Reviewerly/exHarmony huggingface dataset:
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.
Witnessing non-objectivity in the framework of strong quantum Darwinism

Abstract Quantum Darwinism is a compelling theory that describes the quantum-to classical transition as the emergence of objectivity of quantum systems. Spectrum broadcast structure and strong quantum Darwinism are two extensions of this theory with emphasis on state structure and information respectively. The complete experimental verification of these three frameworks, however, requires quantum state tomography over both the system and accessible environments, thus limiting the feasibility and scalability of experimental tests. Here, we introduce a subspace-dependent objectivity operation and construct a witness that detects non-objectivity by comparing the dynamics of the system-environment state with and without the objectivity operation. We then propose a photonic experimental simulation that implements the witnessing scheme. Our work proposes a route to further experimental exploration of the quantum to classical transition.
Maximum likelihood estimation for Gaussian processes under inequality constraints

We consider covariance parameter estimation for a Gaussian process under inequality constraints (boundedness, monotonicity or convexity) in fixed-domain asymptotics. We address the estimation of the variance parameter and the estimation of the microergodic parameter of the Mat\'ern and Wendland covariance functions. First, we show that the (unconstrained) maximum likelihood estimator has the same asymptotic distribution, unconditionally and conditionally to the fact that the Gaussian process satisfies the inequality constraints. Then, we study the recently suggested constrained maximum likelihood estimator. We show that it has the same asymptotic distribution as the (unconstrained) maximum likelihood estimator. In addition, we show in simulations that the constrained maximum likelihood estimator is generally more accurate on finite samples. Finally, we provide extensions to prediction and to noisy observations.
Universal programmable photonic architecture for quantum information processing

We present a photonic integrated circuit architecture for a quantum programmable gate array (QPGA) capable of preparing arbitrary quantum states and operators. The architecture consists of a lattice of phase-modulated Mach-Zehnder interferometers, which perform rotations on path-encoded photonic qubits, and embedded quantum emitters, which use a two-photon scattering process to implement a deterministic controlled-${\ensuremath{\sigma}}_{z}$ operation between adjacent qubits. By appropriately setting phase shifts within the lattice, the device can be programmed to implement any quantum circuit without hardware modifications. We provide algorithms for exactly preparing arbitrary quantum states and operators on the device, and we show that gradient-based optimization can train a simulated QPGA to automatically implement highly compact approximations to important quantum circuits with near-unity fidelity.
Stochastic learning control of inhomogeneous quantum ensembles

In quantum control, the robustness with respect to uncertainties in the system's parameters or driving-field characteristics is of paramount importance and has been studied theoretically, numerically, and experimentally. We test in this paper stochastic search procedures (Stochastic gradient descent and the Adam algorithm) that sample, at each iteration, from the distribution of the parameter uncertainty, as opposed to previous approaches that used a fixed grid. We show that both algorithms behave well with respect to benchmarks and discuss their relative merits. In addition the methodology allows to address high-dimensional parameter uncertainty; we implement numerically, with good results, a three-dimensional and a six-dimensional case.
Deep Direct Visual Odometry

Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.
A New Volcanic Seismic Signal Descriptor and its Application to a Data Set From the Cotopaxi Volcano

This article proposes a new volcano seismic signal descriptor for improving the area under the receiver operating characteristic curve (AUC) in the classification of long-period (LP) and volcano-tectonic (VT) seismic events. It aims to describe a volcanic seismic event from a different and novel point of view that involves image processing techniques instead of classical seismic signal processing strategies, such as frequency or scale analysis. The proposed descriptor allows exploring the seismic signal space for obtaining the determination of the event patterns and, subsequently, the extraction of intensity-, shape-, and texture-based features into a numeric vectorial output for supplying a set of selected machine learning classifiers with different taxonomies. The descriptor was validated on a seismic signal database collected at the Cotopaxi volcano, containing a total of 637 events, including LP, VT, and other types of seismic events (e.g., rockfall or icequakes). An accuracy value of 96% was obtained in the determination of the event patterns using the signal database, while the values of 0.95 and 0.96 were obtained for the AUC when using a feedforward backpropagation artificial neural network classifier on two experimental data sets, containing feature vectors representing signal with and without event overlapping, respectively. The obtained results demonstrate that the proposed descriptor is capable of providing adequate seismic signal representations in a different feature space and that its output provides competitive results in the classification of volcanic seismic events.
Motion Segmentation of RGB-D Sequences: Combining Semantic and Motion Information Using Statistical Inference

