Reviewerly/exHarmony huggingface dataset:
Visual sensor network stimulation model identification via Gaussian mixture model and deep embedded features

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.
Stereoscopic High Speed Camera Based Operational Modal Analysis Using a One-Camera Setup

We perform Operational Modal Analysis (OMA) through Frequency Domain Decomposition (FDD) on a test structure using a high speed camera. Using optical flow algorithms, brightness changes due to displacements of a test structure under load are translated into displacement data. By using a mirror, a split image is generated that enables a stereoscopic view of the structure and thus 3D information can be extracted from the high speed video footage. This setup is used to determine the modal parameters of the structure, especially the mode shapes with high spatial resolution. The mirror that is used to gather a second point of view onto the image sensor enables a cost-efficient stereoscopic measurement compared to a two-camera setup. Moreover, it eliminates the need of synchronizing two separate signals. This is also why OMA is used instead of EMA: as no force signal needs to be recorded, we can perform a full-field modal analysis with inputs from only one single device. The results are evaluated taking into account the accuracy as well as the complexity of the setup of the 3D measurements compared to a setup using triaxial accelerometers.
End-to-end Speech-to-Punctuated-Text Recognition

Conventional automatic speech recognition systems do not produce punctuation marks which are important for the readability of the speech recognition results. They are also needed for subsequent natural language processing tasks such as machine translation. There have been a lot of works on punctuation prediction models that insert punctuation marks into speech recognition results as post-processing. However, these studies do not utilize acoustic information for punctuation prediction and are directly affected by speech recognition errors. In this study, we propose an end-to-end model that takes speech as input and outputs punctuated texts. This model is expected to predict punctuation robustly against speech recognition errors while using acoustic information. We also propose to incorporate an auxiliary loss to train the model using the output of the intermediate layer and unpunctuated texts. Through experiments, we compare the performance of the proposed model to that of a cascaded system. The proposed model achieves higher punctuation prediction accuracy than the cascaded system without sacrificing the speech recognition error rate. It is also demonstrated that the multi-task learning using the intermediate output against the unpunctuated text is effective. Moreover, the proposed model has only about 1/7th of the parameters compared to the cascaded system.
Task Allocation with Load Management in Multi-Agent Teams

In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur.While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources.Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks.In this work, we present a decision-making framework for multiagent teams to learn task allocation with the consideration of load management through decentralized reinforcement learning, where idling is encouraged and unnecessary resource usage is avoided.We illustrate the effect of load management on team performance and explore agent behaviors in example scenarios.Furthermore, a measure of agent importance in collaboration is developed to infer team resilience when facing handling potential overload situations.
Discovering optimal fermion-qubit mappings through algorithmic enumeration

Simulating fermionic systems on a quantum computer requires a high-performing mapping of fermionic states to qubits. A characteristic of an efficient mapping is its ability to translate local fermionic interactions into local qubit interactions, leading to easy-to-simulate qubit Hamiltonians.<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>A</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:math> fermion-qubit mappings must use a numbering scheme for the fermionic modes in order for translation to qubit operations. We make a distinction between the unordered labelling of fermions and the ordered labelling of the qubits. This separation shines light on a new way to design fermion-qubit mappings by making use of the enumeration scheme for the fermionic modes. The purpose of this paper is to demonstrate that this concept permits notions of fermion-qubit mappings that are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>o</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:math> with regard to any cost function one might choose. Our main example is the minimisation of the average number of Pauli matrices in the Jordan-Wigner transformations of Hamiltonians for fermions interacting in square lattice arrangements. In choosing the best ordering of fermionic modes for the Jordan-Wigner transformation, and unlike other popular modifications, our prescription does not cost additional resources such as ancilla qubits.We demonstrate how Mitchison and Durbin's enumeration pattern minimises the average Pauli weight of Jordan-Wigner transformations of systems interacting in square lattices. This leads to qubit Hamiltonians consisting of terms with average Pauli weights 13.9% shorter than previously known. By adding only two ancilla qubits we introduce a new class of fermion-qubit mappings, and reduce the average Pauli weight of Hamiltonian terms by 37.9% compared to previous methods. For <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-mode fermionic systems in cellular arrangements, we find enumeration patterns which result in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math> improvement in average Pauli weight over naïve schemes.
Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

