Large-scale evaluation framework for local influence theories in Twitter

Influence theories constitute formal models that identify those individuals that are able to affect and guide their peers through their activity. There is a large body of work on developing such theories, as they have important applications in viral marketing, recommendations, as well as information retrieval. Influence theories are typically evaluated through a manual process that cannot scale to data voluminous enough to draw safe, representative conclusions. To overcome this issue, we introduce in this paper a formalized framework for large-scale, automatic evaluation of topic-specific influence theories that are specialized in Twitter. Basically, it consists of five conjunctive conditions that are indicative of real influence exertion: the first three determine which influence theories are compatible with our framework, while the other two estimate their relative effectiveness. At the core of these two conditions lies a novel metric that assesses the aggregate sentiment of a group of users and allows for estimating how close the behavior of influencers is to that of the entire community. We put our framework into practice using a large-scale test-bed with real data from 75 Twitter communities. In order to select the theories that can be employed in our analysis, we introduce a generic, two-dimensional taxonomy that elucidates their functionality. With its help, we ended up with five established topic-specific theories that are applicable to our settings. The outcomes of our analysis reveal significant differences in their performance. To explain them, we introduce a novel methodology for delving into the internal dynamics of the groups of influencers they define. We use it to analyze the implications of the selected theories and, based on the resulting evidence, we propose a novel partition of influence theories in three major categories with divergent performance.     

Measuring technological capabilities at the country level: A survey and a menu for choice

Several new measures of national technological capabilities have recently been developed. These attempts are a result of an often-implicit theoretical consensus about the nature of technology. The aim of this article is to compare their methodologies and results. The World Economic Forum (WEF), the UN Development Program (UNDP), the UN Industrial Development Organisation (UNIDO), and the RAND Corporation are the institutions that have provided the measures examined here. We compare these authoritative attempts with our own measure of technological capability, ArCo. The results provide a broadly comparable ranking of countries, although a few significant differences do emerge.     

Measuring design investment in firms: Conceptual foundations and exploratory UK survey

The importance of design to company and national performance has been widely discussed, with a number of studies investigating the value or impact of design on performance. However, none of these studies has measured design investment as an input against which performance can be compared. As yet, there is no established way in which design investment might be measured. Without such a method, we cannot develop a reliable picture, akin to that for R&D spending, on the impact of design spending on company performance.     

Mapping user interest into hyper-spherical space: A novel POI recommendation method

Point-of-interest (POI) recommendation helps users quickly filter out irrelevant POI by considering the spatio-temporal factor. In this paper, we address the problem of check-in preference modeling in POI recommendation, and propose a novel POI recommendation method that depicts user preference by constructing unique hypersphere interest model for each user. Different from existing works, we have done three innovative work. (1) We build a check-in graph and adopt DeepWalk algorithm to learn POI embedding, further aggregating them to obtain a hypersphere interest space with an interest center and interest radius. (2) We established a stacked neural network module by a bidirectional LSTM, a self-attention and a memory network, to grasp memory features contained in check-in histories. (3) We proposed a novel candidate POI filter method that updates ranking score by evaluating the Euclidean distance between the vectors of candidate POI and interest center. We evaluate the performance of our method on the four real-world check-in datasets constructed from Foursquare. The comparison between our method and six baselines demonstrates the outstanding performance on various measurements. Compared to the best baseline method, our method achieves about 50% performance improvement on NDCG. In terms of MRR, Precision and Recall, our method achieves about 37%, 21% and 9% performance improvement over the best baseline method. Further ablation experiments verified the importance and effectiveness of the hypersphere interest model, as removing this component caused significant performance degradation.     

美国女性环保主义初探

女性环保主义包容了妇女和环保这两个极富现实意义的主题。文章以马克思主义的妇女观、环境观和哲学观为指导 ,将妇女运动置于环保层面加以审视和评价 ,进而论述了美国女性环保主义的当今动态、思想根源、发展历程及启迪作用     

粘着信息与用户创新工具箱:一个研究综述

创新源理论与应用是企业创新理论中一个非常重要的研究领域 ,而粘着信息和用户创新工具箱的研究在创新源理论与应用中占据举足轻重的地位。以麻省理工学院冯·希普尔为主要代表的国外创新专家在粘着信息和用户创新工具箱等方面的研究取得了突破性的进展 ,有力地推动了创新源理论与应用的发展。本文详尽而深入地对他们在粘着信息和用户创新工具箱等方面的研究进行了评述 ,并为这些理论的进一步发展和完善指出了研究方向     

How user innovations become commercial products: A theoretical investigation and case study

In this paper we model the pathways commonly traversed as user innovations are transformed into commercial products. First, one or more users recognize a new set of design possibilities and begin to innovate. They then join into communities, motivated by the increased efficiency of collective innovation. User-manufacturers then emerge, using high-variable/low-capital cost production methods. Finally, as user innovation slows, the market stabilizes enough for high-capital, low-variable cost manufacturing to enter. We test the model against the history of the rodeo kayak industry and find it supported. We discuss implications for “dominant design” theory and for innovation practice.     

