Reviewerly/exHarmony huggingface dataset:
Hierarchical feature selection with subtree based graph regularization

Feature selection is an important and challenging task in machine learning and data mining. In many practical problems, the classes have a hierarchical structure. However, some existing feature selection algorithms ignored the dependence among different classes in the hierarchical structure. Other feature selection algorithms only focused on one way dependence among different classes, ignoring two-way dependence. In this paper, we propose a novel feature selection method called hierarchical feature selection with subtree based graph regularization (HFSGR), which is aimed at exploring two-way dependence among different classes. First, we construct a subtree graph using the parent–child relationships of the subtrees in a predefined tree structure, where the subtree is obtained from its internal nodes. Second, we use the l2,1-norm regularization term to encourage nearby subtrees that share similar sparsity patterns. Third, we extend our algorithm to a directed acyclic graph structure so that it can be applied to common situations. Our method is applied to eight datasets with different tree structures. Experimental comparisons of our proposed algorithm with five hierarchical feature selection algorithms, justify its effectiveness and efficiency.
An Optical Image Transmission System for Deep Sea Creature Sampling Missions Using Autonomous Underwater Vehicle

The exploration of oceans using autonomous underwater vehicles (AUVs) is necessary for activities, such as the sustainable management of fishery resources, extraction of seafloor minerals and energy resources, and inspection of underwater infrastructure. As the next step in ocean exploration, AUVs are expected to employ end-effectors to make physical contact with seafloor creatures and materials. We propose a scenario for realizing a sampling mission using an AUV that is equipped to sample marine life. In this scenario, the sampling AUV observes the seafloor while concurrently transmitting the observed images to a surface vessel for inspection by the AUV operators. If the received images show an object of interest, the object is selected as a candidate of sampling target by the operators, who send a sampling command to the AUV. After receiving the command, the AUV returns to the target area and attempts to sample it. In this paper, we propose a system for transmitting images of the seafloor as part of the sampling-mission scenario. The proposed image transmission system includes a process for enhancing images of the deep seafloor, a process for selecting interesting images, and processes for compressing and reconstructing images. The image enhancement process resolves imaging problems resulting from light attenuation, such as color attenuation and uneven illumination. The process for selecting interesting images selects those that contain interesting objects, such as marine life. The selection process prevents the transmission of meaningless images that contain only flat sand on the seafloor. The proposed image compression method, which is based on color depth compression, reduces the amount of data. The combined process of selecting an interesting image and compressing it reduces various problems in acoustic communication, such as low information density and data loss. Instead of an overall image, part of an overall image is transmitted by a set of data packet, and each received data packet is reconstructed onboard the vessel. Because of image compression, the colors of a reconstructed image differ from those of an enhanced image. However, the reconstructed image contains similar colors, and the structural similarity index was found to be 91.4% by evaluating images that were subjected to a 4-b color compression. The proposed image transmission system was tested in the Sea of Okhotsk, and these tests were performed four times in different sea areas (minimum depth 380 m, maximum depth 590 m). The results show that the size of the data for a single image was reduced by a factor of 18 using the proposed image compression process, with each image taking 3.7 s to be transmitted via an acoustic modem (20 kb/s). Of the automatically selected images, 63% contained marine life, and the total transmission success rate was 22%.
Design and Evaluate Immersive Learning Experience for Massive Open Online Courses (MOOCs)

Massive open online courses (MOOCs), a unique form of online education enabled by web-based learning technologies, allow learners from anywhere in the world with any level of educational background to enjoy online education experience provided by many top universities all around the world. Traditionally, MOOC learning contents are always delivered as text-based or video-based materials. Although introducing immersive learning experience for MOOCs may sound exciting and potentially significative, there are a number of challenges given this unique setting. In this paper, we present the design and evaluation methodologies for delivering immersive learning experience to MOOC learners via multiple media. Specifically, we have applied the techniques in the production of a MOOC entitled Virtual Hong Kong: New World, Old Traditions, led by AIMtech Centre, City University of Hong Kong, which is the first MOOC (as our knowledge) that delivers immersive learning content for distant learners to appreciate and experience how the traditional culture and folklore of Hong Kong impact upon the lives of its inhabitants in the 21st Century. The methodologies applied here can be further generalized as the fundamental framework of delivering immersive learning for future MOOCs.
Multidimensional privacy preservation in location-based services

