Reviewerly/exHarmony huggingface dataset:
Commodity Image Retrieval Based on Image and Text Data

With the continuous popularization and development of the Internet, online shopping has gradually become people’s main consumption mode. This paper mainly studies the image retrieval task of goods in e-commerce websites. The main purpose of applying image retrieval in shopping websites is to enable users to search for the expected goods more conveniently and accurately in the massive commodity information. Given the traditional image retrieval process, only the image or text of goods is used as the retrieval object. The query results obtained do not take advantage of the information relevance and complementarity between text and images, which loses the retrieval advantages of goods. To solve the problem, this paper first designs an end-to-end supervised learning algorithm to project heterogeneous data into a common metric space and apply traditional indexing schemes in this space to achieve efficient image retrieval. Secondly, a fusion method is proposed to give better data and higher weight according to the semantic capture quality of input features. Finally, an objective function is proposed, which can correctly embed the fusion features of image and text into their respective feature space, make the fusion features of the same kind of image and text closer to each other, and separate the dissimilar features. The experimental results show that the average accuracy of this method on the test set of commodity data is 70%, which is about 6% higher than the image content-based and text-based image retrieval methods, which proves the effectiveness.
Atomic filter: A weak form of shift operator for graph signals

The shift operation plays a crucial role in the classical signal processing. It is the generator of all the filters and the basic operation for time-frequency analysis, such as windowed Fourier transform and wavelet transform. With the rapid development of internet technology and big data science, a large amount of data is expressed as signals defined on graphs. In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals. It is a fundamental problem since the vertex set of a graph is usually not a vector space and the algebraic operation cannot be defined on the vertex set of the graph. In this paper, based on our understanding on the core role of shift operation in classical signal processing we propose the concept of atomic filters, which can be viewed as a weak form of the shift operator for graph signals. Then, we study the conditions such that an atomic filter is norm-preserving, periodic, or real-preserving. The property of real-preserving holds naturally in the classical signal processing, but no research has been reported on this topic in the graph signal setting. With these conditions we propose the concept of normal atomic filters for graph signals, which degenerates into the classical shift operator under mild conditions if the graph is circulant. Typical examples of graphs that have or have not normal atomic filters are given. Finally, as an application, atomic filters are utilized to construct time-frequency atoms which constitute a frame of the graph signal space.
Customized Database Management based on Digital Signature

Of late, a variety of data associated with individuals and organizations have been connected to the cyberspace. This trend is expected to become more increased in the future. This paper proposes a scheme to manage the database in an open network, particularly picking up the case of financial data. We confirm that the financial data is highly required to be managed maintaining the authenticity of users and transaction contents. Then, in order to manage the financial data in more realistic situations, a data management scheme is suggested that the data with small amounts and high frequency is transacted in an Internet environment, while high-value and infrequent transactions are conducted under a conventional system managed by banks, even connecting them one another. The technology that underpins the security of the data in open networks is a digital signature based on a key generated by a personal device. We summarize the data management scheme by ensuring security based on digital signature and by connecting the data with the database in a private network according to the need from the general prospect.
Social and Technological Renovation in Online Second Language Teaching: A Post-COVID-19 Study of Pedagogy and Its Implication

Jacques de Vaucanson invented the digesting duck in the year 1739, and there was a proposal of launching fully automated cars in the year 2020 made by Tesla Company; one can witness the monstrous development in technology in the last four centuries. Furthermore, in the past few decades, attempts and inventions are made to digitalize every possible knowledge that is available and documented in the form of books, tape recorders, video recorders and so on. Scientists, academicians, educationists, engineers, and people from all discipline get united in the one platform called internet, to access knowledge and to deliver the same in digital form. This internet or famously referred as “the second world/virtual world” is expanding and exploding with knowledge and information. Similarly, way before the COVID-19 pandemic, teaching–learning programmes were digitalized and were offered by educational institutions through online. Online teaching–learning was the most sort out programme that gradually replacing the distance education system in the last decade. One such example is the IGNOU’s success in distance education, which eventually paved way for online learning. Thereby, even though we have rushed into the online platform during the pandemic, we should be aware that online learning is not entirely alien to us. Moving further post-COVID-19, the internet indeed has brought a paradigm shift in the fundamental way of teaching, and this paper is an attempt to understand the culture of technology in academics. Marshall McLuhan in his book “Understanding Media” remarks technology as an extension of human, and the paper carries this idea into the language-learning scenario to find its helpfulness in online second language teaching and learning.
Recovery of geometry, natural illumination, and BRDF from a single light field image

