An Inquiry into Business Intelligence Application in Telecom BOSS

This paper analyzes the application prospect of Business Intelligence (BI) in telecom BOSS construction,describes the technical framework of BI realization and its technical strategies.And it also discusses some typical applications of BI in current construction.It is concluded that BI construction in BOSS is a comprehensive application of a massive amount of accumulated operation support data.

AIGC Scenario Analysis and Research on Technology Roadmap of Internet Industry Application

The explosion of ChatGPT is considered to be a milestone in the normalization of artificial intelligence education applications.On the technical line,the cross-modal AI generation application based on human feedback system is accelerated.In the business model,the scenes to realize interactive functions are constantly enriched.This paper reviews the evolution process of AIGC,closely follows the current situation of the coexistence of business acceleration and technical worries in the application of artificial intelligence education,analyzes the application of AIGC education in 7 subdivided fields,and analyzes the optimization direction of application cases from the perspective of perception-cognition-creation technology maturity matrix.The 3 recommendations and 2 follow-up research directions will promote the scientific application of artificial intelligence education in the AIGC period.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state.The examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior assessment.Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs.Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals effectively.Although these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some limitations.So this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human emotions.The utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over time.The next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster convergence.Then the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram sequentially.The attitude of feature selection is to retain only dominant features by excluding the irrelevant ones.In this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features selection.Finally,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short Term Memory(Bi-LSTM)network to discern emotions.Since the deep Bi-LSTM can hierarchically learn complex features and increases model capacity by achieving more robust temporal modeling,it is more effective than a shallow Bi-LSTM in capturing the intricate tones of emotional content existent in speech signals.The effectiveness and resilience of the proposed SER model were evaluated by experiments,comparing it to state-of-the-art SER techniques.The results indicated that the model achieved accuracy rates of 90.92%,93%,and 92%over the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS),Berlin Database of Emotional Speech(EMO-DB),and The Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets,respectively.These findings signify a prominent enhancement in the ability to emotional depictions identification in speech,showcasing the potential of the proposed model in advancing the SER field.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

The Role of Artificial Intelligence Tax in Reducing the Unemployment Problem

The fear of the impact of artificial intelligence applications in the labor market on unemployment rates has increased.After the total global investment in this field did not exceed eight billion dollars in 2015,the global market for artificial intelligence globally will reach about 70 billion dollars by 2025.Many economic analysts believe that the application of artificial intelligence in industrial fields in particular will produce factories with much fewer employees than the current number,which will cause an increase in unemployment rates.Therefore,countries are trying to adapt to these systems,not only at the level of research and development of these systems,but also on the extent of strategic planning for the change they bring about on the economic level in general,and on the labor market in particular.One of the tools that these countries can use in their attempts to solve this problem is the artificial intelligence tax.

Frontiers of collaborative intelligence systems

The development of information technology has propelled technological reform in artificial intelligence(AI).To address the needs of diversified and complex applications,AI has been increasingly trending towards intelligent,collaborative,and systematized development across different levels and tasks.Research on intelligent,collaborative and systematized AI can be divided into three levels:micro,meso,and macro.Firstly,the micro-level collaboration is illustrated through the introduction of swarm intelligence collaborative methods related to individuals collaboration and decision variables collaboration.Secondly,the meso-level collaboration is discussed in terms of multi-task collaboration and multi-party collaboration.Thirdly,the macro-level collaboration is primarily in the context of intelligent collaborative systems,such as terrestrial-satellite collaboration,space-air-ground collaboration,space-air-ground-air collaboration,vehicle-road-cloud collaboration and end-edge-cloud collaboration.Finally,this paper provides prospects on the future development of relevant fields from the perspectives of the micro,meso,and macro levels.

Intelligent Drilling and Completion:A Review

The application of artificial intelligence(AI)has become inevitable in the petroleum industry.In drilling and completion engineering,AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency(DE),In recent years,numerous studies have focused on intelligent algorithms and their application.Advanced technologies,such as digital twins and physics-guided neural networks,are expected to play roles in drilling and completion engineering.However,many challenges remain to be addressed,such as the automatic processing of multi-source and multi-scale data.Additionally,in intelligent drilling and completion,methods for the fusion of data-driven and physicsbased models,few-sample learning,uncertainty modeling,and the interpretability and transferability of intelligent algorithms are research frontiers.Based on intelligent application scenarios,this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future.This study aims to enhance the berthing of AI techniques in drilling and completion engineering.

2018 special issue on artificial intelligence 2.0：theories and applications

In July 2017,the Chinese government issued a guideline on developing artificial intelligence（AI）,namely,the‘New-Generation Artificial Intelligence Development Plan’,through 2030 to the public,setting a goal of becoming a global innovation center in this field by 2030.According to the development plan,breakthroughs should be made in basic theories of AI in terms of big data intelligence.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Toward Human-centered XAIin Practice:A survey

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI development.Explainable AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human understanding.However,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI practice.We first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of XAI.In addition,a taxonomy of XAI methods is developed for visual computing with analysis of method properties.Holding clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in practice.And we check the availability of open-source XAI tools for stakeholders'use.This survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

Application of informatics in cancer research and clinical practice:Opportunities and challenges

Cancer informatics has significantly progressed in the big data era.We summarize the application of informatics approaches to the cancer domain from both the informatics perspective(e.g.,data management and data science)and the clinical perspective(e.g.,cancer screening,risk assessment,diagnosis,treatment,and prognosis).We discuss various informatics methods and tools that are widely applied in cancer research and practices,such as cancer databases,data standards,terminologies,high‐throughput omics data mining,machine‐learning algorithms,artificial intelligence imaging,and intelligent radiation.We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes,and focus on how informatics can provide opportunities for cancer research and practices.Finally,we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices.It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights.

Development trends of the national secure PNT system based on BDS

Satellite navigation systems are vulnerable.To guarantee the positioning,navigation and timing(PNT)safety of core infrastructure,it is necessary to establish a secure PNT system with hybrid physical principles.In this paper,the augmentations of the BeiDou satellite system(BDS)itself are analysed,namely augmentations through the BDS inter-satellite link,BDS geostationary orbit(GEO)and inclined geostationary orbit(IGSO)satellites,and BDS PNT services supported by low earth orbit(LEO)satellites.Then,taking BDS as the core component,the comprehensive PNT infrastructure seamlessly covering deep space and deep ocean is described,consisting of the deep space PNT constellation,the sea-floor PNT sonar beacon network,and the ground-based low frequency and very low frequency(VLF)long wave radio stations.Moreover,the key technologies of resilient PNT application matching comprehensive PNT and various autonomous perception PNT information are discussed,such as resilient PNT sensor integration,the resilient PNT functional model and the resilient stochastic model.As a future development direction,the key factors of intelligent PNT services are analysed,including the intelligent perception of PNT application scenes,the intelligent optimization of PNT functional and stochastic models and the intelligent fusion of multisource PNT information.