The present investigation suggests a novel control technique that merge the advantage of the adaptive Neuro-Fuzzy inference system (ANFIS) with the proportional integral derivative (PID) controller, abbreviated as (ANFIS-PID), for dealing with the dynamics of the of wheeled mobile robot (WMR).A comparative study of various meta-heuristic optimization algorithms is made.The results revealed the highest efficiency of the suggested ANFIS-PID technique compared to seven designed PID controllers, in terms of settling and rise time, overshoot, and the integral square error (ISE) as a cost function.Various cases, study (circular path, diamond path, zigzag path) have highlighted the over performance of mentioned controller.Moreover, this hybrid technique is fused with backstepping approach for the kinematic control.A lemniscate and a square trajectory were performed to clarify the capability of the mentioned controller.
GCN-Based Text Classification Research

With the development and popularity of the Internet, a huge amount of text data has emerged on the web, and how to accurately and quickly identify the categories of these text data has become a challenge in the field of text classification. To address this challenge, this paper aims to apply deep learning techniques to text classification and build a model that can classify text data in a batch manner and automatically. By studying the current state of the art in text classification needs, this paper proposes a TextGCN model, a text classification method that presents high robustness on small data sets, based on graph convolutional neural networks. This paper makes the model perform better by modifying the IDF formula.
A New Reversible Data Hiding Scheme by Altering Interpolated Pixels Exploiting Neighbor Mean Interpolation (NMI)

Reversible data hiding (RDH) is an important field in image processing which can be implemented using interpolation method. Here, we have proposed a reversible data hiding method which is implemented by adjusting the interpolated pixels after applying NMI interpolation. Two bits of secret data has been embedded at each interpolated pixel of cover image which results in 26.94 dB PSNR value on average and 1.5 bpp payload. Instead of addition or subtraction operation during data embedding, we adjust the interpolated pixels in such a way that eliminates overflow and underflow problem occurred in previous schemes. Further, the results prove that the proposed scheme is superior to the state-of-the-art schemes in terms of image quality and hiding capacity.
On the Expressive Power of Message-Passing Neural Networks as Global Feature Map Transformers

We investigate the power of message-passing neural networks (MPNNs) in their capacity to transform the numerical features stored in the nodes of their input graphs. Our focus is on global expressive power, uniformly over all input graphs, or over graphs of bounded degree with features from a bounded domain. Accordingly, we introduce the notion of a global feature map transformer (GFMT). As a yardstick for expressiveness, we use a basic language for GFMTs, which we call MPLang. Every MPNN can be expressed in MPLang, and our results clarify to which extent the converse inclusion holds. We consider exact versus approximate expressiveness; the use of arbitrary activation functions; and the case where only the ReLU activation function is allowed.
Digital Magnetic Tensor Sensor With ANN Measurement Model for Human Joint Motion Sensing in Sagittal Plane

Motivated by the increased needs of home-based rehabilitation for stroke patients, more and more interest has been drawn towards developing body-fixed sensors for monitoring affected joint motions. Although relatively accurate bone geometries can be obtained by scanning technologies, most human joints are approximated by simple circles and spheres to reduce the highly nonlinear kinematics to a tractable form for motion studies; many human joint-motion sensing challenges remain open. This article presents a novel magnetic tensor sensor (MTS) for noncontact tracing a human joint trajectory and a physics-based measurement model implemented on an artificial neural network (ANN) to account for un-modeled factors. The effects of input configurations and datatypes on measurement accuracy of an MTS/ANN have been numerically investigated with published data and experimentally evaluated on a prototype pantographic exoskeleton worn on a human shank/foot. As demonstrated experimentally, the MTS/ANN system calibrates the sensor intrinsic parameters, accounts for the environmental magnetic effects on the measurements, and can be trained with both offline and user-specific data.
High-performance computing and computational intelligence applications with a multi-chaos perspective