战略联盟中的信任问题研究综述

随着市场竞争的加剧,企业越来越倾向于通过建立战略联盟来提升竞争优势,但是,战略联盟的失败率一直很高,如何保证战略联盟的发展就成为了研究重点,其中,战略联盟中的信任问题得到了越来越多学者的重视.在分析战略联盟信任问题提出的基础上,对战略联盟中信任问题的研究现状进行了综述,最后,提出了未来研究方向,希望本文能够为战略联盟的信任问题研究提供一定的借鉴.     

A nationwide survey starts on lacustrine resources

As a key component of the national initiative for basic S＆T data and infrastructure development, a nationwide survey on the lake water quality, volume and bio-resources has recently been kicked off. The move marks the systematic implementation of the secondround investigation of the country＇s lacustrine resources.     

Personal ontologies: Generation of user profiles based on the YAGO ontology

Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process.     

A study of the metadata creation behavior of different user groups on the Internet

Metadata is designed to improve information organization and information retrieval effectiveness and efficiency on the Internet. The way web publishers respond to metadata and the way they use it when publishing their web pages, however, is still a mystery. The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use. This study will enhance the current understanding of metadata application behavior and provide evidence useful to researchers, web publishers, and search engine designers.     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

A survey on XAI and natural language explanations

The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent years. As a consequence, several surveys have been published to explore the current state of the art on this topic. One aspect that seems to be overlooked by these works is the applied presentation methods and, specifically, the role of natural language in generating the final explanations. This survey reviews 70 XAI papers published between 2006 and 2021 and evaluates their readiness with respect to natural language explanations. Thus, together with a set of hierarchical criteria, we define a multi-criteria decision-making model. Finally, we conclude that only a handful of recent XAI works either considered natural language explanations to approach final users (see, e.g.,(Bennetot et al., 2021)) or implemented a method capable of generating such explanations.     

Textual aggregation approaches in OLAP context: A survey

In the last decade, OnLine Analytical Processing (OLAP) has taken an increasingly important role as a research field. Solutions, techniques and tools have been provided for both databases and data warehouses to focus mainly on numerical data. however these solutions are not suitable for textual data. Therefore recently, there has been a huge need for new tools and approaches that treat and manipulate textual data and aggregate it as well. Textual aggregation techniques emerge as a key tool to perform textual data analysis in OLAP for decision support systems. This paper aims at providing a structured and comprehensive overview of the literature in the field of OLAP Textual Aggregation. We provide a new classification framework in which the existing textual aggregation approaches are grouped into two main classes, namely approaches based on cube structure and approaches based on text mining. We discuss and synthesize also the potential of textual similarity metrics, and we provide a recent classification of them.     

基于用户感知的网络优化策略

为了提升用户感知,加大用户的忠诚度,以便达到稳固扩大市场的目标。文章解析了传统网络优化的不足,提出了基于用户感知的网络优化策略,从用户的体验出发,利用有效的优化手段改善服务客户的网络质量。最后通过实际的应用案例验证此策略的可行性及有效性。     

The effects of communication monitors on user satisfaction

This study analyzes the influence of communication monitors (CM) on overall computer user satisfaction as determined by multiple regression. The variables user expectations, manufacturers and vendors are found to be the most significant variables affecting overall satisfaction, whereas the variables number of systems represented and not using communication monitors have the least significant effect in the user satisfaction model. The implications for improvements in CM by updates to a database are discussed and analyzed.     

浅析知识资本评估的指标体系

随着知识经济的到来，知识资本（IntellectualCapital）已经成为表征企业核心竞争力的一种新的生产要素。那么，如何评估企业的知识资本，以便对它进行有效的管理，为其带来价值增值，是现代企业面临的一个刻不容缓的课题。本文通过对知识资本的概念、特点和结构的分析，认为知识资本评估指标体系的建立应该立足于对在企业运作过程中所产生的知识资本流量－即知识资本各组成部分内部以及相互之间转化关系的衡量一，为知识资本的评估提供一些参考。     

Under the radar: Industry entry by user entrepreneurs

We inductively develop a model of the commercialization process for new products or services user entrepreneurs undertake when entering an industry while drawing on proprietary technology developed in another industry. Extending the growing field of user entrepreneurship, we identify a two-phase approach to industry entry by user entrepreneurs who start “under the radar” of incumbent firms, gain experience, attract a first potential customer base, and then, in a second phase, engage in commercialization. During this process, a community of fellow users is of major importance for the entrepreneur, serving as a knowledge pool for skills development and experimentation with different commercialization paths. We study a nascent group of firms founded by users of video games who became entrepreneurs on entering the animation industry by producing Machinima, a new film genre characterized by shooting film in video games. We explain how user entrepreneurs gain access to complementary assets (video games) for their new use (shooting film), how they deal with intellectual property issues when using other firms’ assets, and how user entrepreneurs combine domain knowledge about film production with their experience in video games and the art of Machinima. Our propositions hold implications for management and policy.