Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that eliminates the need for human intervention. Specifically, the generator, relying solely on basic product information, first uses a unified design module to integrate the three key elements of a poster (background, text, and layout) into a cohesive output. Then, an element rendering module encodes these elements into condition tokens, efficiently and controllably generating the product poster. Based on the generated poster, the optimizer enhances its Click-Through Rate (CTR) by leveraging online feedback. It systematically replaces elements to gather fine-grained CTR comparisons and utilizes Isolated Direct Preference Optimization (IDPO) to attribute CTR gains to isolated elements. Our work is supported by AutoPP1M, the largest dataset specifically designed for product poster generation and optimization, which contains one million high-quality posters and feedback collected from over one million users. Experiments demonstrate that AutoPP achieves state-of-the-art results in both offline and online settings.

Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is nontrivial, mainly because assessing the blocked influence of an IBM problem solution typically requires plenty of expensive Monte Carlo simulations (MCSs). This work presents a novel approach that enables solving IBM problems with hundreds of thousands of nodes and edges in seconds. The key idea is to construct a fast-to-evaluate surrogate model called neural influence estimator (NIE) offline as a substitute for the time-intensive MCSs, and then combine it with optimization algorithms to address IBM problems online. To this end, a learning problem is formulated to build the NIE that takes the false-and-true information instance as input, extracts features describing the topology and interrelationship between two seed sets, and predicts the blocked influence. A well-trained NIE can generalize across different IBM problems given a social network, and can be readily combined with existing IBM optimization algorithms. The experiments on 25 IBM problems with up to millions of edges show that the NIE-based optimization method can be up to four orders of magnitude faster than MCSs-based optimization method to achieve the same optimization quality. Moreover, given a one-minute limit, the NIE-based method can solve IBM problems with up to hundreds of thousands of nodes, which is at least one order of magnitude larger than what can be solved by existing methods.

Abstract Although the market demand for smart devices (SDs) in the Internet of Things (IoT) era is surging, the corresponding thunderstorm protection measures have rarely attracted attention. This paper presents a thunderstorm prediction method with elevation correction, to reduce the thunderstorm damage to SDs by visually tracking thunderstorm activities. First, a self‐made three‐dimensional atmospheric electric field apparatus (3DAEFA) deployed in IoT is developed to collect real‐time AEF data. A 3DAEFA‐based localization model is established, and the localization formula after correction is derived. AEF data predicted by the bi‐directional long short‐term memory (BiLSTM) model are input to this formula to obtain thunderstorm point charge localization results. Then, the localization skill is evaluated. Finally, the proposed method is assessed in experiments, under single and multiple point charge conditions. There are significant reductions of at least 33.1% and 8.8% in ranging and elevation angle errors, respectively. Particularly, this post‐prediction correction reduces the deviation of fitted point charge moving paths by at most 0.189 km, demonstrating excellent application effects. Comparisons with radar charts and existing methods testify that this method can effectively predict thunderstorms.

This paper presents a Bayesian data fusion framework for estimating journey time variability that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework offers a generalized statistical foundation for making full use of multiple traffic data sources to estimate the vehicular journey time variability. Feeding data collected from multiple data sources are classified based on the associated traffic conditions, and the corresponding estimation biases of the individual data sources are determined by arbitrary distributions. The proposed framework is implemented and tested on a Hong Kong corridor with actual data collected from the field. Different statistical distributions of prior and likelihood knowledge are applied and compared. The findings of the case study show significant improvement in the journey time estimations of the proposed method compared with the individual measurements. The results also highlight the benefit of incorporating a traffic state classifier and prior knowledge in the fusion framework. This study contributes to the development of reliability-based intelligent transportation systems based on advanced traffic data analytics.

A deep-learning-powered two-stage method for automating the inspection of railway track major components is developed in this article. Rails and two types of fasteners: 1) bolts and 2) clippers, are considered as major targeted objects in this study. Based on railway images, the developed method realizes the accurate railway track inspection via two stages: 1) the initial detection and 2) the detection calibration. At stage I, a squeeze and excitation participated YOLOv3 model is developed to generate initial detection results. A domain-logic-based hybrid model (DLHM) developed with the domain knowledge is introduced to enhance the detection performance at stage II. The DLHM consists of two modules: 1) a module for the problematic region calibration and 2) another module for the symmetric region calibration. The developed DLHM offers a high probability on inspecting overlooked or misclassified interested objects generated from stage I. The effectiveness of the proposed method for detecting railway tracks is validated with field collected railway images. An overall 95.2% mAP can be achieved via the proposed method. Four state-of-the-art deep-learning-based methods are considered as benchmarks to verify advantages of the proposed method. Via a deep comparative analytics, we show that the proposed method offers a state-of-the-art performance in the railway track major component inspection task.

