Reviewerly/exHarmony huggingface dataset:
Parallel Subdomain Synthesis of Cell Mapping for Capturing Global Invariant Sets in Higher-Dimensional Dynamical Systems

Unaffordable computational cost and memory storage induced by the curse of dimensionality has become the bottleneck of numerical methods in different fields. In the global analysis of nonlinear dynamical systems, the capability of numerical methods, like cell mapping methods, are mostly feasible only to a system dimension less than four. Although cell mappings are naturally parallelizable that may be used to greatly enhance the computational efficiency, it is still not enough to release the computational burden on a higher-dimensional system of greater than seven, not to mention the memory in dealing with millions of billion cells. In this paper, the subdomain synthesis method, which partitions the chosen region in state space into subdomains suitable for operating in a computational unit and then synthetizes the so-called virtual invariant sets to get the underlying global invariant sets, is promoted to be parallelizable on the subdomains so as to build a two-layer massively parallel architecture in both cell and subdomain levels. The proposed approach can be implemented by GPU Cluster that can maximize the powerful computation capability of hardwares. Examples with global invariant sets in very fine distances of a Jerk system and in bifurcations of a twelve-dimensional nonsmooth rotor system are presented for the first time to demonstrate then the feasibility of the proposed approach.
Sample balancing of curves for lens distortion modeling and decoupled camera calibration

State-of-the-art camera calibration methods usually estimate the pinhole model and lens distortion together by strict point correspondence. However, in many applications, only part of the full calibration result is required. Simpler calibration patterns like straight lines are studied in many works to calibrate only the lens distortion but lack precision compared with conventional methods. In this paper, we study the geometric invariants of line straightness and proposed a method to accurately estimate the normal vector of the original line from the distorted curve. We discovered that unbalanced samples have a significant influence on estimation accuracy. We proposed a sample balancing method and linear COD determination methods based on normal geometry. The proposed method requires little positioning restrictions of the camera or the calibration pattern, and lines are very easily found or established patterns. The proposed method is demonstrated with both synthetic data and real images. We show by experiment that camera calibration can be decoupled, and more flexible calibration patterns can be used for each stage.
Blockchain-Based Data Owner Rating in Medical Record Data Sharing using Ethereum

Some hospitals have different Electronic Medical Records (EMR) systems in affect health services for patients. Electronic Medical Record data among hospitals can eliminate the process of reduplicating Electronic Medical Record data. Data sharing is still centralized and fails to provide logs and events that are trusted, secure, immutable, auditable, and decentralized. In addition, Electronic Medical Record data sharing is important to reward a hospital as a Data Owner for contributing to sharing its Electronic Medical Record data. Our paper provided an Electronic Medical Record data sharing scheme among hospitals with a blockchain solution using an Ethereum smart contract. Access to encrypted data kept on a decentralized storage platform is made possible through a re-encryption method used in conjunction with oracles. We provided the reward mechanism for each Hospital that shared their Electronic Medical Record data through rating techniques with other hospitals as Data Requesters. As a result, we improvised a proposed data-sharing scheme that is focused on Electronic Medical Record data in hospitals to solve the problem of differences in Electronic Medical Record data of patients among hospitals. We evaluated the proposed system with cost analysis and security analysis.
Comparison between Grad-CAM and EigenCAM on YOLOv5 detection model

This paper presents a comparison between Grad-CAM and EigenCAM methods on the YOLOv5 detection model. Explainable Artificial Intelligence (XAI) is a tool to understand and interpret machine learning models. XAI is used to understand which pixel from an image contributes significantly to the model’s final output. EigenCAM is quite simple but a very powerful technique for producing visual explanations, but Grad-CAM is better for a low illumination environment.
Design of Testing Environment for Line-Follower Robot with Obstacles

To solve shortest path problem using Q-Learning, one testing method is needed. The testing method is showing Q-Learning result using robot and arena with obstacles. This article will explain obstacles and challenges related to the robot kinematics and arena design to fulfill those needs. To be specific, the robot is needed to move in particular manner in testing ground so the test can be conducted properly and the result can be visualized. As the proposed solution, the movement of the robot will use DC motor as the actuator and be complemented by the reading of infrared sensor and compass sensor as reference for the control method. The real condition of the arena and robot shape may impede the robot movement and cause uncertainty. In that condition, further design is needed so the robot movement in the testing area for the shortest path case can run properly.
A Low-power Sleep Apnea Monitoring IC with a Duty-Recovered Body Channel Communication Receiver

