Reviewerly/exHarmony huggingface dataset:
Multi-view Embedding with Adaptive Shared Output and Similarity for unsupervised feature selection

The problem of multi-view feature selection, a kind of feature learning pattern, has raised considerable interests in the past decade. It is crucial for feature selection to maintain both the overall structure and locality of the original features. The existing unsupervised feature selection methods mostly preserve either global or local structures, and compute the sparse representation for each view individually. Besides, several methods introduce a predefined similarity matrix among different views and fix it in the learning process, which consider less correlation between each single view. Thus, we focus on the multi-view feature selection and propose a new method, Multi-view Embedding with Adaptive Shared Output and Similarity (ME-ASOS). This method introduces embedding directly into multi-view learning, mapping the high-dimensional data to a shared subspace with the view-wise multi-output regular projections and learns a common similarity matrix through an improved algorithm to characterize structures across different views. A regulation parameter is used to largely eliminate the adverse effect of noisy and unfavorable features for global structures and another regularization term is used in local structure to avoid the trivial solution and add a prior of uniform distribution. Compared with 5 existing algorithms, the experimental results on 4 real-world datasets has shown that method ME-ASOS captures more related information between different views, selects better discriminative features and obtains superior accuracy and higher efficiency.
A framework for reporting and dealing with end-user security policy compliance

Purpose It is widely acknowledged that non-compliance of employees with information security polices is one of the major challenges facing organisations. This paper aims to propose a model that is intended to provide a comprehensive framework for raising the level of compliance amongst end-users, with the aim of monitoring, measuring and responding to users’ behaviour with an information security policy. Design/methodology/approach The proposed model is based on two main concepts: a taxonomy of the response strategy to non-compliant behaviour and a compliance points system. The response taxonomy comprises two categories: awareness raising and enforcement of the security policy. The compliance points system is used to reward compliant behaviour and penalise non-compliant behaviour. Findings A prototype system has been developed to simulate the proposed model and work as a real system that responds to the behaviour of users (reflecting both violations and compliance behaviour). In addition, the model has been evaluated by interviewing experts from academic and industry. They considered the proposed model to offers a novel approach for managing end users’ behaviour with the information security policies. Research limitations/implications Psychological factors were out of the research scope at this stage. The proposed model may have some psychological impacts upon users; therefore, this issue needs to be considered by studying the potential impacts and the best solutions. Originality/value Users being compliant with the information security policies of their organisation is the key to strengthen information security. Therefore, when employees have a good level of compliance with security policies, this positively affects the overall security of an organisation.
Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps

This paper studies the problem of detection and tracking of general objects with semistatic dynamics observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, the robot can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.
Intelligent retrieval and classification in three-dimensional biomedical images — A systematic mapping

The massive generation of data has raised new research topics, such as methods to store and to retrieve large volumes of information. Some medical image modalities, such as Magnetic Resonance Imaging, generate hundreds images series and many research groups have presented studies to develop intelligent techniques to classify and to retrieve this information. However, these studies are dispersed in several databases, and cataloged by using different terms. In this paper we present an analysis of these studies, through a Systematic Mapping that identifies methods and techniques currently being used in this scenario. In addition, we provide a perspective about the type of scientific literature the researchers have been disclosed their studies as well as impact on the scientific literature in dissemination of this knowledge domain. Some challenges and research opportunities are also highlighted in order to propitiate advances in the area.
Exploring Active Learning Based on Representativeness and Uncertainty for Biomedical Data Classification

Nowadays, there is an abundance of biomedical data, such as images and genetic sequences, among others. However, there is a lack of annotation to such volume of data, due to the high costs involved to perform this task. Thus, it is mandatory to develop techniques to ease the burden of human annotation. To reach such goal active learning strategies can be applied. However, the state-of-the-art active learning methods, generally, are not feasible to lead with real-world datasets. Another important issue, that is generally neglected by these methods, is related to the conception that the classifier tends to learn more and more at each iteration. Their adopted selection criteria do not properly exploit the knowledge of the classifier. Therefore, in this paper, we propose the use of an active learning approach, in order to leverage the learning process, including the proposal of a novel active learning strategy. The main difference of our proposed strategy is related to the participation of the classifier in an extremely active way in its learning process. So, we can better maximize and prioritize the knowledge that is obtained by the classifier at each iteration, making use of this knowledge in a more appropriate and useful way when selecting more informative samples. To do so, in our selection criteria, we give significant importance to the classifications suggested by the classifier. In addition, jointly with the participation and the knowledge of the classifier, we consider both uncertainty and representativeness criteria through a fine-grained analysis of the samples. Experimental results show that our novel active learning approach outperforms state-of-the-art active learning methods, considering several supervised classifiers. Hence, dealing with real dataset problems in a better way, equalizing the tradeoff between annotation task and higher accuracy rates.
Adaptive virtual sensors using SNPER for the localized construction and elastic net regularization in nonlinear processes

