Reviewerly/exHarmony huggingface dataset:
Using HMD for Immersive Training of Voice-Based Operation of Small Unmanned Ground Vehicles

Voice recognition systems provide a method of hands-free control of robotic systems that may be helpful in law enforcement or military domains. However, the constraints of the operational environment limit the capabilities of the on-board voice recognition system to a keyword-based command system. To effectively use the system, the users must learn the available commands and practice pronunciation to ensure accurate recognition. Virtual reality simulation provides users the opportunity to train with the voice recognition system and the robot platform in realistic interactive scenarios. Training using virtual reality with a head-mounted display may increase immersion and sense of presence compared to using a keyboard and monitor. A small pilot study compared user experience in the desktop mode and the virtual reality mode of our voice recognition system training tool. Participants controlled a simulated unmanned ground vehicle in two different modes across four different environments. The results revealed no significant differences in simulator sickness, sense of presence, or perceived usability. However, when asked to choose between the desktop mode and the head-mounted display mode, results indicate users’ overall preference for the head-mounted display. However, the users also perceive the head-mounted display to be more complex, less consistent, and more difficult to learn to use. The desktop mode was perceived as easier to use and users reported being more confident when using it.
CNOT Gate Mappings to Clifford+T Circuits in IBM Architectures

IBM architectures impose some restrictions the quantum circuits that can be implemented. Only gates from Clifford+T gate library can be used and not all of the CNOT gates are available. Some of the CNOT need to be mapped into a sequence of gates. In this paper we present a set of mappings to conform to the restrictions imposed by IBM's architectures. These mappings require fewer gates than SWAP gates. It is well known, that permuting the qubits will yield circuits with different number of gates. The design in this paper uses efficient mappings with qubit permutations to obtain circuits with a reduced number of gates. Results have shown that the proposed approach reduces circuits by up to 64% compared with Qiskit and up to 42% compared with another mapping algorithm.
Local Feature Tensor Based Deep Learning for 3D Face Recognition

A local feature tensor similarity based deep learning approach is proposed in this paper for 3D face recognition. Once a set of salient points on the 3D mesh are detected, three scale and rotation invariant features are extracted to represent local surface around each salient point. The local features of all the salient points are concatenated to produce a 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> order feature tensor to represent a 3D face. Similarity of two 3D faces can thus be measured by a similarity tensor calculated using the two feature tensors. To address the unavailability of large 3D face samples, a feature tensor based data augmentation approach is proposed to augment the number of feature tensors. Experimental results show that the ResNet model trained using the augmented feature tensors achieves the best performance among state of the art competitors, i.e. 99.71% and 96.2% accuracy are achieved for Bosphorus and BU3DFE database, respectively.
A new hybrid ant colony algorithms for the traveling thief problem

The Traveling Thief Problem (TTP) is a new problem recently proposed in the literature. The TTP combines two well-known optimization problems: the knapsack problem (KP) and the travelling salesman problem (TSP). In this paper, new hybrid ant colony algorithms are presented. We study and compare different approaches for solving the TTP. The first approach is a centralized and static metaheuristic, the second is a dynamic metaheuristic and the third is a distributed metaheuristic. The obtained results prove that our algorithms are efficient for instances of TTP.
The iterated extended set membership filter applied to relative localization between autonomous vehicles based on GNSS and UWB ranging

Abstract This paper presents a novel Iterated Extended Set Membership Filter (IESMF) with an application to relative localization. For safe operation of formations of automatic vehicles, consistent uncertainty estimates are of crucial importance. Here, a localization filter that provides ellipsoidal regions that are guaranteed to contain another vehicles position is presented. The proposed iterative update step can appreciably reduce the size of the a posteriori state ellipsoid. The idea of using SIVIA as a baseline to quantify conservativeness is introduced. Another novelty is that we take into account parametric uncertainty of the observation equation. The proposed filter is applied to a two unmanned aircraft systems (UAS) localization problem in simulation with observation noise obtained from real sensors. Simulation results illustrate the effective reduction of filter conservativeness by a small number of iterative updates.
The Design of the Body:Suit:Score, a Full-Body Vibrotactile Musical Score