The experience of the COVID-19 pandemic, which has accelerated many chaotic processes in modern society, has highlighted in a very serious and urgent way the need to understand complex processes in order to achieve the common well-being. Modern high performance computing technologies, quantum computing, computational intelligence are shown to be extremely efficient and useful in safeguarding the fate of mankind. These technologies are the state-of-the-art of IT evolution and are fundamental to be competitive and efficient today. If a company is familiar with these techniques and technologies, it will be able to deal with any unexpected and complicated scenarios more efficiently and effectively. The main contribution of our work is a set of best practices and case studies that can help the researcher address computationally complex problems. We offer a range of software technologies, from high performance computing to machine learning and quantum computing, which represent today the state-of-the-art to deal with extremely complex computational issues, driven by chaotic events and not easily predictable. In this chapter we analyze the different technologies and applications that will lead mankind to overcome this difficult moment as well as to understand more and more deeply the profound aspects of very complex phenomena. In this environment of rising complexity, in terms of technology, algorithms, and changing lifestyles, it is critical to emphasize the importance of achieving maximum efficiency and outcomes while protecting the integrity of everyone's personal data and respecting the human being as a whole.
Automatic Error Analysis for Document-level Information Extraction

Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts. Evaluation of the approaches, however, has been limited in a number of dimensions. In particular, the precision/recall/F1 scores typically reported provide few insights on the range of errors the models make. We build on the work of Kummerfeld and Klein (2013) to propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. We employ our framework to compare two state-of-the-art document-level template-filling approaches on datasets from three domains; and then, to gauge progress in IE since its inception 30 years ago, vs. four systems from the MUC-4 (1992) evaluation.
Enhancing Variational Generation Through Self-Decomposition

In this article we introduce the notion of Split Variational Autoencoder (SVAE), whose output <i>x</i> is obtained as a weighted sum &#x03C3; &#x2299; <i>x</i><sub>1</sub> + (1 - &#x03C3;) &#x2299; <i>x</i><sub>2</sub> of two generated images <i>x</i><sub>1</sub>,<i>x</i><sub>2</sub>, and &#x03C3; is a learned compositional map. The composing images <i>x</i><sub>1</sub>,<i>x</i><sub>2</sub>, as well as the &#x03C3;-map are automatically synthesized by the model. The network is trained as a usual Variational Autoencoder with a negative loglikelihood loss between training and reconstructed images. No additional loss is required for <i>x</i><sub>1</sub>,<i>x</i><sub>2</sub> or &#x03C3;, neither any form of human tuning. The decomposition is nondeterministic, but follows two main schemes, that we may roughly categorize as either &#x201C;syntactic&#x201D; or &#x201C;semantic&#x201D;. In the first case, the map tends to exploit the strong correlation between adjacent pixels, splitting the image in two complementary high frequency sub-images. In the second case, the map typically focuses on the contours of objects, splitting the image in interesting variations of its content, with more marked and distinctive features. In this case, according to empirical observations, the Fr&#x00E9;chet Inception Distance (FID) of <i>x</i><sub>1</sub> and <i>x</i><sub>2</sub> is usually lower (hence better) than that of <i>x</i>, that clearly suffers from being the average of the former. In a sense, a SVAE forces the Variational Autoencoder to make choices, in contrast with its intrinsic tendency to average between alternatives with the aim to minimize the reconstruction loss towards a specific sample. According to the FID metric, our technique, tested on typical datasets such as Mnist, Cifar10 and CelebA, allows us to outperform all previous purely variational architectures (not relying on normalization flows).
FasterVideo: Efficient Online Joint Object Detection and Tracking

