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Abstract We consider incorporating topic information into message-response matching to boost responses with rich content in retrieval-based chatbots. To this end, we propose a topic aware attentive recurrent neural network in which representations of the message and the response are enhanced by the topic information. The model first leverages the message and the response represented by recurrent neural networks (RNNs) to weight topic words given by a pre-trained LDA model and forms topic vectors as linear combinations of the topic words. It then refines the representations of the message and the response with the topic vectors through an attention mechanism. The attention mechanism weights the hidden sequences of the message and the response not only by themselves but also by their topic vectors. Thus both the parts that are important to matching and the parts that are semantically related to the topics are highlighted in the representations.Empirical studies on public data and human annotated data show that our model can significantly outperform state-of-the-art methods and rank more responses with rich content in high positions. [ABSTRACT FROM AUTHOR]

The rise of social networks has provided people with platforms to display their lives and emotions, often in multimodal forms such as images and descriptive texts. Capturing the emotions embedded in the multimodal content of social networks involves great research challenges and practical values. Existing methods usually make sentiment predictions based on a single-round reasoning process with multimodal attention networks, however, this may be insufficient for tasks that require deep understanding and complex reasoning. To effectively comprehend multimodal content and predict the correct sentiment tendencies, we propose the Scanning, Attention, and Reasoning (SAR) model for multimodal sentiment analysis. Specifically, a perceptual scanning model is designed to roughly perceive the image and text content, as well as the intrinsic correlation between them. To deeply understand the complementary features between images and texts, an intensive attention model is proposed for cross-modal feature association learning. The multimodal joint features from the scanning and attention models are fused together as the representation of a multimodal node in the social network. A heterogeneous reasoning model implemented with a graph neural network is constructed to capture the influence of network communication in social networks and make sentiment predictions. Extensive experiments conducted on three benchmark datasets confirm the effectiveness and superiority of our model compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Artemia Cyst-shell in-situ growth of La-nanocluster with size of sub-10 nm. • The charged amino-acids inside matrix motivates nano-La(OH) 3 formation. • The unique super-porous skeleton structure benefits for phosphate diffusion and sequestration. • An outstanding treatment capacity of 10,980 L/kg further proves its applicability. Phosphate removal by nanocomposite is a promising approach in water remediation. But it is still challenged by the small size nanoparticles formation and kinetic diffusion under the nanopore confinement. Herein, we report a different strategy, which relies on the deposition of nano-La(OH) 3 particles inside the porous biomass Artemia Cyst-shells matrix. The charged amino acid components of matrix drivers for the in-situ growth of rod-like nano-La(OH) 3 with size below 10 nm, while the broad-porous skeleton structures enable and accelerate the accessibility of trace phosphate to nano-La(OH) 3 sorption sites available, resulting in the efficient and fast phosphate removal performance. This nanocomposite exhibited excellent adsorption capacity as high as 180.5 mg/g, which is beyond of most adsorbents by recent-ten-year survey. It also revealed superior selectivity against various competing ions with a distribution coefficient (K d) up to ∼ 34000 mL/g, exceeding commercial ion-exchange resin by 15 times and outdoing 10 times the performance of well-known Fe based nanocomposites Lewatit FO36. An outstanding treatment capacity of 10980 L/kg under the continuous operation and the satisfactory regeneration property (> 95 %) further proves its applicability. This technology provides a new perspective approach for nanocomposite design and wastewater determination. It is also promising alleviating problems of trace phosphate deep purification in drinking water and wastewater medium. [ABSTRACT FROM AUTHOR]

Automatic Exploit Generation (AEG) involves automatically discovering paths in a program that trigger vulnerabilities, thereby generating exploits. While there is considerable research on heap-related vulnerability detection, such as detecting Heap Overflow and Use After Free (UAF) vulnerabilities, among contemporary heap-automated exploit techniques, only certain automated exploit techniques can hijack program control flow to the shellcode. An important limitation of this approach is that it cannot effectively bypass Linux's protection mechanisms. To solve this problem, we introduced Automatic Advanced Heap Exploit Generation (AAHEG). It first applies symbolic execution to analyze heap-related primitives in files and then detects potential heap-related vulnerabilities without a source code. After identifying these vulnerabilities, AAHEG builds an exploit abstract syntax tree (AST) to identify one or more successful exploit strategies, such as fast bin attack and Safe-unlink. AAHEG then selects exploitable methods via an abstract syntax tree (AST) and performs final testing to produce the final exploit. AAHEG chose to generate advanced heap-related exploits because the exploits can bypass Linux protections. Basically, AAHEG can automatically detect heap-related vulnerabilities in binaries without source code, build an exploit AST, choose from a variety of advanced heap exploit methods, bypass all Linux protection mechanisms, and generate final file-form exploit based on pwntools which can pass local and remote testing. Experimental results show that AAHEG successfully completed vulnerability detection and exploit generation for 20 Capture The Flag (CTF) binary files, 11 of which have all protection mechanisms enabled. [ABSTRACT FROM AUTHOR]

