LLukas22/scidocs huggingface dataset:
ONLI: An ontology-based system for querying DBpedia using natural language paradigm

The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users. 2015 Elsevier Ltd. All rights reserved.
Improving Adherence to Cardiovascular Therapies: An Economic Evaluation of a Randomized Pragmatic Trial.

OBJECTIVE
Preplanned economic analysis of a pragmatic trial using electronic-medical-record-linked interactive voice recognition (IVR) reminders for enhancing adherence to cardiovascular medications (i.e., statins, angiotensin-converting enzyme inhibitors [ACEIs], and angiotensin receptor blockers [ARBs]).


METHODS
Three groups, usual care (UC), IVR, and IVR plus educational materials (IVR+), with 21,752 suboptimally adherent patients underwent follow-up for 9.6 months on average. Costs to implement and deliver the intervention (from a payer perspective) were tracked during the trial. Medical care costs and outcomes were ascertained using electronic medical records.


RESULTS
Per-patient intervention costs ranged from $9 to $17 for IVR and from $36 to $47 for IVR+. For ACEI/ARB, the incremental cost-effectiveness ratio for each percent adherence increase was about 3 times higher with IVR+ than with IVR ($6 and $16 for IVR and IVR+, respectively). For statins, the incremental cost-effectiveness ratio for each percent adherence increase was about 7 times higher with IVR+ than with IVR ($6 and $43 for IVR and IVR+, respectively). Considering potential cost offsets from reduced cardiovascular events, the probability of breakeven was the highest for UC, but the IVR-based interventions had a higher probability of breakeven for subgroups with a baseline low-density lipoprotein (LDL) level of more than 100 mg/dl and those with two or more calls.


CONCLUSIONS
We found that the use of an automated voice messaging system to promote adherence to ACEIs/ARBs and statins may be cost-effective, depending on a decision maker's willingness to pay for unit increase in adherence. When considering changes in LDL level and downstream medical care offsets, UC is the optimal strategy for the general population. However, IVR-based interventions may be the optimal choice for those with elevated LDL values at baseline.
Fixed Points in Peano Arithmetic with Ordinals

Abstract Jager, G., Fixed points in Peano arithmetic with ordinals, Annals of Pure and Applied Logic 60 (1993) 119-132. This paper deals with some proof-theoretic aspects of fixed point theories over Peano arithmetic with ordinals. It studies three such theories which differ in the principles which are available for induction on the natural numbers and ordinals. The main result states that there is a natural theory in this framework which is a conservative extension of Peano arithmetic.
Efficacy and safety of vilazodone 20 and 40 mg in major depressive disorder: a randomized, double-blind, placebo-controlled trial

Vilazodone is a selective serotonin reuptake inhibitor and 5-HT1A partial agonist approved for major depressive disorder (MDD) treatment in adults. This was a 10-week, multicenter, double-blind, placebo-controlled and active-controlled, fixed-dose trial (NCT01473381). Adult patients with MDD (Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text revision criteria) were randomized 1 : 1 : 1 : 1 to vilazodone 20 or 40 mg/day, citalopram 40 mg/day, or placebo. Primary efficacy: Montgomery-Åsberg Depression Rating Scale (MADRS); secondary efficacy: Clinical Global Impressions-Severity and sustained response (MADRS total score≤12 for at least the last two consecutive double-blind visits). The intent-to-treat population comprised 1133 patients, (placebo=281; vilazodone 20 mg/day=288; vilazodone 40 mg/day=284; citalopram=280). MADRS and Clinical Global Impressions-Severity score change from baseline to week 10 was significantly greater for vilazodone 20 mg/day, vilazodone 40 mg/day, and citalopram versus placebo. Sustained response rates were numerically higher, but not significantly different, in all active treatment groups versus placebo. The most common adverse events (≥5% of vilazodone patients, twice the rate of placebo) were diarrhea, nausea, vomiting (vilazodone 40 mg/day only), and insomnia. Improved sexual function (Changes in Sexual Functioning Questionnaire scores) was seen in all groups; between-group differences were not significant. Vilazodone 20 and 40 mg/day demonstrated efficacy and tolerability in the treatment of MDD.
Specific active immunotherapy with a VEGF vaccine in patients with advanced solid tumors. results of the CENTAURO antigen dose escalation phase I clinical trial.

