The growing presence of mobile and IoT devices has led to massive decentralized and evolving data, driving the rise of Federated Learning (FL) to enable collaborative training without data sharing. However, traditional FL assumes static data distributions, which is unrealistic for dynamic real-world environments. To address this challenge, Federated Class-Incremental Learning (FCIL) has emerged as a promising framework that enables flexible adaptation to newly introduced classes over time. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal client drift caused by data heterogeneity and often incur significant computational and communication overhead, limiting practical deployment. To address these challenges simultaneously, we propose a novel approach, Spatial-Temporal Statistics Aggregation (STSA), which provides a unified framework to aggregate feature statistics both spatially (across clients) and temporally (across stages). The aggregated feature statistics are unaffected by data heterogeneity and can be used to update the classifier in closed form at each stage. Additionally, we introduce STSA-E, a communication-efficient variant that enables the server to approximate global second-order feature statistics using first-order statistics uploaded from clients. Theoretical analysis shows that it achieves similar performance to STSA with much lower communication overhead. Extensive experiments on three widely used FCIL datasets, with varying degrees of data heterogeneity, show that our method outperforms state-of-the-art FCIL methods in terms of performance, flexibility, and both communication and computation efficiency. The code is available at https://github.com/Yuqin-G/STSA.

Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.

Retrieval-augmented generation (RAG) paradigms can integrate external knowledge to enhance and validate the output of Large Language Models (LLMs) thereby mitigating generative hallucinations and broadening the model's knowledge scope. Despite advancements, existing RAG methods still suffer from uncertainty of prediction during the multi-round retrieval-generation process, and a lack of the ability to balance the adequacy and redundancy of retrieved information. To address these challenges, we propose CIRAG, an approach that combines the RAG process with collective intelligence. Inspired by the crowd of wisdom, CIRAG simulates individual independent decision-making and information aggregation within a crowd. Specifically, CIRAG first enhances retrieval diversity by expanding queries based on extracted entities, then combines frequency-based and semantic-based reranking to form a multi granularity fusion reranking thereby assessing better relevance, and integrate multiple information sources for accurate content generation. By undertaking these steps in an integrated manner, CIRAG enables the model to acquire comprehensive and non-redundant information for generating responses. We conduct extensive experiments with HotPotQA and 2WikiMultihopQA datasets, popular benchmark for retrieval-based, multi-step question-answering. Experimental results show that our approach surpasses existing advanced RAG framework while providing high portability in query expansion as well as strong comprehensiveness exhibited in the collective intelligence.

Sarcasm Explanation in Dialogue (SED) task aims to uncover the underlying meaning of sarcastic expressions in multimodal dialogues. While previous studies have largely focused on modeling dialogue content, they often neglect the influence of speakers and the interactions between utterances. To address this gap, we propose a novel framework called CISI, which integrates personalized communication styles, inter-speaker interaction relationships, and sarcasm-centric multimodal cues to enhance SED. To capture how personalized styles influence sarcasm expression, we model speakers' communication styles using Satir's Communication Model in psychology. Furthermore, we model the flow of sarcasm through discourse parsing, constructing explicit conversational interaction and dependencies between speakers. Lastly, we design a multimodal fusion module that aligns modality-specific cues with sarcasm-related semantics to enhance understanding. Extensive experiments on the WITS dataset demonstrate that CISI achieves superior performance. We also obtain competitive results on the MUStARD dataset for dialogue-level multimodal sarcasm detection, further showcasing the generalizability of CISI.

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.

With the extensive utilization of deep hashing, there exists a surging interest in studying adversarial attacks against it. Previous methods have demonstrated the superior white-box attack performance against deep hashing. However, the more challenging and realistic targeted black-box attack has not yet been explored sufficiently, which will result in an over-estimation on model robustness. In this paper, we focus on targeted black-box attack based on transferability, and propose a novel Targeted Transferable Attack method against deep hashing with Generative Adversarial Network (TTA-GAN). Specifically, we first propose a new Iterative Anchor code Optimization (IAO) method to generate anchor code with superior representative semantics of target label, which can improve both targeted white-box and black-box performances. Then, we propose a generation-based method to directly generate targeted transferable adversarial example by training a conditional generator and a discriminator. Moreover, to further promote the targeted transferability, we conduct multiple input transformations on the generated adversarial example to alleviate the overfitting phenomenon on source model. Finally, we extend our method to a novel model ensemble attack method TTA-GANens to preserve the representative semantics on multiple models, specialized for deep hashing. Extensive experiments demonstrate the superior targeted black-box attack performance than the state-of-the-art methods.

Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.

Kernel$k$-means is a fundamental unsupervised learning in data mining. Its computational requirements are typically at least quadratic in the number of data, which are prohibitive for large-scale scenarios. To address these issues, we propose a novel randomized sketching approach SKK based on the circulant matrix. SKK projects the kernel matrix left and right according to the proposed sketch matrices to obtain a smaller one and accelerates the matrix-matrix product by the fast Fourier transform based on the circulant matrix, which can greatly reduce the computational requirements of the approximate kernel$k$-means estimator with the same generalization bound as the exact kernel$k$-means in the statistical setting. In particular, theoretical analysis shows that taking the sketch dimension of$\sqrt{n}$is sufficient for SKK to achieve the optimal excess risk bound with only a fraction of computations, where$n$is the number of data. The extensive experiments verify our theoretical analysis, and SKK achieves the state-of-the-art performances on 12 real-world datasets. To the best of our knowledge, in randomized sketching, this is the first time that unsupervised learning makes such a significant breakthrough.

In this paper, by introducing Generalized Bernstein condition, we propose the first O(√p over n∈ ) high probability excess population risk bound for differentially private algorithms under the assumptions G-Lipschitz, L-smooth, and Polyak-Łojasiewicz condition, based on gradient perturbation method. If we replace the properties G-Lipschitz and L-smooth by α-Hölder smoothness (which can be used in non-smooth setting), the high probability bound comes to O(n-α over 1+2α) w.r.t n, which cannot achieve O (1/n) when α ∈(0,1]. To solve this problem, we propose a variant of gradient perturbation method, max1,g -Normalized Gradient Perturbation (m-NGP). We further show that by normalization, the high probability excess population risk bound under assumptions α-Hölder smooth and Polyak-Łojasiewicz condition can achieve O (√p over n∈), which is the first O (1/n) high probability excess population risk bound w.r.t n for differentially private algorithms under non-smooth conditions. Moreover, experimental results show that m-NGP improves the performance of the differentially private model over real datasets.

Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low compute cost. However, the stability of DARTS is very inferior, especially skip connections aggregation that leads to performance collapse. Though existing methods leverage Hessian eigenvalues to alleviate skip connections aggregation, they make DARTS unable to explore architectures with better performance. In the paper, we propose operation-level progressive differentiable neural architecture search (OPP-DARTS) to avoid skip connections aggregation and explore better architectures simultaneously. We first divide the search process into several stages during the search phase and increase candidate operations into the search space progressively at the beginning of each stage. It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations. Besides, to keep the competition between operations relatively fair and select the operation from the candidate operations set that makes training loss of the supernet largest. The experiment results indicate that our method is effective and efficient. Our method’s performance on CIFAR-10 is superior to the architecture found by standard DARTS, and the transferability of our method also surpasses standard DARTS. We further demonstrate the robustness of our method on three simple search spaces, i.e., S2, S3, S4, and the results show us that our method is more robust than standard DARTS. Our code is available at https://github.com/zxunyu/OPP-DARTS.

Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.

Cross-modal retrieval between videos and texts has attracted growing attention due to the rapid growth of user-generated videos on the web. To solve this problem, most approaches try to learn a joint embedding space to measure the cross-modal similarities, while paying little attention to the representation of each modality. Video is more complicated than the commonly used visual feature, since the audio and caption on the screen also contain rich information. Recently, the aggregations of multiple features in videos boost the benchmark of the video-text retrieval system. However, they usually handle each feature independently, which ignores the interchange of high-level semantic relations among these multiple features. Moreover, despite the inter-modal ranking constraint where semantically-similar texts and videos should stay closer, the modality-specific requirement, i.e. two similar videos/texts should have similar representations, is also significant. In this paper, we propose a novel Multi-Feature Graph ATtention Network (MFGATN) for cross-modal video-text retrieval. Specifically, we introduce a multi-feature graph attention module, which enriches the representation of each feature in videos with the interchange of high-level semantic information among them. Moreover, we elaborately design a novel Dual Constraint Ranking Loss (DCRL), which simultaneously considers the inter-modal ranking constraint and the intra-modal structure constraint to preserve both the cross-modal semantic similarity and the modality-specific consistency in the embedding space. Experiments on two datasets, i.e. MSR-VTT and MSVD, demonstrate that our method achieves significant performance gain compared with the state-of-the-arts.