• Push-out tests were performed on timber-concrete composite (TCC) shear connections with glued-in rod (GIR). • Most of the TCC shear connections with GIR mainly failed by the double-hinge yield mode of GIR. • Both the test configurations and loading rates showed influence on the shear performance of TCC shear connectors. • Calculation modes for shear capacity and shear stiffness were proposed according to typical failure modes of specimens. To examine the effect of test configurations and loading protocols on the performance of timber-concrete composite (TCC) shear connectors, a total of twenty-seven TCC shear connections with glued – in threaded steel rod (GIR) were designed to experience push-out tests. The experimental variables include rod diameter, test configurations (single - and double - shear), and loading protocols including the loading protocol (EN26891) and the displacement rate (0.5 mm/min, 2 mm/min, and 4 mm/min). The results indicate that both the test configurations and the displacement rates showed different influences on the performance of the shear connections. Most of the tested TCC shear connections with GIR mainly failed by the double – hinge yield mode of GIR. Double – shear specimens with 16 mm – diameter GIR exhibited a 49 % higher maximum load compared to the single – shear specimens. The slip modulus of the specimens tested at a displacement rate of 4 mm/min was about 40 % lower than that of the specimens tested at the displacement rates of 0.5 mm/min and 2 mm/min, respectively. Among the single-shear specimens, the 16 mm– diameter GIR showed the lowest ductility, by 65 % and 59 %, as compared to the 10 mm – and 12 mm – diameter specimens. Finally, theoretical analytical formulas were proposed to predict the load carrying capacity and the slip modulus of the TCC shear connections with GIR. The experimental slip modulus was also compared with the results obtained by existing calculation models. [ABSTRACT FROM AUTHOR]

Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks. [ABSTRACT FROM AUTHOR]

Lattice-based cryptosystems have recently acquired much importance. In this work, we construct a fuzzy identity based signature (FIBS) scheme based on the Small Integer Solution (SIS) Problem. FIBS schemes allow a user with identity id to issue a signature which could be verified under identity id′ if and only if id and id′ are close to each other. To our best knowledge, no lattice based FIBS schemes were known before, and the existing security model of FIBS schemes is not correct indeed. We propose a modified security model and prove that our scheme is existentially unforgetable against adaptively chosen message and identity attacks in the random oracle model. To break the bottleneck of designing lattice-based FIBS scheme, the secret key of each identity bit is generated by employing the Bonsai Tree techniques in the fuzzy extract algorithm. We also use some techniques to prove its security. Then we show the performance comparisons of all existing FIBS schemes. Finally, we give its application in biometric authentication. [ABSTRACT FROM AUTHOR]

We introduce the concept of Affiliation-Hiding Authenticated Asymmetric Group Key Agreement (AH-AAGKA) and construct a concrete one-round AH-AAGKA protocol. An AH-AAGKA protocol allows the participants of a group to establish a common encryption key associated with several decryption keys; each of which can only be computed by the corresponding legitimate group member. An AH-AAGKA protocol has the following privacy feature. For a member i of a group G, if i participates in an AH-AAGKA protocol, any protocol participant j cannot learn whether i is a member of G, unless j himself is a member of group G. Our scheme demonstrates new features in comparison with other existing AH-AGKA protocols. If non-group members participate in our protocol, honest parties can identify these non-group members. Our scheme also captures Unlinkability and Perfect Forward Secrecy (PFS), which are missing in other existing schemes. We propose a novel security model to prove that our protocol holds PFS and present a new privacy model to prove that our scheme meets Affiliation-Hiding property. [ABSTRACT FROM AUTHOR]

The Programmable Logic Controller (PLC) is located at the junction of the virtual network and physical reality in the Industrial Control System (ICS), which is vulnerable to attacks due to its weak security. Specifically, firmware tampering attacks take the firmware under the PLC operating system as the primary attack target. The firmware provides the bridge between PLC's hardware and software, which means tampering against the firmware can be more destructive and harmful than other attacks. However, existing defense and forensics methods against firmware tampering attacks are asymmetrical, which directly leads to the proliferation of such attacks and the difficulty of forensic tracing. How to accurately, quickly, and efficiently conduct forensics for such attacks is an urgent problem. In this paper, we designed and implemented a reliable detection method based on Joint Test Action Group (JTAG) and memory comparison—Aye, which can detect mainstream firmware tampering attacks reliably. To determine the effectiveness and reliability of Aye, we selected a widely used PLC to observe Aye's performance in defense and forensics by simulating the two latest PLC firmware tampering attack methods. The experimental results show that Aye can effectively defend against firmware tampering attacks, helping improve the efficiency and accuracy of such attack detection and forensics. [ABSTRACT FROM AUTHOR]

Identity (ID) uniqueness is essential in distributed hash table (DHT)-based systems, as peer lookup and resource searching rely on ID matching. However, many DHT implementations in the wild, such as Kad and Mainline, do not enforce such uniqueness. Most previous works and measurements on DHTs do not take into account that IDs among peers may not be unique. Unfortunately, we observe that a significant portion of peers, i.e. 19.5% of the peers in Kad and 4.0% of the peers in Mainline, do not have unique IDs. These repetitions would mislead the measurements and modeling on those networks. We further focus on investigating the repetition in Kad considering its wider usage and more serious situation of repetition. We observe that there are a large number of peers that frequently change their UDP ports, and there are a few IDs that repeat for a large number of times and all peers with these IDs do not respond to Kad protocol. We also analyze the effects of ID repetitions under simplified settings and find that the current repetition degrades Kad's performance on publishing and searching, but has insignificant effect on lookup process. These measurement and analysis are useful to further determine the sources of repetitions and are also useful for finding suitable parameters in publishing and searching processes in DHT networks without compulsive ID uniqueness. [ABSTRACT FROM AUTHOR]

