Large Multimodal Models (LMMs) face notable challenges when encountering multimodal knowledge conflicts, particularly under retrieval-augmented generation (RAG) frameworks, where the contextual information from external sources may contradict the model’s internal parametric knowledge, leading to unreliable outputs. However, existing benchmarks fail to reflect such realistic conflict scenarios. Most focus solely on intra-memory conflicts, while context-memory and inter-context conflicts remain largely unaddressed. Furthermore, commonly used factual knowledge-based evaluations are often overlooked, and existing datasets lack a thorough investigation into conflict detection capabilities.To bridge this gap, we propose MMKC-Bench, a benchmark designed to evaluate factual knowledge conflicts in both context-memory and inter-context scenarios. MMKC-Bench encompasses four types of multimodal knowledge conflicts and includes 1,881 knowledge instances and 3,997 images across 32 broad types, collected through automated pipelines with human verification. We evaluate four representative series of LMMs on both model behavior analysis and conflict detection tasks. Our findings show that while current LMMs are capable of recognizing knowledge conflicts, they tend to favor internal parametric knowledge over external evidence. We hope MMKC-Bench will foster further research in multimodal knowledge conflict and enhance the development of multimodal RAG systems.

Detecting Out-Of-Distribution (OOD) samples in image classification is crucial for model reliability. With the rise of Vision-Language Models (VLMs), CLIP-OOD has become a research hotspot. However, we observe the Low Focus Attention phenomenon from the image encoders of CLIP, which means the attention of image encoders often spreads to non-in-distribution regions. This phenomenon comes from the semantic mismalignment and inter-class feature confusion. To address these issues, we propose a novel fine-tuned OOD detection method with the Double loss constraint based on Optimal Transport (DOT-OOD). DOT-OOD integrates the Double Loss Constraint (DLC) module and Optimal Transport (OT) module. The DLC module comprises the Aligned Image-Text Concept Matching Loss and the Negative Sample Repulsion Loss, which respectively (1) focus on the core semantics of ID images and achieve cross-modal semantic alignment, (2) expand inter-class distances and enhance discriminative. While the OT module is introduced to obtain enhanced image feature representations. Extensive experimental results show that in the 16-shot scenario of the ImageNet-1k benchmark, DOT-OOD reduces the FPR95 by over 10% and improves the AUROC from 94.48% to 96.57% compared with SOTAs.

Out-of-distribution (OOD) intent detection aims to identify user queries that exceed predefined intent categories and is a crucial technology for ensuring interaction reliability and user experience in practical applications such as dialogue systems and intelligent customer service. Although large language models (LLMs) perform excellently in natural language processing tasks, they encounter difficulties with zero-shot OOD intent detection. This article reveals the dual biases of LLMs in zero-shot OOD intent detection: 1) LLMs tend to prefer the intents that are presented earlier in the intent list; and 2) LLMs frequently misclassify unknown intent as in-distribution (ID) intent. These biases severely limit the deployment of LLMs in intent-recognition systems that require high reliability. To address this problem, we propose a novel semantics-based sorting-filtering and querying-confirming (SF-QC) method: the semantic sorting-filtering (SF) module dynamically adjusts the intent list to mitigate position preference, and then the querying-confirming (QC) module deeply validates the initial ID response to reduce OOD misclassification, starting from the root cause of biases to guide the LLM to make unbiased judgments. Experiments on two mainstream intent datasets show that our proposed SF-QC approach has up to 7.82% (Macro-F1) and 9.76% (ACC) improvement in overall performance, and up to 14.9% (Macro-F1) and 20.66% (ACC) improvement in OOD performance over the chain-of-thought (CoT) method. The excellent detection performance of SF-QC provides key technical support for the robust deployment of LLMs in practical applications such as dialogue systems, helping to reduce system risks and enhance user experience.