This paper presents an innovative method for motion segmentation in RGB-D dynamic videos with multiple moving objects. The focus is on finding static, small or slow moving objects (often overlooked by other methods) that their inclusion can improve the motion segmentation results. In our approach, semantic object based segmentation and motion cues are combined to estimate the number of moving objects, their motion parameters and perform segmentation. Selective object-based sampling and correspondence matching are used to estimate object specific motion parameters. The main issue with such an approach is the over segmentation of moving parts due to the fact that different objects can have the same motion (e.g. background objects). To resolve this issue, we propose to identify objects with similar motions by characterizing each motion by a distribution of a simple metric and using a statistical inference theory to assess their similarities. To demonstrate the significance of the proposed statistical inference, we present an ablation study, with and without static objects inclusion, on SLAM accuracy using the TUM-RGBD dataset. To test the effectiveness of the proposed method for finding small or slow moving objects, we applied the method to RGB-D MultiBody and SBM-RGBD motion segmentation datasets. The results showed that we can improve the accuracy of motion segmentation for small objects while remaining competitive on overall measures.
Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search

Mapping logical quantum circuits to Noisy Intermediate-Scale Quantum (NISQ) devices is a challenging problem which has attracted rapidly increasing interests from both quantum and classical computing communities. This paper proposes an efficient method by (i) selecting an initial mapping that takes into consideration the similarity between the architecture graph of the given NISQ device and a graph induced by the input logical circuit; and (ii) searching, in a filtered and depth-limited way, a most useful SWAP combination that makes executable as many as possible two-qubit gates in the logical circuit. The proposed circuit transformation algorithm can significantly decrease the number of auxiliary two-qubit gates required to be added to the logical circuit, especially when it has a large number of two-qubit gates. For an extensive benchmark set of 131 circuits and IBM's current premium Q system, viz., IBM Q Tokyo, our algorithm needs, in average, 0.4346 extra two-qubit gates per input two-qubit gate, while the corresponding figures for three state-of-the-art algorithms are 0.6047, 0.8154, and 1.0067 respectively.
Smart Security System for Suspicious Activity Detection in Volatile Areas

The latest progress in the technology has led to automation and digitization in almost every fields, and has influenced a wide scope of application. This has caused enormous amount of data flow from each sectors, where the information contained in the data acts as the important component for the progress of the single person, organization, state, country and so on. These data with valuable information can be used in the constructive and the destructive perceptive based on the hands that handle it. So protective measures become very essential for preserving the data from unwanted access. This paves for developing a system to identify the suspicious movement in the volatile areas like military regimes, hospitals and financial organizations to safe the data. The method put forward in the paper incorporates the motion sensors and the face identification system to detect the suspicious activities and report to the lawful person. The algorithm for the system was developed using the python and tested for various sets of exemplary real time video recordings to know the accuracy in the detection.
Reducing information asymmetry with ICT

Purpose The purpose of this paper is to investigate loan price and quantity effects of information sharing offices with information and communication technology (ICT), in a panel of 162 banks consisting of 42 African countries for the period 2001–2011. Design/methodology/approach The empirical evidence is based on a panel of 162 banks in 42 African countries for the period 2001–2011. Misspecification errors associated with endogenous variables and unobserved heterogeneity in financial access are addressed with generalized method of moments and instrumental quantile regressions. Findings The findings uncover several major themes. First, ICT when integrated with the role of public credit registries significantly lowered the price of loans and raised the quantity of loans. Second, while the net effects from the interaction of ICT with private credit bureaus (PCBs) do not improve financial access, the corresponding marginal effects show that ICT could complement the characteristics of PCBs to reduce loan prices and increase loan quantity, but only when certain thresholds of ICT are attained. The authors compute and discuss the policy implications of these ICT thresholds for banks with low, intermediate and high levels of financial access. Originality/value This is one of the few studies to assess how the growing ICT can be leveraged in order to reduce information asymmetry in the banking industry with the ultimate aim of improving financial access in a continent where lack of access to finance is a critical policy syndrome.
Predicting human complexity perception of real-world scenes

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction versus feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalized for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network (CNN) trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75 020 2-alternative forced choice paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r = 0.83. By contrast, feature-based models used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.
Autonomous navigation of MAVs in unknown cluttered environments

