Purpose This study aims to explore the impact of information security knowledge on information security compliance behavior using neuroscience science. Design/methodology/approach This study uses electroencephalography (EEG) equipment to collect brain cognitive data from participants under two specific conditions. The conditions are before and after information security knowledge interventions contextualized with the “Wannacry” ransomware incident. Findings Firstly, the information security knowledge interventions lead to differences in the participants’ behavioral data under the two conditions, with participants showing increased attention to security warnings after intervention. Secondly, the P300 waveform is observed in the decision-making process for information security compliance behavior both before and after intervention, suggesting that the cognitive processes of the brain are influenced by information security knowledge. Finally, the amplitude of the P300 waveform is larger after intervention compared to before, and the energy in the β frequency band induced by decision-making for information security compliance behavior is higher before intervention than after. Originality/value Few studies have focused on the corresponding research regarding how to enhance information security compliance behavior. This study confirms that information security knowledge can significantly improve information security compliance behavior encouraging users to proactively implement security measures to counter information security threats. The findings reveal the impact of EEG on the cognitive processes involved in decision-making for information security compliance behavior and offer a novel approach for improving information security behavior through information security knowledge interventions.

Music generation task is commonly considered as a note-by-note prediction problem. Moreover, prediction models generating one musical note at a time may ignore the overall coherence because the music phrase is incomplete and unable to demonstrate musicality. To address these issues, in this study, we propose a feasible monophonic music generation framework that can simulate subsequent trends for each predicted musical note. The framework generates a musical note mainly in three steps: 1) a sequence prediction model is used to predict the most potential candidates, 2) the subsequent trends for each candidate are modeled and evaluated, and 3) the best candidate is selected as the final result. We use the Monte-Carlo tree search algorithm because of its great capability of discovering near-optimal results. We establish a method of training a value network that can assess musical coherence to evaluate the simulated sequences. Further, we used a smoothed polynomial upper confidence trees algorithm to improve the accuracy and efficiency of the search process. An accurate dataset labeled by us, which contains 36 transcribed samples from real-world pop songs, was used to validate our framework. Compared with the note-by-note sequence prediction model, our framework exhibits a better sense of musicality. Our framework can be applied to generate symbolic monophonic music, particularly the main melody track in pop music.

The temporal order of user behaviors, which implies the user's preference in the near future, plays a key role in sequential recommendation systems. To capture such patterns from user behavior sequences, many recent works borrow ideas from language models and consider it a next item prediction problem. It is reasonable, but the gap between the user behavior data and the text data is ignored. Generally speaking, user behaviors are more arbitrary than sentences in natural languages. A behavior sequence usually carries multiple intentions, and the exact order does not matter a lot. But a sentence in a text tends to express one meaning and different orders of the words may bring very different meanings. To address these issues, this study considers user behavior as a mixture of multiple subsequences. Specifically, we introduce a subsequence extraction module, which assigns the items in a sequence into different subsequences, with respect to their relationship. Then these subsequences are fed into the downstream sequence model, from which we obtain several user representations. To train the whole system in an end-to-end manner, we design a new training strategy where only the user representation near the target item gets supervised. To verify the effectiveness of our method, we conduct extensive experiments on four public datasets. It is compared with several baselines and achieves better results in most cases. Further experiments explore the properties of our model and we also visualize the result of the subsequence extraction.

Deep reinforcement learning algorithms have made great progress in video games. However, there are still some problems, such as sample inefficiency and poor generalization. In this paper, we highlight that these problems are partially caused by the inability of convolutional neural networks (CNNs) to reason with the underlying relations between the objects in the image observations. Based on this point, we try to alleviate these problems in a more efficient and explainable way, including learning the representations of objects and reasoning the relations between them with a relation network (RN). Each pixel in the feature maps is treated as an object and our model explicitly learns the relations between object pairs. The relations are summarized through an attention mechanism and then fed into the downstream fully-connected layers. In the experiments, our model is compared with baseline models in three typical object based Atari games. Under the same hyperparameter settings, our model still achieves better sample efficiency and generalization capability. Further studies throw light on the impact of hyperparameters and verify the interpretability of the model.

A common shortfall of supervised learning for medical imaging is the greedy need for human annotations, which is often expensive and time-consuming to obtain. This paper proposes a semi-supervised classification method for three kinds of apicomplexan parasites and non-infected host cells microscopic images, which uses a small number of labeled data and a large number of unlabeled data for training. There are two challenges in microscopic image recognition. The first is that salient structures of the microscopic images are more fuzzy and intricate than natural images’ on a real-world scale. The second is that insignificant textures, like background staining, lightness, and contrast level, vary a lot in samples from different clinical scenarios. To address these challenges, we aim to learn a distinguishable and appearance-invariant representation by contrastive learning strategy. On one hand, macroscopic images, which share similar shape characteristics in morphology, are introduced to contrast for structure enhancement. On the other hand, different appearance transformations, including color distortion and flittering, are utilized to contrast for texture elimination. In the case where only 1% of microscopic images are labeled, the proposed method reaches an accuracy of 94.90% in a generalized testing set.

This paper deals with the problem of detecting protein complexes from protein-protein interaction (PPI) network using a spectral clustering method. A complete PPI network is crucial for detection performance. However, experimentally identified PPIs are usually very limited, resulting in incomplete PPI networks. To solve this problem, we propose a deep transfer learning based predictor for the PPI prediction, consisting of a semi-supervised SVM classifier and a deep feature extractor of convolution neural network (CNN). Considering the fact that the similarities of gene ontology (GO) annotations contribute to protein interaction, and the difference of subcellular localizations contribute to negative interactions, we pre-train the deep CNN feature extractor in deep GO annotation and subcellular localization predictors and then transfer it to the PPI prediction. In this way, we have a deep PPI detector enhanced with transfer learning of GO annotation and subcellular localization prediction. Experimental results show that the proposed method outperforms the state-of-the-art methods on benchmark datasets.

Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth captions. It usually leads to over-penalization and thus a bad correlation to human judgment. Recently, the latest one-to-one metric BERTScore can achieve high human correlation in system-level tasks while some issues can be fixed for better performance. In this paper, we propose a novel metric based on BERTScore that could handle such a challenge and extend BERTScore with a few new features appropriately for image captioning evaluation. The experimental results show that our metric achieves state-of-the-art human judgment correlation.

In online advertising systems, having a good knowledge of user behavior is crucial for click-through rate (CTR) prediction. In recent years, many researchers turn to seek a better way of user representation by modeling the behavior sequences with recurrent neural network (RNN). However, recurrent layers implicitly adopt the assumption that elements with different orders are fundamentally different, which is inefficient in many practical scenarios with much uncertainty and complicated hidden states. In this paper, we follow the paradigm of Relation Network (RN), and propose a new model called Similitude Attentive Relation Network (SARN). The user behavior is modeled as a graph, where nodes correspond to the visited items and edges correspond to the relations. To capture the latent user interest better, the model concentrates on the relations between items, rather than the translation on the time series. More specifically, the model tries to learn the similarity between items in a semantic space through a learnable dot-product operation and blend both of the item representations and relational information together as the final relations. We define our user representation on an attentive pooling of the relations directly. To verify the effectiveness of our method, extensive experiments on two public datasets and one real-world online advertising dataset are conducted. Experimental results show that our methods achieve usually better performance than others. Besides, we explore the properties of our model by controlled experiments and show the learned relational knowledge by visualizing the inner states of SARN.