Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

Towards Safe Autonomous Driving:Decision Making with Observation‑Robust Reinforcement Learning

Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous driving.To address these issues and further improve safety,automated driving is required to be capable of handling perception uncertainties.Here,this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous vehicles.Specifically,an adversarial agent is trained online to generate optimal adversarial attacks on observations,which attempts to amplify the average variation distance on perturbed policies.In addition,an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain bound.Lastly,the safe decision making scheme is evaluated on a lane change task under complex highway traffic scenarios.The results show that the developed approach can ensure autonomous driving performance,as well as the policy robustness against adversarial attacks on observations.

Curricular Robust Reinforcement Learning via GAN-Based Perturbation Through Continuously Scheduled Task Sequence

Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as cooperative Boston Dynamics robots.However,robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real world.Existing efforts have been made to approach this problem,such as performing random environmental perturbations in the learning process.However,one cannot guarantee to train with a positive perturbation as bad ones might bring failures to RL.In this work,we treat robust RL as a multi-task RL problem,and propose a curricular robust RL approach.We first present a generative adversarial network(GAN)based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current policy.Furthermore,with these progressive tasks,we can realize curricular learning and finally obtain a robust policy.Extensive experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.

Robust cross-modal retrieval with alignment refurbishment

Cross-modal retrieval tries to achieve mutual retrieval between modalities by establishing consistent alignment for different modal data.Currently,many cross-modal retrieval methods have been proposed and have achieved excellent results;however,these are trained with clean cross-modal pairs,which are semantically matched but costly,compared with easily available data with noise alignment(i.e.,paired but mismatched in semantics).When training these methods with noise-aligned data,the performance degrades dramatically.Therefore,we propose a robust cross-modal retrieval with alignment refurbishment(RCAR),which significantly reduces the impact of noise on the model.Specifically,RCAR first conducts multi-task learning to slow down the overfitting to the noise to make data separable.Then,RCAR uses a two-component beta-mixture model to divide them into clean and noise alignments and refurbishes the label according to the posterior probability of the noise-alignment component.In addition,we define partial and complete noises in the noise-alignment paradigm.Experimental results show that,compared with the popular cross-modal retrieval methods,RCAR achieves more robust performance with both types of noise.