UNLABELLED
CIGB-247 is a novel cancer therapeutic vaccine that uses a human VEGF variant molecule as antigen, in combination with a bacterial adjuvant. In mice, CIGB-247 has anti-tumor and anti-metastatic effects. The vaccine induces anti-VEGF blocking antibodies and a cellular response targeting tumor cells producing VEGF, and has proven to be safe in mice, rats, rabbits and non-human primates. Herein we report the results of a Phase I clinical trial (code name CENTAURO) where safety, tolerance, and immunogenicity of CIGB-247 were studied in 30 patients with advanced solid tumors, at three antigen dose levels. Individuals were subcutaneously immunized for 8 consecutive weeks with 50, 100 or 400 μg of antigen, and re-immunized on week twelve. On week sixteen, evaluations of safety, tolerance, clinical status, and immunogenicity (seroconversion for anti-VEGF IgG, serum VEGF/KDR-Fc blocking ability, and gamma-IFN ELISPOT with blood cells stimulated in vitro with mutated VEGF) were done. Surviving patients were eligible for off-trial additional 4-week re-immunizations with 400 μg of antigen. Immunogenicity and clinical status were again studied on weeks 25 and 49. Vaccination was shown to be safe at the three dose levels, with only grade 1-2 adverse events. CIGB-247 was immunogenic and higher numbers of individuals positive to the three immune response tests were seen with increasing antigen dose. Off-protocol long-term vaccination produced no additional adverse events or negative changes in immunogenicity. Eleven patients are still alive, with overall survivals ranging from 20 to 24 months. Twelve of the thirty patients exhibited objective clinical benefits, and two individuals have complete responses. Most patients with higher survivals are positive in the three immune response tests. In summary, this is the first clinical testing report of a cancer therapeutic vaccine based on a human VEGF related molecule as antigen. The CIGB-247 vaccine is safe, immunogenic, and merits further clinical development.


REGISTRATION NUMBER AND NAME OF TRIAL REGISTRY
RPCEC00000102. Cuban Public Clinical Trial Registry (WHO accepted Primary Registry). Available from: http://registroclinico.sld.cu/.
Phi.sh/$oCiaL: the phishing landscape through short URLs

Size, accessibility, and rate of growth of Online Social Media (OSM) has attracted cyber crimes through them. One form of cyber crime that has been increasing steadily is phishing, where the goal (for the phishers) is to steal personal information from users which can be used for fraudulent purposes. Although the research community and industry has been developing techniques to identify phishing attacks through emails and instant messaging (IM), there is very little research done, that provides a deeper understanding of phishing in online social media. Due to constraints of limited text space in social systems like Twitter, phishers have begun to use URL shortener services. In this study, we provide an overview of phishing attacks for this new scenario. One of our main conclusions is that phishers are using URL shorteners not only for reducing space but also to hide their identity. We observe that social media websites like Facebook, Habbo, Orkut are competing with e-commerce services like PayPal, eBay in terms of traffic and focus of phishers. Orkut, Habbo, and Facebook are amongst the top 5 brands targeted by phishers. We study the referrals from Twitter to understand the evolving phishing strategy. A staggering 89% of references from Twitter (users) are inorganic accounts which are sparsely connected amongst themselves, but have large number of followers and followees. We observe that most of the phishing tweets spread by extensive use of attractive words and multiple hashtags. To the best of our knowledge, this is the first study to connect the phishing landscape using blacklisted phishing URLs from PhishTank, URL statistics from bit.ly and cues from Twitter to track the impact of phishing in online social media.
What Do Client Developers Concern When Using Web APIs? An Empirical Study on Developer Forums and Stack Overflow

Popularity of service-oriented computing makes more and more companies and organizations provide their services through Web Application Program Interfaces (Web APIs). The Web APIs are considered to offer a convenient way to integrate web services to client applications. However, the integration process is often challenging. For example, updated Web APIs may be no longer compatible with the current version of client applications, thus break the client applications. To help the integration process, it is of significant interest to understand the challenges that are encountered by client developers. Developer forums and Stack Overflow are commonly used by client developers to seek help from fellow peers. In this paper, we mine both developer forums and Stack Overflow to find the common challenges encountered by client developers. We perform an empirical study on 32 Web APIs with a total of 92,471 discussions. To extract topics from all discussions, we apply a topic modeling technique called Latent Dirichlet Allocation (LDA). The results show that on average five dominant topics can cover at least 50% of questions regarding each Web API. We further investigate how topics evolve across Web APIs, and find five patterns. As a summary, our findings highlight a list of dominant concerns and persistent concerns for each Web API that Web API providers should pay more attention to.
Preadolescent disordered eating predicts subsequent eating dysfunction.

OBJECTIVE
This article tested whether disordered eating in the spring of sixth grade can be predicted by the behaviors of fifth grade elementary school children.