Abstract Recently, there have been many advances in the algorithms required for autonomous navigation in unknown environments, such as mapping, collision avoidance, trajectory planning, and motion control. These components have been integrated into drones with high‐end computers and graphics processors. However, further development is required to enable compute‐constrained platforms with such autonomous navigation capabilities. To address this issue, in this paper, we present an autonomous navigation framework for reaching a goal in unknown three‐dimensional cluttered environments. The framework consists of three main components. The first component is a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. The second component is a stochastic approach to generate a path to a given goal, taking into account the field of view constraints on the space that is assumed to be safe for navigation. The third method is a fast algorithm for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty, and disturbances. We provide a qualitative and quantitative comparison with existing reaching a goal and exploration methods, showing the superior performance of our approach. Additionally, we present indoors and outdoors experiments using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation, in the most computational constrained platform, of autonomous navigation in unknown cluttered environments demonstrated to date. Open source code is available at: https://github.com/IntelLabs/autonomousmavs . The video of the experimental results can be found in https://youtu.be/79IFfQfvXLE .
MapGAN: An Intelligent Generation Model for Network Tile Maps

In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators.
Exact variance of von Neumann entanglement entropy over the Bures-Hall measure

The Bures-Hall distance metric between quantum states is a unique measure that satisfies various useful properties for quantum information processing. In this work, we study the statistical behavior of quantum entanglement over the Bures-Hall ensemble as measured by von Neumann entropy. The average von Neumann entropy over such an ensemble has been recently obtained, whereas the main result of this work is an explicit expression of the corresponding variance that specifies the fluctuation around its average. The starting point of the calculations is the connection between correlation functions of the Bures-Hall ensemble and these of the Cauchy-Laguerre ensemble. The derived variance formula, together with the known mean formula, leads to a simple but accurate Gaussian approximation to the distribution of von Neumann entropy of finite-size systems. This Gaussian approximation is also conjectured to be the limiting distribution for large dimensional systems.
Application Research of Text Classification Based on Random Forest Algorithm

In view of the poor classification effect of traditional random forest algorithm due to the low quality of text feature extraction, a random forest method for text information is proposed. In view of the difficulty in controlling the quality of traditional random forest decision trees, a weighted voting mechanism is proposed to improve the quality of decision trees. This algorithm uses tr-k method based on text feature extraction to improve the quality and diversity of text features, and uses the latest Bert word vector generation model to represent the text. Experimental data in Python environment show that this method can achieve better results in text classification than IDF based random forest algorithm and original random forest algorithm.
Information and communication technologies as a source of education

It is well known that the application of ICT in education helps us to develop skills for competent and efficient problem solving in the field of professional activities. This paper aims to examine the attitudes of students towards the use of ICT as a source of education. The methodology adopted was that of original research, with a qualitative and quantitative approach. This study included 175 students from Serbia and Bulgaria. The variables of this study were gender and the years of study. In order to assess the attitudes of students towards the use of ICT in education a five-point Likert scale was used across forty-five statements. The results of the research show that students are very interested in using ICT as a source of education. Thus, it is evident that information and communication technologies have become very important pedagogical resources in approaching the teaching and learning processes in an innovative way.&#x0D; Keywords: attitudes of students; ICT in education; ICT; education technology; education.&#x0D;
Activating Hidden Metrological Usefulness

We consider bipartite entangled states that cannot outperform separable states in any linear interferometer. Then, we show that these states can still be more useful metrologically than separable states if several copies of the state are provided or an ancilla is added to the quantum system. We present a general method to find the local Hamiltonian for which a given quantum state performs the best compared to separable states. We obtain analytically the optimal Hamiltonian for some quantum states with a high symmetry. We show that all bipartite entangled pure states outperform separable states in metrology. Some potential applications of the results are also suggested.
Graph-based path planning for intelligent UAVs in area coverage applications

In recent times, Unmanned Air Vehicles (UAVs) are being deployed for several tasks of terrain coverage such as surveillance, photogrammetry, smart irrigation, civil security, and disaster management. In these applications, one of the most vital issues to be addressed is, covering the area under observation with minimum traversal for the UAV. So, the problem addressed in this paper is as follows: For a given geographic area and the given parameters describing the UAV’s coverage capacity, the problem is to find an optimal route that covers the given geographic area. In this paper, an optimal path planning algorithm for the area under observation, given as a closed curve, is proposed. The algorithm partitions the given area of interest into multiple non-uniform rectangles while considering other parameters such as the flying altitude of the UAV and obstacles that could be encountered during its flight. The problem is transformed into Traveling Salesman Problem by constructing a graph from the rectangular partitioning. Effective approximate solutions are provided to this problem, using the Minimum Spanning Tree (MST) approximation algorithm and Ant Colony Optimization (ACO). The experimental results show that ACO outperforms the MST based algorithm as it does not get stuck in local minima.