Reviewerly/exHarmony huggingface dataset:
Neural Network Independence Properties with Applications to Adaptive Control

Neural networks form a general purpose architecture for machine learning and parameter identification. The simplest neural network consists of a single hidden layer connected to a linear output layer. It is often assumed that the components of the hidden layer correspond to linearly independent functions, but proofs of this are only known for a few specialized classes of network activation functions. This paper shows that for wide class of activation functions, including most of the commonly used activation functions in neural network libraries, almost all choices of hidden layer parameters lead to linearly independent functions. These linear independence properties are then used to derive sufficient conditions for persistence of excitation, a condition commonly used to ensure parameter convergence in adaptive control.
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control

While ${\mathcal{H}_\infty }$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performance and robustness is known to exist, it has not been quantitatively characterized. Toward addressing this issue, we borrow from the increasingly ubiquitous notion of adversarial training from machine learning to construct a class of controllers which are optimized for disturbances consisting of mixed stochastic and worst-case components. We find that this problem admits a stationary optimal controller that has a simple analytic form closely related to suboptimal ${\mathcal{H}_\infty }$ solutions. We then provide a quantitative performance-robustness tradeoff analysis, in which system-theoretic properties such as controllability and stability explicitly manifest in an interpretable manner. This provides practitioners with general guidance for determining how much robustness to incorporate based on a priori system knowledge. We empirically validate our results by comparing the performance of our controller against standard baselines, and plotting tradeoff curves.
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes

This paper proposes a physically consistent Gaussian Process (GP) enabling the data-driven modelling of uncertain Lagrangian systems.The function space is tailored according to the energy components of the Lagrangian and the differential equation structure, analytically guaranteeing properties such as energy conservation and quadratic form.The novel formulation of Cholesky decomposed matrix kernels allow the probabilistic preservation of positive definiteness.Only differential input-to-output measurements of the function map are required while Gaussian noise is permitted in torques, velocities, and accelerations.We demonstrate the effectiveness of the approach in numerical simulation.
Where to look for tiny objects? ROI prediction for tiny object detection in high resolution images

In this paper, we focus on the detection of tiny objects. The goal is to improve the performance of tiny object detection while preserving the average precision and recall metrics compared to a brute-force, sliding-window approach. We extend the object categories in COCO detection metrics from small, medium, and large by defining tiny, very tiny, and micro-objects. We propose an evaluation protocol for all six object sizes. To detect tiny objects, we offer a novel ROI proposal method based on a two-level nested U-structure architecture U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net. For this purpose, we experiment with multiple dilation techniques as well as Region of Interest (ROI) aggregation methods. We evaluate our method using the Mapillary Traffic Sign Dataset. The obtained detection strategy outperforms the single-step prediction approach and is comparable to the quality obtained with the use of the sliding-window, being nearly 7 times faster.
Text Document Classification Using Deep Learning Techniques

The advancement in technology has resulted in expansion in the volume of online text documents. It is interesting to note that in the previous two years alone, more electronic data has been created than ever before by the entire human species. As a result, it is now essential to accurately classify this data according to their content, which also helps in further processing and the extraction of valuable features. Text based document classification is one of the very important problems in Natural Language Processing (NLP). Manual document classification techniques rely heavily on human power to examine and label documents based on their content. Whereas, traditional Machine Learning (ML) based algorithms require manual feature extraction prior to classification which requires choosing the best algorithm to extract handcrafted features. Both these strategies are not only time-consuming but also prone to error, and require choosing the best available algorithms. On the other hand, Deep Learning (DL) based algorithms do not require human intervention as they perform deep feature extraction and classification automatically with much better performance than the traditional ML based frameworks. In this paper, we present a completely automated and robust document classification method to classify online digital documents using DL based methods i.e. BERT and RoBERTa. The proposed technique achieved highest accuracy of 98.9% and can be deployed to classify digital text documents with a high performance.
Spatial motion planning with Pythagorean Hodograph curves

