of words

A standard word embedding algorithm, such as ‘word2vec’, embeds each word as a dense vector of a preset dimensionality, the components of which are learned by maximizing the likelihood of predicting the context around it. However, as an inherent linguistic phenomenon, it is evident that there is a varying degree of difficulty in identifying words from their contexts. This suggests that a variable granularity in word vector representation may be useful to obtain sparser and more compressed word representations, requiring less storage space. To that end, in this paper, we propose a word vector training algorithm that uses a variable number of components to represent words. Given a text collection of documents, our algorithm, similar to the skip-gram approach of word2vec, learns to predict the context of a word given the current instance of a word. However, in contrast to skip-gram, which uses a static number of dimensions for each word vector, we propose to dynamically increase the dimensionality as a stochastic function of the prediction error. Our experiments with standard test collections demonstrate that our word representation method is able to achieve comparable (and sometimes even better) effectiveness than skip-gram word2vec, using a significantly smaller number of parameters (achieving compression ratio of around 65%).
Application of Dueling DQN and DECGA for Parameter Estimation in Variogram Models

Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested model. Most recently, the evolutionary algorithms(EA), including genetic algorithm(GA), are exploited to calculate the parameters of variogram model, which can obtain a more accurate solution. These methods have some hyper-parameters to set and suffer from the well-recognized premature convergence and slow global convergence problem of EA. In this paper, a double elite co-evolutionary genetic algorithm(DECGA) and deep reinforcement learning(dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance. The DECGA can get the global optimal solution faster than GA with the help of dueling DQN, which can set the hyper-parameters according to the state of DECGA. To verify the effectiveness of the proposed method(DDQNGA), we conduct experiments on the agricultural heavy metal database. Experimental results demonstrate that our method can obtain parameter estimation more accurately. The method proposed in this paper have a certain practical value in the field of geostatistics and environmental engineering.
A Farthest First Traversal based Sampling Algorithm for k-clustering

The farthest-first-traversal (fft) algorithm originally was used by Rosenkrantz et al. in an analysis of heuristics for the traveling salesman problem. This algorithm has been extensively studied for several sampling techniques. In this work, we present a modification of ProTraS algorithm given by Ros and Guillaume, which is also based on the fft algorithm, for sampling datasets for both k-means and k-median clustering algorithms. Unlike ProTraS, proposed algorithm takes the size of samples as an input. The algorithm is implemented in the Spark platform and tested for benchmark datasets. We also estimate the algorithm by comparing with the adaptive sampling and lightweight coreset algorithms, using the adjust Rand index.
Free-Form Gaze Passwords from Cameras Embedded in Smart Glasses

Contemporary personal mobile devices support a variety of authentication approaches, featuring different levels of security and usability. With cameras embedded in smart glasses, seamless, hands-free mobile authentication based on gaze is possible. Gaze authentication relies on knowledge as a secret, and gaze passwords are composed from a series of gaze points or gaze gestures. This paper investigates the concept of free-form mobile gaze passwords. Instead of relying on gaze gestures or points, free-form gaze gestures exploit the trajectory of the gaze over time. We collect and investigate a set of 29 different free-form gaze passwords from 19 subjects. In addition, the practical security of the approach is investigated in a study with 6 attackers observing eye movements during password input to subsequently perform spoofing. Our investigation indicates that most free-form gaze passwords can be expressed as a set of common geometrical shapes. Further, our free-form gaze authentication yields a true positive rate of 81% and a false positive rate with other gaze passwords of 12%, while targeted observation and spoofing is successful in 17.5% of all cases. Our usability study reveals that further work on the usability of gaze input is required as subjects reported that they felt uncomfortable creating and performing free-form passwords.
Transfer Clustering Based on Gaussian Mixture Model

Gaussian mixture model is a helpful method for data mining. However, when the data is scarce, the traditional clustering algorithm based on Gaussian mixture model is not effective anymore. To solve this issue, this paper presents a novel transfer clustering algorithm based on Gaussian mixture model, which utilizes the information of the data in the source domain to impact to cluster the data of the target domain. In this method, traditional Gaussian mixture model is first applied in the source domain to extract the mean and the covariance of each Gaussian distribution. Then the data in the target domain is clustered under the influence of the extracted mean and covariance from the source domain. Experiments on synthetic datasets demonstrate the efficiency of the proposed algorithm compared with the traditional Gaussian mixture model.
A Deep Dive Into Security and Privacy Issues of Blockchain Technologies

Blockchain is a technology that is developed using a combination of various techniques such as mathematics, algorithms, cryptography, economic models, and so on. Blockchain is a public ledger of all cryptocurrency transactions that are digitized and decentralized. All the transactions of cryptocurrencies are stored in chronological order to help users in tracking the transactions without maintaining any central record of the transactions. Application prospects of blockchain are promising and have been delivering the result since its inception. Blockchain technology has evolved from initial cryptocurrency to new age smart contracts and has been implemented and applied in many fields. Although many studies have been carried out on the security and privacy issues of the blockchain, a systematic examination on the security of blockchain systems is still missing. In this chapter, we will try to demonstrate a systematic illustration on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. This chapter will discuss the security and privacy of the blockchain along with their impact with regards to different trends and applications. The chapter is intended to discuss key security attacks and the enhancements that will help develop a better blockchain systems.