Reviewerly/exHarmony huggingface dataset:
An Epistemic Logic for Multi-agent Systems with Budget and Costs

In Artificial Intelligence, Multi-Agent Systems are able to model many kinds of collective behavior and have a wide range of application. Logic is often used to model aspects of agents’ reasoning process. In this paper, we discuss social aspects of such systems. We propose a logical framework (Logic of “Inferable”) which reasons about whether a group of agents can perform an action, highlighting the concepts of action cost and budget that the group must have available in order to perform actions. The focus is on modeling the group dynamics of cooperative agents: if an agent of a group performs an action, that action to be considered as performed by the whole group, and the group can support a component agent in performing actions not affordable by that agent alone.
Research on the Application of Machine Learning Big Data Mining Algorithms in Digital Signal Processing

Traditional digital signal processing technology based on DSP and FPGA is more suitable for real-time signal processing, and is limited by data scale and frequency resolution, making it unsuitable for offline data processing, analysis and mining under large-scale data Application. At present, the industrial big data analysis platform can use Spark as a calculation engine for real-time signal processing and offline signal processing acceleration, but the analysis platform lacks mathematical calculation solutions such as digital signal processing suitable for distributed parallel calculation engines. This article is based on time the parity decomposition is selected, and the fast Fourier transform is realized by MATLAB software. Based on an example of the application of the compiled FFT source program, this article analyses the frequency spectrum of discrete-time and continuous-time signals of limited length.
RSAN: Residual Subtraction and Attention Network for Single Image Super-Resolution

The single-image super-resolution (SISR) aims to recover a potential high-resolution image from its low-resolution version. Recently, deep learning-based methods have played a significant role in super-resolution field due to its effectiveness and efficiency. However, most of the SISR methods neglect the importance among the feature map channels. Moreover, they can not eliminate the redundant noises, making the output image be blurred. In this paper, we propose the residual subtraction and attention network (RSAN) for powerful feature expression and channels importance learning. More specifically, RSAN firstly implements one redundance removal module to learn noise information in the feature map and subtract noise through residual learning. Then it introduces the channel attention module to amplify high-frequency information and suppress the weight of effectless channels. Experimental results on extensive public benchmarks demonstrate our RSAN achieves significant improvement over the previous SISR methods in terms of both quantitative metrics and visual quality.
3CS Algorithm for Efficient Gaussian Process Model Retrieval

Gaussian Process Models (GPMs) are Bayesian machine learning models that have been widely applied in the domain of pattern recognition due to their ability to infer from unreliable, noisy, or highly idiosyncratic data. Retrieving a complex GPM describing the data's inherent statistical patterns, such as trends, seasonalities, and periodicities, is a key requirement for various pattern recognition tasks. In this paper, we propose a novel approach for efficient large-scale GPM retrieval: the Concatenated Composite Covariance Search (3CS) algorithm. By making use of multiple local kernel searches on dynamically partitioned data, the 3CS algorithm is able to overcome the performance limitations of state-of-the-art GPM retrieval algorithms and to efficiently retrieve GPMs for large-scale data up to three orders of magnitude as fast as state-of-the-art algorithms.
Convex Neural Autoregressive Models: Towards Tractable, Expressive, and Theoretically-Backed Models for Sequential Forecasting and Generation

Three features are crucial for sequential forecasting and generation models: tractability, expressiveness, and theoretical backing. While neural autoregressive models are relatively tractable and offer powerful predictive and generative capabilities, they often have complex optimization landscapes, and their theoretical properties are not well understood. To address these issues, we present convex formulations of autoregressive models with one hidden layer. Specifically, we prove an exact equivalence between these models and constrained, regularized logistic regression by using semi-infinite duality to embed the data matrix onto a higher dimensional space and introducing inequality constraints. To make this formulation tractable, we approximate the constraints using a hinge loss or drop them altogether. Furthermore, we demonstrate faster training and competitive performance of these implementations compared to their neural network counterparts on a variety of data sets. Consequently, we introduce techniques to derive tractable, expressive, and theoretically-interpretable models that are nearly equivalent to neural autoregressive models.
Robust Latent Representations Via Cross-Modal Translation and Alignment