METHOD
Measurements of disordered eating were collected from 1906 children (mean age = 10.86 years) at Time 1 (spring of fifth grade), Time 2 (fall of sixth grade), and Time 3 (spring of sixth grade).


RESULTS
A number of fifth grade children reported disordered eating during the previous 2 weeks: 12.1% reported objective binge episodes, 4.8% reported purging food, and 9.8% reported restricting food intake. These behaviors predicted disordered eating during the spring of sixth grade. In addition, fifth grade pubertal onset predicted higher levels of restricting for girls.


CONCLUSION
A substantial number of fifth grade children reported disordered eating behaviors, and these behaviors predicted disordered eating behaviors in the spring of sixth grade. Disordered eating can be studied at least as early as fifth grade.
Efficient Additive Kernels via Explicit Feature Maps

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ2. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al. [1], which is data dependent, and the explicit map of Maji and Berg [2] for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101 [3], Daimler-Chrysler pedestrians [4], and INRIA pedestrians [5].
Relevant association rule mining from medical dataset using new irrelevant rule elimination technique

Association rule mining (ARM) is an emerging research in data mining. It extracts interesting association or correlation relationship in the large volume of transactions. Apriori based algorithms have two steps. First step is to find the frequent item set from the transactions. Second step is to construct the association rule. If ARM applied with medical dataset, it produces huge quantity of rules; most of these rules are irrelevant to the transaction. These irrelevant rules consume more memory space and misguide the decision making. Here irrelevant rule reduction is important. This paper proposes the n-cross validation technique to reduce association rules which are irrelevant to the transaction set. The proposed approach used partition based approaches are supported to association rule validation. The proposed algorithm called as PVARM (Partition based Validation for Association Rule Mining). The proposed PVARM algorithm is tested with T40I10D100K and heart disease prediction. The performance analysis attempted with Apriori, most frequent rule mining algorithm and non redundant rule mining algorithm to study the efficiency of proposed PVARM. The proposed work reduces large number of irrelevant rules and produces new set of rules with high confidence. It is much use to mine medical relevant rule mining.
Citizens' requirement analysis in Chinese e-government

E-government in China is experiencing a flourish period of development till now. Government delivers high quality services to citizens by means of e-government. However, it shows that in many aspects, some problems occur during the process of e-government construction. In order to facilitate the further development of Chinese e-government, the requirement of citizens as one of the important customers of government should be truly considered and represented.
The Eifel retransmission timer

We analyze two alternative retransmission timers for the Transmission Control Protocol (TCP). We first study the retransmission timer of TCP-Lite which is considered to be the current de facto standard for TCP implementations. After revealing four major problems of TCP-Lite's retransmission timer, we propose a new timer, named the Eifel retransmission timer, that eliminates these. The strength of our work lies in its hybrid analysis methodology. We develop models of both retransmission timers for the class of network-limited TCP bulk data transfers in steady state. Using those models, we predict the problems of TCP-Lite's retransmission timer and develop the Eifel retransmission timer. We then validate our model-based analysis through measurements in a real network that yield the same results.
Dual Supply Voltages and Dual Clock Frequencies for Lower Clock Power and Suppressed Temperature-Gradient-Induced Clock Skew

Two new clocking methodologies based on supply voltage and frequency scaling are proposed in this paper for lowering the power consumption and the temperature-fluctuation-induced skew without degrading the clock frequency. The clock signal is distributed globally at a scaled supply voltage with a single clock frequency with the first clocking methodology. Alternatively, dual supply voltages and dual signal frequencies are employed with the second methodology that provides enhanced power savings. The optimum supply voltage that minimizes clock skew is 44% lower than the nominal supply voltage in a 0.18 ¿m TSMC CMOS technology. Novel multi-threshold voltage level converters and frequency multipliers are employed at the leaves of the clock trees in order to maintain the synchronous system performance. The temperature-fluctuation-induced skew and the power consumption are reduced by up to 80% and 76%, respectively, with the proposed dual supply voltage and dual frequency clock distribution networks as compared to a standard clock tree operating at the nominal supply voltage with a single clock frequency.
A new representation and associated algorithms for generalized planning

Article history: Received 5 January 2010 Received in revised form 6 October 2010 Accepted 13 October 2010 Available online xxxx
Examining the associations between HIV-related stigma and health outcomes in people living with HIV/AIDS: a series of meta-analyses

OBJECTIVE
To conduct a systematic review and series of meta-analyses on the association between HIV-related stigma and health among people living with HIV.


DATA SOURCES
A structured search was conducted on 6 electronic databases for journal articles reporting associations between HIV-related stigma and health-related outcomes published between 1996 and 2013.


