Data Intelligence

Over the past years, a large number of fake news detection algorithms based on deep learning have emerged. However, they are often developed under different frameworks, each mandating distinct utilization methodologies, consequently hindering reproducibility. Additionally, a substantial amount of redundancy characterizes the code development of such fake news detection models. To address these concerns, we propose FaKnow, a unified and comprehensive fake news detection algorithm library. It encompasses a variety of widely used fake news detection models, categorized as content-based and social context-based approaches. This library covers the full spectrum of the model training and evaluation process, effectively organizing the data, models, and training procedures within a unified framework. Furthermore, it furnishes a series of auxiliary functionalities and tools, including visualization, and logging. Our work contributes to the standardization and unification of fake news detection research, concurrently facilitating the endeavors of researchers in this field. The open-source code and documentation can be accessed at https://github.com/NPURG/FaKnow and https://faknow.readthedocs.io, respectively.

Zhu Yiyuan

Li Yongjun

Wang Jialiang

Gao Ming

Wei Jiali

FaKnow: A Unified Library for Fake News Detection

FaKnow：用于虚假新闻检测的统一库

Making medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory of medicines, and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload. To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient, researchers have exploited electronic health records (EHRs) in automatically recommending medication. In recent years, medication recommendation using EHRs has been a salient research direction, which has attracted researchers to apply various deep learning (DL) models to the EHRs of patients in recommending prescriptions. Yet, in the absence of a holistic survey article, it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges. To fill this research gap, this survey reports on state-of-the-art DL-based medication recommendation methods. It reviews the classification of DL-based medication recommendation (MR) models, compares their performance, and the unavoidable issues they face. It reports on the most common datasets and metrics used in evaluating MR models. The findings of this study have implications for researchers interested in MR models.

Zafar Ali

Yi Huang

Irfan Ullah

Junlan Feng

Chao Deng

Nimbeshaho Thierry

Asad Khan

Asim Ullah Jan

Xiaoli Shen

Wu Rui

Guilin Qi

Deep Learning for Medication Recommendation: A Systematic Survey

药物推荐中的深度学习：系统性综述

Abstract Existing visual scene understanding methods mainly focus on identifying coarse-grained concepts about the visual objects and their relationships, largely neglecting fine-grained scene understanding. In fact, many data-driven applications on the Web (e.g., news-reading and e-shopping) require accurate recognition of much less coarse concepts as entities and proper linking them to a knowledge graph (KG), which can take their performance to the next level. In light of this, in this paper, we identify a new research task: visual entity linking for fine-grained scene understanding. To accomplish the task, we first extract features of candidate entities from different modalities, i.e., visual features, textual features, and KG features. Then, we design a deep modal-attention neural network-based learning-to-rank method which aggregates all features and maps visual objects to the entities in KG. Extensive experimental results on the newly constructed dataset show that our proposed method is effective as it significantly improves the accuracy performance from 66.46% to 83.16% compared with baselines.

Qiushuo Zheng

Hao Wen

Meng Wang

Visual Entity Linking via Multi-modal Learning

基于多模态学习的视觉实体链接

ABSTRACT Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Recently, few-shot models have been used for Named Entity Recognition (NER). Prototypical network shows high efficiency on few-shot NER. However, existing prototypical methods only consider the similarity of tokens in query sets and support sets and ignore the semantic similarity among the sentences which contain these entities. We present a novel model, Few-shot Named Entity Recognition with Joint Token and Sentence Awareness (JTSA), to address the issue. The sentence awareness is introduced to probe the semantic similarity among the sentences. The Token awareness is used to explore the similarity of the tokens. To further improve the robustness and results of the model, we adopt the joint learning scheme on the few-shot NER. Experimental results demonstrate that our model outperforms state-of-the-art models on two standard Few-shot NER datasets.

