A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies,robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing,due to the large applicabilities and its utilities in a number of academic and real environments.There are a wide range of solutions to provide image watermarking frameworks,while each one of them is attempted to address an efficient and applicable idea.In reality,the traditional techniques do not have sufficient merit to realize an accurate application.Due to the fact that the main idea behind the approach is organized based on contourlet representation,the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique.In a word,the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy.In fact,the motivation of the approach is that the suggested complexity can be of novelty,which consists of the contourlet representation,the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image.There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism,while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks.The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image.And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis,as well.Subsequently,the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade,saturation or amount of gray and their brightness via value or luminance and also hue,saturation and intensity representations,as long as the performance of the whole of channels are concentrated to be presented.The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

Testability Estimation of Framework Based Applications

Quality of software systems is highly demanded in today’s scenario. Highly testable system enhances the reliability also. More than 50% of test effort-time and cost are used to produce a highly testable system. Thus, design-for-testability is needed to reduce the test effort. In order to enhance the quality, productivity and reduced cost of the software organizations are promoting to produce the reuse-oriented products. Incorporating reuse technology in both aspects-software development process and test process may payoff many folds. Keeping this view, our study focus the testability of the object-oriented framework based software systems and identify that flexibility at the variable points of the object-oriented framework, chosen for framework instantiation, greatly affects the testability of object-oriented framework based software at each level of testing. In the current paper, we propose a testability model considering the flexible aspect of the variable point to estimate testability in the early phase, requirement analysis phase, of development process of the framework based software. The proposed model helps to improve the testability of the software before the implementation starts thus reducing the overall development cost.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

Amine-grafted on lanthanide metal-organic frameworks:Three solid base catalysts for Knoevenagel condensation reaction

A post-synthetic modification strategy has been used to prepare three solid base catalysts, including Er（btc）（ED）075（H2O）0.25 （2, btc = 1,3,5-benzenetricarboxylates, ED = 1,2-ethanediamine）, Er（btc）（PP）0.55（H20）0.45 （3, PP = piperazine）, and Er（btc）（DABCO）0.15（H2O）0.85 （4, DABCO = 1,4- diazabicyclo[2.2.2]octane）, by grafting three different diamines onto the coordinatively unsaturated Er（III） ions into the channels of the desolvated lanthanide metal-organic framework （Er（otc））. The resulting metal-organic frameworks were characterized by elemental analysis, thermogravimetric analysis, powder X-ray diffraction, and N2 adsorption. Based on its higher loading ratio of the diamine, as well as its greater stability and porosity, catalyst 2 exhibited higher catalytic activity and reusability than catalysts 3 and 4- for the Knoevenagel condensation reaction. The catalytic mechanism of 2 has also been investigated using size-selective catalysis tests.

EAFoC： Enterprise Architecture Framework Based on Commonality

The recent rapid development in information systems （ISs） has resulted in a critical need for integration and interoperability between heterogeneous ISs in various domains, using specific commonalities. However, stovepipe systems have been caused due to inconsistencies in planning IS architecture among stakeholders. So far, there has been no research on an enterprise architecture framework （EAF） that can satisfy with the coefficient factors of system architecture （SA） and enterprise architecture （EA）. This paper proposes a new EAF that can resolve the problems inherent in existing legacy EAFs and their features. EAFoC （Enterprise Architecture Framework based on Commonality） is based on commonality that can be satisfied as the coefficient factors in both SA and EA within a common information technology （IT） domain. Thus, it should be possible to integrate an established heterogeneous framework for each stakeholder＇s view. Consequently, the most important contribution of this paper is to establish the appropriate EAFoC for the development of consistent IS architecture, smooth communication among stakeholders, systematic integration management of diversified and complicated new IT technologies, interoperability among heterogeneous ISs, and reusability based on commonality with other platforms.

