In psychometry, psychometric profiles of an individual subject as well as correlations among the psychometric profiles are crucial supports for assessing the psychometric preference of the individual subject. Intuitively, while sufficient psychometric profiles in terms of amount and types yield high assessment accuracy, too many psychometric profiles increase workloads of subjects, because measuring a psychometric profile of an individual subject requires filling out one psychological questionnaire at least. The trade-off between assessment accuracy and participant burden constitutes a core challenge in psychometric research, because it directly affects both the statistical validity of findings and the cognitive feasibility of large-scale evaluation. Existing assessment frameworks often struggle to reconcile efficiency with precision. This challenge underscores the need for a new modeling paradigm capable of reducing measurement redundancy while preserving interpretability and reliability. Therefore, inspired by the semantic field, this paper defines a new concept, namely$\mathrm{\emph {profile}}$$\mathrm{\emph {field}}$, and proposes a novel framework, i.e.,$\mathrm{\emph {PsycHin}}$, to achieve the trade-off. Specifically,$\mathrm{\emph {PsycHin}}$can be decomposed into three phases:$i$) modeling the psychologically cognitive processes, where$\mathrm{\emph {PsycHin}}$learns multiple explicit and implicit correlations among psychometric profiles based on a heterogeneous information network and defines a profile meta structure to guide instance extraction;$ii$) fitting data and calculating similarities among subjects, where$\mathrm{\emph {PsycHin}}$utilizes$\mathrm{\emph {tanh}}$to make up for the rating biases caused by the discrete scores from options of psychological questionnaires and employs entropy to measure the cognitive levels of subjects on same objects; and$iii$) constructing profile fields, where$\mathrm{\emph {PsycHin}}$calculates the importance of psychometric profiles and filters the noisy ones by leveraging the contribution rates of psychometric profiles. Our experimental results not only show that$\mathrm{\emph {PsycHin}}$performs well, but also demonstrate that incorporating the heterogeneous information network into psychometric preference assessment does improve the performance of$\mathrm{\emph {PsycHin}}$.

Conventional cross-domain recommendation models, which require centrally collecting varieties of original data from users, usually meet a challenge that users are reluctant to provide their real feedbacks because of privacy concerns. Thus, federated learning is incorporated into cross-domain recommendation, since it aggregates parameters of local models trained on user sides to train a global recommendation model, instead of centralized data collection. However, the deviations between the global model and local ones, which are caused by users’ data with non-independent and identical distributions, significantly challenge existing federated learning-based models in terms of alleviating data sparsity and cold-start problems. This article proposes a novel end-to-end federated contrastive learning-based model towards cross-domain recommendation, namely${{Fed-CLR}}$. It first uses an inference model to characterize interaction distributions of users in source domain(s), then reconstructs historical interactions of users in target domain(s) with a generative model, and finally performs federated contrastive learning at model level (including inner-model and inter-model) to help reduce deviations between the global model and local ones. Particularly, a constraint mechanism, namely${{Con-Mec}}$, is proposed to achieve consistency reinforcement from the aspect of inner- and inter-models. The experimental results on three real-world datasets not only show that${{Fed-CLR}}$outperforms the state-of-the-art peers, but also demonstrate that${{Fed-CLR}}$achieves a faster convergence speed than classical federated learning-based models.

Causal feature selection has recently attracted much more attention because it can improve the interpretability of predictive models. However, the existing causal feature selection framework needs to discover the PC (i.e., parents and children) of each variable in the PC of a target variable for spouses discovery, which is time-consuming on high-dimensional data. To tackle this issue, we propose a novelCausalFeatureSelection framework with efficient spouses discovery, called CFS. Specifically, by exploiting the dependency change property between a variable and its non-PC, the proposed framework only discovers the PC of the variables in some children of the target variable for spouses discovery. Furthermore, based on the proposed CFS framework and existing PC discovery algorithms, we propose four new causal feature selection algorithms. Using benchmark Bayesian networks and real-world datasets, we experimentally validated the efficiency and accuracy of the proposed algorithms compared with seven state-of-the-art causal feature selection algorithms.

With the explosive growth of short texts on users' preferences, learning discriminative and coherent latent topics from short texts is a critical work, since many practical applications require semantic understandings that short texts convey explicitly and implicitly. However, existing short text topic learning methods face the challenge of fully capturing semantically related co-occurrence phrases. Therefore, this paper proposes a novel Heterogeneous Information Network-based Short Text Topic learning approach (HIN-ShoTT) in terms of parts of speech, without depending on any auxiliary information. Specifically, HIN-ShoTT can be decomposed into three phases: i) seeking semantic relations among words, where HIN-ShoTT models multiple semantic relations among words based on a Heterogeneous Information Network (HIN) in terms of parts of speech; ii) extracting co-occurrence phrases and filtering noises, where HIN-ShoTT defines parts-of-speech meta structures to guide co-occurrence phrase extraction and a self-adapting threshold filtering module is proposed for discarding noises; and iii) inferring topics, where HIN-ShoTT models the generative process of co-occurrence phrases to make topic learning effective with the abundant corpus-level information. Our experimental results on three real-world datasets not only show that HIN-ShoTT performs well, but also demonstrate that it is feasible to incorporate HIN into short text topic learning for accuracy improvement.

