Reviewerly/exHarmony huggingface dataset:
Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for Multiple Wheeled Mobile Robots

This paper proposes the distance-and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs’ velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.
A Novel Real-Time Obstacle Avoidance Method in Guidance Layer for AUVs' Path Following

This paper addresses the real-time obstacle avoidance problem in 2D/3D path following missions for autonomous underwater vehicles (AUVs). To efficiently solve this problem, a novel universal obstacle avoidance framework based on the guidance layer is proposed for under-actuated AUVs to follow arbitrary referenced path. Based on this, a real-time avoidance line-of-sight guidance law is designed to avoid multi-static or slow time-varying obstacles that appear on the desired route and are pretreated by the oriented bounding box method. The strategy of single-obstacle avoidance, multi-obstacle avoidance, and vertical-obstacle avoidance is designed according to the geometrical features of unknown obstacles. Following the command of guidance law, kinematic and dynamic controllers are designed based on integral terminal sliding mode control to enhance robustness under environmental disturbances and parameter perturbation whether in path following or obstacle avoidance missions. Finally, Lyapunov theory proves stability of control system while numerical simulations demonstrate effectiveness and robustness of proposed control scheme.
T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE , a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.
Cooperative Multiagent Reinforcement Learning With Partial Observations

In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agent can observe the states and actions of all the other agents in the network. This can be impractical in large-scale problems, where sharing the state and action information with multi-hop neighbors may incur significant communication overhead. The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus. Specifically, to calculate the local policy gradients, we develop a new distributed zeroth-order policy gradient estimator that relies on one-point residual-feedback which, compared to existing zeroth-order estimators that also rely on one-point feedback, significantly reduces the variance of the policy gradient estimates improving, in this way, the learning performance. We show that the proposed distributed zeroth-order policy optimization method with constant stepsize converges to the neighborhood of a policy that is a stationary point of the global objective function. The size of this neighborhood depends on the agents' learning rates, the exploration parameters, and the number of consensus steps used to calculate the local estimates of the global accumulated rewards. Moreover, we provide numerical experiments that demonstrate that our new zeroth-order policy gradient estimator is more sample-efficient compared to other existing one-point estimators.
Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.
Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

With the ever-increasing diversification of people's interests and preferences, artwork has become one of the most popular commodities or investment goods in E-commerce, and it increasingly attracts the attention of the public.Currently, many real-world or virtual artworks can be found in E-commerce, and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users.Existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in E-commerce, which considerably influences the quality of experience of potential users, especially when they need to select a set of artworks instead of a single artwork.Inspired by this limitation, we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ART com-rec .Through ART com-rec , the recommender system can output a set of artworks (e.g., an artwork composite solution) in E-commerce by considering the keywords typed by a user to indicate his or her personalized preferences.Finally, we validate the feasibility of the ART com-rec approach by a set of simulated experiments on a real-world PW dataset.
Efficient Barrett Modular Multiplication Based on Toom-Cook Multiplication

The Toom-Cook multiplier (TCM) is the most suitable method for multiplying large integers with key widths typically employed in ECC or RSA due to its computational efficiency. In this brief, we improve the accuracy of hardware TCM algorithm, and propose a TCM-based Barrett modular multiplication algorithm. We provide detailed mathematical proofs for the determinantion of parameters, error ranges, and input-output bounds, which serve to validate the robustness and reliability of our proposed algorithm. Further, the hardware architecture and the ASIC implementation results under 40nm, 65nm, 90nm process are presented. Compared with existing works, the proposed modular multiplier exhibits superior performance in terms of Area-Time-Product.
Cerise: Program Verification on a Capability Machine in the Presence of Untrusted Code

A capability machine is a type of CPU allowing fine-grained privilege separation using capabilities , machine words that represent certain kinds of authority. We present a mathematical model and accompanying proof methods that can be used for formal verification of functional correctness of programs running on a capability machine, even when they invoke and are invoked by unknown (and possibly malicious) code. We use a program logic called Cerise for reasoning about known code, and an associated logical relation, for reasoning about unknown code. The logical relation formally captures the capability safety guarantees provided by the capability machine. The Cerise program logic, logical relation, and all the examples considered in the paper have been mechanized using the Iris program logic framework in the Coq proof assistant. The methodology we present underlies recent work of the authors on formal reasoning about capability machines [Georges et al. 2021 ; Skorstengaard et al. 2019a ; Van Strydonck et al. 2022 ], but was left somewhat implicit in those publications. In this paper we present a pedagogical introduction to the methodology, in a simpler setting (no exotic capabilities), and starting from minimal examples. We work our way up to new results about a heap-based calling convention and implementations of sophisticated object-capability patterns of the kind previously studied for high-level languages with object-capabilities, demonstrating that the methodology scales to such reasoning.
A novel fixed-time error-monitoring neural network for solving dynamic quaternion-valued Sylvester equations