Abstract: In the traditional identity-based cryptography, when a user holds multiple identities as its public keys, it has to manage an equal number of private keys. The recent advances of identity-based cryptography allow a single private key to map multiple public keys (identities) that are selectable by the user. This approach simplifies the private key management. Unfortunately, the existing schemes have a heavy computation overhead, since the private key generator has to authenticate all identities in order to generate a resultant private key. In particular, it has been considered as a drawback that the data size for a user is proportional to the number of associated identities. Moreover, these schemes do not allow dynamic changes of user identities. When a user upgrades its identities, the private key generator (PKG) has to authenticate the identities and generate a new private key. To overcome these problems, in this paper we present an efficient dynamic identity-based key exchange protocol with selectable identities, and prove its security under the bilinear Diffie–Hellman assumption in the random oracle model. [Copyright &y& Elsevier]

Background: A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms. The prediction procedure is usually formulated as binary classification problem. Training binary classifier needs both positive examples and negative ones that have almost the same size. However, from various annotation database, we can only obtain few positive genes annotation for most offunctional terms, that is, there are only few positive examples for training classifier, which makes predicting directly gene function infeasible. Results: We propose a novel approach SPE_RNE to train classifier for each functional term. Firstly, positive examples set is enlarged by creating synthetic positive examples. Secondly, representative negative examples are selected by training SVM(support vector machine) iteratively to move classification hyperplane to a appropriate place. Lastly, an optimal SVM classifier are trained by using grid search technique. On combined kernel ofYeast protein sequence, microarray expression, protein-protein interaction and GO functional annotation data, we compare SPE_RNE with other three typical methods in three classical performance measures recall R, precise P and their combination F: twoclass considers all unlabeled genes as negative examples, twoclassbal selects randomly same number negative examples from unlabeled gene, PSoL selects a negative examples set that are far from positive examples and far from each other. Conclusions: In test data and unknown genes data, we compute average and variant of measure F. The experiments showthat our approach has better generalized performance and practical prediction capacity. In addition, our method can also be used for other organisms such as human. [ABSTRACT FROM AUTHOR]

Abstract With the popularity of social network, social media data embedding has attracted extensive research interest and boomed many applications, such as image classification and cross-modal retrieval. In this paper, we examine the scenario of social images containing multimodal content (e.g., visual content and textual tags) and connecting with each other (e.g., two images submitted to the same group). In such a case, both the multimodal content and link information provide useful clues for representation learning. Therefore, simply learning the embedding from network structure or data content results in sub-optimal social image representation. In this paper, we propose a Deep Multimodal Attention Networks (DMAN) to combine multimodal content and link information for social image embedding. Specifically, to effectively incorporate the multimodal content, a visual-textual attention model is proposed to encode the fine-granularity correlation between multimodal content, i.e., the alignment between image regions and textual words. To incorporate the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the first-order proximity and the second-order proximity among images. Then the two modules are integrated into a joint deep model for social image embedding. Once the representation has been learned, a wide variety of data mining problems can be solved by using the task-specific algorithms designed for handling vector representations. Extensive experiments are conducted to demonstrate the effectiveness of our approach on multi-label classification and cross-modal search. [ABSTRACT FROM AUTHOR]

Power grid is a complex system which closely links the power generation and power consumer through transmission and distribution networks. With the development of smart grid, smart grid is more open to external communication systems, it also has exposed some problems in the network attacks. A new false data injection attack (called the unobservable attack) that can bypass the traditional BDD and inject random errors into state estimation. We propose an improved extreme learning machine (ELM) for attack detection. The artificial bee colony (ABC) incorporates the thought of differential evolution algorithm (DE) to optimize ELM for improving detection precision. In this paper, Autoencoder is used to reduce the dimensionality of the measurement data, which makes the low-dimensional data information basically and fully represent high-dimensional data. We verify the performance of the proposed method on IEEE bus systems, and prove that the proposed method can effectively detect such unobservable attack. [ABSTRACT FROM AUTHOR]

Named entity disambiguation (NED) refers to the task of mapping entity mentions in running texts to the correct entries in a specific knowledge base (e.g., Wikipedia). Although there has been a lot of work on NED for long and formal texts like Wikipedia and news, the task is not well studied for questions in community question answering (CQA). The challenges of the task include little context for mentions in questions, lack of ground truth for learning, and language gaps between CQA and knowledge bases. To overcome these problems, we propose a topic modelling approach to NED for questions. Our model performs learning in an unsupervised manner, but can take advantage of weak supervision signals estimated from the metadata of CQA and knowledge bases. The signals can enrich the context of mentions in questions, and bridge the language gaps between CQA and knowledge bases. Besides these advantages, our model simulates people’s behavior in CQA and thus is intuitively interpretable. We conduct experiments on both Chinese and English CQA data. The experimental results show that our method can significantly outperform state-of-the-art methods when we apply them to questions in CQA. [ABSTRACT FROM AUTHOR]

