Reviewerly/exHarmony huggingface dataset:
Analysis of the development direction of the history of American war photography in the context of big data

Abstract Due to America’s leadership in the photography industry, American wars have been repeatedly brought to the screen. The American photographer’s attitude of excellence in this kind of photography has also forged the classics of the film industry. After the contemporary era, with the advent of the consumer and picture-reading era, a clearer narrative strategy has emerged in cinema that differs from the earlier film adaptations of war literature. In order to explore the aesthetic implications and ethical values inherent in American war photography, as well as to correct some previous biases, we aim to provide a comprehensive interpretation of American war photography from a more objective and rational perspective, and to provide references for the appreciation and creation of other film and television works. This paper analyzes the narrative strategies of contemporary American war literature film adaptations through three aspects of respect for literariness in narrative and compromise with commerciality. Meanwhile, this paper will explain the connotation of big data in photography, analyze the application of big data in photography, and elaborate the development of big data in photography. The ultimate goal is to promote the joint growth of big data and photography industry, and to advance the flourishing of photography business. In the context of new media, photojournalists can no longer follow the original mode of work, but should recognize the current plight of the industry and seize the opportunity to transform in time in order to better meet the new requirements of the development of the field of news communication.
Using CNN Algorithm Detection of Suspicious Activity from Video Surveillance

Abstract: Detecting suspicious human activity involves predicting the positions of body parts or joints from an image or video. This problem has been extensively studied in computer vision for over 15 years. It is crucial because there are numerous applications that can benefit from activity detection. For instance, human pose estimation is used in various areas such as video surveillance, tracking and understanding animal behaviour, detecting sign language, improving human-computer interaction, and capturing motion without markers.
Algebraic characterization of Ifa main divination codes

Ifa is an age long divination system vastly consulted across west Africa, the Caribbeans and the Americas. The system goes with the use of signature codes named Odu Ifa which are 256 in number consisting of 16 Oju Odu (major) and 240 Omo Odu (minor). We consider the algebraic structure of this divination system by writing the codes representing the main Odu as 4 × 2 matrices which are found to satisfy group axioms under addition mod 2. We construct the Cayley table that gives an abelian group which we dubbed “self-inversed group”. Other properties of group structure are outlined.
Stability of scientific big data sharing mechanism based on two-way principal-agent

&lt;abstract&gt; &lt;p&gt;In the era of big data, facing the data-intensive scientific paradigm shift and the explosion of scientific big data, there is an urgent need for alliance cooperation between heterogeneous research groups to actively open and share scientific big data to support China's economic development, technological innovation and national security. Therefore, the study of scientific big data sharing mechanism has very important practical significance. We think science big data sharing is an ecosystem that is constantly evolving to higher-order ecological evolution. Based on the dual perspectives of psychological contract and contractual contract, the scientific big data sharing strategy evolution mechanism and sharing strategy incentive mechanism are explored.The research finds that the cooperation of scientific research groups is bound by psychological contract and contractual contract; stochastic evolutionary game has stronger explanatory power for sharing strategy evolution, complementarity is positive indicator, random interference and moral risk are negative indicators; two-way principal agent can describe Alliance members are mutually entrusted, and the shared strategy incentive contract consists of fixed wages and incentive wages, which are proportional to risk.&lt;/p&gt; &lt;/abstract&gt;
Live Inventory Tracking System Using IOT

Abstract: Inventory Management System software is important for organisations that operate hardware stores, where the store owner keeps track of sales and purchases. Poor inventory management leads to disgruntled customers, excess money in warehouses, and slower sales. With this initiative, paper work, human mistake, manual delay, and process acceleration are all avoided. An inventory management system can help a store owner keep track of sales and available goods, as well as calculate when and how much to repurchase. Inventory Management System is a Windows application for Windows operating systems that focuses on inventory control and the ability to generate all essential reports.
Lattice-based ring signcryption for consortium blockchain

Ring signcryption with no group administrator satisfies the decentralization and blockchain anonymity. In this article, we construct new lattice-based ring signcryption scheme suitable for consortium blockchain (CB-LRSCS), in which the smart contract controls the process of signcryption and unsigncryption to make the system be fair and reliable. CB-LRSCS can protect the user privacy by reducing the connection between blockchain and user information, and it satisfies the reliability in ethereum environment. CB-LRSCS also has the characteristics of high efficiency, anti-quantum, anti-forgery, confidentiality and unconditional anonymity, and it can be applied in the electronic finance system.
The application of body symbols in contemporary art from the perspective of Da Vinci’s theory