A Triple Nexus Water-Energy-Housing (WEH) Framework Modelling towards Improved Decision-Making in Humanitarian Operations

Given the challenges facing most humanitarian operations worldwide, a change of approach is needed to ensure greater sustainability of humanitarian settlements right from the planning stage. Some studies attribute unsustainability to inadequate provision of basic resources and highlight the apparent bottlenecks that prevent access to the meaningful data needed to plan and remedy problems. Most operations have relied on an “ad hoc ism” approach, employing parallel and disconnected data processing methods, resulting in a wide range of data being collected without subsequent prioritization to optimize interconnections that could enhance performance. There have been little efforts to study the trade-offs potentially at stake. This work proposes a new framework enabling all subsystems to operate in a single system and focusing on data processing perspective. To achieve this, this paper proposes a Triple Nexus Framework as an attempt to integrate water, energy, and housing sector data derived from a specific sub-system within the overall system in the application of Model-Based Systems Engineering. Understanding the synergies between water, energy, and housing, Systems Engineering characterizes the triple nexus framework and identifies opportunities for improved decision-making in processing operational data from these sectors. Two scenarios illustrate how an integrated platform could be a gateway to access meaningful operational data in the system and a starting point for modeling integrated human settlement systems. Upon execution, the model is tested for nexus megadata processing, and the optimization simulation yielded 67% satisfactory results, demonstrating that an integrated system could improve sustainability, and that capacity building in service delivery is more than beneficial.

Market based framework for multiple AUVs cooperation

A“Market” based framework for multiple AUVs team is introduced in this paper.It is a distributed meta-level task allocation framwork. The formulation and the basic concepts of the “Market” such as “goods” and “price” are discussed first, then the basic algorithm of the “auction”. The loosely coupled v-MDTSP tasks are considered as an example of the task allocation mission. A multiple AUV team controller and a detailed algorithm are developed for such applications. The simulation results show that the controller has the advantages such as robustness and low complexity and it can achieve better optimization results than the classical central controller (such as GA) in some tasks. And the comparison of two different local solvers also implies that we should get the reasonable task allocation even not using the high quality algorithm, which can considerably decrease the cooperation computation.

A thiophene-containing covalent triazine-based framework with ultramicropore for CO2 capture

In this work, a 2D covalent triazine-based framework was prepared by using 1,3-dicyanobenzo[c]thiophene（DCBT） as monomer to effectively capture CO. The resulting CTF-DCBT was characterized by FT-IR, XPS, PXRD, elemental analysis, SEM, TEM, and Nadsorption-desorption.The results indicate that CTF-DCBT is partially crystalline and has ultramicropore（6.5 A?） as well as high heteroatom contents（11.24 wt% and 12.61 wt% for N and S, respectively）. In addition, the BET surface area and total pore volume of CTF-DCBT are 500 m/g and 0.26 cm/g, respectively. CTF-DCBT possesses excellent thermal stability（450 °C） and chemical stability towards boiling water, 4 M HCl, and 1 M Na OH.The COadsorption capacity of CTF-DCBT is 37.8 cm/g at 1 bar and 25 °C. After six adsorption-desorption cycles, there is no obvious loss of COuptake observed. Due to the ultramicropore and high heteroatom contents, CTF-DCBT has high isosteric heats of adsorption for COand high selectivities of COover Nand CH. At 25 °C, the CO/Nand CO/CHselectivities are 112.5 and 10.3, respectively, which are higher than those of most POFs. Breakthrough curves indicate that CTF-DCBT could effectively separate CO/Nand CO/CHmixtures.

Carbon-based derivatives from metal-organic frameworks as cathode hosts for Li–S batteries