Mining frequent and high utility itemsets from a transactional database is a significant task in the field of data mining and has attracted increasing attention in the past several years. Recently, researchers focus on designing multiobjective evolutionary algorithms (MOEAs) for the task of mining frequent and high utility itemsets, which has shown promising performance. In this paper, we continue this research line by further exploring the potential of MOEAs for mining frequent and high utility itemsets. Tb be specific, we suggest a closed itemset property based multi-objective evolutionary approach, termed as CP-MOEA, where two individual updating strategies are designed for improving the quality of mining frequent and high utility itemsets. We find that if the superset of an itemset is closed, then this itemset must be dominated by its superset, termed as closed itemset property. The proposed two individual updating strategies exploit this property of closed itemset to guide the evolution of the population at certain times. The experimental results on six real datasets demonstrate the effectiveness of the proposed algorithm CP-MOEA comparing to the state-of-the-art baseline.

Existing studies show that extracting a complete keyphrase candidate set is the first and crucial step to extract high quality keyphrases from documents. Based on a common sense that words do not repeatedly appear in an effective keyphrase, we propose a novel algorithm named KCSP for document-specific keyphrase candidate search using sequential pattern mining with gap constraints, which only needs to scan a document once and automatically specifies appropriate gap constraints for words without users’ participation. The experimental results confirm that it helps improve the quality of keyphrase extraction.

Currently, there have been several high performance OCR products for Chinese or for English. However, no one OCR technique can be simultaneously fit for both the English and the Chinese due to the large differences between Chinese and English. On the other hand, Chinese/English mixed document increases drastically with the globalization, so it is rather important to study the Chinese/English mixed document processing. Obviously, the key problem to resolve is how to split the mixed document into two parts: Chinese part and English part, so that the different OCR techniques can be applied to different parts. To further improve the previous system performance, a novel Chinese/English split algorithm based on global information is proposed and a rule for language identification is achieved by Bayesian formula. Experiment shows, the system error rate drops from 1.52% to 0.87% on magazine samples and from 1.32% to 0.75% on book samples, more than 2/5 of errors are excluded, which provides an experimental support for our research work.

In this paper, we study the major modules of on-line handwritten chemical expressions recognition. We propose a novel two-level algorithm to recognize expressions. In the first level, structural information is used to distinguish different parts and recognize substances. Then the algorithm segments expressions fatherly and recognizes isolated symbols. To meet the demand of actual applications, the paper also designs an XML-based system to help users save, modify and search the recognition result. The experiment shows that the presented algorithm is reliable.

In this paper, we study the major modules of on-line handwritten chemical expressions recognition. We propose a novel algorithm that combines two separate methods to segment expressions, one of which is based on structural information and the other on partial recognition. The algorithm improves the traditional algorithm at the stage of recognition, which consists of a substance recognizer and a character recognizer. To meet the demand of actual applications, the paper also designs a standard feature set to deal with the related issues and presents a flexible process of human-computer interaction to help users modify the recognition result. The experimental results show that the presented algorithm is reliable.

The construction of Latin based European OCR system is studied in this paper. Compared with English, other Latin based European languages use more characters, which is called European special characters in this paper to be distinct from English letters. To construct a European system with high performance, the key is the recognition of the European special characters. In this paper, the European special characters are automatically divided into three subsets by the different handwritten position. And two solutions are proposed, one solution in which is used to recognize "i", "j " and the European special characters in subset 1, while another solution is used to recognize other English characters, digits and the European special character in other subsets. Experiment shows, the new system is more effective than the old one, which provides an experimental support for our research work.

Palmprint identification, a subcategory of biometrics identification, has become a hot research area, and image enhancement is a key problem in offline palmprint identification. Since the physiological characteristics and image quality of palmprints are different from those of fingerprints, existing algorithms on fingerprint image enhancement cannot be directly applied in offline palmprint images. Taking into account the characteristics of palmprint images, an enhancement algorithm specific to offline palmprint images is proposed in this paper. We have performed a series of experiments and provide the enhanced palmprint images in the experiment section. Moreover, we evaluate our algorithm by comparing it with the method only using a low-pass filter to smooth the images under the criteria of GI value. Besides, the running time of each step is given to show the efficiency of the algorithm. The result shows that our algorithm is capable of attaining the objectives of offline palmprint enhancement efficiently.

As a universal technical language, mathematics has been widely applied in many fields, and it is more accurate than any other languages in describing information. Therefore, numerous mathematical formulas exist in all kinds of documents. There is no doubt that automatic mathematical formulas processing is very important and necessary, of which extract formulas from document images is the first step. In this paper, formulas extraction methods which are not based on recognition results are presented: isolated formulas are extracted based on Parzen window and embedded expressions are extracted based on 2-D structures detection. Experiments show that our methods are very effective in formulas extraction. 1.

We present a system for automatic Chinese business card recognition. Chinese business card is characterized by its complex layout - Chinese-English mixed character sets and diverse typesetting. Our goal is to extract meaningful information item from the scanned image, such as "holder-name", "title", "address", etc. The automatic recognition task is divided into three stages: 1) document geometrical layout analysis; 2) bilingual character recognition; 3) text understanding and information extraction. The final results of this system are stored in database for later use. The whole system has been tested with card images scanned at 400 dpi and results are reported.