nils-herrmann/clean_scirepeval huggingface dataset:
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.
A Multi-token Authorization Strategy for Secure Mobile Cloud Computing

Cloud computing is an emerging paradigm shifting the shape of computing models from being a technology to a utility. However, security, privacy and trust are amongst the issues that can subvert the benefits and hence wide deployment of cloud computing. With the introduction of omnipresent mobile-based clients, the ubiquity of the model increases, suggesting a still higher integration in life. Nonetheless, the security issues rise to a higher degree as well. The constrained input methods for credentials and the vulnerable wireless communication links are among factors giving rise to serious security issues. To strengthen the access control of cloud resources, organizations now commonly acquire Identity Management Systems (IdM). This paper presents that the most popular IdM, namely OAuth, working in scope of Mobile Cloud Computing has many weaknesses in authorization architecture. In particular, authors find two major issues in current IdM. First, if the IdM System is compromised through malicious code, it allows a hacker to get authorization of all the protected resources hosted on a cloud. Second, all the communication links among client, cloud and IdM carries complete authorization token, that can allow hacker, through traffic interception at any communication link, an illegitimate access of protected resources. We also suggest a solution to the reported problems, and justify our arguments with experimentation and mathematical modeling.
Unexpectedly high frequency of genital involvement in women with clinical and histological features of oral lichen planus.

The main aims of this cross-sectional study were: (i) to assess the frequency of genital (vulval) lichen planus (VLP) and vulval lichen sclerosus (VLS) in women affected with oral lichen planus (OLP), regardless of the genital symptoms reported; and (ii) to verify whether any demographic, clinical, or histological features of OLP are associated with a higher risk of vulvo-vaginal involvement. Fifty-five women, presenting OLP, consecutively underwent gynaecological examination and, if they demonstrated positive clinical signs of VLP, underwent biopsy. After a drop-out of 14 subjects, 31/41 (75.6%) were found to have signs of genital involvement, of which 13/31 (44.0%) were asymptomatic. Following genital biopsy, 27/31 (87.1%) had histologically confirmed VLP or VLS. Following both univariate and multivariate statistical analyses, no significant association was found between gynaecological concomitance and demographic, clinical, histological features of OLP. This unpredictably common genital involvement in females with OLP emphasizes the importance of routinely performing both oral and gynaecological examinations, to facilitate an early and correct therapeutic approach.
MULTISCALE ENTANGLEMENT RENORMALIZATION ANSATZ

The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERAlayers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.
Extending media richness theory to explain social media adoption by microbusinesses

This study uses the canonical action research method to investigate social media adoption in microbusinesses. A post positivist approach is followed based on a predetermined premise that along with Media Richness Theory (MRT), theories of access/quality and social interaction may be required to explain the media choice preference. It is found that in terms of richness, the social media interface of the Facebook business page is considered richer than email in its capacity to express by photos, and to transmit web links but it does not necessarily lead to greater use of the tool. Individual differences from social interaction theory and the ability to learn the “command language” from access/quality theory dominated the use behaviour leading to the owner’s inability to take advantage of this rich media. The incapability of MRT to explain rich communication through email is carried forwarded to social media adoption. The access/quality theory is found to be operating as a theory-in-use over the espoused theory of MRT. The microbusiness owner tries to minimise cost by minimising accessibility attributes while expecting maximum quality attributes. Overall, the access/quality theory best explained the adoption process with limited role from the MRT, and a decisive role by individual differences from social interaction theories for understanding the difference in level of adoption within the participating microbusinesses.
Gender , Entrepreneurial Self-Efficacy , and Entrepreneurial Career Intentions : Implications for Entrepreneurship Education '