This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising.
Sketch-based semantic retrieval of medical images

The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.
Adaptive Shape Servoing of Elastic Rods Using Parameterized Regression Features and Auto-Tuning Motion Controls

The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects' non-linear properties and high-dimensional geometric configuration. In this paper, we propose an efficient shape servoing framework to manipulate elastic objects through real-time visual feedback automatically. The proposed parameterized regression features are used to construct a compact (low-dimensional) feature vector (Bézier and NURBS) that quantifies the object's shape, thus enabling the establishment of an explicit shape servo-loop. To automatically manipulate the object into a desired configuration, our adaptive controller can iteratively estimate the sensorimotor model that relates the robot's motion and shape changes. This valuable capability enables the effective deformation of objects with unknown mechanical models. An auto-tuning algorithm is introduced to adjust the controller's gain and, thus, modulate the shaping motions based on optimal performance criteria. To validate the proposed framework, a detailed experimental study with vision-guided robot manipulators is presented.
Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021

Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.
Quantum Key Distribution Based on the Quantum Eraser

Quantum information and quantum foundations are becoming popular topics for advanced undergraduate courses. Many of the fundamental concepts and applications in these two fields, such as delayed choice experiments and quantum encryption, are comprehensible to undergraduates with basic knowledge of quantum mechanics. In this paper, we show that the quantum eraser, usually used to study the duality between wave and particle properties, can also serve as a generic platform for quantum key distribution. We present a pedagogical example of an algorithm to securely share random keys using the quantum eraser platform and propose its implementation with quantum circuits.
Comparative Performance of Hay Densification Machines

Densification and baling of hay material is required to reduce its volume for easy transport. Three types of densification machines viz. i) stationary baler; ii) movable baler; and iii) field baler were evaluated for densification of different hay materials. The output from stationary, movable and field baler were 0.045, 0.21 and 0.86 t.h-1, respectively. Densification with stationary baler needed all the hay material to be carried to the densification yard, thus increasing man power requirement and costs while movable baler could be moved to the place of availability of raw material. Field baler could collect and bale the harvested hay material spread on field. Field baler also operated under stationary condition with manual feeding of hay material heaped at one place. The cost of operation with stationary, movable and field baler were Rs.14400, 4700 and 1500 Rs.t-1 of bales formed, respectively.
Numerical Composition of Differential Privacy

We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of \emph{privacy loss random variables} to quantify the privacy loss of DP algorithms.The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\tilde{O}(\sqrt{k})$. This improves over the best prior method by Koskela et al. (2021) which requires $\tilde{\Omega}(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.
Grounded language acquisition through the eyes and ears of a single child

Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child’s everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child’s input.
Who Wants to Shop With You: Joint Product–Participant Recommendation for Group-Buying Service

Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.
Proposal for sequential Stern–Gerlach experiment with programmable quantum processors

The historical significance of the Stern–Gerlach (SG) experiment lies in its provision of the initial evidence for space quantization. Over time, its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory. To date, the practical implementation of the sequential SG experiment has not been fully achieved. In this study, we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment. The specific parametric shallow quantum circuits, which are suitable for the limitations of current noisy quantum hardware, are given to replicate the functionality of SG devices with the ability to perform measurements in different directions. Surprisingly, it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit. With the utilization of the identical circuits, it is also feasible to implement Wheeler’s delayed-choice experiment. We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments, and the simulation results demonstrate a strong alignment with theoretical predictions. With the rapid advancement of cloud-based quantum computing, such as BAQIS Quafu, it is our belief that the proposed solution is well-suited for deployment on the cloud, allowing for public accessibility. Our findings not only expand the potential applications of quantum computers, but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.