Users of mobile communication services can reap great benefits and take advantage of the convenience offered by various location-based services (LBSs). Privacy disclosure in LBSs poses a serious threat to the user's security, and thus, existing solutions adopt K-anonymity principles to protect the location privacy of users. However, existing solutions have rarely considered the comprehensive protection of user privacy offered by LBSs such as location privacy, trajectory privacy, and query privacy. In this paper, we propose a multidimensional privacy preservation (MPP) scheme that provides full protection for user privacy without any need for a trusted third party (TTP). The MPP scheme employs a semi-trusted middle entity to perform user anonymization and result-blind filtering while unaware of any sensitive information regarding the mobile users. We utilize the Hilbert curve to transform user locations, and preserve users' query contents using encryption technology. The proposed scheme provides enhanced security protection in both snapshot and continuous LBSs. Extensive experiments were conducted to verify the effectiveness and efficiency of the proposed scheme.
Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity

The imperfections in the RF frontend of different transmitters can be used to distinguish them. This process is called transmitter identification using RF fingerprints. The nonlinearity in the power amplifier of the RF frontend is a significant cause of the discrepancy in RF fingerprints, which enables transmitter identification. In this work, we use deep learning to identify different transmitters using their nonlinear characteristics. By developing a nonlinear model generator based on extensive measurements, we were able to extend the evaluation of transmitter identification to include a larger number of transmitters beyond what exists in the literature. We were also able to study the impact of transmitter variability on identification accuracy. Additionally, many other factors were considered including modulation type, length of data used for identification, and type of data being transmitted whether identical or random under a realistic channel model. Simulation results were compared with experiments which confirmed similar trends.
Toward a cybercrime classification ontology: A knowledge-based approach

In recent years there has been an increase in cybercrimes and its negative impacts on the lives of individuals, organizations, and governments. It has been argued that a better understanding of cybercrime is a necessary condition to develop appropriate legal and policy responses to cybercrime. While a universally agreed-upon classification scheme would facilitate the development of such understanding and also collaborations, current classification schemes are insufficient, fragmented and often incompatible since each focuses on different perspectives (e.g., role of the computer, attack, attacker's or defender's viewpoint), or uses varying terminologies to refer to the same thing, making consistent cybercrime classifications improbable. In this paper we present and illustrate a new cybercrime ontology that incorporates multiple perspectives and offers a more holistic viewpoint for cybercrime classification than prior works. It should therefore prove to be a more useful tool for cybercrime stakeholders.
Application of probability to enhance the performance of fuzzy based mobile robot navigation

This paper presents a new path planning algorithm based on Probability and Fuzzy Logic (PFL) as a duality technique to enhance the performance of Fuzzy Logic alone. Fuzzy Logic interacts with the grading of obstacles existed in the path and probability lies over the decision to move the mobile robot. The fuzzy grading correspondence with the probabilistic decision is the primary function of moving the mobile robot towards the goal and the secondary is path planning which lies over the probability distribution function. The distance–speed combination rule is developed for effective navigation. The single and multiple mobile robot systems have been tested successfully in a dense environment in presence of obstacles (static and dynamic) and moving goal. The obtained results are optimal when compared to other navigational approaches in sense of navigational path length and time in the static and dynamic environment.
On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification

In this paper, we propose a recursive variant of the Parzen kernel density estimator (KDE) to track changes of dynamic density over data streams in a nonstationary environment. In stationary environments, well-established traditional KDE techniques have nice asymptotic properties. Their existing extensions to deal with stream data are mostly based on various heuristic concepts (losing convergence properties). In this paper, we study recursive KDEs, called recursive concept drift tracking KDEs, and prove their weak (in probability) and strong (with probability one) convergence, resulting in perfect tracking properties as the sample size approaches infinity. In three theorems and subsequent examples, we show how to choose the bandwidth and learning rate of a recursive KDE in order to ensure weak and strong convergence. The simulation results illustrate the effectiveness of our algorithm both for density estimation and classification over time-varying stream data.
Minimum-Time Trajectory Planning Under Intermittent Measurements

