Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

Description,Language,Other Minds,Reduction,and Phenomenology

How to think a unique and determinative turn in analytic philosophy of mind?To answer this question this article first presents an attempt to render clear that analytic phenomenology,by contrast with conceptions of phenomenology of the XXth century,beneficially dispenses with several methodological and conceptual assumptions that were assumed to be compulsory,as phenomenological reduction,a notion of synthesis,and a philosophical notion of the a priori.It then presents some eventual difficulties to the achievement of a phenomenological turn within analytic philosophy,which are,the neglect of historicity,abstractionism,the acknowledgement of the place of language in our lives,and solipsism.It finally presents several demands that concern the felicity of contemporary analytic phenomenologies,namely,anti-abstractionism,fallibilism,attention to polyadic relations,and the integration of ecological and decolonial concerns of our cultures.

The Thematic Function of Dream Description in My Platonic Sweetheart

Mark Twain is one of the most famous writers of the nineteenth century,his works have a large number of descriptions of dreams,in Mark Twain’s short story My Platonic Sweetheart,the author describes a dream that constantly repeats itself in his life.The dream description in the novel is not only part of the narrative structure of the article,but also expresses the theme of the article,through the close reading of the text,taking dream description as the starting point,the author of this thesis analyzes the dream description in My Platonic Sweetheart,exploring the thematic role of dream description in the novel,and analyzing what the author wants to express and how the author expresses his spiritual pursuit through dream description.

Audio Description for Educational Videos on COVID-19 Response:A Corpus-Based Study on Linguistic and Textual Idiosyncrasies

Audio description(AD),unlike interlingual translation and interpretation,is subject to unique constraints as a spoken text.Facilitated by AD,educational videos on COVID-19 anti-virus measures are made accessible to the visually disadvantaged.In this study,a corpus of AD of COVID-19 educational videos is developed,named“Audio Description Corpus of COVID-19 Educational Videos”(ADCCEV).Drawing on the model of Textual and Linguistic Audio Description Matrix(TLADM),this paper aims to identify the linguistic and textual idiosyncrasies of AD themed on COVID-19 response released by the New Zealand Government.This study finds that linguistically,the AD script uses a mix of complete sentences and phrases,the majority being in Present Simple tense.Present participles and the“with”structure are used for brevity.Vocabulary is diverse,with simpler words for animated explainers.Third-person pronouns are common in educational videos.Color words are a salient feature of AD,where“yellow”denotes urgency,and“red”indicates importance,negativity,and hostility.On textual idiosyncrasies,coherence is achieved through intermodal components that align with the video’s mood and style.AD style varies depending on the video’s purpose,from informative to narrative or expressive.

Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

Exploring the Characteristics of Data Breaches: A Descriptive Analytic Study

Data breaches have massive consequences for companies, affecting them financially and undermining their reputation, which poses significant challenges to online security and the long-term viability of businesses. This study analyzes trends in data breaches in the United States, examining the frequency, causes, and magnitude of breaches across various industries. We document that data breaches are increasing, with hacking emerging as the leading cause. Our descriptive analyses explore factors influencing breaches, including security vulnerabilities, human error, and malicious attacks. The findings provide policymakers and businesses with actionable insights to bolster data security through proactive audits, patching, encryption, and response planning. By better understanding breach patterns and risk factors, organizations can take targeted steps to enhance protections and mitigate the potential damage of future incidents.

On the Description of Nature in Jane Eyre

For the novel Jane Eyre, people appreciate it with different reasons. The author employs stylistic approaches to illustrate her own reasons why she enjoys it by analyzing its unique, realistic yet emotional and symbolic description of nature alone.

Quantitative description of characteristics of high-capacity channels in unconsolidated sandstone reservoirs using in situ production data

Quantitative description of the high-capacity channels in unconsolidated sandstone reservoirs, into which water was injected to improve oil recovery, is a hot topic in the field of reservoir development. This paper presents a novel approach to describing quantitatively the characteristics of connected macropores in unconsolidated sandstone reservoirs using in situ production data. Based on physical simulation for formation mechanisms of high capacity channels and interwell tracer test data, a mathematical model was established to describe high-capacity channels by grey correlation theory, flow mechanism of fluid in porous media and reservoir engineering, and a program was developed to describe quantitatively the channel characteristics. The predicted results were consistent with field monitoring data （80%）, so this model could be economically and effectively used to identify high-capacity channels.