We propose an inverse rendering model for light fields to recover surface normals, depth, reflectance, and natural illumination. Our setting is fully uncalibrated, with the reflectance modeled with a spatially constant Blinn-Phong bidirectional reflectance distribution function (BRDF) and illumination as an environment map. While previous work makes strong assumptions in this difficult scenario, focusing solely on specific types of objects such as faces or imposing very strong priors, our approach leverages only the light field structure, where a solution consistent across all subaperture views is sought. The optimization is based primarily on shading, which is sensitive to fine geometric details that are propagated to the initial coarse depth map. Despite the problem being inherently ill posed, we achieve encouraging results on synthetic as well as real-world data.
Fuzzy Controlled Adaptive Follow the Gap Obstacle Avoidance Algorithm

Follow the Gap Method (FGM) and Improved Follow the Gap Method (FGM-I) are geometric obstacle avoidance algorithms for navigation. In these methods, the vehicle detects the gaps around the object and navigates to the midpoint of the optimal gap calculated according to a defined function. One missing point of these algorithms is failure to the goal point when there is an obstacle near to it. Another drawback is that early consideration of obstacles causes long trajectories. In this paper, Adaptive Follow the Gap (A-FGM) is presented to overcome these two points. In A-FGM, a fuzzy controlled evaluation radius is set and only obstacles within this region are included in the evaluation. A differential drive robot is used in simulations and results show that A-FGM increases the success rate of reaching the goal and efficiency of previous algorithms. The source code of the developed approach is shared on GitHub 1.
Semantic Segmentation-based Algorithm for Urban Road Waterlogging Disaster Detection

Although Semantic segmentation is a key step of image prehension. Single use of convolutional networks for semantic segmentation makes it difficult to distinguish the same class of objects with large contour deviations, while the higher-level features will lose some the detailed information. Currently, Networks such as ACFNet and DANet have introduced attention mechanism to improve scene classification by obtaining rich contextual information through self-controlled system, but they do not combine both global scope and class feature relationships in local space to further advance intra class consistency and inter-class divisibility of features. In terms of this problem, semantic segmentation network of categorical attention with spatial constraints has been proposed, which contains two submodules, one using the category spatial distribution to introduce local spatial location information of features, and the other using the global category average strength to introduce global strength information of category features. By selecting a kind of appropriate backbone network, this network model obtains the feature map from the backbone network and stacks the features into the original features after two submodules of global category strength and category local space processing, finally, performing classification processing by the classification layer and up-samples to the input image size to complete the pixel-level label prediction. The experiment result demonstrates that this proposed segmentation network has higher accuracy than existing segmentation networks.
Securing Web Applications

Web-based services are common targets for hackers, and the need for ensuring their security keeps on rising. Attackers often take advantage of the vulnerabilities of different web applications using several mechanisms and thus steal and manipulate valuable information. Therefore, the attack vectors are also increasing since there is a wide variety of internet users. Exploitations caused by different types of cyberattacks results in data loss, identity theft, financial loss, and various other adversaries on both humans and infrastructure. Therefore, investigating various attack vectors and countermeasures can facilitate and encourage future research and create awareness among web application users and developers.
Beyond Formalism and Functionalism in Separation of Powers Law

