LLukas22/scidocs huggingface dataset:
Towards Neural Network-based Reasoning

We propose Neural Reasoner , a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks. Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions. Despite the model complexity, Neural Reasoner can still be trained effectively in an end-to-end manner. Our empirical studies show that Neural Reasoner can outperform existing neural reasoning systems with remarkable margins on two difficult artificial tasks (Positional Reasoning and Path Finding) proposed in [8]. For example, it improves the accuracy on Path Finding(10K) from 33.4% [6] to over 98%.
Assessment in Massive Open Online Courses

Open online distance learning in higher education has quickly gained popularity, expanded, and evolved, with Massive Open Online Courses (MOOCs) as the most recent development. New web technologies allow for scalable ways to deliver video lecture content, implement social forums and track student progress in MOOCs. However, we remain limited in our ability to assess complex and open-ended student assignments. In this paper, we present a study on various forms of assessment and their relationship with the final exam score. In general, the reliability of both the self-assessments and the peer assessments was high. Based on low correlations with final exam grades as well as with other assessment forms, we conclude that self-assessments might not be a valid way to assess students’ performance in MOOCs. Yet the weekly quizzes and peer assessment significantly explained differences in students’ final exam scores, with one of the weekly quizzes as the strongest explanatory variable. We suggest that both self-assessment and peer assessment would better be used as assessment for learning instead of assessment of learning. Future research on MOOCs implies a reconceptualization of education variables, including the role of assessment of students’ achievements.
Pattern recognition in multivariate time series - A case study applied to fault detection in a gas turbine

Advances in information technology, together with the evolution of systems in control, automation and instrumentation have enabled the recovery, storage and manipulation of a large amount of data from industrial plants. This development has motivated the advancement of research in fault detection, especially based on process history data. Although a large amount of work has been conducted in recent years on the diagnostics of gas turbines, few of them present the use of clustering approaches applied to multivariate time series, adopting PCA similarity factor (SPCA) in order to detect and/or prevent failures. This paper presents a comprehensive method for pattern recognition associated to fault prediction in gas turbines using time series mining techniques. Algorithms comprising appropriate similarity metrics, subsequence matching and fuzzy clustering were applied on data extracted from a Plant Information Management System (PIMS) represented by multivariate time series. A real case study comprising the fault detection in a gas turbine was investigated. The results suggest the existence of a safe way to start the turbine that can be useful to support the development of a dynamic system for monitoring and predicting the probability of failure and for decision-making at operational level. & 2015 Elsevier Ltd. All rights reserved.
Steganalysis via a Convolutional Neural Network using Large Convolution Filters

For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovative classification procedures, can be deduced by a deep learning approach called Convolutional Neural Networks (CNN). These kind of deep learning networks is so well-suited for classification tasks based on the detection of variations in 2D shapes that it is the state-of-the-art in many image recognition problems. In this article, we design a CNN-based steganalyzer for images obtained by applying steganography with a unique embedding key. This one is quite different from the previous study of Qian et al. and its successor, namely Pibre et al. The proposed architecture embeds less convolutions, with much larger filters in the final convolutional layer, and is more general: it is able to deal with larger images and lower payloads. For the “same embedding key” scenario, our proposal outperforms all other steganalyzers, in particular the existing CNN-based ones, and defeats many state-of-the-art image steganography schemes. ∗This is a corrected version of the eprint J.-F. Couchot, R. Couturier, C. Guyeux, and M. Salomon (2016): Steganalysis via a Convolutional Neural Network using Large Convolution Filters. Publication: eprint arXiv:1605.07946. Publication Date: 05/2016. In this version, we used improperly prepared stego images with the same stego key, resulting in embedding changes pretty much in the same places. This error has been pointed out by a reviewer after submission to IEEE Transactions on Information Forensics & Security. †FEMTO-ST Institute, UMR 6174 CNRS University Bourgogne Franche-Comté, Belfort DISC (department of computer science and complex systems) AND team ‡raphael.couturier@univ-fcomte.fr 1 ar X iv :1 60 5. 07 94 6v 2 [ cs .M M ] 2 7 Ju l 2 01 6
Issues in implementing ERP: A case study

Information technology/systems play a major role in improving the competitiveness of organizations. There are numerous enterprise information software packages available in the market. One of them is enterprise resource planning (ERP). ERP can provide significant improvements in efficiency across a company, but only when implemented correctly. Otherwise, an ERP system could be a curse and drag the whole enterprise into spiraling inefficiency. Planning for ERP systems and their implementations requires an integrated approach to meet the requirements of various functional areas. With a brief overview of ERP implementations, this paper describes some experiences of an ERP implementation in a water corporation. The case study reveals some of the intricacies during the planning and implementation stages that may occur in any company in any part of the world. Suggestions are offered in resolving the issues of implementing ERP. 2002 Elsevier Science B.V. All rights reserved.
ROS: an open-source Robot Operating System

This paper gives an overview of ROS, an opensource robot operating system. ROS is not an operating system in the traditional sense of process management and scheduling; rather, it provides a structured communications layer above the host operating systems of a heterogenous compute cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.
The extended edit distance metric

The problem of similarity search has attracted increasing attention recently, because it has many applications. Time series are high dimensional data objects. In order to utilize an indexing structure that can effectively handle large time series databases, we need to reduce the dimensionality of these data objects. One of the promising techniques of dimensionality reduction is symbolic representation, which allows researchers to avail from the wealth of text-retrieval algorithms and techniques. To improve the effectiveness of similarity search we propose an extension to the well-known edit distance that we call the extended edit distance. This new distance is applied to symbolic sequential data objects. We test the proposed distance on time series data bases in classification task experiments. We also compare it to other distances that are well known in the literature for symbolic data objects, and we also prove, mathematically, that our new distance is metric.
Intraoperative imaging with a 3D C-arm system after zygomatico-orbital complex fracture reduction.