Traditional data-driven virtual sensors were constructed upon the macro-perspective of the manifold structure using generalized models. They did not focus on the local relationships among the data samples through micro-perspective of manifold proximity indicating the local relationships among the data samples. In the case with the quantity of data points fewer than the dimensions of the data variables, the virtual sensor model is likely to be unstable, ill-conditioned, and computationally expensive. And the real-world data often vary with time. It is difficult, in the long term, to sustain good performance by a single fixed model. This paper addresses the aforementioned issues by proposing three algorithms (NPER, SNPER, and LW-SNPER) to successively improve the virtual sensor modeling performance for nonlinear, high-dimensional and time-varying processes. It is shown through the numerical cases and a real semiconductor process that the proposed algorithms perform better than the other regular regression algorithms.
Fixing Incremental Computation

Incremental computation has recently been studied using the concepts of change structures and derivatives of programs, where the derivative of a function allows updating the output of the function based on a change to its input. We generalise change structures to change actions, and study their algebraic properties. We develop change actions for common structures in computer science, including directed-complete partial orders and Boolean algebras. We then show how to compute derivatives of fixpoints. This allows us to perform incremental evaluation and maintenance of recursively defined functions with particular application generalised Datalog programs. Moreover, unlike previous results, our techniques are modular in that they are easy to apply both to variants of Datalog and to other programming languages.
Self-Adaptive Monte Carlo Tree Search in General Game Playing

For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately.Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game.We propose adapting such methods for Rolling Horizon Evolutionary Algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent's win rate.On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach.We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.
Estimating cognitive load from speech gathered in a complex real-life training exercise

Speech-enabled applications are becoming prevalent, providing opportunities for real-time detection of speaker characteristics. Estimation of cognitive load from speech is one type of speaker characteristic that can provide insight into the human state in complex, highly dynamic human-machine teaming scenarios and be used to adapt interaction with the user to their current cognitive state. Cognitive load estimation from speech experiments are typically performed on speech gathered in laboratory settings. By contrast, this research is performed on a real-life dataset that was not created for the purpose of cognitive load assessment. Speech was extracted from recordings of a military simulation exercise in which air battle managers communicated with pilots flying simulated aircraft. This paper assesses whether cognitive load can be estimated from speech self-labelled by exercise participants and collected in a realistic setting, and examines how well cognitive load estimation methods translate from the laboratory setting to the real-world. Analysis suggests that participants’ self-assessment of workload at periodic intervals can be used to label speech to create 2-class cognitive load classifiers. The analysis also shows that including some target speaker speech in speaker independent training data results in higher classification accuracy than when classifiers are built solely from speaker dependent data.
Parameter-Free Selective Segmentation With Convex Variational Methods

Selective segmentation methods involve incorporating user input to partition an image into a foreground and background. These methods are often sensitive to some aspect of the user input in a counter intuitive manner, making their use in practice difficult. The most robust methods often involve laborious refinement on the part of the user, and sometimes editing/supervision. The proposed method reduces the burden of the user by simplifying the requirements in the input. Specifically, the fitting term does not depend on a distance function, and so no selection parameter is introduced. Instead, we consider how the user input relates to some general intensity fitting term to ensure the approach is less sensitive to the decisions or intuition of the user. We give comparisons to existing approaches to show the advantages of the new selective segmentation model.
Quality-aware dual-modal saliency detection via deep reinforcement learning

Incorporating various modes of information into the machine learning procedure is becoming a new trend And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL). Specifically, we take the weighting of each domain as a decision-making problem and teach an agent learn to interact with the environment. The agent can tune the weight of each domain through discrete action selection and obtain a positive reward if the saliency results are improved. The target of the agent is to achieve maximum rewards after finished its sequential action selection. We validate the proposed algorithms on dual-modal saliency detection in a coarse-to-fine way. The coarse saliency maps are generated from an encoder–decoder framework which is trained with content loss and adversarial loss. The final results can be obtained via adaptive weighting of maps from each domain. Extensive experiments on two kinds of salient object detection benchmarks all obtained a significant gain by fusing two-modalities with our proposed quality-aware deep neural network.
Geographic profiling for serial cybercrime investigation

Today's cybercrimes are much more difficult to detect and prosecute than traditional crimes. In the investigation of cybercrimes, law enforcement agencies follow similar techniques to traditional crimes that, however, have to be modified to meet the unique conditions and requirements of virtual space. This paper examines cybercrime profiling techniques prevalent today, and focuses on the feasibility of applying geographic profiling technique to cyber offenders. The primary assumption of the research is that for most types of cybercrime, the steps during the procedure of committing criminal act are not random. For example, the choice of the victim, the choice of crime location, similar characteristics, follow a certain logic, which could provide information about the offender's crime. Testing the utility of a geographical profiling has been carried out on real cybercrime samples obtained by law enforcement agencies. This paper aims to apply the concept of geographic profiling to the issue of cybercrime that involve a physical world, targeting two types of cybercrimes: credit card skimming and spear phishing. Specially developed GeoCrime geographic profiling software designed to assist in the mapping, spatial and statistical analysis of cybercrime patterns was used. The results of the study have shown the possibility of applying geographic profiling to certain types of cybercrimes and under the certain conditions. The importance of geographic profiling is also emphasized, especially in situations where little is known about the offender, such as in cybercrime, where offenders use the Internet to hide their identities and activities.
Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