Wen Wen

Yongbin Liu

Qiang Lin

Chunping Ouyang

Few-shot Named Entity Recognition with Joint Token and Sentence Awareness

少样本命名实体识别与联合词和句子意识

Asmat Ali

Munir Ahmad

Muhammad Nawaz

Farha Sattar

Identifying and Prioritizing Spatial Data Required for Effective Agriculture Policymaking: A Comprehensive Analysis Using Analytical Hierarchy Process

识别和优先排序农业政策制定所需空间数据：基于层次分析法的综合分析

In addressing challenges within the field of Natural Language Processing (NLP), supervised fine-tuning is an efficient technique that allows pre-trained Large Language Models to adapt to specific tasks. This is especially crucial for low-resource languages, such as Tibetan, where the demand for high-quality fine-tuning datasets is particularly pronounced. This paper introduces the Tibetan Instruction-Following Dataset (TIFD), comprising 11,535 JSON objects, each with four attributes: a unique identifier, instructions, input, and output. These attributes correspond to the task's unique ID, the instruction describing the task, supplementary input for the task instruction, and the response generated by GPT-4. In the data preprocessing phase, unsuitable data for Tibetan fine-tuning were first filtered out. To ensure the diversity of instructions, the Tibetan instructions were effectively vectorized using LaBSE, followed by the selection of diverse Tibetan instruction data using a method based on the K-Center-Greedy algorithm. After preliminary English-Tibetan machine translation of responses generated by GPT-4, a web program was developed to facilitate Tibetan proofreaders in referencing the original English and corresponding Chinese translations for rectifying errors in words, grammar, and structure in the Tibetan data. Subsequently, five Tibetan language professionals were invited to manually proofread the data, checking and modifying the data format and Tibetan content, and removing erroneous and meaningless tasks, thereby ensuring the quality and reliability of the TIFD. Using TIFD to supervise the fine-tuning of the Tibetan large language model TiLamb has enhanced TiLamb's Tibetan dialogue and instruction-following capabilities. The TIFD serves as a vital resource for Tibetan data, playing a significant role in the development of Tibetan NLP technology and in the effective customization of Large Language Models (LLMs).

Wenhao Zhuang

Dawa Cairen

Yuan Sun

TIFD: Tibetan Instruction-Following Dataset for Large Language Models Supervised Fine-Tuning

TIFD：面向大语言模型监督微调的藏语指令遵循数据集

Focusing on the construction and application of research entity datasets based on scientific literature, it emphasizes the crucial role of data quality in enhancing natural language processing applications in scientific literature mining, addressing the gaps in detailed descriptions and evaluations of datasets in existing research, and promoting advancements in scientific literature entity recognition technology. Through extensive online research and literature review, 22 open-source research entity datasets were analyzed, focusing on the dataset lifecycle's collection, annotation, release, and application stages. A set of quality assessment methods for research entity datasets is proposed, along with a discussion of the challenges in dataset construction and strategies for efficient use. The study emphasizes the importance of high-quality datasets for natural language processing applications, proposing evaluation methods and sharing strategies intended to serve as a reference for constructing, selecting, and using scientific literature entity datasets.

Qinya Xu

Qianqian Yu

Li Qian

Analysis and Evaluation of Open Source Scientific Entity Datasets

开源科学实体数据集的分析与评估

Jolyon Ralph

Pavel Martynov

Xiaogang Ma

David Von Bargen

Wenjia Li

Jingyi Huang

Joshua Golden

Lucia Profeta

Anirudh Prabhu

Shaunna Morrison

Xiang Que

Jiyin Zhang

Identifier Service in the Mindat Database: Persistent and Structured Access to Massive Records of Minerals and Other Natural Materials

Mindat数据库中的标识符服务：持久化和结构化访问大量矿物及其他天然材料记录

Saima Kanwal

Ali Raza

Chunyan Bai

Dawei Zhang

Jing Wenn

Dileep Kumar

An Effective Machine Learning Approach with Hyper-parameter Tuning for Sentiment Analysis

一种有效的情感分析机器学习方法及其超参数调优

Xin Dai

Zihan Yu

Chenglin Liang

Cuiying Gao

Qidan He

Dan Wu

Zichen Xu

Detecting Novel Malware Classes with a Foundational Multi-Modality Data Analysis Model

检测新型恶意软件类别的基础多模态数据分析模型

Abbreviation	Data Intellegence
Journal Impact	1.16
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q3)

ISSN	2096-7004, 2641-435X
h-index	22

Publication Information	Publisher: China National Publications Import and Export (Group) Corporation，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2019，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

Data Intelligence

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