The Constitution’s text, structure, and history suggest that some governmental functions strictly and exclusively appertain to a particular branch, and to the exercise of a single vested power. Many governmental functions, however, are nonexclusive: their exercise has some combination of legislative, executive, and judicial characteristics and, as a result, can be exercised by different branches exercising different vested powers. This Article proposes to reorient our thinking around exclusive and nonexclusive governmental functions and demonstrates how doing so clarifies many existing doctrines and debates in administrative and separation of powers law.This approach differs in important respects from standard functionalist and formalist accounts. If functionalism is concerned with identifying the “core” functions of the three branches, the reorientation proposed here would require identifying “exclusive” functions. The central question, however, is not “balance” or “aggrandizement” but whether an exercise of governmental power falls exclusively within a single vested power as a matter of text, structure, and history. And this approach rejects the proposition often associated with formalism that government functions must always be categorized as either legislative, executive, or judicial, and that there is little functional overlap among the three vested powers. To be sure, the Founders paid little attention to the functional overlap among the vested powers of government; but the idea of functionally overlapping government powers accurately reflects the nature of those powers and follows from the Constitution’s text, structure, and history. Although some scholars and judges have hinted at similar ideas, functional overlap has not been their focus and conflation of powers and functions continues to plague judicial opinions and the scholarly literature. This Article seeks to reorient separation of powers law systematically toward exclusive and nonexclusive functions. Doing so clarifies many doctrinal puzzles, from the nature of legislative rules and judicial deference to “quasi” powers and nondelegation. Other insights also emerge, for example with respect to James Madison’s suggestion that the Comptroller of the Treasury was neither executive nor judicial but “rather distinct from both, though it partakes of each,” and to the recently contested question of whether the Supreme Court can hear direct appeals from military courts.
Research on Mining of Government Data Based on Enhanced-Object Exchange Model

In order to acquire the required knowledge from the vast amounts of government affairs data, accurate data mining has important practical significance. Therefore, a precision mining method of government affairs big data based on E-OEM model is studied. The method firstly uses web crawlers to capture the vast amounts of government data. Government data then performs data cleaning, pattern normalization, format standardization, index information collection and pre-processing, and finally, according to the purpose of data mining, the Kirkpatrick model is used to complete the mining effect evaluation. Experimental comparison specifically includes correlation analysis, cluster analysis, classification analysis, exception analysis and evolution analysis as well as five others. The results show that: compared with the three methods in previous studies, the F1-score obtained by the application of the research method is higher (0.852), which proves that the quality of government affairs big data mining is better.
Bionic orientation method based on polarization imaging in HDR scenes

An increasing number of bio-inspired navigation approaches have been designed based on polarization cameras. However, digital cameras can sense a much narrower field of vision than the vision of insects or human beings. In this study, we propose an adaptive skylight polarized orientation method for high dynamic range (HDR) scenes. Initially, we built a model of the image acquisition pipeline that can recover HDR irradiance maps from polarization images. Subsequently, the orientation method was designed based on a combination of the irradiance maps and the least squares methods. Some preprocessing steps were utilized to eliminate occlusion interference. In addition, an autoexposure adjustment method was proposed using information entropy and heuristic segmentation. Finally, the experimental results show that the proposed method can improve the accuracy of bionic orientation and adaption to skylight with occlusions and interference in natural conditions.
Application of Latent Class Analysis in Automotive Project Management

This paper deals with the project slippage (delay) analysis in automotive industry using the cluster analysis techniques, particularly latent class analysis (LCA). This technique is based on identifying unmeasured class membership among various objects of the whole data set, in this case projects within the luxury vehicles manufacturing program. The aim here is to identify those common root causes of slippage in various vehicle engineering projects that are independent of the technical discipline where a particular project belongs to and thus contribute to overall project management performance. This analytical method has been chosen to reflect the nature of the analyzed data that is categorical as opposite to continuous one that a majority of clustering techniques operate with.
Defamation in Cyberpunk

The law of libel, which balances rights of expression and reputation, has found unique conceptual and ontological challenges in the virtual utopia of cyberpunk culture. A presumption that law is to sociology as ontology is to humans allows this essay to examine whether United States libel laws can reconcile with the ephemeral technologies and assertive demands of cyberpunk. In revisiting goals of libel law, the essay discusses the critical questions of whether bots and cyborgs have a reputation, whether artificial intelligence is more ideology than technology, whether legal positivism is moot, and whether post-professional and post-national environments will universalize the principles of libel law.
Android-Based Mobile Grade Viewer Application Using PHP