STUDY ELIGIBILITY CRITERIA
Controlled studies, cohort studies, case-control studies and cross-sectional studies in people living with HIV were considered for inclusion.


OUTCOME MEASURES
Mental health (depressive symptoms, emotional and mental distress, anxiety), quality of life, physical health, social support, adherence to antiretroviral therapy, access to and usage of health/social services and risk behaviours.


RESULTS
64 studies were included in our meta-analyses. We found significant associations between HIV-related stigma and higher rates of depression, lower social support and lower levels of adherence to antiretroviral medications and access to and usage of health and social services. Weaker relationships were observed between HIV-related stigma and anxiety, quality of life, physical health, emotional and mental distress and sexual risk practices. While risk of bias assessments revealed overall good quality related to how HIV stigma and health outcomes were measured on the included studies, high risk of bias among individual studies was observed in terms of appropriate control for potential confounders. Additional research should focus on elucidating the mechanisms behind the negative relationship between stigma and health to better inform interventions to reduce the impact of stigma on the health and well-being of people with HIV.


CONCLUSIONS
This systematic review and series of meta-analyses support the notion that HIV-related stigma has a detrimental impact on a variety of health-related outcomes in people with HIV. This review can inform the development of multifaceted, intersectoral interventions to reduce the impact of HIV-related stigma on the health and well-being of people living with HIV.
Low-dose oral prednisone improves clinical and ultrasonographic remission rates in early rheumatoid arthritis: results of a 12-month open-label randomised study

INTRODUCTION
In early rheumatoid arthritis (RA), low-dose oral prednisone (PDN) co-medication yields better clinical results than monotherapy with disease-modifying anti-rheumatic drugs (DMARDs). In addition, ultrasonography (US) evaluation reveals rapid and significant effects of glucocorticosteroids on subclinical synovitis. No data currently exist that examine the clinical and US results offered by glucocorticoid co-medication over DMARD monotherapy in early RA patients.


METHODS
Two hundred and twenty patients with early RA (< 1 year from clinical onset) were treated according to a low disease activity (LDA) targeted step-up protocol including methotrexate (MTX) and, in the active treatment arm, low-dose (6.25 mg/day) oral PDN over 12 months. Clinical disease activity measures were collected at baseline, 2, 4, 6, 9 and 12 months, and US examination of hands was performed at baseline, 6 and 12 months. Grey-scale and power Doppler (PD) synovitis were scored (0 to 3) for each joint. At 12 months, clinical remission according to the disease activity score among 28 joints was defined as the clinical outcome, and a total joint PD score of 0 (PD negativity) as the imaging outcome.


RESULTS
Each group included 110 patients with comparable demographic, clinical, laboratory and US characteristics. At 12 months, the LDA rate was similar in the two groups, whilst the clinical remission rate (risk ratio = 1.61 (95% confidence interval = 1.08, 2.04)) and PD negativity rate (risk ratio = 1.31 (95% confidence interval = 1.04, 1.64)) were significantly higher in the MTX+PDN group.


CONCLUSION
In early RA, despite a similar response rate in terms of LDA, low-dose oral PDN co-medication led to a higher proportion of clinical remission and PD negativity compared with MTX monotherapy, thus ensuring a better disease activity control.


TRIAL REGISTRATION NUMBER
Current Controlled Trials ISRCTN2486111.
Fairness Property and TCP-Level Performance of Unified Scheduling Algorithm in HSDPA Networks

Channel-state-aware scheduling strategies on wireless links play an essential role for enhancing throughput performance of elastic data traffic by exploiting channel fluctuations. Especially in HSDPA (High-Speed Downlink Packet Access), while various channel-state-aware schedulers have been proposed, each of them has pros and cons, and thus, none of them can be the best solution for all situations and policies in the base station management. To respond to the variety of the base station management, in our previous paper, a unified scheduler that can flexibly vary its characteristics by adjusting its parameters has been proposed, but only the basic packet-level performance was evaluated. In this paper, therefore, the detailed performance characteristics of the proposed unified scheduler are investigated. First, it is shown that the unified scheduler achieves a general fairness criterion—(p, α)-Proportional Fairness—in terms of the asymptotic behavior of the long-term averaged throughput. Second, the TCP-level throughput performance of the unified scheduler with some underlying link-layer technologies actually employed in HSDPA is evaluated by network simulation. In addition, two scenarios to determine the parameters of the scheduler—the semi-proportional fair scheduling and the semi-greedy scheduling—are examined, which demonstrates the flexibility and effectiveness of the unified scheduler.