Reviewerly/exHarmony huggingface dataset:
Generic saturation-induced phase-error correction algorithm for phase-measuring profilometry

Abstract Intensity saturation causes partial incorrect intensities in captured images, leading to obvious phase errors in high-dynamic-range phase-measuring profilometry. Most existing methods require numerous projected patterns or additional hardware equipment to retrieve the three-dimensional shape. This paper proposes a comprehensive saturation-induced phase-error correction method by combining an average-phase compensation method, applying four-step phase-shifting (PS) patterns, with a phase repair method employing a total variation minimization (TVM) model. The periodic characteristic of the saturation-induced phase error is analyzed. The phase error can be efficiently compensated by averaging the initial and auxiliary phase, which is calculated utilizing a set of PS patterns with a phase offset of <?CDATA $\pi $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>π</mml:mi> </mml:math> /4. Furthermore, a judgment condition is provided to detect invalid points in overexposed shiny areas where the initial calculated phases are wrong. The corrected phases are repaired utilizing the TVM model from the compensated phase information surrounding invalid points. Simulations and experiments show that the proposed method can simultaneously correct the phase in non-uniform, high-reflectivity scenes and shiny areas with high accuracy using relatively few images. The phase error is reduced by nearly 80%.
CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks

Node classification plays a critical role in numerous network applications, and has attracted increasing attention in recent years. Existing state-of-the-art studies aim at maintaining common information between the topology graph and the feature graph in an implicit way, i.e., adopting a common convolution with parameter sharing strategy to preserve common information between the two graphs. Despite their effectiveness, these studies are still far from satisfactory due to the complex correlation information between the two spaces. To address this issue, we present a novel method named Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks (CNIM-GCN). CNIM-GCN preserves the common information between the feature space and topology space in an explicit way by introducing a consensus graph for information propagation. A multi-channel graph convolutional networks is developed for effectively fusing information from different graphs. In addition, we further incorporate two types of consistency constraints, i.e., structural consistency constraint and reconstruction consistency constraint, to maintain the consistency between different channels. The former is leveraged to keep the consistency between different spaces at the structural relationship level, while the latter is used to preserve a consistency between the final node representation and the original node feature representation. We carry out extensive experiments on five real-world datasets, including ACM, BlogCatalog, CiteSeer, Flickr and UAI2010. Experimental results show that our proposed approach CNIM-GCN is superior to the state-of-the-art baselines.
Smooth Exploration System: A novel ease-of-use and specialized module for improving exploration of whale optimization algorithm

Whale optimization algorithm (WOA) is a swarm-based optimization algorithm that has recently attracted extensive interest and attention because of its excellent exploitation ability. However, exploration process of WOA needs to be more valued and requires specialized mechanisms to maximize exploration ability. Therefore, based on the insight into the underlying logic of exploration process, a novel smooth exploration system (SES) is proposed to improve exploration process of WOA, and a variant of WOA is proposed called smooth WOA (SWOA). The SES comprises three mechanisms: unordered dimension sampling, random crossover, and sequential mutation. In detail, inspired by the sampling theory, unordered dimension sampling is used to adjust the sparsity of population and the proportion between exploration and exploitation. The random crossover and the sequential mutation complement each other to cover a vast search space. The performance of the SWOA is verified by comparing it with seven variants of WOA and six advanced algorithms based on 31 benchmark functions from CEC2015, CEC2021, and CEC2022. A total of six qualitative methods are introduced to analyze the performance of the SES comprehensively. Coupling coordination evaluation is introduced to present the synergy of mechanisms within the SES, which is an improvement of the ablation experiment and facilitates the consideration of rationality of each evolutionary strategy. Comprehensive qualitative analyses and fair comparisons demonstrate the remarkable performance of the SWOA.
Efficient and robust Levenberg–Marquardt Algorithm based on damping parameters for parameter inversion in underground metal target detection

The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algorithm has been proposed in this paper to improve its accuracy and efficiency of parameter estimation with low SNR. The NMLM algorithm can also converge globally under certain conditions and converge quadratically under the error-bound condition, which updates based on the gain ratio and determines the damping factor based on the gradient value. An experimental investigation has been conducted under different SNRs and the amount of data, and the results indicate the new algorithm can accurately and quickly estimate the parameters of underground metal targets under the lower SNRs and smaller amounts of data. More specifically, the estimation accuracy of position, principal axes polarizability, and orientation of the metal target can be increased by up to 1.6 cm, 0.00013, and 2.31 degree, respectively. This paper also verifies that the new algorithm finds the optimal solution for standard numerical problems with low errors and a finite number of iterations.
A Survey on Application of Blockchain Technology in Drug Supply Chain Management