This paper focuses on finding robust paths for a robotic system by taking into account the state uncertainty and the probability of collision.We are interested in dealing with intermittent exteroceptive measurements (e.g., collected from vision).We assume that these cues provide reliable measurements that will update a state estimation algorithm wherever they are available.The planner has to manage two tasks: reaching the goal in a minimum time and collecting sufficient measurements to reach the goal state with a given confidence level.We present a robust perception-aware bi-directional A* planner for differentially flat systems such as a unicycle and a quadrotor UAV and use a derivative-free Kalman filter to approximate the belief dynamics in the flat space.We also propose an efficient way of ensuring continuity and feasibility by exploiting the convex-hull property of B-spline curves.
Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty

Electrical energy production using renewable energies is one of the most important challenges in recent years. Among renewable energies, it is worth highlighting photovoltaic and thermoelectric systems due to their adaptation to the Canary Islands. One of the most important issues to ensure the stability for solar power systems, mostly in insular grids as Canary Islands, is the precise knowledge of solar radiation. In this paper, we focus in Gridded Satellite data suitability for modelling Global Horizontal Irradiation (GHI) in islands with complicated orography, as Canary Islands. Solar radiation data retrieved from CM SAF and McClear model were analysed and compared with 22 ground measurement stations in Canary Islands. Moreover, this analysis presents the results of including a site-adaptation methodology for improving satellite suitability. We used different procedures to perform this site adaptation depending on the solar radiation conditions (clear sky or cloudy sky hours), the location of the measurement station (we establish two clusters according to the climate conditions) and the season. This study could provide information about satellite models suitability in islands and a better knowledge of solar radiation behavior. Furthermore, accurate satellite radiation data for wide spatial and temporal coverage could improve solar radiation modelling and forecasting.
Majorana Loop Stabilizer Codes for Error Mitigation in Fermionic Quantum Simulations

Fermion-to-qubit mappings that preserve geometric locality are especially useful for simulating lattice fermion models (e.g., the Hubbard model) on a quantum computer. They avoid the overhead associated with geometric non-local parity terms in mappings such as the Jordan-Wigner transformation and the Bravyi-Kitaev transformation. As a result, they often provide quantum circuits with lower depth and gate complexity. In such encodings, fermionic states are encoded in the common +1 eigenspace of a set of stabilizers, akin to stabilizer quantum error-correcting codes. Here, we discuss several known geometric locality-preserving mappings and their abilities to correct/detect single-qubit errors. We introduce a geometric locality-preserving map, whose stabilizers correspond to products of Majorana operators on closed paths of the fermionic hopping graph. We show that our code, which we refer to as the Majorana loop stabilizer code (MLSC) can correct all single-qubit errors on a 2-dimensional square lattice, while previous geometric locality-preserving codes can only detect single-qubit errors on the same lattice. Compared to existing codes, the MLSC maps the relevant fermionic operators to lower-weight qubit operators despite having higher code distance. Going beyond lattice models, we demonstrate that the MLSC is compatible with state-of-the-art algorithms for simulating quantum chemistry, and can offer those simulations the same error-correction properties without additional asymptotic overhead. These properties make the MLSC a promising candidate for error-mitigated quantum simulations of fermions on near-term devices.
Improved discrete artificial fish swarm algorithm combined with margin distance minimization for ensemble pruning

Ensemble pruning aims to achieve a good result in classification using a smaller size of classifiers by finding the optimal sub-ensemble. Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. There is a tradeoff between the diversity and the accuracy of classifiers, which makes the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures or heuristic algorithms separately. Those pruning approaches based on diversity measures cannot exactly find the tradeoff; Those algorithms based on heuristic algorithms are not also to exhaustively search for it extracted from an initial pool of classifiers with a medium-scale or large-scale size. To address the issue, Improved Discrete Artificial Fish swarm algorithm combined with Margin distance minimization for Ensemble Pruning (IDAFMEP) is proposed using a combination of diversity measure and heuristic algorithm. First, the classifiers in a constructed initial pool are pre-pruned using Margin Distance Minimization (MDM), which can downsize the classifiers who perform badly, and markedly alleviate the computational complexity of ensemble pruning. Second, the final ensemble is efficiently achieved from the retaining classifiers after pre-pruning based on MDM using the proposed Improved discrete Artificial Fish Swarm Algorithm (IDAFSA). Experimental results on 29 datasets from the UCI Machine Learning Repository demonstrate that IDAFMEP can achieve better results than the original ensemble and other state-of-the-art pruning approaches, and that its validity and effectiveness. It provides a new research idea for ensemble pruning.
A Self-Evolving Fuzzy System Which Learns Dynamic Threshold Parameter by Itself