Multi-modal learning relates information across observation modalities of the same physical phenomenon to leverage complementary information. Most multi-modal machine learning methods require that all the modalities used for training are also available for testing. This is a limitation when signals from some modalities are unavailable or severely degraded. To address this limitation, we aim to improve the testing performance of uni-modal systems using multiple modalities during training only. The proposed multi-modal training framework uses cross-modal translation and correlation-based latent space alignment to improve the representations of a worse performing (or weaker) modality. The translation from the weaker to the better performing (or stronger) modality generates a multi-modal intermediate encoding that is representative of both modalities. This encoding is then correlated with the stronger modality representation in a shared latent space. We validate the proposed framework on the AVEC 2016 dataset (RECOLA) for continuous emotion recognition and show the effectiveness of the framework that achieves state-of- the-art (uni-modal) performance for weaker modalities.
Galois Field Arithmetic Operations using Xilinx FPGAs in Cryptography

Cryptography algorithms are standards for any security-based industry. Internationally widely accepted and used cryptography algorithms like AES, DES rely heavily on finite field arithmetic which needs to be performed efficiently, to meet execution speed and design constraints. This paper aims to provide a concise perspective on designing efficient architectures in finite field arithmetic. In this paper, we propose Galois field arithmetic using irreducible polynomial to generate the S-box for AES using 128, 192, and 256-bit Keys. Cryptographic algorithms are more prone to side-channel attacks, so we implemented this algorithm instead of using a lookup table-based approach. The proposed Galois Field implementation of arithmetic operations are unique which can be extended to any primitive polynomial of any word size GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ). A novel scheme is proposed for AES S-box, Inverse S-box, and validated using a Xilinx Virtex-7 FPGA.
Optimal Incentive and Load Design for Distributed Coded Machine Learning

A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first k (out of the total n) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform's optimal incentive mechanism for motivating proper workers' participation in coded machine learning, despite the multi-dimensional incomplete information about heterogeneous workers' computation performances and costs. A key contribution of this work is to summarize workers' multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform's efficient selection of workers under incomplete information with a linear computation complexity. Although the exact overall runtime is intractable, we characterize the platform's (asymptotically) optimal load assignment to heterogeneous workers in coded machine learning. When the platform has incomplete information about workers' costs, it is optimal to assign loads only based on workers' computation performances; when the platform further lacks workers' computation performance information, it is optimal to design the loads to be cost-dependent and performance-dependent.
A New Hybrid Meta-Heuristics Algorithms to Solve APP Problems

Abstract In this paper, a new hybrid algorithm for linear programming model based on Aggregate production planning problems is proposed. The new hybrid algorithm of a simulated annealing (SA) and particle swarm optimization (PSO) algorithms. PSO algorithm employed for a good balance between exploration and exploitation in SA in order to be effective and efficient (speed and quality) for solving linear programming model. Finding results show that the proposed approach is achieving within a reasonable computational time comparing with PSO and SA algorithms.
Verification of the Incremental Merkle Tree Algorithm with Dafny

The Deposit Smart Contract (DSC) is an instrumental component of the Ethereum 2.0 Phase 0 infrastructure. We have developed the first machine-checkable version of the incremental Merkle tree algorithm used in the DSC. We present our new and original correctness proof of the algorithm along with the Dafny machine-checkable version. The main results are: 1) a new proof of total correctness; 2) a software artefact with the proof in the form of the complete Dafny code base and 3) new provably correct optimisations of the algorithm.
Spatiotemporal clustering using Gaussian processes embedded in a mixture model

