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关键词： Multi-label classification   data mining   scientific documents   citations  

摘要： The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.

摘要： We thank Damian Walker and Subhash Chandir for their letter in response to our benefit–risk analysis on sustaining routine childhood immunisation in African countries during the COVID-19 pandemic. Walker and Chandir highlight the uncertainty in ou...

关键词： Feature extraction   Dictionaries   Image reconstruction   Adaptation models   Data models   Computer science   Analytical models   Adaptive neighborhood preservation   image representation   self-expressive latent dictionary pair learning (SLatDPL)   structured twin-incoherence  

摘要： The projective dictionary pair learning (DPL) model jointly seeks a synthesis dictionary and an analysis dictionary by extracting the block-diagonal coefficients with an incoherence-constrained analysis dictionary. However, DPL fails to discover the underlying subspaces and salient features at the same time, and it cannot encode the neighborhood information of the embedded coding coefficients, especially adaptively. In addition, although the data can be well reconstructed via the minimization of the reconstruction error, useful distinguishing salient feature information may be lost and incorporated into the noise term. In this article, we propose a novel self-expressive adaptive locality-preserving framework: twin-incoherent self-expressive latent DPL (SLatDPL). To capture the salient features from the samples, SLatDPL minimizes a latent reconstruction error by integrating the coefficient learning and salient feature extraction into a unified model, which can also be used to simultaneously discover the underlying subspaces and salient features. To make the coefficients block diagonal and ensure that the salient features are discriminative, our SLatDPL regularizes them by imposing a twin-incoherence constraint. Moreover, SLatDPL utilizes a self-expressive adaptive weighting strategy that uses normalized block-diagonal coefficients to preserve the locality of the codes and salient features. SLatDPL can use the class-specific reconstruction residual to handle new data directly. Extensive simulations on several public databases demonstrate the satisfactory performance of our SLatDPL compared with related methods.

关键词： Computer science   Databases   Computational modeling   Neural networks   Computer architecture   Solids   Reflection  

摘要： Artificial intelligence (AI) is a recent subfield of the already young field of computer science, which has clear mathematical foundations in complexity and queuing theories as well as solid engineering in compilers, databases, and computer architecture. AI, in contrast, has been notably ill-defined and has been mostly an applied practice of two kinds: 1) symbolic reasoning and 2) neural networks. Both have an extensive scientific basis but the claim of both to be "AI" is disputed. Certainly, this is unnecessary;but the dispute arises because the actual application of AI seems unambitious given its name. We examine this through a personal retrospective and suggest some future research.

摘要： In their modelling study, Kaja Abbas and colleagues (October, 2020) state that routine childhood immunisation is “at risk of suspension” and “should be sustained in Africa as much as possible… during the COVID-19 pandemic”. It is now more than 6 m...

关键词： Quality of service   Public transportation   Companies   Computer science   Service computing   Smart devices   Service sharing   grouping   services with capacity limit   asymmetric similarity  

摘要： Sharing a service among multiple users could bring benefit to users by reducing their service price and also benefit service providers by allowing them to make more profit. Shared services usually have a capacity limit. To construct a win-win situation between users and providers through service sharing, it is necessary to reasonably organize users with similar service requests into groups under the limits of group sizes. This paper explores methods to enable user grouping for sharing services with the limit on the group size. Based on user request descriptions and evaluation, a connection relation is built to present which two users could be in a group. Semi-groups can be derived from the connection relation and two grouping schemes satisfying the group size limit are further determined through proposed algorithms to meet different service expectations. Finally, a case study and a simulation are given to demonstrate the effectiveness of the proposed methods in grouping users to provide higher service benefits to both users and providers.

关键词： capital punishment   culture of death   formal and material cooperation   gun manufacturing   John Paul II   Alphonsus Liguori  

摘要： The article reflects on the nature of moral choices and acts that participate in what John Paul II identified as a "culture of death." As social beings, our actions are tied to a "chain of cooperators" (i.e., fellow agents), and because of these unavoidable connections, Noonan argues that assessing whether or how a particular act culpably cooperates in a culture of death requires a casuist regard. He shows how casuists like Alphonsus Liguori, when assessing situations that force upon individuals the choice between costly self-sacrifice or acting so as to contribute to undesirable evils, took account of the human agent's fallen context and the particularities of the case-including intention, proximity, foreseeable results, and the gravity of those results-to argue for the liceity of the less heroic alternative. This article was originally given as an address under the same title at the University of Notre Dame's Center of Ethics and Culture in 2000. Noonan's examination of how our actions cooperate, or not, in a culture of death remains as valuable for us today as it was when he first delivered it. The journal expresses its gratitude to Cathleen Kaveny for bringing this work to our attention and to Mary Lee Noonan, the late judge's widow, for allowing us to publish it. The original text has been lightly edited to bring it into conformity with journal style, correct minor typos, and supply missing details in Noonan's bibliographic references. In addition, the editors have added a few contemporary updates (noted as such).

关键词： Distributed Processing   Public Domain Software   Software Engineering   Git Hub   Perceptible Race   Qualitative Study   Nonmerged Pull Requests   Software Development Management   Cultural Differences   Psychology   Corporate Acquisitions   Computer Science   Cognition  

摘要： This article is a qualitative study that analyzes whether there is evidence of bias based on perceptible race in the written comments of nonmerged pull requests in GitHub.

关键词： Climate crisis   environment and society   environmental destruction   sociology of the downstream  

摘要： Japanese environmental sociology has developed many original perspectives, including the social structure of victims, the benefit versus victimized zone theory, and life environmentalism. Developments in the field can be divided into three stages. The first, to 2001, is characterized by the early organizing of environmental sociologists, including the establishment of the Japanese Association for Environmental Sociology (JAES), and its accompanying journal, and a focus on local environmental destruction case studies. The second, to 2011, is characterized by the institutionalization of the field, while the third was triggered by the 2011 Tohoku earthquake and tsunami and the Fukushima nuclear disaster. The third stage is currently one of diversification and a crisis of academic identity.

关键词： Computer Science Education   Educational Courses   Educational Institutions   Engineering Education   Further Education   Gender Issues   Software Engineering   Affirmative Action   College Admissions   Gender Gap   Mathematics Programs   Female Enrollment   Combined Computer Science   Software Engineering Degree   Software Engineering   Market Research   Gender Issues   STEM   Engineering Profession   Computer Science Education   Gender Equity  

摘要： Affirmative action in college admissions has been touted as a way to close the gender gap in science, technology, engineering, and mathematics programs. We report a study of one such program, where we discovered a positive effect on female enrollment and a smaller effect on a combined computer science and software engineering degree.