Reviewerly/exHarmony huggingface dataset:
AUV Path Planning Based on Improved RRT and Bezier Curve Optimization

In view of the complexity of the marine environment, considering the limitations of the AUV fuel and other factors, in order to reduce the energy consumption of the AUV in the unknown environment, taking an AUV as the research object, a path planning algorithm of RRT is designed and implemented. An AUV path planning algorithm based on improved RRT (Rapidly-exploring random tree, RRT) and Bezier optimization is proposed to accelerate the search, avoid tortuous paths and improve path smoothness. In this method, the cost formula of A* algorithm is used as the sampling standard, and the pruning strategy is used to simplify the path. Then, Bezier curve fitting is used to generate the initial solution of the planning problem, so as to improve the feasibility of generating the path. In addition, local re-planning is used to solve the obstacle avoidance problem in the unknown environment. In the simulation experiment, the algorithm is compared with the conventional RRT method, and the results show that the algorithm has a great improvement in search speed, smoothness, feasibility and other aspects.
Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.
Differentially Private Two-Party Set Operations

Private set intersection (PSI) allows two parties to compute the intersection of their data without revealing the data they possess that is outside of the intersection. However, in many cases of joint data analysis, the intersection is also sensitive. We define differentially private set intersection and we propose new protocols using (leveled) homomorphic encryption where the result is differentially private. Our circuit-based approach has an adaptability that allows us to achieve differential privacy, as well as to compute predicates over the intersection such as cardinality. Furthermore, our protocol produces differentially private output for set intersection and set intersection cardinality that is optimal in terms of communication and computation complexity. For a client set of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> and a server set of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> is smaller than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> , our communication complexity is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(m)$</tex> while previous circuit-based protocols only achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n+m)$</tex> communication complexity. In addition to our asymptotic optimizations which include new analysis for using nested cuckoo hashing for PSI, we demonstrate the practicality of our protocol through an implementation that shows the feasibility of computing the differentially private intersection for large data sets containing millions of elements.
Analyzing Sentiment using IMDb Dataset

Text is the largest repository of human knowledge acquired over thousands of years. This knowledge will impart even more meaning if mined for deeper insights. Sentiment Analysis (SA) provides a traditional machine learning (ML) solution to this problem by putting Natural Language Processing (NLP) to work. In the proposed work, we have performed SA on the IMDb movie reviews dataset taken from Kaggle's Bag of Words meets Bag of Popcorn challenge to demonstrate how valuable insights can be drawn from a bulk of textual data collected from the internet. We derive these insights by applying four traditional ML algorithms namely, Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT). Furthermore, results of these four algorithms were compared on the basis of six evaluation metrics - confusion matrix, accuracy, precision, recall, F1 measure, and Area Under Curve (AUC).
Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

Among the most prevalent motion planning techniques, sampling and trajectory optimization have emerged successful due to their ability to handle tight constraints and high-dimensional systems, respectively. However, limitations in sampling in higher dimensions and local minima issues in optimization have hindered their ability to excel beyond static scenes in offline settings. Here we consider highly dynamic environments with long horizons that necessitate a fast on-line solution. We present a unified approach that leverages the complementary strengths of sampling and optimization, and interleaves them both in a manner that is well suited to this challenging problem. With benchmarks in multiple synthetic and realistic simulated environments, we show that our approach performs significantly better on various metrics against baselines that employ either only sampling or only optimization. Project page: https://sites.google.com/view/jistplanner
A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

The robotic task sequencing problem (RTSP) appears in various forms across many industrial applications and consists of developing an optimal sequence of motions to visit a set of target points defined in a task space. Developing solutions to problems involving complex spatial constraints remains challenging due to the existence of multiple inverse kinematic solutions and the requirements for collision avoidance. So far existing studies have been limited to relaxed RTSPs involving a small number of target points and relatively uncluttered environments. When extending existing methods to problems involving greater spatial constraints and large sets of target points, they either require substantially long planning times or are unable to obtain high-quality solutions. To this end, this article presents a clustering-based algorithm to efficiently address spatially constrained RTSPs involving several hundred to thousands of points. Through a series of benchmarks, we show that the proposed algorithm outperforms the state-of-the-art in terms of solution quality and planning efficiency for large, complex problems, achieving up to 60% reduction in task execution time and 91% reduction in computation time.
Entity Typing Based on RDF2Vec Using Supervised and Unsupervised Methods

Knowledge Graphs have been recognized as the foundation for diverse applications in the field of data mining, information retrieval, and natural language processing. So the completeness and the correctness of the KGs are of high importance. The type information of the entities in a KG, is one of the most vital facts. However, it has been observed that type information is often noisy or incomplete. In this work, the task of fine-grained entity typing is addressed by exploiting the pre-trained RDF2Vec vectors using supervised and unsupervised approaches.
Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search

