基于深度学习的矩阵分解推荐模型

通过对深度学习和矩阵分解技术进行结合,设计一个深度神经网络对用户和物品进行特征提取,形成用户隐向量和物品隐向量的方法,计算这两个隐向量的内积得到用户对物品的评分预测.为提高推荐精度,提出使用显式数据和隐式数据并设计新的损失函数能够同时计算这两类数据损失的方法.在两个公开数据集上的实验结果表明,该方法比基线模型在HR和N...     

基于深度学习的无人机识别算法研究

无人机的广泛运用,在给人们带来便利的同时,也引发了不良影响。比如,无人机飞入禁飞区引发安全问题,由于不正当的使用侵犯公民的隐私等,因此需要构建一个无人机警察系统,对无人机实施监控,遏制乱飞现象。采用传统的识别方法,灵活性不足,精度也不够高。为此提出一种基于深度学习的无人机识别算法,通过训练一个基于卷积神经网络(CNNs)的学习网络,得出一个高效的识别模型,实现无人机和非无人机间的分类。模型的测试结果表明,该方法具有较高的识别率。     

A multi-biometric iris recognition system based on a deep learning approach

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris-V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

A novel scalable intrusion detection system based on deep learning

International Journal of Information Security - This paper successfully tackles the problem of processing a vast amount of security related data for the task of network intrusion detection. It...     

The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach

The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers’ satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers’ characteristics and the similarity of customers’ satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.     

Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into account

In a context-aware ubiquitous learning environment, learning systems are aware of students’ locations and learning status in the real world via the use of sensing technologies which provide personalized guidance or support. In such a learning environment that guides students to observe and learn from real-world targets, various physical world constraints need to be taken into account when planning learning paths for individuals. In this study, an optimization problem is formulated by taking the relevance of real-world learning targets and the environmental constraints into account when determining personalized learning paths in the real world to maximize students’ learning efficacy. Moreover, a hyper-heuristic approach is proposed to efficiently find quality learning paths for individual students. To evaluate the performance of the proposed approach, the teachers’ feedback was collected and analyzed based on the learning activities conducted in an elementary school natural science course; in addition, the performances of the proposed algorithm and other approaches were compared based on a set of test data.     

A novel framework to alleviate the sparsity problem in context-aware recommender systems

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.     

A context-aware reminder system for elders based on fuzzy linguistic approach

To date, several reminder systems for elders have been developed, but when and how to prompt the reminding message has not been fully explored yet. This paper presents a context-aware system that provides reminding messages for elders based on fuzzy linguistic model in order to properly deliver a reminder in an appropriate time and way. By separating the user activity contexts and contexts utilized to trigger a reminder, it is much easier for the elderly or their care givers to schedule and maintain reminders. We first adopt a fuzzy linguistic model to determine the prompting level based on the interrupt degree of user current activity and the urgent level of to-be-prompt reminder. An adaptive mapping strategy is then presented to transfer the prompting level into machine-readable parameters. Finally, the proposed approach is verified through our system prototype and experiments.     

Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

The main strengths of collaborative filtering (CF), the most successful and widely used filtering technique for recommender systems, are its cross-genre or ‘outside the box’ recommendation ability and that it is completely independent of any machine-readable representation of the items being recommended. However, CF suffers from sparsity, scalability, and loss of neighbor transitivity. CF techniques are either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. An important contribution of this paper is a hybrid fuzzy-genetic approach to recommender systems that retains the accuracy of memory-based CF and the scalability of model-based CF. Using hybrid features, a novel user model is built that helped in achieving significant reduction in system complexity, sparsity, and made the neighbor transitivity relationship hold. The user model is employed to find a set of like-minded users within which a memory-based search is carried out. This set is much smaller than the entire set, thus improving system’s scalability. Besides our proposed approaches are scalable and compact in size, computational results reveal that they outperform the classical approach.     

A novel infrared video surveillance system using deep learning based techniques

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.     

A context-aware adaptive learning system using agents

Evolution of Web technologies has made e-learning a popular common way of education and training. As an outcome, learning content adaptation has been the subject of many research projects lately. This paper suggests a framework for building an adaptive Learning Management System (LMS). The proposed architecture is based upon multi-agent systems and uses both Sharable Content Object Reference Model (SCORM) 2004 and semantic Web ontology for learning content storage, sequencing and adaptation. This system has been implemented upon a well known open-source LMS and its functionalities are demonstrated through the simulation of a scenario mimicing the real life conditions. The result reveals the system effectiveness for which it appears that the proposed approach may be very promising.     

A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches.     

A context-aware approach to automated negotiation using reinforcement learning

Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate.     

A closed-loop healthcare processing approach based on deep reinforcement learning

Multimedia Tools and Applications - In healthcare, the human body is a controlled input-output system, which generates different observations with the variations of external interventions. The...     

A context-aware sensing strategy with deep reinforcement learning for smart healthcare

Health sensing system (HSS), offering a variety of health services, has attracted considerable research attention in the area of smart healthcare. However, continuous sensing inevitably brings dramatic energy consumption of mobile sensing devices. On the other hand, the reduction of sensing time duration causes excessive delay in sensing a user state change and the missing of critical physiologic signal. Thus, the trade-off between energy consumption and delay constitutes a primary challenge in the design of HSS. In this paper, we propose an adaptive sensing strategy to intelligently determine the trigger time for sensing physiological parameters at a HSS. Furthermore, human context recognition (HCR) is adopted to design context-aware sensing strategy, where the health condition, sensing requirements, and dependence on physiological data are considered simultaneously. To devise the sensing strategy, we first generate a dynamic observation model. Next, we propose a sort retention double-DQN based sensing strategy. In comparison to traditional double-DQN, the proposed approach can effectively enhance learning stability and sample efficiency. With SRD-DQN, we can obtain the optimized solution for the schedule of the successive window according to the current state. We implement blood pressure and heart rate monitoring simulations to evaluate the performance of the proposed sensing strategy. Simulation results reveal that the sensing strategy can effectively restrain energy consumption and delay, and SRD-DQN converges faster than traditional DQN.     

Social movie recommender system based on deep autoencoder network using Twitter data

Neural Computing and Applications - Recommender systems attempt to provide effective suggestions to each user based on their interests and behaviors. These recommendations usually match the...     

A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems

Multimedia Tools and Applications - Recommender systems are intelligent programs to suggest relevant contents to users according to their interests which are widely expressed as numerical ratings....