Abstract Motion recognition methods can distinguish body symbols and play an important role in real-time safety monitoring of pedestrians. In order to solve the problem of lack of data for deep learning-based action recognition models, a human motion synthesis method with hidden state initialization is proposed. Commonly used human motion synthesis methods utilize Recurrent Neural Network (RNN) to automatically generate subsequent motion sequences by taking several frames of motion data as input. In previous work, the initial hidden state of the RNN is usually set to zero or randomly initialized hidden state, resulting in a jump between the end frame of motion input and the first frame of motion synthesis, which affects the quality of the generated motion. To solve this problem, a method for estimating the initial hidden state estimation is proposed, which takes the initial hidden state as the independent variable, uses the objective function of the neural network as the optimization objective, and uses the gradient descent method to optimize the solution to obtain a suitable initial hidden state. The proposed motion model with initial hidden state estimation reduces the prediction error in the first frame by 63.51% and 6.90%, respectively, and the total error in 10 frames by 50.00% and the total errors of 10 frames were reduced by 50.00% and 4.89%, respectively. The experimental results show that the proposed motion model with initial hidden state estimation has better motion synthesis quality and motion prediction accuracy than the method without initial hidden state estimation; the proposed method improves the quality of motion synthesis by accurately estimating the first frame hidden state of the RNN human motion model, which can provide reliable data support for motion recognition models in real-time security monitoring.
Eye movements are made to the centre of gravity of texture-defined targets

Saccadic localisation of targets of various properties has been extensively studied, but rarely for texture-defined figures. In this paper, three experiments that investigate the way information from a texture target is processed in order to provide a signal for eye movement control are presented. Participants made saccades to target regions embedded in a background structure, and the saccade landing position and latency were measured. The textures comprised line elements, with orientations of the lines configured to form the figure and ground. Various orientation profile configurations (Block, Blur, and Cornsweet), were used in order to measure the role of edge profiles in driving eye movements and producing salience. We found that in all cases the visual system is in fact able to effectively segregate a texture figure from the ground in order to accurately plan a saccade to the target-figure. While saccadic latency was the highest for the Blur profile, the mean saccadic landing position was mostly unaffected by the various profiles (Experiment 1). More specifically, we showed that saccades were directed to the centre-of-gravity of the target (Experiment 2). We also found that figures with information of orientation contrast at both the edge and centre of figure (i.e. Block) produced the highest level of saliency in attracting eye movements (Experiment 3). Overall, the results show that saccades are planned on the representation of the whole target shape rather than a local salient region based on orientation contrast cues, and that the various texture profiles were important only to the extent that they affected the time to programme a saccade.
A Low-Overhead Auditing Protocol for Dynamic Cloud Storage Based on Algebra

With the widespread adoption of cloud storage, ensuring the integrity of outsourced data has become increasingly important. Various cloud storage auditing protocols based on public key cryptography have been proposed. However, all of them require complex cryptographic operations and incur significant storage and communication costs. To address the issues of significant storage overhead for data tags, high computational complexity of cryptographic algorithms, and limited efficiency of dynamic data algorithms in signature algorithm-based cloud storage outsourcing data integrity verification protocols, we propose a dynamic auditing protocol called AB-DPDP, which is based on algebra. Our protocol reduces the computational complexity of tag generation by utilizing basic algebraic operations instead of the traditional cryptographic method used in most current auditing protocols. To reduce storage overhead and protect private data, our protocol stores only tags, allowing for data to be restored through these tags, as opposed to storing both tags and data on the cloud server. To accommodate for more frequent and efficient data dynamics, we propose the dynamic index skip table data structure. Furthermore, the security of our proposed protocol is thoroughly proven based on the security definition of secure cloud storage. Finally, through theoretical analysis and experimental evaluation, we demonstrate the advantages of our scheme in terms of data privacy, storage overhead, communication overhead, computation overhead, and data dynamic efficiency.
Construction of a text complexity grading model for English textbooks in the context of globalization