Image captioning is one essential work in the multi-modal area, which employs computer vision and natural language processing technology together to describe image content. Most current methods employ the encoder–decoder framework to achieve satisfactory results. Recently, transformers have been extensively utilized in image captioning tasks and earned satisfactory results. Nevertheless, transformers pay more attention to the global features in images and divide images into fixed sizes and process them separately. Those models can not capture the relationship between the internal components in image, such as the relationships between objects, regions and object attributes. In this article, we introduce a novel Residual Swin Transformer and Actor-Critic (RSTAC) in the image captioning task. RSTAC consists of two modules: Residual Swin Transformer and Actor-Critic modules. In particular, we employ a residual network to preserve the vanilla feature in the first place and several residual Swin Transformer blocks with convolution operations to obtain the mid-level features, which are also composed of a residual network and several Swin Transformer blocks and convolution operations. It aims to explore the internal correlation between image content and obtain multi-view features in the image. Then, a policy network is utilized to explore possible words and forecast the future generation word. A value network is employed to calculate the reward of generated sentences whose goal is directly optimizing non-differentiable quality metrics and enhancing the performance of generated sentences. Experiments reveal that our model surpasses other competitive models and performs better on the MSCOCO and Flickr30k datasets. [ABSTRACT FROM AUTHOR]

In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance. [ABSTRACT FROM AUTHOR]

Existing knowledge-grounded dialogue (KGD) systems access the knowledge from an external knowledge base, then generate the context-coherent response accordingly. However, the knowledge access capability is constrained to the scale of a knowledge base. On the one hand, a small-scale knowledge base makes a model hard to generalize on unseen topics, while the improper shift of topics may induce an unsmooth conversation flow. On the other hand, a large-scale knowledge base requires a strong retrieval component to accurately index the context-relevant knowledge from many plausible candidates, costing significant amounts of time and resources. To address this, we regard the language model as a virtual knowledge base and propose homogenizing internalized knowledge of different language models into hybrid prompts. The hybrid prompts are a set of continuous vectors learned to represent knowledge inherently encoded in different language models. Furthermore, we devise a two-stage knowledge-grounding manner, in which both the knowledge internalized in language models and the knowledge provided by evidence can be jointly optimized to generate a knowledgeable response. We compare our proposed method with two groups of methods, including methods with explicit knowledge retrieval and those with implicit knowledge access. Experimental results on three knowledge-grounded dialogue corpora demonstrate advantages over these competitive methods. [ABSTRACT FROM AUTHOR]

Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features. After that, these visual features are fused with language representations for the decoder to generate the text summary. However, the cascaded way employs separate encoders for different modalities, which makes it hard to learn the joint vision and language representation. In addition, they also ignore the semantics alignment between paragraphs and images for multi-modal summarization tasks, which are crucial to a precise summary. To tackle these issues, in this paper, we propose ViL-Sum to jointly model paragraph-level Vision-Language Semantic Alignment and Multi-Modal Summarization. Our ViL-Sum contains two components for better learning multi-modal semantics and aims to align them. The first one is a joint multi-modal encoder. The other one is two well-designed tasks for multi-task learning, including image reordering and image selection. Specifically, the joint multi-modal encoder converts images into visual embeddings and attaches them with text embedding as the input of the encoder. The reordering task guides the model to learn paragraph-level semantic alignment, and the selection task guides the model to select summary-related images in the final summary. Experimental results show that our proposed ViL-Sum outperforms current state-of-the-art methods on most automatic and manual evaluation metrics. In further analysis, we find that two well-designed tasks and a joint multi-modal encoder can effectively guide the model to learn reasonable paragraph-image and summary-image relations. [ABSTRACT FROM AUTHOR]

Due to the rich spatio-temporal visual content and complex multimodal relations, Video Question Answering (VideoQA) has become a challenging task and attracted increasing attention. Current methods usually leverage visual attention, linguistic attention, or self-attention to uncover latent correlations between video content and question semantics. Although these methods exploit interactive information between different modalities to improve comprehension ability, inter- and intra-modality correlations cannot be effectively integrated in a uniform model. To address this problem, we propose a novel VideoQA model called Cross-Attentional Spatio-Temporal Semantic Graph Networks (CASSG). Specifically, a multi-head multi-hop attention module with diversity and progressivity is first proposed to explore fine-grained interactions between different modalities in a crossing manner. Then, heterogeneous graphs are constructed from the cross-attended video frames, clips, and question words, in which the multi-stream spatio-temporal semantic graphs are designed to synchronously reasoning inter- and intra-modality correlations. Last, the global and local information fusion method is proposed to coalesce the local reasoning vector learned from multi-stream spatio-temporal semantic graphs and the global vector learned from another branch to infer the answer. Experimental results on three public VideoQA datasets confirm the effectiveness and superiority of our model compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

There is an abundance of information found on microblog services due to their popularity. However the potential of this trove of information is limited by the lack of effective means for users to browse and interpret the numerous messages found on these services. We tackle this problem using a two-step process, first by slicing up the search results of current retrieval systems along multiple possible genres. Then, a summary is generated from the microblog messages attributed to each genre. We believe that this helps users to better understand the possible interpretations of the retrieved results and aid them in finding the information that they need. Our novel approach makes use of automatically acquired information from external search engines in each of these two steps. We first integrate this information with a semi-supervised probabilistic graphical model, and show that this helps us to achieve significantly better classification performance without the need for much training data. Next we incorporate the extra information into graph-based summarization, and demonstrate that superior summaries (up to 30% improvement in ROUGE-1) are obtained. [ABSTRACT FROM AUTHOR]

The notion of Affiliation-Hiding Authenticated Group Key Agreement (AH-AGKA) protocols was first introduced by Jarecki et al. in CT-RSA 2007, where they presented two concrete AH-AGKA protocols. In this paper, we show that Jarecki et al.'s second protocol has some drawbacks. We propose a new affiliation-hiding protocol. Differing from Jarecki et al.'s protocol, our protocol is asymmetric. Compared with existing AH-AGKA protocols, our scheme not only exhibits the affiliation-hiding property, but also holds the properties of detectability and perfect forward secrecy. [ABSTRACT FROM PUBLISHER]