The prevalence of social media platforms has accelerated the propagation of fake news, with carefully crafted multimodal fake news often gaining public trust and causing severe impacts. Methods based on multimodal small language models (MSLMs) struggle in scenarios with scarce labeled data. Large vision-language model (LVLM)-based approaches, though alleviating this limitation, often face two major problems: They rely too much on text-image consistency when predicting the authenticity of news. They judge which samples need to introduce external knowledge based on LVLMs’ direct outputs, which is not accurate because of LVLMs’ overconfidence. This article proposes a novel LVLM-based multiview multimodal fake news detection (MV-MFND) framework to address these problems. MV-MFND leverages LVLMs to analyze text, image, and text-image pairs separately, thus predicting authenticity from multiple perspectives. MV-MFND determines which samples need to introduce external knowledge by analyzing the softmax probability of specific tokens output by the LVLM. Our framework MV-MFND achieves SOTA performance on three real-world datasets Fakeddit, Twitter, and MR2-en on Qwen.

Understanding emotions in art paintings and generating comments about emotions is a highly challenging task due to their rich semantics and complex expressions. However, existing related methods tend to neglect the critical role of artistic emotion features in artworks at the visual level, while general vision language models(VLMs) lack a comprehensive grasp of art domain knowledge. To address this, we propose a knowledge-driven framework based on artistic emotion knowledge, which helps VLMs better comprehend emotions in artworks. The framework primarily comprises the artistic-emotion visual tower and the ANP cross-attention module. Specifically, the artistic-emotion visual tower introduces additional emotion tokens before visual features are fed into transformer layers. It integrate low-level art features into emotion tokens and infuses high-level art features into emotion tokens in the penultimate transformer layer. Moreover, the attention mechanism of visual tower is optimized by emotion bias, which enhances the model’s sensitivity to art features related to emotions. The ANP cross-attention module will extract affective entities directly from paintings as adjective-noun pairs and apply cross-attention with visual tower outputs. We have conducted extensive experiments on Artemis 1.0 and Artemis 2.0 datasets. The results demonstrate that our framework effectively improves the performance of VLMs in the task of understanding artistic emotions on eight metrics, outperforming existing methods.

Recent research has uncovered the vulnerability of Large Language Model (LLM) instruction tuning via BackDoor Attacks (BDA). Given a poisoned LLM after instruction tuning, input with clean instruction performs normally while input with poisoned instruction would give the target response set by the attacker. We have witnessed that existing works usually require a 5% Poisoning Ratio (PR) to achieve a high Attack Success Rate (ASR). However, more poisoned samples in the training data may increase the risk of exposing the BDA. We observe that using certain specific instructions makes the model tend to predict target labels. Thus, in this work, we utilize the target label bias to generate poisoned instructions as triggers, so as to achieve a high ASR with less poisoned data. We propose Label Bias Enhanced (LBE) backdoor attacks based on instruction tuning, including the module of Bias Instruction Generation, Bias Instruction Selection and Model Contamination Module. We perform experiments on three datasets and evaluate the BDA performance on three popular open-sourced LLMs (Llama2, ChatGLM3 and Vicuna). Results show that our proposed LBE can achieve the highest ASR (close to 100%) with 1% poisoning ratio, exceeding the baselines with an ASR improvement ranging from 2% to 20%.

Open-ended visual storytelling presents a formidable challenge for current text-to-image models, which frequently struggle to preserve both narrative coherence and consistent character depictions across generated sequences. To address this, we introduce StoryCrafter, a multi-character diffusion model that leverages a novel instance-level cross-attention module with supervised fine-tuning to ensure precise text-character alignment and consistent multi-character interactions throughout the narrative. Further, we propose Direct-Diffusion Group Relative Policy Optimization (D2GRPO), a novel RLHF stage that optimizes denoising strategies using automated story-aligned rewards, selecting the best candidate frames from a generated group. We evaluate our approach through human assessments and vision language model (VLM) scoring, measuring text-to-image alignment, style and character consistency, and fine-grained detail quality. Experiments on three benchmarks demonstrate that StoryCrafter outperforms existing methods, achieving 7% improvements in storytelling consistency and 10% in character accuracy, while outperforming baselines in both human and VLM evaluations.