Abstract Text complexity is an important construct in the study of English reading instruction in the context of globalization and in the field of applied linguistics, and the main purpose of its study is to match learners with reading texts that are appropriate to their language level in order to improve learning outcomes. Focusing on text complexity, in addition to focusing on lexical and syntactic dimensions, discourse complexity features are added to explore the application in the construction of text complexity hierarchy models based on feature optimization to be examined. In this study, with the text processing software developed by Kyle’s team, we used BNCbaby as the reference corpus to extract fine-grained indicators such as vocabulary, syntax and discourse complexity, and used principal component analysis to optimize the indicators by dimensionality reduction and determine the principal component features used to build the model. Different classification algorithms are used to construct separate models and compare their performance. Models constructed based on common traditional readability formulas and other single-dimensional features have significant advantages over models constructed from feature sets. In addition, the feature set and modeling method have good grading ability for other domestic textbook datasets and good performance in grading prediction for several different datasets, with strong generalization and generalization ability. This study integrates multivariate linguistic features with neural networks to construct a text complexity grading model, which provides a new path for text complexity research. The research results not only have certain theoretical significance for text complexity research, but also have high application value in the field of applied linguistics, which can provide references for the selection of students’ reading materials, the writing and adaptation of teaching materials, and the planning and test design of reading courses.
Innovative research on propagation modes of short video works based on digital technology

Abstract In today’s information explosion, simple text and pictures have long lost the market, replaced by the multidimensional expression form which integrates text, pictures, sound and other elements and mobilizes people’s vision, hearing, smell and other senses. Therefore, in the context of digital technology, this paper deeply explores the innovative propagation modes of short video works by establishing the CONVLSTM propagation model. The results show that the innovation of the similarity symbol system in the content of short video works is 65%. The innovation of symbol interpretation in the content of short video works is 73%. Then, with respect to the form strategy of short video works, the innovations of short video works on Weibo, Douyin, Kuaishou and Wechat platforms are 54%, 93%, 24% and 45% respectively. Therefore, it is concluded that the unique expression of Douyin short video works is the most innovative point, which differentiate from the traditional TV-style short video works on Wechat and Weibo platforms.
Practice and research on non-foreign heritage culture inheritance education in universities based on information fusion technology

Abstract It has gradually become a trend to infiltrate the knowledge of non-heritage culture and inherit the spirit of non-heritage culture and ethnic crafts in art education in colleges and universities. The purpose of this paper is to discuss the significance of ethnic crafts in non-heritage culture to art education in colleges and universities, the problems faced by the integration of ethnic crafts in non-heritage culture into art education in colleges and universities, and the transmission path of ethnic crafts in non-heritage culture in art education in colleges and universities. We hope to enhance deep connection between college art education and non-heritage culture by increasing research efforts, so that students will pay attention to the preservation of non-heritage culture and pass on the ethnic crafts of non-heritage culture in college studies. This paper develops research education and the cultivation of intangible cultural heritage through information fusion-based technology. The construction objectives of the multi-source information integration platform are proposed, and the overall architecture, functions, interfaces, and hardware architecture of the platform are designed. Analyze the connection between information fusion and information integration, and clarify the implementation process of multi-source information fusion technology. The two-level information fusion model of diagnosis constructed in this paper can avoid the error of diagnosis results caused by relying on neural network fault diagnosis alone, and the accuracy of fault diagnosis is optimized, which can be applied to the construction of working face. It solves the fusion problem that fault diagnosis results from different information sources exist with each other. It can provide scientific guidance for operation and management units to make decisions, and help to improve the level of education of NRM culture transmission.
Key management scheme of distributed IoT devices based on blockchains

Abstract With the rapid development of Internet of Things devices and communication technology, the number of devices connected to the Internet of Things is growing exponentially. However, in the context of the rapid increase in the number of devices, data security issues such as data leakage and data tampering also follow. Therefore, communication security between devices is particularly important. However, in the traditional network environment, the identity authentication and information encryption methods to ensure communication security mostly adopt centralized network structure, relying on the central equipment to manage the access edge equipment. However, due to the heterogeneity of IoT devices and limited resources, the traditional centralized management scheme does not fully comply with the IoT system. In order to solve the above problems, this paper proposes a blockchain distributed IoT device key management scheme under the blockchain technology framework, which proposes a blockchain based distributed IoT architecture, and designs a hash chain based identity authentication scheme and a key distribution scheme based on network security coding on the basis of this architecture. Because blockchain technology has the characteristics of openness, immutability, traceability and fault tolerance, it can ensure the privacy of data transmission in the IoT scene, thus providing a secure communication environment. In this paper, the performance indexes related to the scheme are evaluated, and the experimental analysis confirms the efficiency and scalability of the proposed scheme.
Remote Penetration Testing with Telegram Bot

