Ensemble Feature Learning-Based Event Classification for Cyber-Physical Security of the Smart Grid

The power grids are transforming into the cyber-physical smart grid with increasing two-way communications and abundant data flows. Despite the efficiency and reliability promised by this transformation, the growing threats and incidences of cyber attacks targeting the physical power systems have exposed severe vulnerabilities. To tackle such vulnerabilities, intrusion detection systems (IDS) are proposed to monitor threats for the cyber-physical security of electrical power and energy systems in the smart grid with increasing machine-to-machine communication. However, the multi-sourced, correlated, and often noise-contained data, which record various concurring cyber and physical events, are posing significant challenges to the accurate distinction by IDS among events of inadvertent and malignant natures. Hence, in this research, an ensemble learning-based feature learning and classification for cyber-physical smart grid are designed and implemented. The contribution of this research are (i) the design, implementation and evaluation of an ensemble learning-based attack classifier using extreme gradient boosting (XGBoost) to effectively detect and identify attack threats from the heterogeneous cyber-physical information in the smart grid; (ii) the design, implementation and evaluation of stacked denoising autoencoder (SDAE) to extract highlyrepresentative feature space that allow reconstruction of a noise-free input from noise-corrupted perturbations; (iii) the design, implementation and evaluation of a novel ensemble learning-based feature extractors that combine multiple autoencoder (AE) feature extractors and random forest base classifiers, so as to enable accurate reconstruction of each feature and reliable classification against malicious events. The simulation results validate the usefulness of ensemble learning approach in detecting malicious events in the cyber-physical smart grid

The Investigation of Post-Experience Advertising Effect of Memory of Collegiate Sporting Event Experience

While memory of previous travel experience significantly influences travelers’ intention to re-visit the destination (Kozak, 2001), the current study aims to investigate the memory of collegiate sport event experience, more specifically, provide answers for the question about whether post-experience advertising impact the memory of collegiate sport event experience. The theoretical framework is based on Braun’s (1999) research, which is proposing that advertising received after an actual product experience can providing a backward-framing effect and have significant influence on how that product experience was remembered and evaluated. Results of the current study are expected to help collegiate sport event marketers understanding how their marketing campaigns influence consumers’ memories and perceptions of collegiate sport event experience

The Exploration of Influential Factors of On Sport Tourist Expenditure in Mega Sport Event

Sport events are considered as important business and many countries are competing fiercely to host major events. This is because of the money that events generate and the marketing value of destination from hosting major events. The purpose of this study is exploring the potential factors that can influence sport tourists’ spending during mega sport events. The surveys consisted of 136 questionnaires that were completed one day before 2012 Super Bowl in Indianapolis. Findings of the survey indicate that team identification, previous Super Bowl attendance, and level of the professional football involvement may influence sport tourists’ spending during mega sport event. The contribution of this research is providing several possible directions for both destination and event managers to cultivate marketing strategy to increase the value and amounts generated by the event

Some New Results on the Lotka-Volterra System with Variable Delay

This paper discusses the stochastic Lotka-Volterra system with time-varying delay. The nonexplosion, the boundedness, and the polynomial pathwise growth of the solution are determined once and for all by the same criterion. Moreover, this criterion is constructed by the parameters of the system itself, without any uncertain one. A two-dimensional stochastic delay Lotka-Volterra model is taken as an example to illustrate the effectiveness of our result

Expression Syntax Information Bottleneck for Math Word Problems

Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features. The key idea of ESIB is to encourage multiple models to predict the same expression syntax tree for different problem representations of the same problem by mutual learning so as to capture consistent information of expression syntax tree and discard latent-specific redundancy. To improve the generalization ability of the model and generate more diverse expressions, we design a self-distillation loss to encourage the model to rely more on the expression syntax information in the latent space. Experimental results on two large-scale benchmarks show that our model not only achieves state-of-the-art results but also generates more diverse solutions. The code is available.Comment: This paper has been accepted by SIGIR 2022. The code can be found at https://github.com/menik1126/math_ESI

Phase Match for Out-of-Distribution Generalization

The Fourier transform, serving as an explicit decomposition method for visual signals, has been employed to explain the out-of-distribution generalization behaviors of Convolutional Neural Networks (CNNs). Previous research and empirical studies have indicated that the amplitude spectrum plays a decisive role in CNN recognition, but it is susceptible to disturbance caused by distribution shifts. On the other hand, the phase spectrum preserves highly-structured spatial information, which is crucial for visual representation learning. In this paper, we aim to clarify the relationships between Domain Generalization (DG) and the frequency components by introducing a Fourier-based structural causal model. Specifically, we interpret the phase spectrum as semi-causal factors and the amplitude spectrum as non-causal factors. Building upon these observations, we propose Phase Match (PhaMa) to address DG problems. Our method introduces perturbations on the amplitude spectrum and establishes spatial relationships to match the phase components. Through experiments on multiple benchmarks, we demonstrate that our proposed method achieves state-of-the-art performance in domain generalization and out-of-distribution robustness tasks

Self-consistent Reasoning For Solving Math Word Problems

Math word problems (MWPs) is a task that automatically derives solution expression from a giving math problems in text. The previous studies suffer from spurious correlations between input text and output expression. To mitigate this issue, we propose a self-consistent reasoning framework called SCR, which attempts to adopt a pruning strategy to correct the output distribution shift so as to implicitly fix those spurious correlative samples. Specifically, we firstly obtain a sub-network by pruning a roberta2tree model, for the sake to use the gap on output distribution between the original roberta2tree model and the pruned sub-network to expose spurious correlative samples. Then, we calibrate the output distribution shift by applying symmetric Kullback-Leibler divergence to alleviate spurious correlations. In addition, SCR generates equivalent expressions, thereby, capturing the original text's logic rather than relying on hints from original text. Extensive experiments on two large-scale benchmarks demonstrate that our model substantially outperforms the strong baseline methods.Comment: Submitted to IEEE ICASSP 202

More than Encoder: Introducing Transformer Decoder to Upsample

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such a schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model long-range dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic U-Net structure with lateral connections and achieve state-of-the-art performance on Synapse multi-organ segmentation, Medical Segmentation Decathlon (MSD) Brain, and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement.Comment: Accepted by BIBM202

Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR

<p>Abstract</p> <p>Background</p> <p>Real-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.</p> <p>Results</p> <p>A systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (<it>ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b</it>) and seven new candidates (<it>SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2</it>). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of <it>SKIP16, UKN1 </it>and <it>UKN2 </it>was overall the most stable. A combination of <it>ACT11, UKN1 </it>and <it>UKN2 </it>would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. <it>ACT11, TUA5 </it>and <it>TIP41 </it>were the most stably expressed when the photoperiod was altered, and <it>TIP41, UKN1 </it>and <it>UKN2 </it>when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with <it>ACT11, UKN2 </it>and <it>TUB4 </it>being the most stable genes. The relative gene expression level of <it>GmFTL3</it>, an ortholog of Arabidopsis <it>FT </it>(<it>FLOWERING LOCUS T</it>) was detected to validate the reference genes selected in this study.</p> <p>Conclusion</p> <p>None of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.</p