一种基于隐式信任感知的MOOCs推荐方法

[目的/意义]随着MOOCs迅猛发展和普及,如何利用智能推荐技术为学习者从海量的MOOC中"寻找最佳课程"成为MOOC发展中需要解决的重要课题。[方法/过程]基于自我知觉理论和学习行为投入框架,充分利用学习行为日志和评分数据挖掘学习者之间的隐式信任关系,并通过信任传播建立MOOC社区信任网络,从而构建动态结合兴趣和隐式信任感知的混合推荐方法。为解决数据稀疏问题,提出基于信任的联合概率矩阵分解模型(TA-PMF),将课程评分矩阵、信任关系矩阵的分解相结合来挖掘用户及课程潜在特征,进而实现评分预测。[结果/结论]真实数据集测试结果表明,与显性评分值相比,学习行为投入信息对信任度构建贡献权重达到0.7;TA-PMF方法对MOOC推荐具有较好的适用性,且能在一定程度上缓解冷启动问题。     

TASR: Adversarial learning of topic-agnostic stylometric representations for informed crisis response through social media

The impact of crisis events can be devastating in a multitude of ways, many of which are unpredictable due to the suddenness in which they occur. The evolution of social media (for example Twitter) has given directly affected individuals or those with valuable information a platform to effectively share their stories to the masses. As a result, these platforms have become vast repositories of helpful information for emergency organizations. However, different crisis events often contain event-specific keywords, which results in the difficult extraction of useful information with a single model. In this paper, we put forward TASR, which stands for Topic-Agnostic Stylometric Representations, a novice deep learning architecture that uses stylometric and adversarial learning to remove topical bias to better manage the unknown surrounding unseen events. As an alternative to domain adaptive approaches requiring data from the unseen event, it reduces the work for those responding to the onset of a crisis. Overall, we conduct a comprehensive study of the situational properties of TASR, the benefits of its architecture including its topic-agnostic and explainable properties, and how it improves upon comparable models in past research. From two experiments, on average, TASR is able to outperform state-of-the-art methods such as transfer learning and domain adoption by 11% in AUC. The ablation study illustrates how different architecture choices of TASR impact the results and that TASR has been optimized for this task. Finally, we conduct a case study to show that explainable results from our model can be used to help guide human analysts through crisis information extraction.     

基于机器学习的自适应个性化数字图书馆模型研究

由于信息数量和种类增加,用户对数字图书馆期待更多的智能服务.本文提出通过引入机器学习技术来解决此问题,首先简要介绍了机器学习技术,说明了可适应个性化数字图书馆局限性,然后提出基于机器学习的自适应个性化数字图书馆模型,最后探讨了用户模型的自动创建.实践表明,此模型可满足用户对信息的需要,简化信息查找过程.     

Flexible sample selection strategies for transfer learning in ranking

Ranking is a central component in information retrieval systems; as such, many machine learning methods for building rankers have been developed in recent years. An open problem is transfer learning, i.e. how labeled training data from one domain/market can be used to build rankers for another. We propose a flexible transfer learning strategy based on sample selection. Source domain training samples are selected if the functional relationship between features and labels do not deviate much from that of the target domain. This is achieved through a novel application of recent advances from density ratio estimation. The approach is flexible, scalable, and modular. It allows many existing supervised rankers to be adapted to the transfer learning setting. Results on two datasets (Yahoo’s Learning to Rank Challenge and Microsoft’s LETOR data) show that the proposed method gives robust improvements.     

组织学习视角行业惯例的演化过程案例研究

   本研究关注从传统农业技术行业到现代农业物联网行业的转型,选择组织学习视角、采用案例研究方法对转型过程中行业主导企业和跟随企业的认知和行动倾向演化进行研究,揭示组织惯例的演化过程机理并构建演化过程模型。研究得出：行业惯例的演化主要经历主导企业组织惯例的演化、行业跟随企业组织惯例的演化以及新行业惯例的形成三个阶段;组织学习是促使行业惯例以及行业中个体企业的组织惯例实现演化的关键,其中主导企业依赖试错学习实现主动式演化,而跟随企业依赖效仿学习实现被动推动式演化;尽管行业惯例演化过程中不同类型的企业依赖于不同的学习模式,但组织惯例演化的根本均在于通过参与者的有效沟通与互动形成新的共同理解和一致性行动。     

产业集群共享性资源对创业人才学习的影响研究

创业人才通过获取特定的资源和信息提升个人创业能力,产业集群内部丰富的资源为创业人才学习提供了有利条件,基于资源观角度探讨产业集群对创业人才学习的影响,可以为构建集群创业人才学习机制提供新的理论视角.首先通过文献分析和问卷调查的方法,建立创业人才学习的四个内容维度结构:自我学习,业务学习,关系管理学习和企业管理学习.在此基础上,通过问卷调查实证检验了集群共享性资源对创业人才学习的影响.结果表明,集群共享性资源的三个维度(集体声誉、资源的交换与组合强度以及当地机构的参与程度)对创业人才学习具有不同程度的影响.     