We present the body:suit:score, a vibrotactile musical score that displays instructions and signals directly on musicians’ bodies using an array of vibrating motors. Initially inspired by the third author’s need for a new kind of score in his compositional practice, the body:suit:score facilitates the exploration of novel musical experiences and performer-audience relationships that are impossible or impractical without such a score, such as allowing the performers to freely move about the performance space amidst the audience. The body:suit:score has been designed through a close collaborative process between artists and technicians. A number of notable design elements and functionalities have emerged in response to this process, and in particular due to the strong requirements for usability and maintainability of the system.
Evolving mean-update selection methods for CMA-ES

This paper details an investigation of the extent to which performance can be improved for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) by tuning the selection of individuals used for the mean-update algorithm. A hyper-heuristic is employed to explore the space of algorithms which select individuals from the population. We show the increase in performance obtained with a tuned selection algorithm, versus the unmodified CMA-ES mean-update algorithm. Specifically, we measure performance on instances from several real-valued benchmark function classes to demonstrate generalization of the improved performance.
Feature grouping-based parallel outlier mining of categorical data using spark

This paper proposes a feature-grouping based parallel outlier mining method called POS for high-dimensional categorical datasets. Existing methods of outlier mining are inadequate to deal with datasets which are so voluminous and complex. We solve this problem by proposing a parallel framework using the Spark platform for categorical and mass data. POS is composed of two modules, which are parallel feature grouping, and parallel outlier mining. Additionally, Vertical transformation is utilized to improve the performance of POS. We implement our POS on the Spark platform and evaluate it using synthetic and real-world datasets. Our experimental results confirm that POS is a promising and practical parallel algorithm to mine outliers in high-dimensional categorical datasets because POS achieves high performance in terms of extensibility and scalability.
A Comparison of Machine Learning Algorithms for Detecting XSS Attacks

With the rapid increase of web applications, the problem of XSS (cross-site scripting) attacks in Web applications is becoming more and more serious. In the face of more and more complex and changeable XSS attacks, the traditional XSS defense method cannot solve the problem of XSS security, inefficient and accurate recognition effect is poor. Therefore, this paper summarizes the method of XSS recognition based on the machine learning algorithm, classifies different machine learning algorithms according to the recognition strategy, analyzes their advantages and disadvantages, and finally looks forward to the development trend of XSS defense research, hoping to play a reference role for the following researchers.
Learning to Infer Relations for Future Trajectory Forecast

Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes. To this end, we propose a relation-aware framework for future trajectory forecast, which aims to infer relational information from the interactions of road users with each other and with environments. Extensive evaluations on a public benchmark dataset demonstrate the robustness and efficacy of the proposed framework as observed by performances higher than the state-of-the-art methods.
Unlimited budget analysis

Performance analysis of randomised search heuristics is a rapidly growing and developing field. We contribute to its further development by introducing a novel analytical perspective that we call unlimited budget analysis. It has its roots in the very recently introduced approximation error analysis and bears some similarity to fixed budget analysis. The focus is on the progress an optimisation heuristic makes towards a set goal, not on the time it takes to reach this goal, setting it far apart from runtime analysis. We present the framework, apply it to simple mutation-based algorithms, covering both, local and global search. We provide analytical results for a number of simple example functions for unlimited budget analysis and compare them to results derived within the fixed budget framework for the same algorithms and functions.
Analysis–Synthesis Learning With Shared Features: Algorithms for Histology Image Classification

Objective: The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. Dictionary learning within a sparse representation-based classification (SRC) framework has been shown to be successful for feature discovery. However, there exist stiff practical challenges: 1) computational complexity of SRC can be onerous in the decision stage since it involves solving a sparsity constrained optimization problem and often over a large number of image patches; and 2) images from distinct classes continue to share several geometric features. We propose a novel analysis-synthesis model learning with shared features algorithm (ALSF) for classifying such images more effectively. Methods: In the ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. Unlike SRC, no explicit optimization is needed in the inference phase leading to much reduced computation. Crucially, we introduce the learning of a low-rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance tradeoff. Results: The ALSF is evaluated on three challenging and well-known datasets: 1) spleen tissue images; 2) brain tumor images; and 3) breast cancer tissue dataset, provided by different organizations. Conclusion: Experimental results demonstrate both complexity and performance benefits of the ALSF over state-of-the-art alternatives. Significance: Modeling shared features with appropriate quantitative constraints lead to significantly improved classification in histopathology.
Unsupervised learning of depth estimation based on attention model and global pose optimization