For the pursuit of ubiquitous computing, distributed computing systems containing the cloud, edge devices, and Internet-of-Things devices are highly demanded. However, existing distributed frameworks do not tailor for the fast development of Deep Neural Network (DNN), which is the key technique behind many intelligent applications nowadays. Based on prior exploration on distributed deep neural networks (DDNN), we propose Heterogeneous Distributed Deep Neural Network (HDDNN) over the distributed hierarchy, targeting at ubiquitous intelligent computing. While being able to support basic functionalities of DNNs, our framework is optimized for various types of heterogeneity, including heterogeneous computing nodes, heterogeneous neural networks, and heterogeneous system tasks. Besides, our framework features parallel computing, privacy protection and robustness, with other consideration for the combination of heterogeneous distributed system and DNN. Extensive experiments demonstrate that our framework is capable of utilizing hierarchical distributed system better for DNN and tailoring DNN for real-world distributed system properly, which is with low response time, high performance, and better user experience.
Stochastic investigation of long-term persistence in two-dimensional images of rocks

Determining the geophysical properties of rocks and geological formations is of high importance in many fields such as geotechnical engineering. In this study, we investigate the second-order dependence structure of spatial (two-dimensional) processes through the statistical perspective of variance vs. scale (else known as the climacogram) instead of covariance vs. lag (e.g. autocovariance, variogram etc.) or power vs. frequency (e.g. power spectrum, scaleogram, wavelet transform etc.) which traditionally are applied. In particular, we implement a two-dimensional (visual) estimator, adjusted for bias and for unknown process mean, through the (plot of) variance of the space-averaged process vs. the spatial scale. Additionally, we attempt to link the climacogram to the type of rocks and provide evidence on stochastic similarities in certain of their characteristics, such as mineralogical composition and resolution. To this end, we investigate two-dimensional spatial images of rocks in terms of their stochastic microstructure as estimated by the climacogram. The analysis is based both on microscale and macroscale data extracted from greyscale images of rocks. Interestingly, a power-law drop of variance vs. scale (or else known as long-term persistence) is detected in all scales presenting a similar power-exponent. Furthermore, the strengths and limitations of the climacogram as a stochastic tool are discussed and compared with the traditional tool in spatial statistics, the variogram. We show that the former has considerable strengths for detecting the long-range dependence in spatial statistics.
Feature Tracking by Two-Step Optimization

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results' plausibility for simulation data.
Maximal fusion of facts on the web with credibility guarantee

The Web became the central medium for valuable sources of information fusion applications. However, such user-generated resources are often plagued by inaccuracies and misinformation as a result of the inherent openness and uncertainty of the Web. While finding objective data is non-trivial, assessing their credibility with a high confidence is even harder due to the conflicts of information between Web sources. In this work, we consider the novel setting of fusing factual data from the Web with a credibility guarantee and maximal recall. The ultimate goal is that not only the information should be extracted as much as possible but also its credibility must satisfy a threshold requirement. To this end, we formulate the problem of instantiating a maximal set of factual information such that its precision is larger than a pre-defined threshold. Our proposed approach is a learning process to optimize the parameters of a probabilistic model that captures the relationships between data sources, their contents, and the underlying factual information. The model automatically searches for best parameters without pre-trained data. Upon convergence, the parameters are used to instantiate as much as factual information with a precision guarantee. Our evaluations of real-world datasets show that our approach outperforms the baselines up to 6 times.
Large stark tuning of InAs/InP quantum dots

InAs/InP quantum dots are excellent sources of telecom single-photon emission and are among the most promising candidates for scalable quantum photonic circuits. However, geometric differences in each quantum dot lead to slightly different emission wavelengths and hinder the possibility of generating multiple identical quantum emitters on the same chip. Stark tuning is an efficient technique to overcome this issue as it can control the emission energy of individual quantum dots through the quantum-confined Stark effect. Realizing this technique in InAs/InP quantum dots has previously been limited to shifts of less than 0.8 meV due to jumps in the emission energy because of additional charges at high electric field intensities. We demonstrate up to 5.1 meV of Stark tuning in the emission wavelength of InAs/InP quantum dots. To eliminate undesirable jumps to the charged state, we use a thin oxide insulator to prevent carrier injection from the contacts, thereby significantly improving the tuning range of the Stark effect. Moreover, the single-photon nature and narrow linewidth of the quantum dot emission are preserved under a wide range of applied electric fields. Using photoluminescence intensity measurements and time-resolved lifetime spectroscopy, we confirmed that this Stark tuning range is limited by carrier tunneling at high electric fields. This result is an important step toward integrating multiple identical quantum emitters at telecom wavelengths on a chip, which is crucial for realizing complex quantum photonic circuits for quantum information processing.