This paper presents a two-stage prediction-based control scheme for embedding the environment’s geometric properties into a collision-free Pythagorean Hodograph spline, and subsequently finding the optimal path within the parameterized free space. The ingredients of this approach are twofold: First, we present a novel spatial path parameterization applicable to any arbitrary curve without prior assumptions in its adapted frame. Second, we identify the appropriateness of Pythagorean Hodograph curves for a compact and continuous definition of the path-parametric functions required by the presented spatial model. This dual-stage formulation results in a motion planning approach, where the geometric properties of the environment arise as states of the prediction model. Thus, the presented method is attractive for motion planning in dense environments. The efficacy of the approach is evaluated according to an illustrative example.Video: https://youtu.be/Sl6x8l7RJK8
Fairness-Aware Maximal Clique in Large Graphs: Concepts and Algorithms

Cohesive subgraph mining on attributed graphs is a fundamental problem in graph data analysis. Existing cohesive subgraph mining algorithms on attributed graphs do not consider the <i>fairness</i> of attributes in the subgraph. In this article, we, for the first time, introduce fairness into the widely-used clique model to mine fairness-aware cohesive subgraphs. In particular, we propose three novel fairness-aware maximal clique models on attributed graphs, called weak fair clique, strong fair clique and relative fair clique, respectively. To enumerate all weak fair cliques, we develop an efficient backtracking algorithm called <inline-formula><tex-math notation="LaTeX">$\mathsf{WFCEnum}$</tex-math></inline-formula> equipped with a novel colorful <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core based pruning technique. We also propose an efficient enumeration algorithm called <inline-formula><tex-math notation="LaTeX">$\mathsf{SFCEnum}$</tex-math></inline-formula> to find all strong fair cliques based on a new attribute-alternatively-selection search technique. To further improve the efficiency, we also present several non-trivial ordering techniques for both weak and strong fair clique enumerations. To enumerate all relative fair cliques, we design an enhanced colorful <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core based pruning technique for 2D attributes, and develop two efficient search algorithms: <inline-formula><tex-math notation="LaTeX">$\mathsf{RFCRefineEnum}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathsf{RFCAlterEnum}$</tex-math></inline-formula> for arbitrary dimension attributes. The results of extensive experiments on four real-world graphs demonstrate the efficiency, scalability and effectiveness of the proposed algorithms.
DASP: A Framework for Driving the Adoption of Software Security Practices

Implementing software security practices is a critical concern in modern software development. Industry practitioners, security tool providers, and researchers have provided standard security guidelines and sophisticated security development tools to ensure a secure software development pipeline. But despite these efforts, there continues to be an increase in the number of vulnerabilities that can be exploited by malicious hackers. There is thus an urgent need to understand why developers still introduce security vulnerabilities into their applications and to understand what can be done to motivate them to write more secure code. To understand and address this problem further, we propose DASP, a framework for diagnosing and driving the adoption of software security practices among developers. DASP was conceived by combining behavioral science theories to shape a cross-sectional interview study with 28 software practitioners. Our interviews lead to a framework that consists of a comprehensive set of 33 drivers grouped into 7 higher-level categories that represent what needs to happen or change so that the adoption of software security practices occurs. Using the DASP framework, organizations can design interventions suitable for developers’ specific development contexts that will motivate them to write more secure code.
Easy-to-Hard Structure for Remote Sensing Scene Classification in Multitarget Domain Adaptation

Multitarget domain adaptation (MTDA) is a transfer learning task that uses knowledge extracted from a labeled source domain to adapt across multiple unlabeled target domains. The MTDA setting is more complicated than the single-source-single-target domain adaptation (S3TDA) setting because domain shift not only exists in each pair of a source–target domain but also exists among different target domains. In addition, multiple-target domains have their own unique characteristics because they are often collected from various conditions. The semantic information in each target domain can be damaged when they are naïvely merged into a single-target domain. Therefore, the trained model struggles to distinguish between representations in the combined target domain, which degrades the classification performance. Furthermore, the knowledge transferability from the source domain to multiple-target domains in prior studies leaves room for improvement because they only focus on exploiting the relationship of source–target pairs while failing to consider the correlation among multiple-target domains. This article introduces an easy-to-hard adaption structure to solve these problems in MTDA. The proposed method consists of three components: Extracting source representations, Hierarchical intratarget feature Alignment, and Collaborative intertarget feature Alignment, called EHACA. These components are used to encode the semantic information in each target domain and explore the relationships between the source and target domains, and among different target domains. The proposed method shows outstanding classification performance over five remote sensing datasets of MTDA tasks, surpassing state-of-the-art approaches in most experimental scenarios.
Pruning-aware Sparse Regularization for Network Pruning

Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity. In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. Moreover, it restricts the network’s capacity, which leads to under-fitting. To solve this problem, we propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization. MaskSparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask, rather than all the filters of the model. Before the fine-grained sparse regularization of MaskSparity, we can use many methods to get the pruning mask, such as running the global sparse regularization. MaskSparsity achieves a 63.03% float point operations (FLOPs) reduction on ResNet-110 by removing 60.34% of the parameters, with no top-1 accuracy loss on CIFAR-10. On ILSVRC-2012, MaskSparsity reduces more than 51.07% FLOPs on ResNet-50, with only a loss of 0.76% in the top-1 accuracy. The code of this paper is released at https://github.com/CASIA-IVA-Lab/MaskSparsity . We have also integrated the code into a self-developed PyTorch pruning toolkit, named EasyPruner, at https://gitee.com/casia_iva_engineer/easypruner .
Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Summary Sliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.
BatTS: a hybrid method for optimizing deep feedforward neural network

Deep feedforward neural networks (DFNNs) have attained remarkable success in almost every computational task. However, the selection of DFNN architecture is still based on handcraft or hit-and-trial methods. Therefore, an essential factor regarding DFNN is about designing its architecture. Unfortunately, creating architecture for DFNN is a very laborious and time-consuming task for performing state-of-art work. This article proposes a new hybrid methodology (BatTS) to optimize the DFNN architecture based on its performance. BatTS is a result of integrating the Bat algorithm, Tabu search (TS), and Gradient descent with a momentum backpropagation training algorithm (GDM). The main features of the BatTS are the following: a dynamic process of finding new architecture based on Bat, the skill to escape from local minima, and fast convergence in evaluating new architectures based on the Tabu search feature. The performance of BatTS is compared with the Tabu search based approach and random trials. The process goes through an empirical evaluation of four different benchmark datasets and shows that the proposed hybrid methodology has improved performance over existing techniques which are mainly random trials.
The Evolution of Artificial Intelligence in the Digital Economy: An Application of the Potential Dirichlet Allocation Model

The most critical driver of the digital economy comes from breakthroughs in cutting-edge technologies such as artificial intelligence. In order to promote technological innovation and layout in the field of artificial intelligence, this paper analyzes the patent text of artificial intelligence technology using the LDA topic model from the perspective of the patent technology subject based on Derwent patent data. The results reveal that AI technology is upgraded from chips, sensing, and algorithms to innovative platforms and intelligent applications. Proposed countermeasures are necessary to advance the digitalization of the global economy and to achieve economic globalization in terms of industrial integration, building ecological systems, and strengthening independent innovation.
Stabilization of a D.C. Motor Speed Controller Using Type-1 Fuzzy Logic Systems Designed with the Chicken Search Optimization Algorithm

In this paper, a Chicken Search Optimization algorithm (CSO) is presented particularly in the implementation of the Type-1 Fuzzy Logic System (FLS) to find the optimal values of the Membership Functions (FMs) with the goal of stabilizing a D.C. Motor Speed Controller. CSO algorithm presents three type of role in the individuals which as; chicks, hens, and cocks, each role is analyzed and the intelligent behavior of the chicken to find the food is simulating in a real problem control applied to the Fuzzy Logic Controller (FLC) to stabilize the speed of 40 rads/s through starting at an origin point. Several type of metrics are shown; ITSE, ISE, ITAE, IAE, MSE, RMSE. A comparative analysis is implemented to demonstrated and verified that CSO algorithm presents excellent results compared to Harmony Search (HS) and Differential Evolutional (DE) for the stabilization of the D.C. Motor Speed Controller.
A viewpoint-based evaluation method for future Automotive Architectures