Lithium-sulfur batteries(Li–S batteries) are promising candidates for the next generation high-energy rechargeable Li batteries due to their high theoretical specific capacity(1672 m Ahg-1) and energy density(2500 Wh kg-1). The commercialization of Li–S batteries is impeded by several key challenges at cathode side, e.g. the insulating nature of sulfur and discharged products(Li2S 2 and Li2S), the solubility of long-chain polysulfides and volume variation of sulfur cathode upon cycling. Recently, the carbonbased derivatives from metal-organic frameworks(MOFs) has emerged talent in their utilization as cathode hosts for Li–S batteries. They are not only highly conductive and porous to enable the acceleration of Li +/e-transfer and accommodation of volumetric expansion of sulfur cathode during cycling, but also enriched by controllable chemical active sites to enable the adsorption of polysulfides and promotion of their conversion reaction kinetics. In this review, based on the types of MOFs(e.g. ZIF-8, ZIF-67, Prussian blue, Al-MOF, MOF-5, Cu-MOF, Ni-MOF), the synthetic methods, formation process and morphology, structural superiority of MOFs-derived carbon frameworks along with their electrochemical performance as cathode host in Li–S batteries are summarized and discussed.

Synthesis, Structure and Electrocatalytic Activity of Perfluorinated Covalent Triazine-based Framework

Using the bottom-up method, we synthesized a series of perfluorinated covalent triazine-based frameworks(FCTFs) with porous structures for catalysis oxygen reduction reaction(ORR). The evolved FCTFs by high-temperature carbonization show an apparent variation in electrocatalytic activity toward the ORR dependent on the type of F. The samples synthesized at 900 ℃(FCTF-900) exhibits advantages in terms of high activity, high durability, and methanol-tolerant as an efficient electrocatalyst for ORR, manifests a comparable or even better activity as compared with the commercial Pt/C catalysts not only in alkaline media but also in acidic and neutral electrolyte.

Using a Task-based Teaching Framework in Lesson Designing

Task-basedteachingandlearningis becoming more and more popularin China now,andthe aimof taskis to create a real purpose for language use and provide a natural context for language study· This article introduces what a task-based teaching frameworkis and the three phases of pre-task, while-task and post-task,combined withsomethoughts about howwe shouldconvertthosetasksinto actual classroomactivities while using the task-based teaching framework in designing the lessons and howthe teacher and students participate inthe lessons·

大数据驱动的产品创新商务智能——基于SSP框架的分析

人工智能改变产品创新主体,引发产品创新战略与流程变革。本文依据大数据合作资产理论,从企业与用户互动的适应性创新视角,通过逻辑推演分析提出大数据驱动的产品创新商务智能主体—战略—流程框架,以探讨人工智能影响制造产品创新的新机制。研究表明:首先,人与人工智能双向卷入形成要素组合与知识积累的双向反馈式增强机制,促使创新主体发生结构性改变,形成产品创新战略与流程变革的新基础。其次,企业人工智能认知战略与竞争战略、组织文化、商业模式和生态系统的匹配,共同影响人工智能在创新流程中的一体化应用。最后,模糊前端的全流程扩散、需求—解决方案对的非线性迭代和智能制造的生成式自测构成大数据驱动的产品创新流程的新特征。研究表明,主体—战略—流程框架适合分析大数据驱动的产品创新商务智能的新机制,可为人工智能与产品创新的关系研究提供新视角。

Two Generic Frameworks of Multiple Viewpoints Oriented Requirements Method and Their Comparison

Traditional requirements method has some problems when it is used for large distributed systems. Multiple viewpoints oriented requirements method (MVORM) is a new method for resolving these problems. This paper develops two generic formal frameworks of MVORM, framework based on refinement relation (FBRR) and framework based on implementation relation (FBIR). They are generic, because no assumptions are made about the development process or the formal description languages to be used. Three kinds of specification relations and three kinds of specification transformations are discussed over FBIR and FBRR. This paper also compares the equivalence between FBIR and FBRR. We point out that an equivalent FBIR can be found for any FBRR, but reverse transformation is not always possible. We think FBIR is better than FBRR on most cases.