Object detection and tracking in videos represent essential and computationally demanding building blocks for current and future visual perception systems. In order to reduce the efficiency gap between available methods and computational requirements of real-world applications, we propose to re-think one of the most successful methods for image object detection, Faster R-CNN, and extend it to the video domain. Specifically, we extend the detection framework to learn instance-level embeddings which prove beneficial for data association and re-identification purposes. Focusing on the computational aspects of detection and tracking, our proposed method reaches a very high computational efficiency necessary for relevant applications, while still managing to compete with recent and state-of-the-art methods as shown in the experiments we conduct on standard object tracking benchmarks (Code available at https://github.com/Malga-Vision/fastervideo ).
Explaining Intelligent Agent’s Future Motion on Basis of Vocabulary Learning With Human Goal Inference

Intelligent agents (IAs) that use machine learning for decision-making often lack the explainability about what they are going to do, which makes human-IA collaboration challenging. However, previous methods of explaining IA behavior require IA developers to predefine vocabulary that expresses motion, which is problematic as IA decision-making becomes complex. This paper proposes Manifestor, a method for explaining an IA’s future motion with autonomous vocabulary learning. With Manifestor, an IA can learn vocabulary from a person’s instructions about how the IA should act. A notable contribution of this paper is that we formalized the communication gap between a person and IA in the vocabulary-learning phase, that is, the IA’s goal may be different from what the person wants the IA to achieve, and the IA needs to infer the latter to judge whether a motion matches that person’s instruction. We evaluated Manifestor by investigating whether people can accurately predict an IA’s future motion with explanations generated with Manifestor. We compared Manifestor’s vocabulary with that from optimal acquired in a situation in which the communication-gap problem did not exist and that from ablation, which was learned with a false assumption that an IA and person shared a goal. The experimental results revealed that vocabulary learned with Manifestor improved people’s prediction accuracy as much as with optimal, while ablation failed, suggesting that Manifestor can enable an IA to properly learn vocabulary from people’s instructions even if a communication gap exists.
Generating a Structured Summary of Numerous Academic Papers: Dataset and Method

Writing a survey paper on one research topic usually needs to cover the salient content from numerous related papers, which can be modeled as a multi-document summarization (MDS) task. Existing MDS datasets usually focus on producing the structureless summary covering a few input documents. Meanwhile, previous structured summary generation works focus on summarizing a single document into a multi-section summary. These existing datasets and methods cannot meet the requirements of summarizing numerous academic papers into a structured summary. To deal with the scarcity of available data, we propose BigSurvey, the first large-scale dataset for generating comprehensive summaries of numerous academic papers on each topic. We collect target summaries from more than seven thousand survey papers and utilize their 430 thousand reference papers’ abstracts as input documents. To organize the diverse content from dozens of input documents and ensure the efficiency of processing long text sequences, we propose a summarization method named category-based alignment and sparse transformer (CAST). The experimental results show that our CAST method outperforms various advanced summarization methods.
Weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition

Existing MARL algorithms have low efficiency in many-agent scenarios due to the complex dynamic interaction when agents growing exponentially. Mean-field theory has been introduced to improve the scalability where complex interactions are approximated by those between a single agent and the mean effect from neighbors. However, only considering the averaged actions of neighborhood at last step and ignoring the dynamic influence of neighbors leads to unstable training procedures and sub-optimal solutions. In this paper, the Weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition (MFRAD) framework is proposed by differentiating heterogeneous and hysteresis neighbor effect with weighted mean-field approximation and reward attribution decomposition. The multi-head attention is employed to calculate the weights which formulate the weighted mean-field Q-function. To further eliminate the impact of hysteresis information, reward attribution decomposition is integrated to decompose weighted mean-field Q-value, improving the interpretability of MFRAD and achieving fully decentralized execution without information exchanging. Two novel regularization terms are also introduced to guarantee the consistency of temporal relationship among agents and unambiguity of local Q-value with no agents. Numerical experiments on many-agent scenarios demonstrate the superior performance against existing baselines.