Abstract The categorization of multidimensional data into clusters is a common task in statistics. Many applications of clustering, including the majority of tasks in ecology, use data that is inherently spatial and is often also temporal. However, spatiotemporal dependence is typically ignored when clustering multivariate data. We present a finite mixture model for spatial and spatiotemporal clustering that incorporates spatial and spatiotemporal autocorrelation by including appropriate Gaussian processes (GP) into a model for the mixing proportions. We also allow for flexible and semiparametric dependence on environmental covariates, once again using GPs. We propose to use Bayesian inference through three tiers of approximate methods: a Laplace approximation that allows efficient analysis of large datasets, and both partial and full Markov chain Monte Carlo (MCMC) approaches that improve accuracy at the cost of increased computational time. Comparison of the methods shows that the Laplace approximation is a useful alternative to the MCMC methods. A decadal analysis of 253 species of teleost fish from 854 samples collected along the biodiverse northwestern continental shelf of Australia between 1986 and 1997 shows the added clarity provided by accounting for spatial autocorrelation. For these data, the temporal dependence is comparatively small, which is an important finding given the changing human pressures over this time.
A Survey and Comparison of Some of the Most Prominent QKD Protocols

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Research on Fake Rating Detection Algorithm Based on Deep Learning

In the e-commerce market, fake rating detection has been one of the key and difficult problems that disturb the e-commerce platforms. There have been a large number of researches on this topic all over the world, in which many algorithms and models have been proposed. Most of these algorithms are supervised and semi-supervised. However, few e-commerce platforms are willing to disclose their own transaction data and detection methods, thus it is challenging to perform theoretical studies on the detection algorithms. In order to address this challenge, this paper proposes a semi-supervised fake rating detection method based on deep learning (FRDDL) and e-commerce platform reputation evaluation method under the premise of using a small number of training samples and only considering the behavior characteristics of the buyers. The experimental results on the hotel rating data in Yelp showed that the proposed algorithm had better detection performance than the existing algorithms.
Constructive Master's Thesis Work in Industry: Guidelines for Applying Design Science Research

Context: Software engineering researchers and practitioners rely on empirical evidence from the field. Thus, education of software engineers must include strong and applied education in empirical research methods. For most students, the master's thesis is the last, but also most applied form of this education in their studies. Problem: Especially thesis work in collaboration with industry requires that concerns of stakeholders from academia and practice are carefully balanced. It is possible, yet difficult to do high-impact empirical work within the timeframe of a typical thesis. In particular, if this research aims to provide practical value to industry, academic quality can suffer. Even though constructive research methods such as Design Science Research (DSR) exist, thesis projects repeatably struggle to apply them. Principle solution idea: DSR enables balancing such concerns by providing room both for knowledge questions and design work. Yet, only limited experience exists in our field on how to make this research method work within the context of a master's thesis. To enable running design science master's theses in collaboration with industry, this paper complements existing method descriptions and guidelines. It offers experience and pragmatic advice to students, examiners, and supervisors in academia and industry. Method: This paper itself is based on DSR. Based on 12 design science theses over the last seven years, common pitfalls and good practices are collected from analysing the theses, the student-supervisor interaction, the supervisor-industry interaction, the examiner feedback, and, where available, reviewer comments on publications that are based on such theses. Results: This paper provides concrete advise for framing research questions, structuring a report, as well as for planning and conducting design science research with practitioners.
Local Diagonal Maxima-Minima Pattern-based Edge Detection Technique for Ultrasound and Digital Radiography Images

This paper presents a low dimensional edge descriptor, based on local diagonal and non-diagonal maxima-minima pattern for medical images. The methods utilize local relationship of a middle pixel to its diagonal/non-diagonal maxima-minima. The scheme reduces the length of feature vector to a great extent without compromising the quality of edge map. The designed methods are validated on digital X-ray for bone fracture detection, dental images and ultrasound images. Improved depth local binary pattern, anisotropic diffusion & Canny edge detection, Canny and Sobel methods are also used for a comparative analysis. The local diagonal maxima-minima pattern and local non-diagonal maxima-minima pattern methods are robust to noise, while preserving the useful edge information. The experimental results reveal the superiority of proposed methods as compared to the state-of-the-art methods in terms of accuracy, Jaccard similarity index, specificity, sensitivity and dice similarity coefficient.
Dual-Stream Network Based On Global Guidance for Salient Object Detection