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. A new data structure with a sort-mapping strategy is proposed to enhance the search ability and reduce memory usage. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

We focus on Stock Movement Forecasting (SMF) using AI techniques to develop modern automated trading systems. Previous studies with deep-learning-based methodology have only considered binary up-or-down trends, ignoring the importance of fine-grained categorization of the stock movements to facilitate decision-making. However, the challenges of SMF arise from the randomness of the global market impacting cross-sectional stocks and the volatility of internal dynamics in each time series. To address these challenges, we present a novel end-to-end learning-to-rank framework that incorporates both market-level and stock-level dynamics. Specifically, we aim to identify cross-sectional stocks that exhibit notable movements at every time step and learn to rank steps with the most significant movements in the temporal dimension. We conduct extensive evaluations of our multi-task learning framework utilizing real-world market data, which demonstrate superior performance when compared to state-of-the-art methods, with improvements in the Gain and Sharpe Ratio by 5–15%.

Chinese is a representative East Asian language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is significant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and effective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to refine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

Nowadays, deep-learning based NLP models are usually trained with large-scale third-party data which can be easily injected with malicious backdoors. Thus, BackDoor Attack (BDA) study has become a trending research to help promote the robustness of an NLP system. Text-based BDA aims to train a poisoned model with both clean and poisoned texts to perform normally on clean inputs while being misled to predict those trigger-embedded texts as target labels set by attackers. Previous works usually choose fixed Positions-to-Poison (P2P) first, then add triggers upon those positions such as letter insertion or deletion. However, considering the positions of words with important semantics may vary in different contexts, fixed P2P models are severely limited in flexibility and performance. We study the text-based BDA from the perspective of automatically and dynamically selecting P2P from contexts. We design a novel Locator model which can predict P2P dynamically without human intervention. Based on the predicted P2P, four effective strategies are introduced to show the BDA performance. Experiments on two public datasets show both tinier test accuracy gap on clean data and higher attack success rate on poisoned ones. Human evaluation with volunteers also shows the P2P predicted by our model are important for classification. Source code is available at https://github.com/jncsnlp/LocatorModel

Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations to enable successful domain adaptation. In this paper, a novel method called learning TransFerable and Discriminative Features for unsupervised domain adaptation (TFDF) is proposed to optimize these two objectives simultaneously. On the one hand, distribution alignment is performed to reduce domain discrepancy and learn more transferable representations. Instead of adopting Maximum Mean Discrepancy (MMD) which only captures the first-order statistical information to measure distribution discrepancy, we adopt a recently proposed statistic called Maximum Mean and Covariance Discrepancy (MMCD), which can not only capture the first-order statistical information but also capture the second-order statistical information in the reproducing kernel Hilbert space (RKHS). On the other hand, we propose to explore both local discriminative information via manifold regularization and global discriminative information via minimizing the proposed class confusion objective to learn more discriminative features, respectively. We integrate these two objectives into the Structural Risk Minimization (RSM) framework and learn a domain-invariant classifier. Comprehensive experiments are conducted on five real-world datasets and the results verify the effectiveness of the proposed method.

Exploiting label correlation is crucially important in multi-label learning, where each instance is associated with multiple labels simultaneously. Multi-label learning is more complex than single-label learning for that the labels tend to be correlated. Traditional multi-label learning algorithms learn independent classifiers for each label and employ ranking or threshold on the classification results. Most existing methods take label correlation as prior knowledge, which have worked well, but they failed to make full use of label dependency. As a result, the real relationship among labels may not be correctly characterized and the final prediction is not explicitly correlated. To address these problems, we propose a novel high-order multi-label learning algorithm of Label collAboration based Multi-laBel learning (LAMB). With regard to each label, LAMB utilizes collaboration between its own prediction and the prediction of other labels. Extensive experiments on various datasets demonstrate that our proposed LAMB algorithm achieves superior performance over existing state-of-the-art algorithms. In addition, one real-world dataset of channelrhodopsins chimeras is assessed, which would be of great value as pre-screen for membrane proteins function.

Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks are not efficient in encoding the part-whole relationships, thus requiring a large number of training samples to achieve robust generalization. This paper proposes an efficient variant of capsule networks (CapsNets) as an alternative to CNNs. Extensive experimental results demonstrate that the proposed CapsNets achieve competitive performances in target cell apoptosis classification, while significantly outperforming CNNs when the number of training samples is small. To utilize temporal information within microscopy videos, we propose a recurrent CapsNet constructed by stacking a CapsNet and a bi-directional long short-term recurrent structure. Our experiments show that when considering temporal constraints, the recurrent CapsNet achieves 93.8% accuracy and makes significantly more consistent prediction than NNs.

Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function pre-screen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate sub-cellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequence-based proteins into consideration, a novel boosting Dual-Modal Dual-Label (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dual-modal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as dual-label, which would be of great value as a pre-screen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of real-world channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with state-of-the-art algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effectiveness of hypothesis reuse mechanism in DMDL.

In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Meta-path and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semi-supervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other state-of-the-art methods.

Biomedical event extraction plays an important role in the field of biomedical text mining, and the event trigger detection is the first step in the pipeline process of event extraction. Event trigger can clearly indicates the occurrence of related events. There have been many machine learning based methods applied to this area already. However, most previous work have omitted two crucial points: (1) Class Difference: They simply regard non-trigger as same level class label. (2) Information Isolation: Most methods only utilize token level information. In this paper, we propose a novel model based on gate mechanism, which identifies trigger and non-trigger words in the first stage. At the same time, we also introduce additional fusion layer in order to incorporate sentence level information for event trigger detection. Experimental results on the Multi Level Event Extraction (MLEE) corpus achieve superior performance than other state-of-the-art models. We have also performed ablation study to show the effectiveness of proposed model components.

MOTIVATION: Automated profiling of cell-cell interactions from high-throughput time-lapse imaging microscopy data of cells in nanowell grids (TIMING) has led to fundamental insights into cell-cell interactions in immunotherapy. This application note aims to enable widespread adoption of TIMING by (i) enabling the computations to occur on a desktop computer with a graphical processing unit instead of a server; (ii) enabling image acquisition and analysis to occur in the laboratory avoiding network data transfers to/from a server and (iii) providing a comprehensive graphical user interface. RESULTS: On a desktop computer, TIMING 2.0 takes 5 s/block/image frame, four times faster than our previous method on the same computer, and twice as fast as our previous method (TIMING) running on a Dell PowerEdge server. The cell segmentation accuracy (f-number = 0.993) is superior to our previous method (f-number = 0.821). A graphical user interface provides the ability to inspect the video analysis results, make corrective edits efficiently (one-click editing of an entire nanowell video sequence in 5-10 s) and display a summary of the cell killing efficacy measurements. AVAILABILITY AND IMPLEMENTATION: Open source Python software (GPL v3 license), instruction manual, sample data and sample results are included with the Supplement (https://github.com/RoysamLab/TIMING2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendat ion and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification.

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

The demand for mining massive short-text data from the Internet has promoted researches on topic models. There exist many schemes trying to solve the sparsity problems brought by short texts, mainly based on data aggregation or model improvement. Among them, Biterm Topic Model changes the way of modeling topics, which is on document-level biterms and has shown creativity and effectiveness. However, this may ignore those semantically similar and rarely co-occurrent word pairs, which are denoted as global biterms in this paper. Inspired by the successful application of word embeddings in GPU-DMM, we exploit word embeddings to extract semantically similar word pairs from the whole corpus to help discover better topics. We call this model as GloSS, which takes advantages of both the approach to model topics and word embeddings. Experimental results on two open-source and real datasets are superior to state-of-the-art topic models for short texts.