基于电子网络辅助大学英语教学模式的探索

电子网络是一种现代技术,在信息传输方面具有许多异常突出的优点,在许多领域得到了广泛的应用。利用电子网络辅助大学英语教学,可以使教学信息资源在师生间快捷地进行传递。方便师生的交流沟通,促进教与学的融合。介绍了在大学英语教学中如何利用电子网络进行教学信息的传输,从而改进教学的方式和方法,促进大学英语教学效率和质量的提高。     

Beyond effective use: Integrating wise reasoning in machine learning development

The introduction of machine learning (ML), as the engine of many artificial intelligence (AI)-enabled systems in organizations, comes with the claim that ML models provide automated decisions or help domain experts improve their decision-making. Such a claim gives rise to the need to keep domain experts in the loop. Hence, data scientists, as those who develop ML models and infuse them with human intelligence during ML development, interact with various ML stakeholders and reflect their views within ML models. This interaction comes with (often conflicting) demands from various ML stakeholders and potential tensions. Building on the theories of effective use and wise reasoning, this mixed method study proposes a model to better understand how data scientists can use wisdom for managing these tensions when they develop ML models. In Study 1, through interviewing 41 analytics and ML experts, we investigate the dimensions of wise reasoning in the context of ML development. In Study 2, we test the overall model using a sample of 249 data scientists. Our results confirm that to develop effective ML models, data scientists need to not only use ML systems effectively, but also practice wise reasoning in their interactions with domain experts. We discuss the implications of these findings for research and practice.     

Learning fair representations by separating the relevance of potential information

Representation learning has recently been used to remove sensitive information from data and improve the fairness of machine learning algorithms in social applications. However, previous works that used neural networks are opaque and poorly interpretable, as it is difficult to intuitively determine the independence between representations and sensitive information. The internal correlation among data features has not been fully discussed, and it may be the key to improving the interpretability of neural networks. A novel fair representation algorithm referred to as FRC is proposed from this conjecture. It indicates how representations independent of multiple sensitive attributes can be learned by applying specific correlation constraints on representation dimensions. Specifically, dimensions of the representation and sensitive attributes are treated as statistical variables. The representation variables are divided into two parts related to and unrelated to the sensitive variables by adjusting their absolute correlation coefficient with sensitive variables. The potential impact of sensitive information on representations is concentrated in the related part. The unrelated part of the representation can be used in downstream tasks to yield fair results. FRC takes the correlation between dimensions as the key to solving the problem of fair representation. Empirical results show that our representations enhance the ability of neural networks to show fairness and achieve better fairness-accuracy tradeoffs than state-of-the-art works.     

产业互联网平台构建路径：创业共创视角的案例研究

产业互联网平台是实现产业数字化与数字产业化的重要方向，产业互联网平台构建路径充满复杂性与挑战性，现有文献尚未作出有效回答。通过兴盛优选案例研究发现：产业互联网平台构建经历了开发探索、聚焦验证和复制扩张的3个阶段，分别面临认知瓶颈、资源瓶颈与能力瓶颈，企业相继采取学习型创业共创、整合型创业共创与赋能型创业共创，先后实现了业务在线化、运营平台化和决策数智化。本文提炼出“瓶颈识别—创业共创—平台构建”的路径模型，揭开了产业互联网平台构建的过程“黑箱”，拓展了创业共创理论的应用边界，为产业互联网平台构建提供了重要启示。     

Text classification from unlabeled documents with bootstrapping and feature projection techniques

Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for accurate learning. While unlabeled documents are easily collected and plentiful, labeled documents are difficultly generated because a labeling task must be done by human developers. In this paper, we propose a new text classification method based on unsupervised or semi-supervised learning. The proposed method launches text classification tasks with only unlabeled documents and the title word of each category for learning, and then it automatically learns text classifier by using bootstrapping and feature projection techniques. The results of experiments showed that the proposed method achieved reasonably useful performance compared to a supervised method. If the proposed method is used in a text classification task, building text classification systems will become significantly faster and less expensive.     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