Abstract: Some expanded fuzzy rough set models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. However, some of them are still sensitive to mislabeled samples and others have only considered relative errors. To remedy these defects, we propose a novel expanded fuzzy rough set model called the variable precision -fuzzy rough set model based on fuzzy granules. Considering the absolute error limit, we introduce the concept of the variable precision -fuzzy rough set firstly. Then we present a theorem to ensure that the approximation operators can be calculated efficiently. The basic properties of the model are also investigated, some of which are analogous to those of fuzzy rough sets. Furthermore, the degenerated variable precision -fuzzy rough set model is shown, and the difference and connection between the degenerated model and the variable precision rough set model introduced by Ziarko are studied. Finally, the definitions of β-fuzzy lower and upper approximation reducts are presented and the attribute reduction methods are proposed. [Copyright &y& Elsevier]

Abstract: Social image tagging is becoming increasingly popular with the development of social website, where images are annotated with arbitrary keywords called tags. Most of present image tagging approaches are mainly based on the visual similarity or mapping between visual feature and tags. However, in the social media environment, images are always associated with multi-type of object information (i.e., visual content, tags, and user contact information) which makes this task more challenging. In this paper, we propose to fuse multi-type of information to tag social image. Specifically, we model social image tagging as a “ranking and reinforcement” problem, and a novel graph-based reinforcement algorithm for interrelated multi-type objects is proposed. When a user issue a tagging request for a query image, a candidate tag set is derived and a set of friends of the query user is selected. Then a graph which contains three types of objects (i.e., visual features of the query image, candidate tags, and friend users) is constructed, and each type of objects are initially ranked based on their weight and intra-relation. Finally, candidate tags are re-ranked by our graph-based reinforcement algorithm which takes into consideration both inter-relation with visual features and friend users, and the top ranked tags are saved. Experiments on real-life dataset demonstrate that our algorithm significantly outperforms state-of-the-art algorithms. [Copyright &y& Elsevier]

Formal concept analysis and rough set analysis are two complementary approaches for analyzing data. This paper studies approaches to constructing fuzzy concept lattices based on generalized fuzzy rough approximation operators. For a residual implicator θ satisfying θ(a, b) = θ(1 - b, 1 - a) and its dual σ, a pair of (θ, σ)-fuzzy rough approximation operators is defined. We then propose three kinds of fuzzy operators, and examine some of their basic properties. Thus, three complete fuzzy concept lattices can be produced, for which the properties are analogous to those of the classical concept lattices. [ABSTRACT FROM AUTHOR]

Network embedding, as a promising way of node representation learning, is capable of supporting various downstream network mining tasks, and has attracted growing research interests recently. Existing approaches mostly focus on learning the low-dimensional node representations by preserving the local or global topology information of a static network. It is difficult for such methods to learn desirable features for the nodes in an incomplete graph whose topology information is sparse or the new nodes in a dynamic graph. It is also challenging for them to deeply incorporate node attributes as the complementary information to improve the network embedding performance. To this end, in this paper we propose a Multi-View Adversarial learning based Network Embedding model named MVANE to deeply fuse the network topology information and node attributes to better perform network embedding on incomplete graphs. The insight is that the network topology and the node attributes are treated as two correlated views. The learned embedding vector of a node should be able to reveal its unique characteristics in both views. Specifically, the adversarial autoencoder is introduced as the basic model of MVANE. Autoencoder can learn a projection function to directly map the input feature vectors into the latent space, which ensures the MVANE learn embeddings for the new nodes through features projection without the need of retraining the model. Meanwhile, the adversarial learning strategy is also applied to better capture the cross-view correlations. The idea is that the learned embeddings in one view can not only reconstruct the inputs in this view, but also generate the features in another view. Under a unified learning framework, the latent representations in different views are fused and jointly reinforced by the proposed self/cross-view learning model. Empirically, we evaluate MVANE over multiple network datasets, and the results demonstrate the superiority of our proposal. • We investigate the novel problem of network embedding on incomplete graphs. • We propose an adversarial learning based network embedding model MVANE. • The experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Social network has become an inseparable part of our daily lives and thus the automatic sentiment analysis on social media content is of great significance to identify people's viewpoints, attitudes, and emotions on the social websites. Most existing works have concentrated on the sentiment analysis of single modality such as image or text, which cannot handle the social media content with multiple modalities including both image and text. Although some works tried to conduct multi-modal sentiment analysis, the complicated correlations between the two modalities have not been fully explored. In this paper, we propose a novel Bi-Directional Multi-Level Attention (BDMLA) model to exploit the complementary and comprehensive information between the image modality and text modality for joint visual-textual sentiment classification. Specifically, to highlight the emotional regions and words in the image–text pair, visual attention network and semantic attention network are proposed respectively. The visual attention network makes region features of the image interact with multiple semantic levels of text (word, phrase, and sentence) to obtain the attended visual features. The semantic attention network makes semantic features of the text interact with multiple visual levels of image (global and local) to obtain the attended semantic features. Then, the attended visual and semantic features from the two attention networks are unified into a holistic framework to conduct visual-textual sentiment classification. Proof-of-concept experiments conducted on three real-world datasets verify the effectiveness of our model. • Bi-directional attention to highlight the emotional regions and words. • Multiple levels to excavate the emotional correlations between image and text. • The experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract Embedding the network into a low-dimensional space has attracted extensive research interest as well as boomed a lot of applications, such as node classification and link prediction. Most existing methods learn the network embedding simply from the network structure. However, the social media data, such as social images, usually contain both multimodal contents (e.g., visual content and text description) and social links among the images. To address this problem, we propose a novel model Attention-based Multi-view Variational Auto-Encoder (AMVAE) to fuse both the links and the multimodal contents for more effectively and efficiently network embedding. Specifically, Bi-LSTM (bidirectional long short-term memory) with attention model is proposed to capture the fine-granularity correlation between different data modalities, such as some words are reflected by specific visual regions. A joint representation of the multimodal contents is accordingly learned. Then, the network structure information and the learned representation for the multimodal contents are considered as two views. To fuse the two views, a multi-view correlation learning based Variational Auto-Encoder (VAE) is proposed to learn the representation of each node. By jointly optimizing the two components into a holistic learning framework, the embedding of network structure and multimodal contents are integrated and mutually reinforced. Experiments on three real-world datasets demonstrate the superiority of the proposed model in two applications, i.e., multi-label classification and link prediction. [ABSTRACT FROM AUTHOR]