Transfer learning has made great achievements in many fields and many excellent algorithms have been proposed. In recent years, many scholars have focused on a new research area called online transfer learning, which is different from general transfer learning. Online transfer learning concentrates on how to build a good classifier on the target domain when the training data arrive in an online/sequential manner. This paper focuses on online transfer learning problem based on a single source domain under homogeneous space. The existing algorithms HomOTL-I and HomOTL-II simply ensemble the classifiers on the source and target domains directly. When the distribution difference between the source domain and the target domain is large, it will not result in a good transfer effect. We are inspired by the idea of the boosting algorithm, that is we could form a strong classification model by a combination of multiple weak classifications. We train multiple classifiers on the source domain in an offline manner using AdaBoost algorithm, combine these classifiers on source domain with the classifier trained in an online manner on the target domain to form multiple weak combination in an ensemble manner. Based on the above ideas, we propose two algorithms AB-HomOTL-I and AB-HomOTLII, which have different ways to adjust the weights. We tested our algorithms on sentiment analysis dataset and 20newsgroup dataset. The results show that our algorithms are superior to other baseline algorithms.

In our daily life, short texts have been everywhere especially since the emergence of social network. There are countless short texts in online media like twitter, online Q&A sites and so on. Discovering topics is quite valuable in various application domains such as content recommendation and text characterization. Traditional topic models like LDA are widely applied for sorts of tasks, but when it comes to short text scenario, these models may get stuck due to the lack of words. Recently, a popular model named BTM uses word co-occurrence relationship to solve the sparsity problem and is proved effectively. However, both BTM and extended models ignore the inside relationship between words. From our perspectives, more related words should appear in the same topic. Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words. Experiments on two real-world short text datasets show great utility of our model.

User complaint data are quite valuable because they can reflect deficiencies of companies. Analyzing these short texts can help companies discover what topics users are complaining about. It is critical to locate and respond to these complaints timely so that companies can improve users’ satisfaction and loyalty. As the data volume is large, topic model can help discover key complaint topics quickly. The complaint data are in the form of short texts and streams, traditional topic models like LDA and BTM are not suitable in this scenario, for the reason that LDA is designed for long texts and BTM can not handle streams. This paper firstly proposes an improved shorttext topic model called PMITI-BTM to generate topics from user complaint data statically, and then further extends this algorithm into a dynamic one to suit the streaming feature of data. To further analyze the topic evolution, we finally propose a clustering algorithm called TDWAP to acquire the evolution process of these topics in different time slices. For each algorithm, we do several experiments to prove its efficiency. Results show that our methods not only can improve the performance of short texts topic discovery, but also can discover the evolution of topics.

In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant space to improve the effectiveness of these short text specific methods. In this paper, we proposed a novel word co-occurrence network based method, referred to as biterm pseudo document topic model (BPDTM), which extended the previous biterm topic model(BTM) for short text. We utilized the word co-occurrence network to construct biterm pseudo documents. The proposed model is promising since it represents words with their semantic adjacent biterms and is able to model the corpus-level semantic relation between two words. Besides, BPDTM naturally lengthens the documents, which alleviate the influence for performance exerted by data sparsity. Experiments demonstrated that our model outperformed two baselines, i.e. LDA and BTM, which proved its effectiveness on short text topic modeling task.

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried out for years so as to detect anomalies as soon as possible. Among them, machine learning is one of the most used techniques. Previous work tries to improve detection by choosing various classifiers, which has achieved some success. But few has considered the different losses each anomaly might cause. As we know, anomalies with higher significance will cause higher losses. In this paper, we aim to minimize the losses by proposing an improved cost-sensitive GBDT algorithm named LF-GBDT. LF-GBDT is designed to optimize a self-defined loss function. Experiments with both traditional classification algorithms such as CART, Adaboost etc. And cost-sensitive algorithms such as MetaCost, CSC show that our method can both improve the detection of important anomalies and reduce the total losses.