基于机器学习算法的研究热点趋势预测模型对比与分析——BP神经网络、支持向量机与LSTM模型

[目的/意义]细粒度分析学科领域热点主题发展脉络并对利用机器学习算法对未来发展趋势进行准确预测研究。[方法/过程]提出一种基于机器学习算法的研究热点趋势预测方法与分析框架，以基因工程领域为例利用主题概率模型识别WOS核心集中论文摘要数据研究热点主题并进行主题演化关联构建，然后选取BP神经网络、支持向量机及LSTM模型等3种典型机器学习算法进行预测分析，最后利用RE指标和精准度指标评价机器学习算法预测效果并对基因工程领域在医药卫生、农业食品等方面研究趋势进行分析。[结果/结论]实验表明基于LSTM模型对热点主题未来发展趋势预测准确度最高，支持向量机预测效果次之，BP神经网络预测效果较差且预测稳定性不足，同时结合专家咨询和文献调研表明本文方法可快速识别基因领域研究主题及发展趋势，可为我国学科领域大势研判和架构调整提供决策支持和参考。     

普适技术、嵌入式系统的研发及在教育体系的应用

本文主要介绍了普适技术,并介绍了运用普适技术、嵌入式系统在我国的中、小学教学过程中的应用,从而体现了该技术的产、学、研的结合,进一步实现了我国教育信息技术化的目标。该技术与课程的结合主要是通过自主开发以计算机、网络、移动技术为核心的普适教育服务系统．将多媒体技术、移动设备技术和网上社区技术支持下的中小学全科教学结合。本研究构建了一个新的自适应学习系统,一个个性化的学习管理系统。该系统扩展性强,可以用于其他的教学方面。     

A comparative study of automated legal text classification using random forests and deep learning

Automated legal text classification is a prominent research topic in the legal field. It lays the foundation for building an intelligent legal system. Current literature focuses on international legal texts, such as Chinese cases, European cases, and Australian cases. Little attention is paid to text classification for U.S. legal texts. Deep learning has been applied to improving text classification performance. Its effectiveness needs further exploration in domains such as the legal field. This paper investigates legal text classification with a large collection of labeled U.S. case documents through comparing the effectiveness of different text classification techniques. We propose a machine learning algorithm using domain concepts as features and random forests as the classifier. Our experiment results on 30,000 full U.S. case documents in 50 categories demonstrated that our approach significantly outperforms a deep learning system built on multiple pre-trained word embeddings and deep neural networks. In addition, applying only the top 400 domain concepts as features for building the random forests could achieve the best performance. This study provides a reference to select machine learning techniques for building high-performance text classification systems in the legal domain or other fields.     

IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning

With the development of information technology and economic growth, the Internet of Things (IoT) industry has also entered the fast lane of development. The IoT industry system has also gradually improved, forming a complete industrial foundation, including chips, electronic components, equipment, software, integrated systems, IoT services, and telecom operators. In the event of selective forwarding attacks, virus damage, malicious virus intrusion, etc., the losses caused by such security problems are more serious than those of traditional networks, which are not only network information materials, but also physical objects. The limitations of sensor node resources in the Internet of Things, the complexity of networking, and the open wireless broadcast communication characteristics make it vulnerable to attacks. Intrusion Detection System (IDS) helps identify anomalies in the network and takes the necessary countermeasures to ensure the safe and reliable operation of IoT applications. This paper proposes an IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology. According to the existing literature and algorithms, this paper introduces the modeling scheme of migration learning model and data feature extraction. In the experimental part, KDD CUP 99 was selected as the experimental data set, and 10% of the data was used as training data. At the same time, the proposed algorithm is compared with the existing algorithms. The experimental results show that the proposed algorithm has shorter detection time and higher detection efficiency.     

LED产业集群中资源优化组织关键路径研究——基于产业链创新协同平台视角

以提升LED产业集群中资源组织效率为目的,基于产业链创新服务平台视角,从产业链平台的业务流程出发,寻找资源组织的关键路径。基于提升资源组织速度的考虑,使用了包括基本信息层、结构信息层、活动信息层、能力信息层和时间信息层的五层资源描述方法构建LED产业平台中虚拟单元。通过LED产业链平台中的应用证明本方法在实践中是有效的。     

Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.     

Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.     

英语学习中学生如何培养元认知策略意识

许多中职院校学生在英语学习中存在着许多问题和困难,这已经成为中职基础英语教育中的一个重大困扰。导致这种困扰的因素有很多,除了英语学习本身的特点,人们首先考虑到的是教学质量和学生的语言天赋,而学生本身的观念和使用的学习策略这两个因素往往被忽视。本文主要从学习策略的三个方面来阐述学生如何培养自身元认知策略意识,如能正确运用,学生就能自己管理自己的学习,摆脱对教师的依赖,加快成为真正的自主学习者的步伐,最终提高自己的英语学习成绩。