This study focused on the development of online Mobile Grade Viewer that allows access to student grades. It used the descriptive research method and employed the QUIS 7.0 questionnaire. The survey involved 350 respondents composed of parents/ guardians, teachers, students and technical experts. Data were treated using frequency count, percentage, and median. The application can access records from the Registrar’s database and can be retrieved through a web browser using a desktop computer, laptop, and mobile phones, allowing greater portability and timely grade querying. The application enabled accounts for students, parents/ guardian, and teachers. It included a log-in page, an account page, an assessment page, a view grade, and summary of grade pages. The application was found by the user-respondents to be easy to operate, easy to learn, consistent and helpful, making it usable to the end-users.
Recognizing the Faces from Variety of Poses and Illumination

The advancement of face recognition systems is still restricted by the conditions achieved by several real applications even though current face recognition systems have attained a moderate level of maturity. A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against face databases. The proposed work introduces a novel approach for eliminating an illumination problem from the facial images using 4-patch Local Binary Pattern (4-patch-LBP) and then a Convolutional Neural Network (CNN) is used to recognize the faces. The method used showed an average recognition performance of 92.3% and a maximum performance of 96% with YouTube Face (YTF) Database.
The Freedom of speech in the Digital Era: Leveraging Its Constitutional and Social Ramifications

Freedom of speech in the digital era comes with a number of open issues, including the role of regulation, of the markets as well as that of the public sphere. In many of the related issues, tensions of constitutional and social nature resurface. This is also the case with some of the recently introduced EU policy initiatives and legal rules to digital speech. While they seem to give ground to EU wide rules on some important issues, their reach can remain limited without a constitutional and social understanding of the matters at hand. Thus, it appears in many ways beneficial to have a wider outlook and analysis of these topics, including the constitutional and social ramifications of regulating freedom of expression today. This article sets an example to that and joins these threads together in its analysis. By doing so, the work aims to contribute towards obtaining a wider outlook into an evolving domain which has global implications.
Design Thinking and Digital Technologies in the Exploration of Science in Early Childhood Education

Contemporary early childhood research investigates ways of extending children’s scientific understanding and knowing in the world around them. The current study followed an innovative approach, adopting the design thinking model of IDEO to promote and deepen children’s scientific thinking and understanding of the ‘water cycle’. Digital technologies such as concept map software, simulations, interactive whiteboard and others, were also successfully integrated and played a significant role in the whole process. A total of 61 children from three Greek kindergartens participated in the study (33 boys and 28 girls) and their mean age was 5.2 years. Teachers’ diaries of the activities following the IDEO phases, digital recordings of children’s responses, drawings and concept maps were utilised for the documentation of the teaching intervention. The results revealed that the IDEO model was very helpful and contributed to reaching positive learning outcomes. Children were active participants throughout the intervention and their scientific knowledge was enhanced. Future research with a design thinking framework in exploring science in early childhood education settings is strongly recommended by also successfully integrating digital tools.
A Sequential Comparative Analysis of Software Change Proneness Prediction Using Machine Learning

Change-prone modules are more likely to produce defects and accumulate technical debt. Thus, developing prediction models for determining change-prone software classes is critical. Such models will allow for more efficient resource utilization during the maintenance phase and will make them more adaptable to future changes. This paper applies the study on a large dataset from a commercial software to investigate the relationships between object-oriented metrics and change-proneness. The study also compared the performance of several machine learning techniques including combining methods that were constructed by combining several single and ensemble classifiers with voting, Select-Best, and stacking scheme. The result of the study indicates a high prediction performance of many of the ensemble classifiers and the combining methods selected and proved that machine learning methods are very beneficial for predicting change-prone classes in software. The study also demonstrated that software metrics are significant indicators of class change-proneness and should be monitored regularly.
Optimal Design of College English Course Network Teaching System under the Background of Artificial Intelligence

Since the 21st century, information technology represented by artificial intelligence, big data and the Internet has developed rapidly, and the application of information technology in English education has also increased. However, due to these problems in the current English online teaching system, there is an urgent need to optimize the existing English online teaching system. This article aims to study the optimization design of the college English course network teaching system under the background of artificial intelligence. Based on the analysis of the characteristics of artificial intelligence, the significance of the English course network teaching system and the problems existing in the college English network teaching system, the existing English network teaching system optimized the design, added corresponding modules according to the existing problems, and finally tested its performance. The test results show that the interactive Q&A module of the college English course network teaching system optimized in this article performs well in response time, which basically meets the needs of this article.