The widespread of websites and web applications makes them the main target of cyber attacks. One way to increase security is to perform a penetration test. This test is carried out using the attacker's point of view to find out vulnerabilities on a website or web application and then exploit these vulnerabilities. The results of the penetration test can be used as recommendations to close the gaps that have been known through testing. Because penetration testing requires special resources such as tools and operating systems, a solution is needed to make penetration testing possible with low resources. Telegram bots that are open source offer a solution to overcome these problems. Using the SDLC waterfall approach, this bot was built to provide penetration testing services by connecting the Kali Linux server as a tools provider and the Telegram bot as an interface to users. As a result, users can access penetration testing tools anywhere and anytime via the Telegram bot. To ensure that the bot can run well, testing is carried out through black box testing and load testing. Telegram bot is a solution for integrated compact automatic mobile penetration tester with low resources. Based on load testing, the maximum limit of users who can access Telegram bots simultaneously is 35 users with the highest load average of 5.4. Based on the results of the User Acceptance Test, the Telegram bot has an acceptance rate score of 88,457 % and a questionnaire score of 774 which is an agreed area.&#x0D;
A Comprehensive Formalization of Propositional Logic in Coq: Deduction Systems, Meta-Theorems, and Automation Tactics

The increasing significance of theorem proving-based formalization in mathematics and computer science highlights the necessity for formalizing foundational mathematical theories. In this work, we employ the Coq interactive theorem prover to methodically formalize the language, semantics, and syntax of propositional logic, a fundamental aspect of mathematical reasoning and proof construction. We construct four Hilbert-style axiom systems and a natural deduction system for propositional logic, and establish their equivalences through meticulous proofs. Moreover, we provide formal proofs for essential meta-theorems in propositional logic, including the Deduction Theorem, Soundness Theorem, Completeness Theorem, and Compactness Theorem. Importantly, we present an exhaustive formal proof of the Completeness Theorem in this paper. To bolster the proof of the Completeness Theorem, we also formalize concepts related to mappings and countability, and deliver a formal proof of the Cantor–Bernstein–Schröder theorem. Additionally, we devise automated Coq tactics explicitly designed for the propositional logic inference system delineated in this study, enabling the automatic verification of all tautologies, all internal theorems, and the majority of syntactic and semantic inferences within the system. This research contributes a versatile and reusable Coq library for propositional logic, presenting a solid foundation for numerous applications in mathematics, such as the accurate expression and verification of properties in software programs and digital circuits. This work holds particular importance in the domains of mathematical formalization, verification of software and hardware security, and in enhancing comprehension of the principles of logical reasoning.
Reading Levels of Leaflets of Common Over-the-Counter Drugs in Ghana

Health communication is critical component of effective health delivery.While there are studies on the readability Patient Information Leaflets (PILs) around the world, so far, there is only one Ghanaian study that explored the language of patient information leaflets for malaria.The present study explores the language of PILs of seven common ailments in Ghana.In all, 68 leaflets were purposely sampled for the study.Using SMOG and Flesch Kincaid grade level readability formulae, the researcher measured the readability of PILs and discovered that the readability of all the PILs was difficult to read except the dewormer leaflets that scored standard reading score.Further statistical analysis reveal that the lexical and syntactic complexity of the leaflets were not statistically different.Finally, an interview with 20 patients confirmed that the leaflets are difficult to read due to the complex words and long sentence structures.It is recommended that the PILs be revised to make the leaflets readable.
Empirical Study of Image Captioning Models Using Various Deep Learning Encoders

Image captioning is the generation of caption for the given image. It is a growing and challenging research topic in the field of computer vision. There have been many approaches for image captioning. Initially, Template based methods were implemented in which a fixed size template was used for fill up with image objects and their properties. Retrieval based approach was used in which some images used to match with the query image and a caption was generated with the help of captions of these images. There were some limitations in these approaches of missing out important objects. Recent approach for the image captioning uses the technique of encoder and decoder. The encoder and decoder based methods have provided remarkable results. However, it is not easy to determine the impact of only the encoder for the image captioning task. In this paper, we have compared the performance of image captioning models with various image encoders such as Visual Geometry Group (VGG), Residual Networks (ResNet), InceptionV3 uses the Gated Recurrent Unit (GRU) as the decoder for the purpose of text generation. The results are compared on the basis of Bilingual Evaluation Understudy (BLEU) score using Flickr8K Dataset. It can be seen that the ResNet provides a better BLEU score as compared to the VGG16, VGG19 and InceptionV3 when it was implemented using the Flickr8K Dataset.