Increasingly advanced deepfake approaches have made the detection of deepfake videos very challenging. We observe that the general deepfake videos often exhibit appearance-level temporal inconsistencies in some facial components between frames, resulting in discriminable spatiotemporal latent patterns among semantic-level feature maps. Inspired by this finding, we propose a predictive representative learning approach termed Latent Pattern Sensing to capture these semantic change characteristics for deepfake video detection. The approach cascades a CNN-based encoder, a ConvGRU-based aggregator and a single-layer binary classifier. The encoder and aggregator are pre-trained in a self-supervised manner to form the representative spatiotemporal context features. Finally, the classifier is trained to classify the context features, distinguishing fake videos from real ones. In this manner, the extracted features can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective deepfake video detector. Extensive experiments prove our approach’s effectiveness, e.g., surpassing 10 state-of-the-arts at least 7.92%@AUC on challenging Celeb-DF(v2) benchmark.

While deep learning has proved success in many critical tasks by training models from large-scale data, some private information within can be recovered from the released models, leading to the leakage of privacy. To address this problem, this paper presents a differentially private deep learning paradigm to train private models. In the approach, we propose and incorporate a simple operation termed grouped gradient clipping to modulate the gradient weights. We also incorporated the smooth sensitivity mechanism into differentially private deep learning paradigm, which bounds the adding Gaussian noise. In this way, the resulting model can simultaneously provide with strong privacy protection and avoid accuracy degradation, providing a good trade-off between privacy and performance. The theoretic advantages of grouped gradient clipping are well analyzed. Extensive evaluations on popular benchmarks and comparisons with 11 state-of-the-arts clearly demonstrate the effectiveness and genearalizability of our approach.

Hashing has become increasingly important for large-scale image retrieval, of which the low storage cost and fast searching are two key properties. However, existing methods adopt large neural networks, which are hard to be deployed in resource-limited devices due to the unacceptable memory and runtime overhead. We address that this huge overhead of neural networks somewhatviolates the appealing properties of hashing. In this paper, we propose a novel deep hashing method, called Binary Neural Network Hashing (BNNH) for fast image retrieval. Specifically, we construct an efficient binarized network architecture to provide lighter model and faster inference, which directly generates binary outputs as the desired hash codes without introducing the quantization loss. Besides, in order to circumvent the huge performance degradation caused by the extremely quantized activations, we introduce a simple yet effective activation-aware loss to explicitly guide the updating of activations in intermediate layers. Extensive experiments conducted on three benchmarks show that the proposed method outperforms the state-of-the-art binarization methods by large margins and validate the efficiency of BNNH.

Given a network and a set of seeds related to each other, the problem of relation discovery from focusing seeds aims to discover the relations among all the seeds. Due to its wide applications, the task has been well studied in the literature. However, when facing applications where maybe not all the seeds relate to each other, methods for the task will discover many vertices unrelated to the seeds. To support such applications, a new problem called adaptive relation discovery from focusing seeds (A-RDFS) is proposed and studied in this article. Given a network and a set of seeds that may not be related to each other, discover additional vertices to reveal the relations among the seeds which are related to each other. To solve the A-RDFS problem, a relation sensitive subgraph structure called the α- relation core is proposed to find vertices related to a subset of the seeds. Thereafter, a metric called the relation quality is proposed to measure the quality of discovered relations. The metric is positively correlated with the α value of each discovered α-relation core. Hence, by maximizing the relation quality, a set of α-relation cores with large α values can be discovered, which reveals the relations among the seeds related to each other. Two algorithms are developed to optimize the relation quality. Then, using the algorithms as subroutines, the OPT-A-RDFS method is designed for the A-RDFS problem. Extensive experimental results demonstrate the performance of our methods.

Dimension reduction is a fundamental data mining task. However, it has limited applicability in high-dimensional scenarios because of stringent computational requirements. To address these issues, we propose ESE, an extremely sparse Johnson-Lindenstrauss transform, which takes a substantial step in dimension reduction. The projection matrix of ESE is an extremely sparse matrix, which has only k nonzero elements by employing the hash functions, where k is the embedded dimension. Theoretical analysis shows that ESE has a smaller time complexity than the existing projection algorithms and keeps the best accuracy (1+ε) for the general case, where 0 <; ε ≪ 1. In particular, the optimal statistical accuracy is achieved requiring log(n)log(d)/ε embedded dimension, where n is the number of data, d is the dimension of data. The extensive experiments verify that ESE has a significant advantage in time with satisfactory accuracy, compared to the state-of-the-art dimension reduction algorithms.