Approximate nearest neighbor algorithms are used to speed up nearest neighbor search in a wide array of applications. However, current indexing methods feature several hyperparameters that need to be tuned to reach an acceptable accuracy–speed trade-off. A grid search in the parameter space is often impractically slow due to a time-consuming index-building procedure. Therefore, we propose an algorithm for automatically tuning the hyperparameters of indexing methods based on randomized space-partitioning trees. In particular, we present results using randomized k-d trees, random projection trees and randomized PCA trees. The tuning algorithm adds minimal overhead to the index-building process but is able to find the optimal hyperparameters accurately. We demonstrate that the algorithm is significantly faster than existing approaches, and that the indexing methods used are competitive with the state-of-the-art methods in query time while being faster to build.
Compacting frequent star patterns in RDF graphs

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved.
Multiparty Session Type-safe Web Development with Static Linearity

Modern web applications can now offer desktop-like experiences from within the browser, thanks to technologies such as WebSockets, which enable low-latency duplex communication between the browser and the server.While these advances are great for the user experience, they represent a new responsibility for web developers who now need to manage and verify the correctness of more complex and potentially stateful interactions in their application.In this paper, we present a technique for developing interactive web applications that are statically guaranteed to communicate following a given protocol.First, the global interaction protocol is described in the Scribble protocol language -based on multiparty session types.Scribble protocols are checked for well-formedness, and then each role is projected to a Finite State Machine representing the structure of communication from the perspective of the role.We use source code generation and a novel type-level encoding of FSMs using multi-parameter type classes to leverage the type system of the target language and guarantee only programs that communicate following the protocol will type check.Our work targets PureScript -a functional language that compiles to JavaScript -which crucially has an expressive enough type system to provide static linearity guarantees.We demonstrate the effectiveness of our approach through a web-based Battleship game where communication is performed through WebSocket connections.
Efficient ion-photon qubit SWAP gate in realistic ion cavity-QED systems without strong coupling

We present a scheme for deterministic ion-photon qubit exchange, namely a SWAP gate, based on realistic cavity-QED systems with 171Yb+, 40Ca+ and 138Ba+ ions. The gate can also serve as a single-photon quantum memory, in which an outgoing photon heralds the successful arrival of the incoming photonic qubit. Although strong coupling, namely having the single-photon Rabi frequency be the fastest rate in the system, is often assumed essential, this gate (similarly to the Duan-Kimble C-phase gate) requires only Purcell enhancement, i.e. high single-atom cooperativity. Accordingly, it does not require small mode volume cavities, which are challenging to incorporate with ions due to the difficulty of trapping them close to dielectric surfaces. Instead, larger cavities, potentially more compatible with the trap apparatus, are sufficient, as long as their numerical aperture is high enough to maintain small mode area at the ion's position. We define the optimal parameters for the gate's operation and simulate the expected fidelities and efficiencies, demonstrating that efficient photon-ion qubit exchange, a valuable building block for scalable quantum computation, is practically attainable with current experimental capabilities.
Visual Sentiment Analysis With Social Relations-Guided Multiattention Networks

These days, social media users tend to express their feelings through sharing images online. Capturing the emotions embedded in these social images involves great research challenges and practical values. Most existing works concentrate on extracting the visual feature from a global view, while ignoring the fact that visual objects are also rich in emotion. How to leverage the multilevel visual features to improve the sentiment analysis performance is important yet challenging. Besides, existing works view each social image as an independent sample while ignoring the rich correlations among social images, which may be helpful in detecting visual emotion. In this article, we propose a novel model called social relations-guided multiattention networks (SRGMANs) to incorporate both the multilevel (region-level and object-level) visual features of a single image and the correlations among multiple social images to conduct visual sentiment analysis. Specifically, we first construct a heterogeneous network consisting of various types of social relations and introduce a heterogeneous network embedding method to learn the network representation for each image. Then, two visual attention branches (region attention network and object attention network) are devised to extract emotional and discriminative visual features. For each branch, we design a self-attention module to capture the emotional dependencies among visual parts. Besides, a network-guided attention module is also designed in each branch to focus on more network-related emotional visual parts with the guidance of the topology information. Finally, the attended visual features from the two attention models, together with network representation features, are combined within a holistic framework to predict the sentiment of social images. Extensive experiments demonstrate the superiority of our model on three benchmark datasets.
The generalized Gell–Mann representation and violation of the CHSH inequality by a general two-qudit state