We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involves matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. This motivates us to propose a new matching framework that can sufficiently carry important information in contexts to matching and model relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interact with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) that models relationships among utterances. Context-response matching is then calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage important information in contexts for matching. [ABSTRACT FROM AUTHOR]

Abstract Sentiment analysis of social media data is crucial to understand people's position, attitude, and opinion toward a certain event, which has many applications such as election prediction and product evaluation. Though great effort has been devoted to the single modality (image or text), less effort is paid to the joint analysis of multimodal data in social media. Most of the existing methods for multimodal sentiment analysis simply combine different data modalities, which results in dissatisfying performance on sentiment classification. In this paper, we propose a novel image–text sentiment analysis model, i.e., Deep Multimodal Attentive Fusion (DMAF), to exploit the discriminative features and the internal correlation between visual and semantic contents with a mixed fusion framework for sentiment analysis. Specifically, to automatically focus on discriminative regions and important words which are most related to the sentiment, two separate unimodal attention models are proposed to learn effective emotion classifiers for visual and textual modality respectively. Then, an intermediate fusion-based multimodal attention model is proposed to exploit the internal correlation between visual and textual features for joint sentiment classification. Finally, a late fusion scheme is applied to combine the three attention models for sentiment prediction. Extensive experiments are conducted to demonstrate the effectiveness of our approach on both weakly labeled and manually labeled datasets. [ABSTRACT FROM AUTHOR]

Recently, a large number of geo-tagged landmark images have been uploaded through various social media services. Usually, these geo-tagged images are annotated by users with GPS and tags related to the landmarks where they are taken. Landmark tagging aims to automatically annotate an image with the tags to describe the landmark where the image is taken. It has been observed that the images and tags show strong correlation with the geographical locations. The widely used assumption by many existing tagging methods is that images are independently and identically distributed is not effective to capture the geographical correlation. In this paper, we study the novel problem of utilizing the geographical correlation among images and landmarks for better tagging landmark images. In particular, we propose an unsupervised feature learning approach to learn the geographically discriminative features across geographical locations, by integrating latent space learning and geographically structural analysis (LSGSA) into a joint model. A latent space learning model is proposed to effectively fuse the heterogeneous features of visual content and tags. Meanwhile, the geographical structure analysis and group sparsity are applied to learn the geographically discriminative features. Then, a geo-guided sparse reconstruction method is proposed to tag images by utilizing the discriminative information of features, in which the landmark-specific tags are boosted by a weighting method. Experiments on the real-world datasets demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

This paper studies response selection for human-computer conversation systems. Existing retrieval-based human-computer conversation systems are intended to reply to user utterances based on existing utterance-response pairs. However, collecting sufficient utterance-response pairs is intractable in practical situations, especially for many specific domains. We introduce DocChat a novel information retrieval approach for human-computer conversation systems that can use unstructured documents rather than semi-structured utterance-response pairs, to react to user utterances. The key of DocChat is a learning to rank model with features designed at various levels of granularity which is proposed to quantify the relevance between utterances and responses directly. We conduct comprehensive experiments on both sentence selection and real human-computer conversation scenarios. Empirical studies of sentence selection datasets shows reasonable improvements and the strong adaptability of our model. We compare DocChat with Xiaoice, a famous open domain chitchat engine in China. Side-by-side evaluation shows that DocChat is a good complement for human-computer conversation systems using utterance-response pairs as the primary source of responses. Furthermore, we release a large scale open-domain dataset for sentence selection which contains 304,413 query-sentence pairs. [ABSTRACT FROM AUTHOR]

Unsupervised extractive summarization aims to extract salient sentences from the document without labeled corpus. Existing methods have achieved promising progress, thanks to the power of large-scale pre-trained language models and high-quality contextualized representations. However, extractive summaries often fail to maintain smooth transitions between sentences and struggle to form a coherent and fluent text due to splicing of sentences. Nevertheless, to the best of our knowledge, very few studies currently focus on unsupervised abstractive summarization. Inspired by the intuitive human process of writing summaries, which involves extracting salient sentences first and then reconstructing them, in this paper, we propose an Extract-then-Abstract framework to generate more coherent and human-like summary. Specifically, we first adopt extractive summarization model as summarizer to generate extractive summary in the extraction stage. Then in the abstraction stage, we propose a BART-based sentence write model to generate more coherent and fluent abstractive summary. To this end, we design a novel parallel data creation method for our rewrite model by proposing an effective sentence sampling strategy without any manual annotation cost. Extensive experiments including automatic evaluation and human evaluation demonstrate that our framework consistently outperforms strong baselines for unsupervised abstractive summarization and can generate more coherent and human-like summary while maintaining in competitive ROUGE scores for unsupervised extractive summarization. [ABSTRACT FROM AUTHOR]