Recently, face image hashing has been proposed in large-scale face image retrieval due to its storage and computational efficiency. However, owing to the large intra-identity variation (same identity with different poses, illuminations, and facial expressions) and the small inter-identity separability (different identities look similar) of face images, existing face image hashing methods have limited power to generate discriminative hash codes. In this work, we propose a deep hashing method specially designed for face image retrieval named deep Discrete Attention Guided Hashing (DAGH). In DAGH, the discriminative power of hash codes is enhanced by a well-designed discrete identity loss, where not only the separability of the learned hash codes for different identities is encouraged, but also the intra-identity variation of the hash codes for the same identities is compacted. Besides, to obtain the fine-grained face features, DAGH employs a multi-attention cascade network structure to highlight discriminative face features. Moreover, we introduce a discrete hash layer into the network, along with the proposed modified backpropagation algorithm, our model can be optimized under discrete constraint. Experiments on two widely used face image retrieval datasets demonstrate the inspiring performance of DAGH over the state-of-the-art face image hashing methods.

Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to its low storage consumption and fast retrieval speed. In real cross-modal retrieval applications, it's hard to obtain label information. Recently, increasing attention has been paid to unsupervised cross-modal hashing. However, existing methods fail to exploit the intrinsic connections between images and their corresponding descriptions or tags (text modality). In this paper, we propose a novel Deep Semantic-Alignment Hashing (DSAH) for unsupervised cross-modal retrieval, which sufficiently utilizes the co-occurred image-text pairs. DSAH explores the similarity information of different modalities and we elaborately design a semantic-alignment loss function, which elegantly aligns the similarities between features with those between hash codes. Moreover, to further bridge the modality gap, we innovatively propose to reconstruct features of one modality with hash codes of the other one. Extensive experiments on three cross-modal retrieval datasets demonstrate that DSAH achieves the state-of-the-art performance.

Paraphrase generation can be modeled as a sequence-to-sequence (Seq2Seq) learning problem. Nonetheless, a typical Seq2Seq model is liable to convey the original meaning incorrectly, as the vectorial representation of the given sentence is sometimes inadequate in recapitulating complicated semantic. Naturally, paraphrases concern the same topic, which can serve as an auxiliary guidance to promote the preservation of source semantic. Moreover, some interesting words for restatements can be derived from the topical information. To exploit topic in paraphrase generation, we incorporate topic words into the Seq2Seq framework through a topic-aware input and a topic-biased generation distribution. Direct supervision signals are also introduced to help dealing with the topic information more accurately. Empirical studies on two benchmark datasets show that the proposed method significantly improves the basic Seq2Seq model, and it is comparable with the state-of-the-art systems.

Detecting curved text in the wild is very challenging. Recently, most state-of-the-art methods are segmentation based and require pixel-level annotations. We propose a novel scheme to train an accurate text detector using only a small amount of pixel-level annotated data and a large amount of data annotated with rectangles or even unlabeled data. A light model is first obtained by training with the pixel-level annotated data and then used to annotate unlabeled or weakly labeled data. A novel strategy which utilizes ground-truth bounding boxes to generate pseudo mask annotations is proposed in weakly-supervised learning. Experimental results on CTW1500 and Total-Text demonstrate that our method can substantially reduce the requirement of pixel-level annotated data. Our method can also generalize well across the two datasets. The performance of the proposed method is comparable with the state-of-the-art methods with only 10% pixel-level annotated data and 90% rectangle-level weakly annotated data.

Cross-modal hashing has attracted considerable attention for large-scale multimodal retrieval task. A majority of hashing methods have been proposed for cross-modal retrieval. However, these methods inadequately focus on feature learning process and cannot fully preserve higher-ranking correlation of various item pairs as well as the multi-label semantics of each item, so that the quality of binary codes may be downgraded. To tackle these problems, in this paper, we propose a novel deep cross-modal hashing method, called Adversary Guided Asymmetric Hashing (AGAH). Specifically, it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability. Furthermore, in order to generate hash codes which can fully preserve the multi-label semantics of all items, we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information. In addition, to ensure higher-ranking correlation of all similar item pairs than those of dissimilar ones, we adopt a new triplet-margin constraint and a cosine quantization technique for Hamming space similarity preservation. Extensive empirical studies show that AGAH outperforms several state-of-the-art methods for cross-modal retrieval.