Obstructive sleep apnea (OSA) is receiving increasingly more attention as it is not only related to the quality of daily life but also to various cardiovascular diseases [1]. Since self-awareness of OSA is difficult and calls for expensive in-hospital examination, a portable inhome sleep apnea monitoring system has been proposed [2]. To meet the requirement of a level-4 sleep test, the sleep monitoring system must acquire at least 3 basic parameters: airflow at the nostril, oxygen saturation (SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) and heart rate (HR) at the fingertip. The system must also have a small form factor for user’s comfort and consume low-power to minimize battery size.
Hybrid Rule-based and Machine Learning System for Assertion Generation from Natural Language Specifications

We propose a hybrid approach for automatic generation of System Verilog assertions from natural-language specifications by combining machine-learning and formal analysis of the input text. The formal analysis focuses on parsing the input text, while the machine learning engine focuses on translating the input text to formal logic and/or assertions. Such a hybrid reduces the burden of a purely machine-learning based translation and increases accuracy in the generated assertions, especially for those complex assertions. In addition, the proposed hybrid approach offers a novel method to validate the assertions generated, in which the results of different machine-learning models are used to determine the confidence of the hybrid approach.
Augmented Placemaking: Urban Technologies, Interaction Design and Public Spaces in a Post-Pandemic World

Abstract The COVID-19 pandemic has brought a sudden and widespread reorganization of public spaces, underpinned by digital technologies. The impromptu, ad hoc and collective character of this reorganization of shared places for social interactions echoes traditional practices of placemaking otherwise advocated for liveable cities, now applied to the new—physical, virtual and, increasingly, hybrid—public spaces people started to inhabit. In this article, I define the notion of ‘augmented placemaking’, referring to a particularly new form of digital placemaking, unleashed by the pandemic. First, I identify key new scenarios for the design of urban interfaces that emerged or gained impetus in response to COVID-19. Following that, I synthesize a model for augmented placemaking based on emerging topologies, discussing the opportunities and challenges they posed, and their potential to inform future HCI. I then conclude by proposing guidelines to inform the design process of augmented placemaking initiatives going forward. Categories and subject descriptors: cities; pervasive computing; smart cities
Sparse RGB-D images create a real thing: A flexible voxel based 3D reconstruction pipeline for single object

Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing, AR/VR, and so on. It is necessary to consider the tradeoff between capturing data at low cost and getting high-quality reconstruction results. In this work, we propose a voxel-based modeling pipeline with sparse RGB-D images to effectively and efficiently reconstruct a single real object without the geometrical post-processing operation on background removal. First, referring to the idea of VisualHull, useless and inconsistent voxels of a targeted object are clipped. It helps focus on the target object and rectify the voxel projection information. Second, a modified TSDF calculation and voxel filling operations are proposed to alleviate the problem of depth missing in the depth images. They can improve TSDF value completeness for voxels on the surface of the object. After the mesh is generated by the MarchingCube, texture mapping is optimized with view selection, color optimization, and camera parameters fine-tuning. Experiments on Kinect capturing dataset, TUM public dataset, and virtual environment dataset validate the effectiveness and flexibility of our proposed pipeline.
An IPDA based target existence assisted Bayesian detector for target tracking in clutter

In this paper, an integrated probabilistic data association (IPDA) based target existence assisted Bayesian detector (TE-BD) is derived for target tracking in the clutter. The key idea of the proposed method is to feed back the predicted target position information and the existence probability from the tracker to the detector to facilitate target detection and tracking, while ensuring radar can terminate track quickly when target disappears. We first derive the prior information assisted target detection model, and then present the general steps of the TE-BD scheme based on the IPDA framework. Simulation results verify the effectiveness of the proposed IPDA-TE-BD w.r.t. the existing works.
DIODE: Dilatable Incremental Object Detection

To accommodate rapid changes in the real world, the cognition system of humans is capable of continually learning concepts. On the contrary, conventional deep learning models lack this capability of preserving previously learned knowledge. When a neural network is fine-tuned to learn new tasks, its performance on previously trained tasks will significantly deteriorate. Many recent works on incremental object detection tackle this problem by introducing advanced regularization. Although these methods have shown promising results, the benefits are often short-lived after the first incremental step. Under multi-step incremental learning, the trade-off between old knowledge preserving and new task learning becomes progressively more severe. Thus, the performance of regularization-based incremental object detectors gradually decays for subsequent learning steps. In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE). For the task-shared parameters, our method adaptively penalizes the changes of important weights for previous tasks. At the same time, the structure of the model is dilated or expanded by a limited number of task-specific parameters to promote new task learning. Extensive experiments on PASCAL VOC and COCO datasets demonstrate substantial improvements over the state-of-the-art methods. Notably, compared with the state-of-the-art methods, our method achieves up to 6.0% performance improvement by increasing the number of parameters by just 1.2% for each newly learned task.
An exquisite multiple image encryption harnessing multi-scroll Lu–Chen and Chua chaotic systems employing domino strategy