• A novel token selection mechanism based on the dynamic self-attention network is proposed to automatically extract important video features. • A vision synchronization network is proposed to align appearance and motion features at the time slice level. • Extensive experiments and analysis confirm the superiority of the proposed model DSAVS. Video Question Answering (VideoQA) has gained increasing attention as an important task in understanding the rich spatio-temporal contents, i.e., the appearance and motion in the video. However, existing approaches mainly use the question to learn attentions over all the sampled appearance and motion features separately, which neglect two properties of VideoQA: (1) the answer to the question is often reflected on a few frames and video clips, and most video contents are superfluous; (2) appearance and motion features are usually concomitant and complementary to each other in time series. In this paper, we propose a novel VideoQA model, i.e., Dynamic Self-Attention with Vision Synchronization Networks (DSAVS), to address these problems. Specifically, a gated token selection mechanism is proposed to dynamically select the important tokens from appearance and motion sequences. These chosen tokens are fed into a self-attention mechanism to model the internal dependencies for more effective representation learning. To capture the correlation between the appearance and motion features, a vision synchronization block is proposed to synchronize the two types of vision features at the time slice level. Then, the visual objects can be correlated with their corresponding activities and the performance is further improved. Extensive experiments conducted on three public VideoQA data sets confirm the effectivity and superiority of our model compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Smart substation is a crucial Cyber-Physical system and is prone to cyber-attack. In this paper, we propose a novel anomaly detection mechanism tailored for detecting the IEC 61850-based network traffic. Three types of traffic features are taken into account for comprehensively characterizing the network traffic during a time window. To eliminate the subjectivity of manually selecting the traffic features, we exploit Discrete Wavelet Transform (DWT) algorithm to secondarily extract the deep features. An improved Locally Linear Embedding (LLE) algorithm is proposed to reduce the dimension of deep feature vectors with more effective dimensionality reduction ability. By doing so, the LSTM (Long Short Term Memory)-based Autoencoder network that can learn to reconstruct the normal traffic time-series behavior, and thereafter uses the reconstruction error to detect the anomalies. We assess the performance of our proposed mechanism with the comprehensive experiments on the real smart substation. The results indicate that the proposed mechanism can be performed in a fast way with satisfactory detection performance. [ABSTRACT FROM AUTHOR]

We propose a multisignature scheme based on the generalized conic curve. This scheme makes use of the difficulties in factorizing large integer and computing discrete logarithm on the Abelian group, is convenient to, plaintext. Furthermore, the numeric Simulation of the multisignature is designed. [ABSTRACT FROM AUTHOR]

The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommendation. However, currently researches on GCN models usually recursively aggregate the information from all the neighbors or randomly sampled neighbor subsets, without explicitly identifying whether the aggregated neighbors provide useful information during the graph convolution. In this paper, we theoretically analyze the affection of the neighbor quality over GCN models' performance and propose the Neighbor Enhanced Graph Convolutional Network (NEGCN) framework to boost the performance of existing GCN models. Our contribution is three-fold. First, we at the first time propose the concept of neighbor quality for both node classification and recommendation tasks in a general theoretical framework. Specifically, for node classification, we propose three propositions to theoretically analyze how the neighbor quality affects the node classification performance of GCN models. Second, based on the three proposed propositions, we introduce the graph refinement process including specially designed neighbor evaluation methods to increase the neighbor quality so as to boost both the node classification and recommendation tasks. Third, we conduct extensive node classification and recommendation experiments on several benchmark datasets. The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks. [ABSTRACT FROM AUTHOR]

Influential spreaders identification in social networks contributes to optimize the use of available resources and ensure the more efficient spread of information. In contrast to common belief that highly connected or core located users are most crucial spreaders, this paper shows that both user's local and global structural properties matter in information diffusion. We propose a new metric, social circle broadness, to measure a user's information spreading influence by qualitatively combining the two above properties. Firstly, a definition of social circle diversity is introduced to measure the dispersion extent of a user's friends distribution in the network. Based on it, a method to calculate each user's local social circle broadness is presented. Preliminary experiments on a coauthor dataset demonstrate the effectiveness of social circle broadness in information diffusion. Furthermore, a social circle weighted PageRank (SCWPR) algorithm is proposed to iteratively rank each user's global social circle broadness. We conduct extensive comparison experiments against six state-of-the-art baseline methods on four real social network datasets. The results show that SCWPR outperforms all of them for influential spreaders identification in information propagation. [ABSTRACT FROM AUTHOR]