One of the most challenging tasks in large-scale multi-label image retrieval is to map images into binary codes while preserving multilevel semantic similarity. Recently, several deep supervised hashing methods have been proposed to learn hash functions that preserve multilevel semantic similarity with deep convolutional neural networks. However, these triplet label based methods try to preserve the ranking order of images according to their similarity degrees to the queries while not putting direct constraints on the distance between the codes of very similar images. Besides, the current evaluation criteria are not able to measure the performance of existing hashing methods on preserving fine-grained multilevel semantic similarity. To tackle these issues, we propose a novel Deep Multilevel Semantic Similarity Preserving Hashing (DMSSPH) method to learn compact similarity-preserving binary codes for the huge body of multi-label image data with deep convolutional neural networks. In our approach, we make the best of the supervised information in the form of pairwise labels to maximize the discriminability of output binary codes. Extensive evaluations conducted on several benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art supervised and unsupervised hashing methods at the accuracies of top returned images, especially for shorter binary codes. Meanwhile, the proposed method shows better performance on preserving fine-grained multilevel semantic similarity according to the results under the Jaccard coefficient based evaluation criteria we propose.

Facing up to the incessant growth of complex networks, more and more researchers start turning to a multilevel computing paradigm with high scalability for clustering. By virtue of iterative coarsening level by level, the clustering results which are obtained from the coarsest network and then projected to the original network, is superior to the ones from mining the original complex network explicitly. Empirical works reflect that the local-aggregation characteristic is a key point for multilevel clustering algorithms, thus techniques like modularity, label propagation etc. are used to discover the micro-clusters for coarsening. In this paper, we propose a scalable clustering algorithm via a triangle folding processing for complex networks(SCAFT). Based on the strong cluster property of triangle, we fold each traversed triangle of the network into a superverex to realize coarsening. And each generated coarsened network by iteration is capable of reserving the cluster structures of last level network, or even the intrinsic cluster structures of original complex network, improving the computational accuracy. What's more, a streaming algorithm is embedded in our novel approach to generate a serial input sequence of vertices, reducing the heavy burdens of memory usage of system. Experimental results on real-world complex networks show that, SCAFT outperforms the state-of-the-art multilevel clustering algorithms in terms of clustering accuracy, running time, especially in memory usage.

Sentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, sentiment lexicons) of different languages are unbalanced in terms of quality and quantity, which arouses interests in cross-lingual sentiment analysis aiming at using the resources in a source language to improve sentiment analysis in a target language. Nevertheless, many existing cross-lingual related works rely on a certain machine translation system to directly adapt the labeled data from the source language to the target language, which usually suffers from inaccurate results generated by the machine translation system. On the other hand, most sentiment analysis studies focus on document-level sentiment classification that cannot solve the aspect dependency problem of sentiment words. For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system. To solve these problems, this paper develops a novel Cross-Lingual Joint Aspect/Sentiment (CLJAS) model to carry out aspect-specific sentiment analysis in a target language using the knowledge learned from a source language. Specifically, the CLJAS model jointly detects aspects and sentiments of two languages simultaneously by incorporating sentiments into a cross-lingual topic model framework. Extensive experiments on different domains and different languages demonstrate that the proposed model can significantly improve the accuracy of sentiment classification in the target language.

MapReduce is a programming framework introduced by Google for large-scale data processing. It is usually used in a scan-centric fashion where all the data are split into blocks and Maps are generated for each block to scan and process the data in the block, then Reduces merge outputs from all the Maps. When a query intends to process only a subset of the data selected by a predicate, this brute-force method may cause extra I/O overhead spent on irrelevant data, and the overhead for initiating so many Maps may be non-trivial given that the actually interesting data for the query is comparatively small in volume. We propose an approach to integrate the index into the MapReduce execution in which only an appropriate number of Maps are generated, each of which accesses the data using an index. This approach incurs random I/O and remote access to data, so the overall performance depends on both system parameters and the query characteristics. We build a cost model for both this index access execution and the traditional full scan execution. This cost model can be used to choose between the two execution modes before executing a query. Experiments show that the index access execution can greatly outperform full scan execution when the selectivity of the predicate is low, and the cost model predicts the actual execution cost very well so can be used to determine the execution plan for a query.