The relationships between gender, entrepreneurial self-efficacy, and entrepreneurial Intentions were examined for two sample groups: adolescents and adult master of business administration (MBA) students. Similar gender effects on entrepreneurial self-efficacy are shown for both groups and support earlier research on the relationship between self-efficacy and career intentions. Additionally, the effects of entrepreneurship education in MBA programs on entrepreneurial self-efficacy proved stronger for women than for men. Implications for educators and policy makers were discussed, and areas for future research outlined.  Women play a substantial role in entrepreneurship throughout the world. In advanced market economies, women own 25% of all businesses and the number of women-owned businesses in Africa, Asia, Eastern Europe, and Latin America are increasing rapidly (Estes, 1999; Jalbert, 2000). In the United States alone, the 6.7 million privately held majority women-owned businesses account for $1.19 trillion in sales and employ 9.8 million people. Moreover, the growth rate of women-owned businesses is impressive (Women-Owned Businesses, 2004). Between 1997 and 2004, employment in women-owned businesses increased by 39% compared to 12% nationally, and revenues rose by 46% compared to 34% among all privately held U.S. businesses. These data reinforce the value of studying women's entrepreneurship, and likely account for the increased attention being paid to this area by scholars and educators. However, current trends mask the fact that men continue to be more active in entrepreneurship than women worldwide. Recent data suggest that the largest gaps occur in middle-income nations where men are 75% more likely than women to be active entrepreneurs, compared with 33% in high-income countries and 41% in low-income countries (Minnitti, Arenius, & Langowitz, 2005). In order to more fully capture the talents of women in new venture creation in the future, a vibrant "pipeline" of potential entrepreneurs is required. However, previous research has shown that this pipeline of women may be weak. Adult men in the United States are twice as likely as women to be in the process of starting a new business (Reynolds, Carter, Gartner, Greene, & Cox, 2002). Furthermore, research on the career interests of teens, the potential entrepreneurs of the next generation, has revealed significantly less interest among girls than among boys in entrepreneurial careers (Kourilsky & Walstad, 1998; Marlino & Wilson, 2003). Many factors undoubtedly contribute to the disparity between men and women in entrepreneurial career interests and behaviors. One factor in particular, entrepreneurial self-efficacy, or the self-confidence that one …
CRM ADOPTION IN A HIGHER EDUCATION INSTITUTION

More and more organisations, from private to public sectors, are pursuing higher levels of customer satisfaction, loyalty and retention. With this intent, higher education institutions (HEI) have adopted CRM – Customer Relationship Management. In order to analyse some of the interesting aspects of this phenomenon n, we conducted an action research in a European Institute. The main research question we answered is “how to adopt a CRM strategy in a Higher Education Institution?” Some of the main findings of this study are (1) even though HEI’s main customer is the student, there are others stakeholders that a CRM project must consider; (2) universities can use their internal resources to implement a CRM project successfully; and (3) using Agile software methodology is an effective way to define clearer, more objective and more assertive technical requirements which result in a CRM software that meet send user’s expectations and organizational strategic goals. These findings can help other HEIs 46 Rigo, G. E., Pedron, C. D., Caldeira, M., Araújo, C. C. S., JISTEM, Brazil Vol. 13, No. 1, Jan/Abr., 2016 pp. 45-60 www.jistem.fea.usp.br planning to adopt CRM as a strategic tool to improve their relationship with the stakeholders ́ community and expand their student body.
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling

Exponential growth of social media consumption demands eective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To eectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We rst mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. en, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a largemargin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an endto-end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the eectiveness of D-Sempre in user interests-social contents modeling, we construct a Twier dataset and conduct extensive experiments on it. As a result, D-Sempre eectively integrates the multimodal data from heterogeneous social media feeds and captures the hidden semantic correlations between users’ interests and social contents.
Image processing techniques for grading & classification of rice

Arelatively faster computer vision system has been discussed to analyze and sort rice kernels. A series of measurements were done using image processing techniques on three varieties of Indian rice namely Markfed Supreme, Markfed Golden (export quality), Hafed Basmati. Area, perimeter, maximum length, maximum width, compactness and elongation were measured. Further, separating the rice varieties by their shape difference was examined. The computer vision system developed has been able to sort rice into sound, cracked, chalky, broken and damaged kernels with an accuracy ranging from 90–95%
Inequalities in Open Source Software Development: Analysis of Contributor’s Commits in Apache Software Foundation Projects