The enormous growth of information in the digital era has urged the researchers to secure the digital content. Guaranteeing security to these aspects remain to be the demanding challenge for the researchers in everyday life. The security needed for the confidential images has grown enormous of ages and in-order to offer exquisite protection for the collection of images in a distinct period of time, Multiple Gray Image Encryption (MGIE) model is proposed here to ensure the prevailing need. This encryption scheme incorporates two chaotic systems namely Chua system and Lu–Chen system due to their complex behavior. The proposed model endeavours the following: (1) source image can be of any type, (2) multiple images can be taken for processing, (3) cris-cross scrambling procedure is followed to absolutely alter the pixel locations, (4) diffusion is accelerated to strengthen the proposed MGIE system. The encryption procedures are interconnected and also provide cascading effect and so termed to be domino encryption strategy. The various experimental measures and performance analyses such as key space, key sensitivity, histogram, correlation, NPCR, UACI, entropy, noise, dataloss, computational complexity, time complexity analysis etc., prove that the MGIE model has inbuilt capacity to blow-away the prevailing attacks. Hence the proposed algorithm is suitable for secure transmission of multiple images.
Tweakable SM4: How to tweak SM4 into tweakable block ciphers?

SM4 is China’s commercial block cipher standard and an ISO/IEC international standard. While SM4 withstood a great deal of analysis, its non-tweakable structure makes it inflexible. In this paper, we study three different methods to turn SM4 into a tweakable block cipher. Based on the Tweak-aNd-Tweak (TNT) introduced by Bao et al. at EUROCRYPT 2020, we propose the first scheme, called TNT-SM4, which can be treated as an instantiation of TNT. For comparative analysis, the 128 and 32 bits tweaks are both adopted, which is denoted as TNT-SM4-128 and TNT-SM4-32, respectively. By taking full advantage of the structural characteristics of SM4, we also propose the second method, which studies how to tweak SM4 into a tweakable block cipher in a direct way. With the design goal of reducing the design, security evaluation, and implementation costs, we use SM4 as is and attach a lightweight linear tweak schedule to it. The biggest challenge is to find the best insertion location for tweaks in both aspects of security and efficiency. We extensively utilize mixed integer linear programming (MILP) to perform a comprehensive search. The third method adopts the same tweak schedule as the key schedule of SM4, which is equivalent to one more key schedule for SM4. We analyze the security of the three schemes, focusing on related-key and related-tweak attacks, and provide a comparative analysis of the three schemes. The results show that our schemes are secure, and a better scheme that makes SM4 more flexible is obtained from the comparison of the three schemes.
Development and comparison of local solar split models on the example of Central Europe

Solar radiation influences many and diverse fields like energy generation, agriculture and building operation. Hence, simulation models in these fields often rely on precise information about solar radiation. Measurements are often restricted to global irradiance, whereby measurements of its single components, direct and diffuse irradiance, are sparse. However, information on both, the direct and diffuse irradiance, is necessary for simulation models to work reliably. In this study, solar separation models are developed using 10-min training data from two different locations in Austria. Direct horizontal irradiance is predicted via regressing the direct fraction using several objective functions. The models are first trained on a data set including data from both locations, and evaluated regarding root mean squared deviation (RMSD), mean bias deviation (MBD), and coefficient of determination (R2) on measured and estimated direct normal irradiance. The two best performing models are then selected for further analysis. This analysis comprises of an evaluation of the models per season, transferability of trained modes between two locations in Austria, a transferability and generalisability study conducted for four more locations in Central Europe, and a comparison with the trusted Engerer model. The solar separation model with polynomial terms up to order three and Ridge regularisation outperforms the second model based a logistic term in combination with mixed quadratic terms as well as the Engerer model.
Model-based deep learning for fiber bundle infrared image restoration