Drug counterfeiting and trafficking are common all over the world. The main reason for this is that the traditional drug supply chain model is opaque, and the database is controlled by the direct interest person, so there is a chance that the actual controller tampers with or falsifies the data for personal gain. The blockchain, as a distributed ledger, can achieve process transparency, data sharing, and autonomous management in the pharmaceutical supply chain through its unique cryptographic algorithms, smart contracts, and consensus mechanisms. However, it introduces a number of new issues, such as scalability, system interoperability, and regulatory policies. This paper discusses the benefits of blockchain technology in enabling the drug supply chain, as well as some academic research and mature industry solutions in this area. We also go over some of the issues that need to be addressed and potential response strategies for using blockchain technology in pharmaceutical supply chain scenarios. Finally, in order to address issues with current drug supply chain regulation, we propose a new model that incorporates the ‘‘governing blockchain by blockchain’’ architecture in order to achieve safe and efficient automated supply chain management.
WSOmark: An adaptive dual-purpose color image watermarking using white shark optimizer and Levenberg–Marquardt BPNN

With the rapid progression in Internet technologies, content authentication and copyright concerns have increased exponentially. To overcome these challenges, this paper proposes WSOmark—a dual purpose multiple embedding strength (MES) based color image watermarking scheme for standard and medical images. It uses a robust watermark for copyright protection, and a semi-fragile watermark for content authentication. White shark optimizer (WSO) is used to optimize the locally relevant MES. However, as the MES optimization process is time-intensive, WSO is employed only once on a set of images to train the Levenberg–Marquardt BPNN (LM-BPNN) model. For the subsequent images, the trained LM-BPNN model is used to generate the MES quickly. In this scheme, for robust watermark embedding the blue channel of the host image is transformed using discrete shearlet transform (DST) and singular value decomposition (SVD), and a robust watermark is embedded into the principal component (PC) coefficients of the approximation sub-band using MES. Human visual system parameters (edge and visual entropy) are used to select the regions for watermarking. Furthermore, a semi-fragile watermark is embedded into the DC coefficients of the green channel for content authentication. A novel CNN-based geometric correction procedure is employed to ensure robustness of WSOmark to geometric attacks. For enhanced security, a combination of SVD-based perceptual hashing technique and an Arnold transform based watermark scrambling technique is employed. The experimental results exhibit that WSOmark achieves an average PSNR and SSIM value of 57.631 dB and 0.999, respectively, and have watermarking capacity of 0.0418 bpp. In addition, WSOmark is robust to geometric, image processing, as well as combination of attacks, and efficiently detects and localize the tampered regions with good accuracy. The comparison of WSOmark with state-of-the-art dual watermarking schemes establishes that WSOmark outperforms the state-of-the-art methods in terms of visual quality, robustness, and capacity. Additionally, it takes less time and provides adequate security.
Text Detection Model for Historical Documents Using CNN and MSER

This article introduces a text detection model for historical documents images. The handwritten characters in historical documents are always difficult to detect because they contain fuzzy or missing ink, or weathering features and stains; these features will seriously affect the detection accuracy. In order to reduce the influence mentioned above, an effective ATD model is proposed to detect the textbox of characters in historical documents image, and ATD model includes a CNN-based text-box generation network and an NMS-based MSER text-box generation model. As a post-processing method, a text merging algorithm is proposed to achieve higher detection accuracy. The test results on historical document datasets such as Yi, English, Latin, and Italian datasets show that the method in this paper has good accuracy, and it has taken a solid step for the detection of historical documents.
<i>CatSQL</i> : Towards Real World Natural Language to SQL Applications

Natural language to SQL (NL2SQL) techniques provide a convenient interface to access databases, especially for non-expert users, to conduct various data analytics. Existing methods often employ either a rule-base approach or a deep learning based solution. The former is hard to generalize across different domains. Though the latter generalizes well, it often results in queries with syntactic or semantic errors, thus may be even not executable. In this work, we bridge the gap between the two and design a new framework to significantly improve both accuracy and runtime. In particular, we develop a novel CatSQL sketch, which constructs a template with slots that initially serve as placeholders, and tightly integrates with a deep learning model to fill in these slots with meaningful contents based on the database schema. Compared with the widely used sequence-to-sequence-based approaches, our sketch-based method does not need to generate keywords which are boilerplates in the template, and can achieve better accuracy and run much faster. Compared with the existing sketch-based approaches, our CatSQL sketch is more general and versatile, and can leverage the values already filled in on certain slots to derive the rest ones for improved performance. In addition, we propose the Semantics Correction technique, which is the first that leverages database domain knowledge in a deep learning based NL2SQL solution. Semantics Correction is a post-processing routine, which checks the initially generated SQL queries by applying rules to identify and correct semantic errors. This technique significantly improves the NL2SQL accuracy. We conduct extensive evaluations on both single-domain and cross-domain benchmarks and demonstrate that our approach significantly outperforms the previous ones in terms of both accuracy and throughput. In particular, on the state-of-the-art NL2SQL benchmark Spider, our CatSQL prototype outperforms the best of the previous solutions by 4 points on accuracy, while still achieving a throughput up to 63 times higher.
GlassDB: An Efficient Verifiable Ledger Database System Through Transparency

