Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang and Shaoping Ma
A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models’ performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users’ interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.
This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.
A prevalent belief is that it is advantageous to have surname initials that are placed early in the alphabet (early surname initials) in academic fields in which authors are ordered alphabetically (alphabetic academic fields), because first authors are more visible. However, it is not certain that the advantage is strong enough to affect academic careers. In this paper, the advantage in having such early surname initials is analyzed by using data from 1,345 course catalogs that span a 100 years. We obtained academic titles and surname initials of 19,353 faculty members who appeared 211,816 times in these course catalogs. Two alphabetic academic fields – economics and mathematics – and four other academic fields that are not alphabetic were analyzed. We found that there are some years when faculty members who have early surname initials are more likely to be full professors. However, there are many other years when faculty members who have early surname initials are less likely to be full professors. We also analyzed the career path of each faculty member. Economists who have early surname initials are found to be more likely to become full professors. However, this result is not significant and does not extend to mathematicians.
This paper reports the results of an international survey on research data management (RDM) services in libraries. More than 240 practicing librarians responded to the survey and outlined their roles and levels of preparedness in providing RDM services, challenges their libraries face, and knowledge and skills that they deemed essential to advance the RDM practice. Findings of the study revealed not only a number of location and organizational differences in RDM services and tools provided but also the impact of the level of preparedness and degree of development in RDM roles on the types of RDM services provided. Respondents’ perceptions on both the current challenges and future roles of RDM services were also examined. With a majority of the respondents recognizing the importance of RDM and hoping to receive more training while expressing concerns of lack of bandwidth or capacity in this area, it is clear that, in order to grow RDM services, institutional commitment to resources and training opportunities is crucial. As an emergent profession, data librarians need to be nurtured, mentored, and further trained. The study makes a case for developing a global community of practice where data librarians work together, exchange information, help one another grow, and strive to advance RDM practice around the world.
Internationalization is important for research quality and for specialization on new themes in the social sciences and humanities (SSH). Interaction with society, however, is just as important in these areas of research for realizing the ultimate aims of knowledge creation. This article demonstrates how the heterogenous publishing patterns of the SSH may reflect and fulfill both purposes. The limited coverage of the SSH in Scopus and Web of Science is discussed along with ideas about how to achieve a more complete representation of all the languages and publication types that are actually used in the SSH. A dynamic and empirical concept of balanced multilingualism is introduced to support combined strategies for internationalization and societal interaction. The argument is that all the communication purposes in all different areas of research, and all the languages and publication types needed to fulfill these purposes, should be considered in a holistic manner without exclusions or priorities whenever research in the SSH is evaluated.
Farhan Saeed, Alan Bury, Stephen Bonsall and Ramin Riahi
The importance of NTS has been realised in many safety critical industries. Recently the maritime domain has also embraced the idea and implemented an NTS training course for both merchant marine deck and engineering officers. NTS encompass both interpersonal and cognitive skills such as situational awareness, teamwork, decision making, leadership, managerial skills, communication and language skills. Well-developed NTS training allow ship’s officers to recognise quickly when a problem is developing and manage the situation safely and efficiently with the available team members. As a result, the evaluation and grading of deck officers’ NTS is necessary to assure safety at sea, reduce the effects of human error on-board ships, and allow ship board operations to be performed safely. This paper identifies the skills necessary for deck officers to effectively perform their duties on the bridge of a ship. To achieve this, initially, a taxonomy of deck officers’ NTS is developed through a review of relevant literature and the conducting of semi-structured interviews with experienced seafarers. Subsequently, NTS weighting data is collected from experienced seafarers to allow the weight of each element of the taxonomy to be established by the use of the Analytical Hierarchy Process (AHP).
Road transport is showing growth in the period of globalization. Its task is to transport cargo as well as people to the required location within the shortest possible time and at the lowest price. Thus, road transport plays a crucial role in enabling the globalization to be developed and improved. However, the internal combustion engine hat prevail among the vehicles of freight and passenger transport are the producers of gaseous emissions from the exhaust gases. Many developed countries of the world has committed themselves, inter alia also trough the Paris Agreement, to reduce global warming, and thus to reduce the production of harmful gaseous emissions. The result is the endeavour to replace the internal combustion engine vehicles that burn carbon fuels with the vehicles powered by electric motors consuming electric energy. The reason of such trying claims that road transport using the internal combustion engine vehicles is environmentally aggressive, and the problem would not be solved by implementation of the vehicles with electric motors. Such claim is based on the fact that an electric car does not produce any of primary emissions. From an overall perspective, it is also necessary to take into account secondary emissions that are produced during the electric energy production by which is the vehicle with electric motor powered. The purpose of this article is to assume the possibility of reducing global pollution by replacing the internal combustion engine vehicles with the vehicles powered by electric motors in dependence with producing the emissions during the production of electric energy.
Dejan Dragan, Abolfazl Keshavarzsaleh, Tomaž Kramberger, Borut Jereb and Maja Rosi
Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.
Özgür Kabadurmuş, Mehmet Serdar Erdoğan, Yiğitcan Özkan and Mertcan Köseoğlu
Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.
Development of sharing economy has brought new opportunities to the development of green logistics. Pallet pooling is a typical type of sharing economy. It has been recently promoted in almost every country in the world. Researchers have reached a common agreement that establishing an effective pallet pool is of great importance to the pallets use efficiency. It is also believed that the implementation of pallet pool will substantially contribute to the economic and social development. Detailed analysis of pallet pooling such as benefit analysis, mode choose, pallet allocation model & algorithm, quality control, etc. are discussed in this paper. Based on literature review, several important problems are listed for future study. These problems are information management, supply chain management, rental pricing, pallets tracking and allocation, quality control, sustainable development (carbon emissions), and “internet plus pallet pooling”. Some suggestions on China’s pallet pooling development are proposed as well.