This paper proposes an online learning algorithm for data streams, namely self-evolving fuzzy system (SEFS). Unlike the fixed control parameters commonly used in evolving fuzzy systems, the SEFS uses online training errors, which measure the quality of an identified model in presenting the dynamics of the data stream, to set a dynamic threshold automatically for rule generation. This self-tuning parameter, which controls the speed and coverage for fuzzy rule generation, helps the SEFS properly deal with the underfitting/overfitting problems relying on two facts: 1) Large training errors present an underfitted model, which is too coarse to represent the highly complicated and rapidly dynamic (e.g., highly nonlinear, nonstationary) behavior of the data segment. Then, finer rules need to be added; and 2) tiny training errors reflect an overfitted model, which can ideally represent any slight dynamic behavior of the data stream. In this case, coarse rule base should be used. Besides, an L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -distance-based geometric similarity measure is proposed in the rule merging phase. With this similarity measure, the SEFS computes the similarity between Gaussian membership functions accurately without making an approximation of the Gaussian membership function beforehand. In addition, a weighted recursive least-squares algorithm with a variable forgetting factor, which minimizes the mean square of the noise-free posterior error signal, is applied to learn the consequent parameters. Several benchmark examples across both artificial and real-life datasets verify that the SEFS has the ability to give better performance compared with many state-of-the-art algorithms.
Stereoscopic image quality assessment by deep convolutional neural network

In this paper, we propose a no-reference (NR) quality assessment method for stereoscopic images by deep convolutional neural network (DCNN). Inspired by the internal generative mechanism (IGM) in the human brain, which shows that the brain first analyzes the perceptual information and then extract effective visual information. Meanwhile, in order to simulate the inner interaction process in the human visual system (HVS) when perceiving the visual quality of stereoscopic images, we construct a two-channel DCNN to evaluate the visual quality of stereoscopic images. First, we design a Siamese Network to extract high-level semantic features of left- and right-view images for simulating the process of information extraction in the brain. Second, to imitate the information interaction process in the HVS, we combine the high-level features of left- and right-view images by convolutional operations. Finally, the information after interactive processing is used to estimate the visual quality of stereoscopic image. Experimental results show that the proposed method can estimate the visual quality of stereoscopic images accurately, which also demonstrate the effectiveness of the proposed two-channel convolutional neural network in simulating the perception mechanism in the HVS.
Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

Underwater imaging is attractive but challenging. Images could be severely degraded by the particles in turbid water because of backscatter generation and signal light attenuation. In this paper, we focus on the scheme of underwater imaging and study the methods of visibility enhancement of turbid underwater images based on polarimetric imaging utilizing active unpolarized illumination. Compared with traditional polarimetric imaging using linearly polarized illumination, using unpolarized illumination ensures the polarization effect of the signal light could be neglected, no matter the depolarization degree of the object is high or low, which expands the application range of underwater polarimetric imaging and makes the underwater polarimetric imaging scheme more reliable and robust. Experimentally, the visibility and the contrast of underwater images are enhanced effectively. In addition, it is demonstrated that our method is applicable for objects of different materials and different imaging distances in turbid water. The contrast of underwater images could be promoted at least 100%, meaning that this kind of technique can be potentially used in many underwater environments.
A factor graph model for unsupervised feature selection

In this paper, a factor graph model for unsupervised feature selection (FGUFS) is proposed. FGUFS explicitly measures the similarities between features; these similarities are passed to each other as messages in the graph model. The importance score of each feature is calculated using the message-passing algorithm, and then feature selection is performed based on the final importance scores. Extensive experiments were performed on several datasets, and the results demonstrate that FGUFS outperforms other state-of-art unsupervised feature selection algorithms on several performance measures.
Towards a unified multi-source-based optimization framework for multi-label learning

In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method.