Depth estimation is a fundamental task for 3D scene perception. Unsupervised learning is a prevalent method for depth estimation due to its generalization ability, and it requires no extra ground truth of depth for training. The typical pipeline for unsupervised solution uses the photometric error between target view and reconstructed view from adjacent frames as supervisory signal, in which the depth and pose are both learned and used for reconstruction. In this paper, we proposed a novel framework for unsupervised learning of depth, which consists of a Details Preserved Depth Network with attention model (DPDN), and Global Pose Calculation (GPC) modules. The attention model is adopted in the depth network to preserve the details of the depth map, which enables the network to maintain the shape of objects and enhance edges of the depth map. Moreover, instead of using the learning-based pose-network with two frames, a global pose estimation is optimized using all previous frames by mathematically minimizing the reprojection error. Experimental results show that our method outperforms existing methods in terms of accuracy and visual quality.
Designing Adaptive Online Mathematics Course Based on Individualization Learning

The article provides a possible option of how first-year students of Russian universities adapt to mastering higher mathematics. The purpose of the research is the development of methodological approaches to designing an online course that recaps school math material in a short period. The concept of individualization of educational activities is used as the main design method. Online training is used as the implementation technology. The article provides an overview of scientific papers on the use of an individual approach to learning and online resources in teaching mathematics. The social survey of first-year students confirmed the relevance of the stated purpose. The authors describe the design methodologies of the online training by taking into account age, social and psychological characteristics of students, and the opportunity for developing individual educational trajectories. The authors set the objectives of the course and structuralize each topic. Instructional content is developed in accordance with step-by-step leveling on mastering educational material. Extra attention is given toward presenting educational information in ways that take into account different types of cognitive processes. Foreign students who receive education in Russian universities are also welcomed to the online course to get familiar with the mathematical terminology in the Russian language.
Local Information Privacy with Bounded Prior

A localized privacy protection notion: local information privacy (LIP) is studied in this paper. As a context-aware notion that considers prior knowledge, the LIP notion is shown to provide increased utility than local differential privacy (LDP). Within the scope of LIP, we further consider scenarios with uncertainty on the prior knowledge, i.e., the prior is bounded within a certain range or the prior is arbitrary. The former case is defined as bounded-prior LIP (BP-LIP), and the latter as worst-case LIP (WC-LIP). The contributions of this paper are three-fold: We first provide theoretical results which show the connections of these new definitions with LDP; Secondly, we present an optimization framework for privacy-preserving data collection, with the goal of minimizing the expected squared error while satisfying BP-LIP and WC-LIP privacy constraints. Utility-privacy tradeoffs are obtained in closed-form. At last, we validate our conclusions by numerical analysis and real-world data simulation. Our results show that the notion of bounded-prior LIP can achieve better utility-privacy tradeoff compared to context free notion of LDP.
Feature Extraction and Selection for Parsimonious Classifiers with Multiobjective Genetic Programming

The objectives of this paper are to investigate the capability of genetic programming to select and extract linearly separable features when the evolutionary process is guided to achieve the same and to propose an integrated system for that. We decompose a c-class problem into c binary classification problems and evolve c sets of binary classifiers employing a steady-state multiobjective genetic programming with three minimizing objectives. Each binary classifier is composed of a binary tree and a linear support vector machine (SVM). The features extracted by the feature nodes and some of the function nodes of the tree are used to train the SVM. The decision made by the SVM is considered the decision of the corresponding classifier. During crossover and mutation, the SVM-weights are used to determine the usefulness of the corresponding nodes. We also use a fitness function based on Golub's index to select useful features. To discard less frequently used features, we employ unfitness functions for the feature nodes. We compare our method with 34 classification systems using 18 datasets. The performance of the proposed method is found to be better than 432 out of 570, i.e., 75.79% of comparing cases. Our results confirm that the proposed method is capable of achieving our objectives.
Toward Generalized Neural Model for VMs Power Consumption Estimation in Data Centers

