Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,trans...
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Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound e...
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Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.
At present,hundreds of cloud vendors in the global market provide various services based on a customer’s *** cloud vendors are not the same in terms of the number of services,infrastructure availability,security stra...
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At present,hundreds of cloud vendors in the global market provide various services based on a customer’s *** cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the ***,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental ***,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable *** existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service ***,they require more time to collect data,simulate and evaluate the *** proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage ***,the parameters are categorized depending on the priority *** weighted average is calculated for each CSP,after which the values are sorted in descending *** experimental results show the unbiased selection of CSPs based on the chosen *** proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.
PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollut...
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PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollution *** PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and *** past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations,making it difficult to further improve the prediction ***,factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities,leading to the change of PM2.5 concentration in *** a PM2.5 directed flow graph is constructed in this *** and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between *** introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between *** on this,a PM2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper.A spatial feature extraction method based on weight aggregation graph attention network(WGAT)is proposed to extract the spatial correlation features of PM2.5 in the flow graph,and a multi-step PM2.5 prediction method based on attention gate control loop unit(AGRU)is *** PM2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration ***,accuracy and validity experiments are conducted on the KnowAir dataset,and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness
This study introduces a new method for multi-objective optimization (MOO) in electrical power systems, aiming to minimise emission release, power losses and fuel costs concurrently. Diverging from traditional techniqu...
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In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, terme...
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This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. U...
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The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying stu...
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Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most ...
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The sustainable livelihoods of farmers in India hinge upon effective crop management and optimal yield. However, the existing manual system for crop selection is time-consuming, relies heavily on experience, and often...
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