As one of the most influential social media platforms, microblogging is becoming increasingly popular in the last decades. Each day a large amount of events appear and spread in microblogging. The spreading of events and corresponding comments on them can greatly influence the public opinion. It is practical important to discover new emerging events in microblogging and predict their future popularity. Traditional event detection and information diffusion models cannot effectively handle our studied problem, because most existing methods focus only on event detection but ignore to predict their future trend. In this paper, we propose a new approach to detect burst novel events and predict their future popularity simultaneously. Specifically, we first detect events from online microblogging stream by utilizing multiple types of information, i.e., term frequency, and user׳s social relation. Meanwhile, the popularity of detected event is predicted through a proposed diffusion model which takes both the content and user information of the event into account. Extensive evaluations on two real-world datasets demonstrate the effectiveness of our approach on both event detection and their popularity prediction. [ABSTRACT FROM AUTHOR]

Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user's relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Abstract: Online social network has become a popular way for users to express themselves, connect and share information with each other. However, in online social networks, the connections between different users are all in binary status, which neglects the relationship strengths between them. Meanwhile, the relationship strength between different users is activity field specific. In different activity fields, such as traveling, shopping, and sport, the relationship strengths between the same users may vary significantly. Therefore, in this paper we propose a general framework to measure the relationship strengths between different users, taking consideration not only the user''s profile information but also the interaction activities and the activity fields. We conduct the experiments on Facebook dataset and the results show that the proposed framework is promising and can be used to improve the performances of various applications. [Copyright &y& Elsevier]

Abstract: Designing elliptic curve password-based authenticated key agreement (ECPAKA) protocols for wireless mobile communications is a challenging task due to the limitation of bandwidth and storage of the mobile devices. Some well-published ECPAKA protocols have been proved to be insecure. We notice that until now none of the existing ECPAKA protocols for wireless mobile communication is provided any formal security analysis. In this paper, we propose a novel protocol and conduct a formal security analysis on our protocol. Compared with other ECPAKA protocol, our protocol meets all basic security properties and is the first ECPAKA protocol with formal security proof for wireless communication. We also explore the suitability of the novel protocol for 3GPP2 specifications and improve the A-Key (Authentication Key) distribution for current mobile cellular systems. [Copyright &y& Elsevier]

Automatic tagging can automatically label images and videos with semantic tags to significantly facilitate multimedia search and organization. However, most of existing tagging algorithms often don't differentiate between tags used to describe visual content, and neglect the semantic correlation of the assigned tag set. In this paper, we propose a novel automatic tagging algorithm which tags a test image or video with an Informative and Correlative Tag (ICTag) set. The assigned ICTag set can provide a more precise description of the multimedia object by exploring both the information capability of individual tags and the tag-to-set correlation. Measures to effectively estimate the information capability of individual tags and the correlation between a tag and the candidate tag set are designed. To reduce the computational complexity, we also introduce a heuristic method to achieve efficient automatic tagging. We conduct extensive experiments on the NUS-WIDE web image dataset downloaded from Flickr and the MCG-WEBV web video dataset downloaded from YouTube. The results confirm the efficiency and effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

• A novel model based on the self-attention mechanism is proposed to learn more effective multi-modal representations. • The DSACA model is proposed to capture the internal dependencies and cross-modal correlation between the image and question sentence. • Extensive experiments and analysis confirm the superiority of the proposed DSACA. Visual Question Answering (VQA) as an important task in understanding vision and language has been proposed and aroused wide interests. In previous VQA methods, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are generally used to extract visual and textual features respectively, and then the correlation between these two features is explored to infer the answer. However, CNN mainly focuses on extracting local spatial information and RNN pays more attention on exploiting sequential architecture and long-range dependencies. It is difficult for them to integrate the local features with their global dependencies to learn more effective representations of the image and question. To address this problem, we propose a novel model, i.e., Dual Self-Attention with Co-Attention networks (DSACA), for VQA. It aims to model the internal dependencies of both the spatial and sequential structure respectively by using the newly proposed self-attention mechanism. Specifically, DSACA mainly contains three submodules. The visual self-attention module selectively aggregates the visual features at each region by a weighted sum of the features at all positions. The textual self-attention module automatically emphasizes the interdependent word features by integrating associated features among the sentence words. Besides, the visual-textual co-attention module explores the close correlation between visual and textual features learned from self-attention modules. The three modules are integrated into an end-to-end framework to infer the answer. Extensive experiments performed on three generally used VQA datasets confirm the favorable performance of DSACA compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To meet the requirements of energy-efficiency and high quality joining in the emerging industries field, a novel thermal explosion (TE) joining method was developed. The dissimilar Ni/316SS joint was first obtained by the TE reaction technology. The Ni-Al mixed powder was selected as TE reaction system, and the reaction product was used as interlayer. The effect of temperature (800, 850 and 900 °C), hold time (5, 10, 15 and 20 min) and joining pressure (0, 10, 20 and 30 MPa) on interfacial microstructure, diffusion behavior and mechanical properties of Ni/316SS joint were investigated. The maximum combustion temperature of Ni-Al interlayer during heating process was 1655 °C and a significant exothermic phenomenon was lasted for 2 s. 316SS(Al), Fe 2 Al 5 (Ni, Cr) and serration-like Ni 5 Al 3 and Ni 3 Al were detected at the interface containing a reaction layer because of element diffusion. The maximum shear strength of the Ni/316SS joint was 65.2 MPa, and the failure is characterized by typical brittleness fracture. The fracture mode is the combination of partial spalling and fracture of the interlayer. The post heat-treatment assisted TE joining technique significantly shortened the bonding process of the dissimilar Ni/316SS joint at moderate temperatures, realizing the low energy and efficient joining between Ni and 316SS. [ABSTRACT FROM AUTHOR]