Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing

The explosion of e-commerce has caused the need for processing and analysis of product titles, like entity typing in product titles. However, the rapid activity in e-commerce has led to the rapid emergence of new entities, which is difficult to be solved by general entity typing. Besides, product titles in e-commerce have very different language styles from text data in general domain. In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing. First, we reformulate the entity typing task into a textual entailment problem to handle new entities that are not present during training. Second, we design a model to automatically generate textual entailment hypotheses using a continuous prompt tuning method, which can generate better textual entailment hypotheses without manual design. Third, we utilize the fusion embeddings of BERT embedding and CharacterBERT embedding with a two-layer MLP classifier to solve the problem that the language styles of product titles in e-commerce are different from that of general domain. To analyze the effect of each contribution, we compare the performance of entity typing and textual entailment model, and conduct ablation studies on continuous prompt tuning and fusion embeddings. We also evaluate the impact of different prompt template initialization for the continuous prompt tuning. We show our proposed model improves the average F1 score by around 2% compared to the baseline BERT entity typing model

Inhibition of HIF-1α Reduced Blood Brain Barrier Damage by Regulating MMP-2 and VEGF During Acute Cerebral Ischemia

Increase of blood brain barrier (BBB) permeability after acute ischemia stroke is a predictor to intracerebral hemorrhage transformation (HT) for tissue plasminogen activator (tPA) thrombolysis and post-endovascular treatment. Previous studies showed that 2-h ischemia induced damage of BBB integrity and matrix metalloproteinase-2 (MMP-2) made major contribution to this disruption. A recent study showed that blocking β2-adrenergic receptor (β2-AR) alleviated ischemia-induced BBB injury by reducing hypoxia-inducible factor-1 alpha (HIF-1α) level. In this study, we sought to investigate the interaction of HIF-1α with MMP-2 and vascular endothelial growth factor (VEGF) in BBB injury after acute ischemia stroke. Rat suture middle cerebral artery occlusion (MCAO) model was used to mimic ischemia condition. Our results showed that ischemia produced BBB damage and MMP-2/9 upregulation was colocalized with Rhodamine-dextran leakage. Pretreatment with YC-1, a HIF-1α inhibitor, alleviated 2-h ischemia-induced BBB injury significantly accompanied by decrease of MMP-2 upregulation. In addition, YC-1 also prevented VEGF-induced BBB damage. Of note, VEGF was shown to be colocalized with neurons but not astrocytes. Taken together, BBB damage was reduced by inhibition of interaction of HIF-1α with MMP-2 and VEGF during acute cerebral ischemia. These findings provide mechanisms underlying BBB damage after acute ischemia stroke and may help reduce thrombolysis- and post-endovascular treatment-related cerebral hemorrhage

Prototyping a novel apparel recommendation system: A feasibility study

This research explores the technical feasibility of developing a knowledge-based apparel style recommendation system through investigations on apparel communication theory, data construction and machine learning techniques. It intends to improve the poor user experiences of online clothes shopping caused by the unpractical style searching, recommendation and personal styling engines. This study started with building up the theoretical foundation of apparel data and recommendation system. Then, an apparel data coding method and two apparel datasets are developed based on the apparel communication system and semiotics theory. ATTRIBUTE dataset captures natural and design features while MEANING dataset labels communicative meanings on style and body. Thirdly, the technical feasibility is investigated by statistics analytical methods to evaluate data relations and machine-learning methods to learn from the training data and predict apparel MEANINGs. The author found that the proposed data might exist non-linear relations, which restricts statistics analytical methods. Instead, machine-learning based methods are applicable as evidenced by three apparel MEANING prediction models. The three models also prove that the new apparel data coding method and ATTRIBUTE dataset could enhance the learning model since it captures more accurate apparel features. Additionally, the most useful data learning method is identified when it firstly learns ATTRIBUTEs from images via CNN model, and then determines MEANINGs from predicted ATTRIBUTEs by LKF classifier. The conclusion from this research is that it is technically feasible to develop an apparel style recommendation system. This research contributes a new method to the field of apparel recommendation system study. It fills the gap of lacking deep understandings of apparel knowledge. The proposed approach made three improvements: (1) a profound theory of apparel as a foundation, (2) a new apparel dataset construction method capturing design features and connotative meanings, and (3) the image-attribute collaborated data training model, which can effectively recognise in-depth design features and make precise predictions on connotative meanings

Robotic Stylist: A design oriented apparel recommendation system

This paper aims to prototype a design-oriented apparel recommendation system based on Artificial Intelligence technology. The Robotic Stylist recommends appropriate clothes to match up with the wearer’s body images and occasions according to the design features of apparel in terms of lines, colors, patterns, prints and textures. Such a system deals with webbased recommendation with real-time results from huge online apparel market to improve users’ experiences while shopping online. A large design evaluation dataset is collected from both fashion experts and peer groups of users via crowdsourcing platform. Artificial Neural Networks are adopted to simulate product judgments process of human brain by training the dataset. The optimization of predicted evaluation results from networks is the solution of recommendations

Apparel-based deep learning system design for apparel style recommendation

Purpose - The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach - This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion. Findings - The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value - The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM

Feature-Based Human Model for Digital Apparel Design

Three-dimensional (3D) body scanning technology opens opportunities for virtual try-on and automatic made-to-measure apparel design. This paper proposes a new feature-based parametric method for modeling human body shape from scanned point clouds of a 3D body scanner $[{rm TC}]^{2}$. The human body model consists of two layers: the skeleton and the cross sections of each body part. First, a simple skeleton model from the body scanner $[{rm TC}]^{2}$ system has been improved by adding and adjusting the position of joints in order to better address some fit issues related to body shape changes such as spinal bending. Second, an automatic approach to extracting semantic features for cross sections has been developed based on the body hierarchy. For each cross section, it is described by a set of key points which can be fit with a closed cardinal spline. According to the point distribution in point clouds, an extraction method of key points on cross sections has been studied and developed. Third, this paper presents an interpolation approach to fitting the key points on a cross section to a cardinal spline, in which different tension parameters are tested and optimized to represent simple deformations of body shape. Finally, a connection approach of body parts is proposed by sharing a boundary curve. The proposed method has been tested with the developed virtual human model (VHM) system which is robust and easier to use. The model can also be imported in a CAD environment for other applications

Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system

Smart apparel recommendation system is a kind of machine learning system applied to clothes online shopping. The performance quality of the system is greatly dependent on apparel data quality as well as the system learning ability. This paper proposes (1) to enhance knowledge-based apparel data based on fashion communication theories and (2) to use deep learning driven methods for apparel data training. The acquisition of new apparel data is supported by apparel visual communication and sign theories. A two-step data training model is proposed. The first step is to predict apparel ATTRIBUTEs from the image data through a multi-task CNN model. The second step is to learn apparel MEANINGs from predicted attributes through SVM and LKF classifiers. The testing results show that the prediction rate of eleven predefined MEANING classes can reach the range from 80.1% to 93.5%. The two-step apparel learning model is applicable for novel recommendation system developments