Adansonia digitata L.(baobab):a review of traditional information and taxonomic description

Adansonia digitata L.(Malvaceae) is commonly known as baobab tree native to Africa.Baobab is a multi-purpose tree which offers protection and provides food,clothing and medicine as well as raw material for many useful items.The fruit pulp,seeds,leaves,flowers.roots,and bark of baobab are edible and they have been studied by scientists for their useful properties.The fruit pulp have very high vitamin C.calcium,phosphorus,carbohydrates,fibers,potassium,proteins and lipids content,which can be used in seasoning as an appetizer and also make juices.Seeds contain appreciable quantities of phosphorus,magnesium,zinc,sodium,iron,manganese,whereas they have high levels of lysine,thiamine,calcium and iron.Baobab has numerous biological properties including antimicrobial,anti-malarial,diarrhoea,anaemia,asthma,antiviral,anti-oxidant and anti-inflammatory activities amongst others.Phytochemical investigation revealed the presence of flavonoids.phytosterols.amino acids,fatty acids,vitamins and minerals.The review summarizes the information on various aspects of traditional information,taxonomic description,medicinal properties and importantly nutritional value.

The Analysis of the Psychological Description in The Scarlet Letter

The Scarlet Letter is the masterpiece of Nathaniel Hawthorne who was a great American novelist. After being , the book brought a variety of dispute. Although many great critics had their authoritative comments on the work, the author of this paper focused on the psycho-logical description in The Scarlet Letter.This paper focus on the analysis of the psychological description of the characters in The Scarlet Letter. This paper is divided into two parts-- The first part is " Personality of the main characters", which depicts the different personality of the characters. The second part is" Wonderful Psychological Description ". The psychology description of characters connected with the portrayal of scenes, the development of plot and the foil of nature surroundings to explore the secret in characters'complicated and contradictory interior world. This kind of psychological description didn't only makes the disposition of the characters distinctive but also works a lot in the development of plot and intensification of the theme. Although Hawthorne took good use of psychological description, he had one shortcoming--to make characters seem negative when the author made psychological depiction lifelikely, which, to some extent, destroyed the brilliant images of characters and weaken the ideoligical meanings. Generally speaking, it is a good literature work for people to study old literature and create today's literature.

Mathematical Model of Hydrodynamic Torque Converter and Analytic Description of Streamline

The mathematical model of a 3-element centripetal-turbine hydrodynamic torque converter and analytic description of fluid flow inside the hydrodynamic torque converter are investigated. A new torus coordinate system is proposed so as to quantitatively describe fluid movement inside the hydrodynamic torque converter. The particle movement inside the hydrodynamic torque converter is decomposed into meridional component movement and torus component movement, and a universal meridional streamline equation is derived. According to the relationship between the converter wheel velocity polygon and its blade angle, a torus streamline differential equation is established. The universal meridional streamline equation is approximated with square polynomials. The approximation error curve is given and the percentage error is not greater than 0.86%. Considered as a function of polar angle, the blade angle cotangent of each converter wheel varies linearly with polar angle. By using integral calculus, torus streamline equations are obtained. As a result, the problem of difficult flow description of the hydrodynamic torque converter is solved and a new analytic research system is established.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on Fuzzy Weighted Support Vector Domain Description

This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the process of AUV multi-fault pattern classification because of the effect of sample sparse density and the uneven distribution of samples, and so on. Thus, a fuzzy weighted support vector domain description (FWSVDD) method based on positive and negative class samples is proposed. In this method, the negative class sample is introduced during classifier training, and the local density and the class weight are introduced for each sample. To improve the multi-fault pattern classifier training speed and fault diagnosis accuracy of FWSVDD, a multi-fault mode classification method based on a hierarchical strategy is proposed. This method adds fault contain detection surface for each thruster and sensor to isolate fault components during fault diagnosis. By considering the problem of pattern classification for a fuzzy sample, which may be located in the overlapping area of hyper-spheres or may not belong to any hyper-sphere in the process of multi-fault classification based on FWSVDD, a relative distance judgment method is given. The effectiveness of the proposed multi-fault diagnosis approach is demonstrated through water tank experiments with an experimental AUV prototype.

Confidence support vector domain description

To accelerate the training of support vector domain description （SVDD）, confidence support vector domain description （CSVDD） is proposed based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of training samples in the userdefined sphere is calculated and taken as the confidence measure, according to which the training samples are ranked in ascending order. Those former ranked ones are selected as the boundary targets for the SVDD training. Simulations on UCI data demonstrate the effectiveness and superiority of CSVDD： the number of training targets and the training time are reduced without any loss of accuracy.

Granular analyzing of weapon SoS demand description

The systematism of weapon combat is the typical characteristic of a modern battlefield. The process of combat is complex and the demand description of weapon system of systems （SOS） is difficult. Granular analyzing is an important method for solving the complex problem in the world. Granular thinking is introduced into the demand description of weapon SoS. Granular computing and granular combination based on a relation of compatibility is proposed. Based on the level of degree and degree of detail, the granular resolution of weapon SoS is defined and an example is illustrated at the end.