While researchers are becoming increasingly interested in studying OSS phenomenon, there is still a small number of studies analyzing larger samples of projects investigating the structure of activities among OSS developers. The significant amount of information that has been gathered in the publicly available open-source software repositories and mailing-list archives offers an opportunity to analyze projects structures and participant involvement. In this article, using on commits data from 263 Apache projects repositories (nearly all), we show that although OSS development is often described as collaborative, but it in fact predominantly relies on radically solitary input and individual, non-collaborative contributions. We also show, in the first published study of this magnitude, that the engagement of contributors is based on a power-law distribution.
Imagination-Based Decision Making with Physical Models in Deep Neural Networks

Decision-making is challenging in continuous settings where complex sequences of events determine rewards, even when these event sequences are largely observable. In particular, traditional trial-and-error learning strategies may have a hard time associating continuous actions with their reward because of the size of the state space and the complexity of the reward function. Given a model of the world, a different strategy is to use imagination to exploit the knowledge embedded in that model. In this regime, the system directly optimizes the decision for each episode based on predictions from the model. We extend deep learning methods that have been previously used for model-free learning and apply them towards a model-based approach in which an expert is consulted multiple times in the agents’ imagination before it takes an action in the world. We show preliminary results on a difficult physical reasoning task where our model-based approach outperforms a model-free baseline, even when using an inaccurate expert.
Opportunities and Challenges of Wireless Sensor Networks in Smart Grid

The collaborative and low-cost nature of wireless sensor networks (WSNs) brings significant advantages over traditional communication technologies used in today's electric power systems. Recently, WSNs have been widely recognized as a promising technology that can enhance various aspects of today's electric power systems, including generation, delivery, and utilization, making them a vital component of the next-generation electric power system, the smart grid. However, harsh and complex electric-power-system environments pose great challenges in the reliability of WSN communications in smart-grid applications. This paper starts with an overview of the application of WSNs for electric power systems along with their opportunities and challenges and opens up future work in many unexploited research areas in diverse smart-grid applications. Then, it presents a comprehensive experimental study on the statistical characterization of the wireless channel in different electric-power-system environments, including a 500-kV substation, an industrial power control room, and an underground network transformer vault. Field tests have been performed on IEEE 802.15.4-compliant wireless sensor nodes in real-world power delivery and distribution systems to measure background noise, channel characteristics, and attenuation in the 2.4-GHz frequency band. Overall, the empirical measurements and experimental results provide valuable insights about IEEE 802.15.4-compliant sensor network platforms and guide design decisions and tradeoffs for WSN-based smart-grid applications.
Exploiting smart-phone USB connectivity for fun and profit

The Universal Serial Bus (USB) connection has become the de-facto standard for both charging and data transfers for smart phone devices including Google's Android and Apple's iPhone. To further enhance their functionality, smart phones are equipped with programmable USB hardware and open source operating systems that empower them to alter the default behavior of the end-to-end USB communications. Unfortunately, these new capabilities coupled with the inherent trust that users place on the USB physical connectivity and the lack of any protection mechanisms render USB a insecure link, prone to exploitation. To demonstrate this new avenue of exploitation, we introduce novel attack strategies that exploit the functional capabilities of the USB physical link. In addition, we detail how a sophisticated adversary who has under his control one of the connected devices can subvert the other. This includes attacks where a compromised smart phone poses as a Human Interface Device (HID) and sends keystrokes in order to control the victim host. Moreover, we explain how to boot a smart phone device into USB host mode and take over another phone using a specially crafted cable. Finally, we point out the underlying reasons behind USB exploits and propose potential defense mechanisms that would limit or even prevent such USB borne attacks.
X-band isoflux pattern antenna for SAR data transmission

In this paper, a design scheme for isoflux pattern antenna suitable for SAR data transmission is presented. An isoflux pattern is suitable for SAR data transmission of LEO satellite requiring wide angle coverage of the earth for the longer time visibility. Also, it is advantageous to provide uniform power density over the earth and constant power transmission to the ground station during the passage on the orbit. Based on the principle of generating isoflux pattern, we design basic isoflux pattern antenna which shows required isoflux characteristics. For improved performance, the basic antenna is optimized with genetic algorithm. The optimized isoflux pattern antenna is designed and implemented and shows rapid skirt characteristics, low side lobe, and low back lobe level.
Municipal Solid Waste Management Problems in Nigeria : Evolving Knowledge Management Solution