Rails of unhealthy conditions are considered as major targets in the rail surface inspection and this study focuses on inspecting five types of major rail surface flaws, corrugations, defects, the shelling, squats, and grinding marks, via analyzing railway images. We propose a deep learning powered rail surface multi-flaw identification framework composed of two main components, a novel rail extractor for extracting rails from the background and a cascading rail surface flaw identifier for precisely identifying different flaws. The novelty of the cascading rail surface flaw identifier includes: 1) An unhealthy rail detector developed based on a DenseNet backbone for recognizing the healthy/unhealthy status on rail surfaces and 2) A rail flaw classifier for identifying flaw types on unhealthy rail surfaces. A new feature joint learning process integrating latent features derived from selected hierarchies of the DenseNet backbone as well as two traditional feature extractors, the local binary pattern and the gray level co-occurrence matrix, is developed to facilitate the rail flaw classifier to offer accurate identification results. The effectiveness of the proposed framework for rail surface multi-flaw identification is validated with datasets provided by an industrial partner in China and collected from online sources. Based on collected datasets, the proposed framework is capable to identify the rail with unhealthy conditions and its flaw type. The overall identification performance can achieve a 98.2% accuracy. Three groups of benchmarking methods are employed to verify advantages of the proposed framework. Computational results demonstrate the impressive performance of the proposed framework in the rail surface multi-flaw identification and its applicability on new datasets.

Standard state-machine replication involves consensus on a sequence of totally ordered requests through, for example, the Paxos protocol. Such a sequential execution model is becoming outdated on prevalent multi-core servers. Highly concurrent executions on multi-core architectures introduce non-determinism related to thread scheduling and lock contentions, and fundamentally break the assumption in state-machine replication. This tension between concurrency and consistency is not inherent because the total-ordering of requests is merely a simplifying convenience that is unnecessary for consistency. Concurrent executions of the application can be decoupled with a sequence of consensus decisions through consensus on partial-order traces, rather than on totally ordered requests, that capture the non-deterministic decisions in one replica execution and to be replayed with the same decisions on others. The result is a new multi-core friendly replicated state-machine framework that achieves strong consistency while preserving parallelism in multi-thread applications. On 12-core machines with hyper-threading, evaluations on typical applications show that we can scale with the number of cores, achieving up to 16 times the throughput of standard replicated state machines.

Stochastic Knapsack Queuing Model (SKQM) can be applied to estimate the network capacity of a telecommunication network where different services are transmitted under various QoS constraints. However, the serious scrutiny and refinement are required before the original model can be fitted into the real-world network deployment. In this paper, we start from the assumption, principle, proof and theoretical result of our improved stochastic knapsack queuing model and apply it to tackle real-world network capacity planning problem in TD-SCDMA system. We develop a general network planning procedure based on SKQM in TD-SCDMA and prove its effectiveness through the case study. Numerical results show that the proposed SKQM have a good performance in capacity planning in TD-SCDMA.

MRP (Material Requirements Planning) is mainly applied in the stock control of dependence requirement i.e. relevant requirement which can decrease the stock level significantly. This paper designs a kind of MRP system that can concerns the production with guarantee period by using Wagner-Whitin Algorithm.

China has entered the 3G era, this paper introduces the concept and function of 3G technology, analysis the characteristics of mobile learning. 3G technology has rich characteristics, which can be used for M-learning. When the 3G technology are applied in mobile learning, mobile learning will have new identities.

We examine the problem of keyboard acoustic emanations. We present a novel attack taking as input a 10-minute sound recording of a user typing English text using a keyboard and recovering up to 96% of typed characters. There is no need for training recordings labeled with the corresponding clear text. A recognizer bootstrapped from a 10-minute sound recording can even recognize random text such as passwords: In our experiments, 90% of 5-character random passwords using only letters can be generated in fewer than 20 attempts by an adversary; 80% of 10-character passwords can be generated in fewer than 75 attempts by an adversary. In the attack, we use the statistical constraints of the underlying content, English language, to reconstruct text from sound recordings without knowing the corresponding clear text. The attack incorporates a combination of standard machine learning and speech recognition techniques, including cepstrum features, Hidden Markov Models, linear classification, and feedback-based incremental learning.

With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization.

We examine the problem of keyboard acoustic emanations. We present a novel attack taking as input a 10-minute sound recording of a user typing English text using a keyboard, and then recovering up to 96% of typed characters. There is no need for a labeled training recording. Moreover the recognizer bootstrapped this way can even recognize random text such as passwords: In our experiments, 90% of 5-character random passwords using only letters can be generated in fewer than 20 attempts by an adversary; 80% of 10-character passwords can be generated in fewer than 75 attempts. Our attack uses the statistical constraints of the underlying content, English language, to reconstruct text from sound recordings without any labeled training data. The attack uses a combination of standard machine learning and speech recognition techniques, including cepstrum features, Hidden Markov Models, linear classification, and feedback-based incremental learning.