As the representative of flexibility in optical imaging media, in recent years, fiber bundles have emerged as a promising architecture in the development of compact visual systems. Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio (SNR) imaging in fiber bundles, the iterative super-resolution reconstruction network based on a physical model is proposed. Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately, the network can efficiently “regenerate” the lost spatial resolution with deep learning. By building and calibrating a dual-path imaging system, the real-world dataset where paired low-resolution (LR) - high-resolution (HR) images on the same scene can be generated simultaneously. Numerical results on both the United States Air Force (USAF) resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise. On the basis of super-resolution reconstruction, compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements. The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band, effectively promoting the engineering application of thermal radiation detection.
Preserving hand-drawn qualities in audiovisual performance through sketch-based interaction

Live coding – the real-time procedural creation of audiovisual works – suggests opportunities to extend hand-drawn animation; however, existing live coding systems are incompatible with manual animation workflows. Manual input is not a primary datatype in existing live coding languages and live coding tools require using symbolic programming environments. We theorize that by applying direct manipulation to the domain of live coding, we can enable animators to create expressive mappings between hand-drawn animations and audio effects in realtime. We present Megafauna, a sketch-based system for audiovisual performance, informed by interviews with professional animators. Megafauna supports the integrated generation and control of hand-drawn animation and audio sequences by enabling animators to directly sketch mapping functions between animation frames and sound generators. We demonstrate the expressive potential of Megafauna by reproducing animated compositions from procedural and manual domains. We evaluate the opportunities of our approach for live production through an expert review of a performance piece created with Megafauna.
Weighted-leader search: A new choice in metaheuristic and its application in real-world large-scale optimization

Aiming at the robustness and real-time requirements of engineering applications, this paper develops a novel metaheuristic algorithm with a simple mathematical framework for numerical optimization, namely the weighted-leader search (WLS). With the WLS, a weighted mean point of the top solutions is developed to evolve the population. Additionally, several simple local search mechanisms are introduced to prevent the algorithm from premature convergence. Moreover, the WLS possesses fewer constant coefficients to reduce the parameter sensitivity. The performance of the WLS is evaluated with the CEC 2017 test suite with 10D, 30D, 50D and 100D problems, the CEC 2008 test suite with 100D, 500D and 1000D problems, large-scale benchmarks with 100D, 500D and 1000D (or beyond) problems and constrained engineering design problems. The statistical results of the experiments indicate that the WLS exhibits an outstanding ability among the state-of-the art algorithms in different competitions, which verifies the accuracy and efficiency of our algorithm in solving complex problems, as well as its robustness in large-scale evaluation. Moreover, the WLS is employed to address the cooperative path planning problem of an unmanned aircraft vehicle (UAV) swarm with 100 members. Through problem model construction, the path planning issue is transformed into a constrained optimization problem and solved by the WLS. The experimental results show that the proposed WLS is able to solve the path planning problem in real time within the constraints and significantly outperforms the other competitors, demonstrating the accuracy and time savings of the algorithm in solving real-world optimization problems.
A new mathematical method to describe the logic of the natural physical world

To model an objective real world with absolute accurate mathematics is the symbol of modern science. However, in real life, the physical parameters could not be measured in infinite high accuracy, which makes the mathematical equations ineffective in the circumstance of turbulence and chaos. As the macro‐ and micro‐physical world could not be unified, it is believed that the laws of macro‐ and micro‐physical world are not the same. This paper proposes a new way of illustrating physical parameters, using the combination of mean ± error instead of a single numeric parameter for illustration, and establishes the corresponding algorithm and equations for means and errors, respectively. The results show that the error part of the Navier–Stokes equations in macro‐physical fluid mechanical world is just Schrodinger equation of micro‐physical quantum mechanical world. Therefore, a new method of researching complex system is proposed, and this method establishes the relationship between macro‐Newtown mechanics and micro‐quantum mechanics, so that in the macro world, the mean will follow the Navier–Stokes equations, and the error will follow the Schrodinger equations.
On fly hybrid swarm optimization algorithms for clustering of streaming data

Clustering is an important data analysis technique for extracting knowledge and hidden patterns in the data. Recently hybrid clustering algorithms have been proposed to solve the local optimum and poor robustness problem due to improper selection of initial cluster centroids in the traditional clustering algorithms. In hybrid algorithms, clustering is solved as an optimization problem by invoking optimization algorithms to find the optimal cluster centroid. Many hybrid clustering algorithms integrating different swarm algorithms and traditional clustering algorithms have been proposed. However, these algorithms were not designed for the on-fly clustering of streaming data. This is necessary for real-time streaming requirements in financial domains like stock markets. This work proposes an on-fly hybrid swarm optimization algorithm for the clustering of streaming data.