Distributed deep learning systems effectively respond to the increasing demand for large‐scale data processing in recent years. However, the significant investment in building distributed learning systems with powerful computing nodes places a huge financial burden on developers and researchers. It will be good to predict the precise benefit, i.e., how many times of speedup it can get compared with training on single machine (or a few), before actually building such big learning systems. To address this problem, this paper presents a novel performance model on training iteration time for heterogeneous distributed deep learning systems based on the characteristics of the parameter server (PS) system with bulk synchronous parallel (BSP) synchronization style. The accuracy of our performance model is demonstrated by comparing real measurement results on TensorFlow when training different neural networks with various kinds of hardware testbeds: the prediction accuracy is higher than 90% in most cases.

In recent years, the widespread availability of Wi‐Fi in various settings, including universities, enterprises, and large shopping centers, has become increasingly prevalent. The user’s time and location information embedded in wireless network systems can reveal individual and group social relationships, which indirectly reflect each person’s psychological well‐being. However, due to challenges in obtaining complete data, the high complexity of related data, and the absence of suitable data analysis models, few studies have analyzed student social behavior using data from university campus networks. This paper employs real‐world data from a renowned Chinese university’s wireless campus network for in‐depth analysis and introduces a novel multiangle semantic trajectory similarity (MA‐STS) algorithm to infer the intimacy and relationship types (such as teacher‐student, friends, classmates, or romantic partners) between users. The experiments demonstrate that the proposed algorithm achieves an accuracy of over 95%.

Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current actions on their next moves into account. In this study, we argue that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks. A novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long-term memory of a session context, whilst taking into account users' current interests from the short-term memory of the last-clicks. The validity and efficacy of the proposed attention mechanism is extensively evaluated on three benchmark data sets from the RecSys Challenge 2015 and CIKM Cup 2016. The numerical results show that our model achieves state-of-the-art performance in all the tests.

Aspect based sentiment classification is a crucial task for sentiment analysis. Recent advances in neural attention models demonstrate that they can be helpful in aspect based sentiment classification tasks, which can help identify the focus words in human. However, according to our empirical study, prevalent content attention mechanisms proposed for aspect based sentiment classification mostly focus on identifying the sentiment words or shifters, without considering the relevance of such words with respect to the given aspects in the sentence. Therefore, they are usually insufficient for dealing with multi-aspect sentences and the syntactically complex sentence structures. To solve this problem, we propose a novel content attention based aspect based sentiment classification model, with two attention enhancing mechanisms: sentence-level content attention mechanism is capable of capturing the important information about given aspects from a global perspective, whiles the context attention mechanism is responsible for simultaneously taking the order of the words and their correlations into account, by embedding them into a series of customized memories. Experimental results demonstrate that our model outperforms the state-of-the-art, in which the proposed mechanisms play a key role.