We formulate and prove the main properties of the generalized Gell-Mann representation for traceless qudit observables with eigenvalues in $[-1,1]$ and analyze via this representation violation of the CHSH inequality by a general two-qudit state. For the maximal value of the CHSH expectation in a two-qudit state with an arbitrary qudit dimension $d\geq2$, this allows us to find two new bounds, lower and upper, expressed via the spectral properties of the correlation matrix for a two-qudit state. We have not yet been able to specify if the new upper bound improves the Tsirelson upper bound for each two-qudit state. However, this is the case for all two-qubit states, where the new lower bound and the new upper bound coincide and reduce to the precise two-qubit CHSH\ result of Horodeckis, and also, for the Greenberger-Horne-Zeilinger (GHZ) state with an odd $d\geq2,$ where the new upper bound is less than the upper bound of Tsirelson. Moreover, we explicitly find the correlation matrix for the two-qudit GHZ state and prove that, for this state, the new upper bound is attained for each dimension $d\geq2$ and this specifies the following new result: for the two-qudit GHZ state, the maximum of the CHSH expectation over traceless qudit observables with eigenvalues in $[-1,1]$ is equal to $2\sqrt{2}$ if $d\geq2$ is even and to $\frac{2(d-1)}{d}\sqrt{2}$ if $d>2$ is odd.
Central limit theorems for coupled particle filters

Abstract In this article we prove new central limit theorems (CLTs) for several coupled particle filters (CPFs). CPFs are used for the sequential estimation of the difference of expectations with respect to filters which are in some sense close. Examples include the estimation of the filtering distribution associated to different parameters (finite difference estimation) and filters associated to partially observed discretized diffusion processes (PODDP) and the implementation of the multilevel Monte Carlo (MLMC) identity. We develop new theory for CPFs, and based upon several results, we propose a new CPF which approximates the maximal coupling (MCPF) of a pair of predictor distributions. In the context of ML estimation associated to PODDP with time-discretization $\Delta_l=2^{-l}$ , $l\in\{0,1,\dots\}$ , we show that the MCPF and the approach of Jasra, Ballesio, et al. (2018) have, under certain assumptions, an asymptotic variance that is bounded above by an expression that is of (almost) the order of $\Delta_l$ ( $\mathcal{O}(\Delta_l)$ ), uniformly in time. The $\mathcal{O}(\Delta_l)$ bound preserves the so-called forward rate of the diffusion in some scenarios, which is not the case for the CPF in Jasra et al. (2017).
Distributed Kalman filtering for uncertain dynamic systems with state constraints

Summary This article addresses the distributed state estimation problem for uncertain time‐varying dynamic systems with state constraints over a sensor network. By using a null space method, the distributed state estimation problem for uncertain dynamic systems with state constraints can be cast into a new unconstrained distributed state estimation problem for reduced uncertain dynamic systems. A constrained distributed Kalman filter is proposed, and it is shown that the full state estimates can be recovered at any time and satisfy the constraints. An optimized upper bound of the estimation error covariance of each sensor is obtained, and the corresponding gains are designed. The application conditions of the proposed algorithm are mild, and they can be off‐line checked. Furthermore, the computational requirements in this article are also reduced compared with the existing results. Finally, the performance of the proposed filter algorithm is demonstrated through numerical simulations.
Simple Stopping Criteria for Information Theoretic Feature Selection

Feature selection aims to select the smallest feature subset that yields the minimum generalization error. In the rich literature in feature selection, information theory-based approaches seek a subset of features such that the mutual information between the selected features and the class labels is maximized. Despite the simplicity of this objective, there still remain several open problems in optimization. These include, for example, the automatic determination of the optimal subset size (i.e., the number of features) or a stopping criterion if the greedy searching strategy is adopted. In this paper, we suggest two stopping criteria by just monitoring the conditional mutual information (CMI) among groups of variables. Using the recently developed multivariate matrix-based Renyi's \alpha-entropy functional, which can be directly estimated from data samples, we showed that the CMI among groups of variables can be easily computed without any decomposition or approximation, hence making our criteria easy to implement and seamlessly integrated into any existing information theoretic feature selection methods with a greedy search strategy.
A Lightweight Image Encryption And Blowfish Decryption For The Secure Internet Of Things

As an innovative technology of the future, the Internet of Things (IoT) is expected to connect billions of users. Increased connectivity is expected to generate mountains of data and data protection can be a threat. Architectural tools, in principle, are smaller in size and low in strength. Due to their sophistication, traditional encryption algorithms are often computationally ineffective and require multiple rounds of coding, effectively wasting restricted device resources. But a less complex algorithm will jeopardize required honesty. In this article we propose a lightweight coding algorithm called Stable IoT (SIT). It is a 64-bit block encryption that requires a 64-bit key to encrypt data. Low complexity coding symmetry algorithm called Stable Power with Affine Shift. The coding component can be implemented using a simple syntax consisting of only basic mathematical operations (AND, OR, XOR, XNOR, SHIFTING, SWAPPING). This will help reduce the encoder, as the more complex key expansion method is only done in decryption. The purpose of this document is to conduct a security review and evaluate the performance of the proposed algorithm. The design of the algorithm is a combination of emotion and a unified switch-switch network. Simulation testing reveals that the algorithm provides adequate protection in only five rounds of encryption. The algorithm is hardware-implemented with a low-cost 8-bit microcontroller and the effects of code size, memory consumption, and encryption / decoding execution cycles are contrasted with reference encryption algorithms.