基于React的电商网站构建与实现

为了满足电商网站所需的各项功能和要求,基于React框架和Node.js进行了电商网站的前后端分离式开发,并设计了关键模块,如用户认证、商品展示、购物车管理及订单处理等。采用了React的组件化开发理念,有效提升了代码的可维护性和可重用性。通过系统测试和性能调优,网站性能和稳定性得到显著提升,包括页面加载时间缩短、数据库查询速度优化以及系统响应性能改善。

基于成果导向教育理念的人工智能开发框架应用课程教学改革与实践

基于成果导向教育理念,对人工智能开发应用课程进行教学改革与实践,阐述了依据专业预期学习成果设计课程教学目标,再分解细化为课程预期学习成果和课程单元预期学习成果,制定课程教学设计方案,最后进行课程教学评价。在实施教学实践后,取得较好的教学效果。

面向深度学习应用的组件式开发框架的设计实现

针对目前深度学习应用缺少有效的开发与部署工具的问题,提出一个面向深度学习应用的组件式开发框架。所提框架根据应用的资源消耗类型进行功能拆分,使用评测引导的资源分配方案进行瓶颈消除,使用分步装箱方案兼顾高CPU利用率和低显存开销的功能放置。基于此框架开发的实时车牌号检测应用,在吞吐优先模式下GPU利用率达到82%,在延迟优先模式下平均应用延迟达到0.73 s,在三种模式下(吞吐优先模式、延迟优先模式以及吞吐/延迟的均衡模式)下,CPU平均利用率达到68.8%。实验结果表明,基于此框架能够进行硬件吞吐与应用延迟的平衡型配置,在吞吐优先模式下高效利用平台的计算资源,在延迟优先模式下满足应用的低延迟需求。相较于MediaPipe,使用本框架能够进行超实时的多人姿态估计应用开发,应用的检测帧率最高提升了1077%。实验结果表明,所提框架能够作为CPU-GPU异构服务器上面向深度学习应用开发部署的有效解决方案。

数字平台生态系统治理——理论逻辑、行动框架与模式划分

数字平台生态系统的持续发展取决于其平台架构设计和治理规则的有效匹配。基于平台的治理规则和治理架构之间的动态镜像关系,从理论逻辑、行动框架和模式划分等方面对数字平台生态系统治理进行分析,结果发现:首先,数字平台生态系统治理是平台所有者基于治理架构设计创建规则来管理平台参与者的一系列活动组合。在治理过程中,治理主体主要面临着决策权集中与分散的平衡、平台边界开放与封闭的取舍两个关键问题;其次,治理主体、治理目标、治理架构和治理规则是数字平台生态系统治理的四个基本要素,四者相互作用共同组成实现数字平台生态系统可持续性的行动框架;最后,基于平台决策权分散还是集中,以及平台边界是封闭还是开放两个维度,数字平台生态系统治理可以划分为专断型治理、引导型治理、合约型治理和自由型治理四类模式。

OBE教育理念下的外语课堂形成性评价:理论与实践构念

OBE(Outcome-Based Education)教育理念强调以学生为中心、以产出为导向、注重教学和学习的持续改进。它不仅关注学习成果,还关注如何评价学习成果。课堂形成性评价是连接“教”与“学”的有效方式,在学业评价范式变革中发挥着越来越重要的作用。本文以分析课堂形成性评价的理论框架、实施工具与方法为基础,对外语课堂形成性评价的理论和实践构念进行系统阐述,对目前存在的问题提出对策和建议,从而为促进外语课堂形成性评价研究和实践提供启示。

聚合物基MOFs复合材料抗菌研究进展

金属有机骨架(MOFs)是一类备受瞩目的新兴材料,在抗菌领域有着广泛的应用。将聚合物与MOFs进行复合制备聚合物基MOFs(PolyMOFs)复合材料,不仅可以增强MOFs的稳定性、机械强度等,还可以进一步扩展其应用范围。该文重点综述了PolyMOFs复合材料在抗菌方面的研究进展,概述了功能性MOFs的制备和主要抗菌机制,以及PolyMOFs复合材料的制备方法;总结了具有抗菌功能的PolyMOFs复合材料在医疗健康、食品保鲜、空气净化、水污染防治、纺织品防护领域的应用;最后对具有抗菌功能的PolyMOFs复合材料目前在抗菌领域面临的挑战和未来包括特定功能增强、制备工艺优化和应用领域拓展等方面的发展方向进行了展望。