The Automotive industry undergoes a major transformation. Alternative powertrains, automated driving and Vehicle-to-Everything (V2X)-communication impose new requirements for vehicle development. Automotive software (SW), hardware (HW) and Vehicle Electronics combined in Electrical/Electronic architectures (E/E-architectures) play a key role in the paradigm change. An evolution from function-oriented, distributed architectures towards domain-or even vehicle-centralized architectures is expected. Even so, the question of evaluating architecture alternatives based on quality characteristics is unsolved. Then again, measuring an E/E-architecture’s quality and suitability is necessary for developing appropriate, use case specific solutions to support a sustainable transformation. By designing a method to evaluate design alternatives for the E/E-architecture of vehicles, combining different viewpoints and evaluation metrics, a contribution to this transformation can be provided. In this work such a viewpoint-based architecture framework enabling a holistic E/E-architecture assessment is introduced. Metrics substantiating these viewpoints are linked to the model. Finally the proposed procedure is demonstrated in the context of a simplified use case.
Natural Language Processing of Specifications for a Prototypical Avionic System to Generate System Design: A Case Study

The basis for the existence of a system is its requirements, which guide the entire process of systems development. The requirements are written in the natural language, which aids the process of generating the specification and, eventually, the system’s design. Interpreting a specification or the system description from the natural language induces ambiguity and inconsistency. To overcome the issues arising from the specification interpretation, we propose automating the process of converting the specification into an analytical diagram. Natural language processing (NLP) research and associated NLP tools developed recently have made this possible. Our method integrates NLP tools and system design principles to create a system architecture and behavior. As an illustration of our approach, we present the requirements and design for a flight avionic system.
Sustainability of entanglement sudden death under the action of memory channel

Abstract Quantum entanglement is an important resource in quantum information science. Its useful effects get disturbed when it is subjected to environmental effect and led to decay of entanglement. The behaviour of entanglement have been studied for maximally entangled mixed state (MEMS) under different cases. Here we proposed a method for the sustainability of entanglement using the concept of memory noise. During this study interesting results were observed for the intensification of entanglement sudden death (ESD) which have many applications in quantum information processing task. Analytical results were derived for MEMS state. After detailed discussion the problem is theoretically analysed and simulation results of ESD are presented.
Feature Map Regularized CycleGAN for Domain Transfer

CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping. In this paper, in order to further constrain the mapping problem and reinforce the cycle consistency between two domains, we also introduce a novel regularization method based on the alignment of feature maps probability distributions. This type of optimization constraint, expressed via an additional loss function, allows for further reducing the size of the regions that are mapped from the source domain into the same image in the target domain, which leads to mapping closer to the bijective and thus better performance. By selecting feature maps of the network layers with the same depth d in the encoder of the direct generative adversarial networks (GANs), and the decoder of the inverse GAN, it is possible to describe their d-dimensional probability distributions and, through novel regularization term, enforce similarity between representations of the same image in both domains during the mapping cycle. We introduce several ground distances between Gaussian distributions of the corresponding feature maps used in the regularization. In the experiments conducted on several real datasets, we achieved better performance in the unsupervised image transfer task in comparison to the baseline CycleGAN, and obtained results that were much closer to the fully supervised pix2pix method for all used datasets. The PSNR measure of the proposed method was, on average, 4.7% closer to the results of the pix2pix method in comparison to the baseline CycleGAN over all datasets. This also held for SSIM, where the described percentage was 8.3% on average over all datasets.
Wireless Network Simulation to Create Machine Learning Benchmark Data

While several wireless network simulators exist, the absence of modern, standardised network datasets may adversely affect the application of machine learning methods to problems involving wireless networks. Due to the difficulty of acquiring and sharing more modern network datasets, many of the rapidly evolving techniques in machine learning are only ever ported to network analysis through archaic network datasets such as KDD'99-creating a divide between communication networks and machine learning. To address this divide, this paper presents a new network simulation framework that brings existing network and machine learning tools together to conveniently generate data consisting of PCAP or raw physical layer data and derived statistics in a format that is directly consumable by machine learning algorithms. The proposed simulation frame-work allows the user to design custom networks through a simple configuration file-based scheme instead of learning a sophisticated network simulator. Measuring our framework's performance on consumer-grade devices, raw data is efficiently generated at up to 1, 200 Ethernet frames per second, and processed into feature vectors of 5.6 samples per second (where each sample uses 1 second of simulation time) or 0.29 samples per second if physical layer signals are used.