The paper attempts a synthesis of problems relating to municipal waste management in Nigeria and proposes a conceptual knowledge management approach for tackling municipal waste problems in cities across Nigeria. The application of knowledge management approach and strategy is crucial for inculcating a change of attitude towards improving the management of waste. The paper is a review of existing literatures, information, policies and data on municipal waste management in Nigeria. The inefficient management of waste by individuals, households, consumers and waste management companies can be attributed to inadequate information on waste management benefits, lack of producers’ involvement in waste management as well as poor implementation of government policies. The paper presents an alternative approach providing solutions promoting efficient municipal waste management. Environment, Knowledge management, Municipal waste management, Nigeria.
SyncGC: A Synchronized Garbage Collection Technique for Reducing Tail Latency in Cassandra

Data-center applications running on distributed databases often suffer from unexpectedly high response time fluctuation which is caused by long tail latency. In this paper, we find that long tail latency of user writes is mainly created by the interference with garbage collection (GC) tasks running in various system layers. In order to address the tail latency problem, we propose a synchronized garbage collection technique, called SyncGC. By scheduling multiple GC instances to execute in sync with each other in an overlapped manner, SyncGC prevents user requests from being interfered with GC instances, thereby minimizing their negative impacts on tail latency. Our experimental results with Cassandra show that SyncGC reduces the 99.99th-percentile tail latency and the maximum latency by 35% and 37%, on average, respectively.
Sokoban: Enhancing general single-agent search methods using domain knowledge

Artificial intelligence (AI) research has developed an extensive collection of methods to solve state-space problems. Using the challenging domain of Sokoban, this paper studies the effect of general search enhancements on program performance. We show that the current state of the art in AI generally requires a large research and programming effort to use domain-dependent knowledge to solve even moderately complex problems in such difficult domains. The application of domainspecific knowledge to exploit properties of the search space can result in large reductions in the size of the search tree, often several orders of magnitude per search enhancement. This applicationspecific knowledge is discovered and applied using application-independent search enhancements. Understanding the effect of these enhancements on the search leads to a new taxonomy of search enhancements, and a new framework for developing single-agent search applications. This is used to illustrate the large gap between what is portrayed in the literature versus what is needed in practice.
Ultra-low on-resistance LDMOS implementation in 0.13µm CD and BiCD process technologies for analog power IC's

Toshiba's 5th generation BiCD/CD-0.13 is a new process platform for analog power applications based on 0.13µm CMOS technology. The process platform has six varieties of rated voltage, 5V, 6V, 18V, 25V, 40V, and 60V. 5 to 18V CD-0.13 process use P-type silicon substrate. 25 to 60V BiCD-0.13 process use N-Epi wafer with N+/P+ buried layer on P type silicon substrate. Each LDMOS recode ultra-low on-resistance compared with that of previous papers, and we will realize the highest performance analog power IC's using this technology.
Board-level IT governance and organizational performance

24 October 2011 Revised: 26 January 2012 2nd  27 August 2012 3rd Revision: 5 November 2012 4th Revision: 11 December 2012  14 December 2012  Research on the strategic management of Information Technology (IT) resources has mostly focused on the oversight provided by the management team as a means to increase organizational performance. In recent years, boards of directors have also increased their involvement in IT matters, and various theoretical lenses suggest that this oversight too has the potential to influence organizational performance. Hence, this study synthesizes the resource-based and contingency views of MIS with corporate governance theories, and examines key antecedents and consequences of board-level IT governance (ITG) using a multi-method approach.  Modelling analysis applied to organization-level data collected from 171 board members suggested that the level of ITG exercised by boards was contingent upon the organization’s ‘IT use mode’, along the two dimensions of need for (a) fast and reliable IT, and (b) new innovative IT. But, the findings further suggested that the contingency approach may be suboptimal because it can cause new ways of leveraging IT to be ignored. High levels of board-level ITG, regardless of existing IT needs, increased organizational performance. This phenomenon was illuminated with applicability checks. Moreover, content analysis and structured interviews with board members further enriched these insights.  (2014) 23, 223–239. doi:10.1057/ejis.2012.61; published online 5 February 2013