Verifiable ledger databases protect data history against malicious tampering. Existing systems, such as blockchains and certificate transparency, are based on transparency logs --- a simple abstraction allowing users to verify that a log maintained by an untrusted server is append-only. They expose a simple key-value interface without transactions. Building a practical database from transparency logs, on the other hand, remains a challenge. In this paper, we explore the design space of verifiable ledger databases along three dimensions: abstraction, threat model, and performance. We survey existing systems and identify their two limitations, namely, the lack of transaction support and the inferior efficiency. We then present GlassDB, a distributed database system that addresses these limitations under a practical threat model. GlassDB inherits the verifiability of transparency logs, but supports transactions and offers high performance. It extends a ledgerlike key-value store with a data structure for efficient proofs, and adds a concurrency control mechanism for transactions. GlassDB batches independent operations from concurrent transactions when updating the core data structures. In addition, we design a new benchmark for evaluating verifiable ledger databases, by extending YCSB and TPC-C benchmarks. Using this benchmark, we compare GlassDB against four baselines: reimplemented versions of three verifiable databases, and a verifiable map backed by a transparency log. Experimental results demonstrate that GlassDB is an efficient, transactional, and verifiable ledger database system.
Behavior Trees and State Machines in Robotics Applications

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the community to use and further develop behavior trees, associated tools, and analysis techniques.
A Cognitive Computing Methodology for Software Requirement Elicitation and Formal Specification

Autonomous software requirement analysis and specifications is not only an ultimate goal of cognitive computing, but also a persistent challenge to theories and technologies of software engineering. A cognitive computing model is demanded to autonomously elicit and rigorously refine software requirements in order to generate a set of formal specifications. This paper presents a novel methodology for the design of a cognitive computing method for Software Requirement Elicitation and Specifications (SRES) based on the latest advances in software science and intelligent mathematics. SRES is implemented as an interactive system for capturing software requirements and generating formal specifications. The SRES methodology and experiments are demonstrated for solving real-world and complex software engineering problems enabled by cognitive computing theories underpinned by intelligent mathematics.
On Abstract Sciences: From Data, Information, Knowledge to Intelligence Sciences

The emergence of abstract sciences as a counterpart of classic concrete sciences is presented in this work. The framework of abstract sciences encompasses data, information, knowledge, and intelligence sciences from the bottom up. It is found that intelligence is the ultimate level of cognitive objects generated in human brains aggregated from data (sensory), information (cognition), and knowledge (comprehension). However, there is a lack of rigorous studies and coherent theories towards the theoretical framework of abstract sciences as the counterpart of classical concrete sciences. This paper explores the cognitive and mathematical models of abstract mental objects in the brain. The taxonomy and cognitive foundations of them are explored. A set of mathematical models of data, information, knowledge, and intelligence is formally created in intelligent mathematics. Based on the cognitive and mathematical models of the cognitive objects, formal properties and relationship of contemporary data, information, knowledge, and intelligence sciences are rigorously explained.
Advances on intelligent algorithms for scientific computing: an overview

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.
Prosodic Modelling based Speaker Identification

The use of prosodic characteristics, mainly pitch and intensity, for speaker identification in noisy environments is examined in this work. To make the acoustic models more resistant to the variability in the speech signal in noisy situations, these features are supplemented with cepstral parameters. As a consequence, two systems for Automatic Speaker Identification (ASI) in the independent mode of text are implemented. The first based on Hidden Markov Models (HMM), whereas Support Vector Machines (SVM) are employed in the second. The addition of prosodic features improves recognition, especially in high-noise environments. The performance of SVM-based systems is better than HMM-based systems
Using Thematic Analysis in Healthcare HCI at CHI: A Scoping Review

CHI papers researching healthcare human-computer interaction (HCI) are increasingly reporting the use of "thematic analysis" (TA). TA refers to a range of flexible and evolving approaches for qualitative data analysis. Its increased use demonstrates a change in research practices, and with that the emergence of new local standards. We need to understand and reflect upon these emerging local practices, including departures from what is advocated as quality TA practice more generally. Toward this, we conducted a scoping review of a decade of CHI publications (2012 – 2021) that researched healthcare and termed their analysis approach "thematic analysis"; 78 papers reporting a total of 100 TAs were included. We contribute a description of 1) the contexts in which TA is being used, 2) the TA approaches being conducted, and 3) how TA is being reported. Drawing on this, we discuss opportunities to improve research practice when using TA in healthcare HCI.