High-level features can help low-level features eliminate semantic ambiguity, which is crucial for obtaining the precise salient object. Some methods use high-level features to provide global guidance for some layers of the network. However, there remain several problems: (1) the global guidance has not been fully mined, which leads to its limited capacity; (2) the semantic gap between global guidance and low-level features is ignored, and simple merging methods will cause feature aliasing. To remedy the problems, we propose a dual-stream network based on global guidance with two plug-ins, global attention based multi-scale high-level feature extraction module (GAMS) to mine global guidance and scale adaptive global guidance module (SAGG) to seamlessly integrate the global guidance into each decoding layer. Comprehensive experiments on the five largest benchmark datasets demonstrate our method outperforms previous state-of-the-art methods by a large margin. Code is available at https://github.com/shuyonggao/DSGGN.
Robust extended Kalman filter estimation with moving window through a quadratic programming formulation

In this work, two new formulations for the extended (MW-EKF) and robust extended Kalman filter with moving window estimation (MW-REKF) are proposed. The MW-EKF and MW-REKF are formulated using an elegant quadratic programming problem that facilitates its implementation and decreases its computational cost. Besides that, the constrained extended Kalman filter (CEKF), constrained extended Kalman filter and smoother (CEKFS) and the moving horizon estimation (MHE) are compared in terms of computational cost and fit to the real data. The comparison is performed over a spherical-quadruple-tank model with different settings aiming to raise each approach's advantages and disadvantages. For both state and parameter estimation the MW-REKF has shown the smoothest and most robust behavior among all methodologies. This technique minimized the effect of the outliers, physical limitations, structural discrepancies, among others. The computational cost of the proposed techniques is only four times higher than CEKF and nine times smaller than the MHE.
Is Walking Necessary for Effective Locomotion and Interaction in VR?

This paper reports on a work-in-progress study to investigate if/how leaning-based interfaces affect simultaneous locomotion and interaction. We compare physical walking and Controller with a seated (i.e., HeadJoystick) and standing (i.e., Naviboard) leaning-based interface. We disambiguated performance in locomotion versus interaction using a novel experimental paradigm, where participants should point toward moving targets using their virtual light-saber while actively following a moving platform.
Quantifying social distancing compliance and the effects of behavioral interventions using computer vision

Social distancing has become a pressing and challenging issue during the Covid-19 pandemic. In a smart cities context, it becomes possible to measure inter-personal distance using networked cameras and computer vision analysis. We deploy a computer vision pipeline based on Retinanet that identifies pedestrians in streaming video frames, then converts their positions to GPS coordinates for distance calculation and further analysis. This processing is applied to nine camera streams at three locations from around Vanderbilt University. We collect 70 hours of baseline distancing data over the course of two weeks, after which time we deploy small behavioral interventions at the three locations aimed at increasing distancing compliance. Another 70 hours of data with the interventions in place will be analyzed against the baseline data to determine if they had an effect on distancing compliance.
Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

This paper describes our approach for the Chinese clinical named entity recognition (CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing (CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record (EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.
Synthesis of Linear Quantum Systems to Generate a Steady Thermal State

The purpose of this article is to synthesize a linear quantum system, which is strictly stable and has a steady thermal state. Specifically, we give a parameterization of a class of stable linear quantum systems that have <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V=\tau I/2$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau &gt; 1$</tex-math></inline-formula> , as their steady covariance matrsices. This is physically important since the covariance matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau I/2$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau &gt; 1$</tex-math></inline-formula> , corresponds to a quantum thermal state. Hence, we can say that these systems will asymptotically evolve into a quantum thermal state. An extension to the case where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V=S\operatorname{diag}(\Lambda,\Lambda ) S^{\top }/2$</tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Lambda &gt; I$</tex-math></inline-formula> being a diagonal matrix and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> being a symplectic matrix will also be considered. Physically, a covariance matrix of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V=S\operatorname{diag}(\Lambda,\Lambda ) S^{\top }/2$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Lambda &gt; I$</tex-math></inline-formula> , corresponds to a mixed Gaussian quantum state. So, we can alternatively say that the corresponding linear quantum systems will asymptotically evolve into a mixed Gaussian quantum state.