The power consumption of IT equipment is always being a big challenge for data centers. The research communities are attempting to solve this problem, by employing various of energy-aware tasks scheduling or VMs consolidation policies. It is crucial for these policies to figure out the major parameters that affect power consumption and their correlation. In this paper, we first identify the major parameters using real cloud services' workload and power consumption data. An important observation is that the parameters are strongly interdependent and the importance of individual parameters varies in different cloud services. Nevertheless, existing power consumption models are unable to fully capture this feature and thus lack generalization. To address this gap, we propose Lapem that adopts a Long Short-term Memory network for power consumption estimation. Lapem further uses an attention mechanism to achieve stable performance and improve generalization. The experimental results demonstrate that Lapem estimates power consumption with a relative error of as low as 2%-5%. More importantly, in comparison with the state-of-the-art models, Lapem reduces the estimation error by more than 23% when generalizing to new cloud services.
ChronoCorrelator: Enriching Events with Time Series

Abstract Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.
MilkyWay: A Toolbox for Prototyping Collaborative Mobile-Based Interaction Techniques

Beside traditional multitouch input, mobile devices provide various possibilities to interact in a physical, device-based manner due to their built-in hardware. Applying such interaction techniques allows for sharing content easily, e.g. by literally pouring content from one device into another, or accessing device functions quickly, e.g. by facing down the device to mute incoming calls. So-called mobile-based interaction techniques are characterized by movements and concrete positions in real spaces. Even though such interactions may provide many advantages in everyday life, they have limited visibility in interaction design due to the complexity of sensor processing. Hence, mobile-based interactions are often integrated, if any, at late design stages. To support testing interactive ideas in early design stages, we propose MilkyWay, a toolbox for prototyping collocated collaborative mobile-based interaction techniques. MilkyWay includes an API and a mobile application. It enables easily building up mobile interactive spaces between multiple collocated devices as well as prototyping interactions based on device sensors by a programming-by-demonstration approach. Appropriate sensors are selected and combined automatically to increase tool support. We demonstrate our approach using a proof of concept implementation of a collaborative Business Model Canvas (BMC) application.
Automatic Classification of Whole Slide Pap Smear Images Using CNN With PCA Based Feature Interpretation

Classification of whole slide image (WSI) cervical cell clusters traditionally involved two stages including segmentation to crop single cell patches followed by the classification of single cell patches. Hence the performance of classification pipeline depends on segmentation accuracy. We propose a first-time-right method which is a segmentation-free direct classification of WSI cervical cell clusters (without the extraction of single cell patches). The proposed method is evaluated on SIPaKMeD and Herlev datasets. Our method significantly outperformed previous methods and baselines with an accuracy of 96.37% on WSI patches (cell clusters) and 99.63% on single cell images. We also propose a PCA based feature interpretation method to visualize and understand the model to make its decisions more transparent. Our solution is promising in the development of automatic whole slide pap smear image classification system.
Prognosis of the energy and instantaneous power consumption in electric vehicles enhanced by visual terrain classification

The use of electric vehicles (EVs) has been viewed as a potential solution to reduce the oil dependence in transportation. Nevertheless, the performance of the EV changes according to the nature and characteristics of the terrain. This work focuses in modelling the instantaneous power consumption (IPC) of an EV as it traverses through different types of terrains, classified using an artificial vision system. The EV is sensorized with voltage and current sensors, accurate real time kinematics device, an artificial vision system and a real time processing unit, to perform field tests. To see the advantages of using terrain information for energy prognosis in EVs, we compared our proposal with the information provided by the manufacturer and previously published approaches. Our system showed to be up to 95% more accurate when tested over clay, gravel, pavement and grass in both short term and long term trials, and it does not need of a complete knowledge of the system’s model.
Counting the Uncountable: Deep Semantic Density Estimation from Space

We propose a new method to count objects of specific categories that are significantly smaller than the ground sampling distance of a satellite image. This task is hard due to the cluttered nature of scenes where different object categories occur. Target objects can be partially occluded, vary in appearance within the same class and look alike to different categories. Since traditional object detection is infeasible due to the small size of objects with respect to the pixel size, we cast object counting as a density estimation problem. To distinguish objects of different classes, our approach combines density estimation with semantic segmentation in an end-to-end learnable convolutional neural network (CNN). Experiments show that deep semantic density estimation can robustly count objects of various classes in cluttered scenes. Experiments also suggest that we need specific CNN architectures in remote sensing instead of blindly applying existing ones from computer vision.