PURPOSE
During the repair of zygomatico-orbital complex (ZMC) fractures, the lateral orbital wall and/or the orbital floor is often reduced by merely reducing the zygoma. Intraoperative 3D imaging can help surgeons decide whether the orbit must be reconstructed as well. The purpose of this study was therefore to assess the usefulness of intraoperative 3D C-arm imaging in evaluating the adequacy of fracture reduction.


METHODS
A total of 21 patients with unilateral ZMC fractures were enrolled in this retrospective study. Four fractures were treated with a closed reduction technique. Seventeen fractures were repaired with open reduction and internal fixation of the zygomaticomaxillary buttress area. Intraoperative 3D C-arm imaging was performed in all cases. All patients underwent postoperative computed tomography and a clinical examination no earlier than 5 months after the procedure.


RESULTS
After reduction of the ZMC fractures, intraoperative 3D scans showed inadequate repair of the orbital floor in 2 patients and inadequate repair of the lateral orbit in 1 patient. Zygoma and zygomatic arch fracture reduction had to be corrected in 1 further case. The other 17 patients did not need an additional procedure. Postoperative imaging showed that no patient required a secondary operation. No postoperative diplopia or enophthalmos developed in any patient.


CONCLUSIONS
Intraoperative 3D C-arm imaging appears to be an effective tool for evaluating ZMC fracture reduction. It helps avoid additional procedures and thus helps reduce morbidity. In addition, there appears to be no need for postoperative imaging.
MONOPOLE-BASED RECTENNA FOR MICROWAVE ENERGY HARVESTING OF UHF RFID SYSTEMS

This paper presents a rectifying antenna (rectenna) for the harvesting of the microwave energy associated to UHF (UltraHigh Frequency) Radio Frequency IDentification (RFID) systems. The proposed device uses a capacitively loaded T-shaped monopole with a coplanar waveguide feeding line as receiving antenna and a five-stage voltage multiplier as rectifier. Experimental results demonstrating an RF-to-DC conversion efficiency of about 54% with an input power density of 80μW/cm2 will be presented and discussed.
Vaccine Mediated Protection Against Zika Virus-Induced Congenital Disease

The emergence of Zika virus (ZIKV) and its association with congenital malformations has prompted the rapid development of vaccines. Although efficacy with multiple viral vaccine platforms has been established in animals, no study has addressed protection during pregnancy. We tested in mice two vaccine platforms, a lipid nanoparticle-encapsulated modified mRNA vaccine encoding ZIKV prM and E genes and a live-attenuated ZIKV strain encoding an NS1 protein without glycosylation, for their ability to protect against transmission to the fetus. Vaccinated dams challenged with a heterologous ZIKV strain at embryo day 6 (E6) and evaluated at E13 showed markedly diminished levels of viral RNA in maternal, placental, and fetal tissues, which resulted in protection against placental damage and fetal demise. As modified mRNA and live-attenuated vaccine platforms can restrict in utero transmission of ZIKV in mice, their further development in humans to prevent congenital ZIKV syndrome is warranted.
Automated perfusion imaging for the evaluation of transient ischemic attack.

BACKGROUND AND PURPOSE
Diffusion-weighted imaging (DWI) is recommended for the evaluation of transient ischemic attack. Perfusion imaging can increase the yield of MRI in transient ischemic attack. We evaluated automated bolus perfusion (the time when the residue function reaches its maximum [TMax] and mean transit time [MTT]) and arterial spin labeling (ASL) sequences for the detection of ischemic lesions in patients with transient ischemic attack.


METHODS
We enrolled consecutive patients evaluated for suspicion of acute transient ischemic attack by multimodal MRI within 36 hours of symptom onset. Two independent raters assessed the presence and location of ischemic lesions blinded to the clinical presentation. The prevalence of ischemic lesions and the interrater agreement were 1,410 assessed.


RESULTS
From January 2010 to 2011, 93 patients were enrolled and 90 underwent perfusion imaging (69 bolus perfusion and 76 ASL). Overall, 25 of 93 patients (27%) were DWI-positive and 14 (15%) were perfusion-positive but DWI-negative (ASL n=9; TMax n=9; MTT n=2). MTT revealed an ischemic lesion in fewer patients than TMax (7 versus 20, P=0.004). Raters agreed on 89% of diffusion-weighted imaging cases, 89% of TMax, 87% o10f010 MTT, and 90% of ASL cases. The interrater agreement was good for DWI, TMax, and ASL (κ=0.73, 0.72, and 0.74, respectively) and fair for MTT (κ=0.43). Diffusion and/or perfusion were positive in 39 of 69 (57%) patients with a discharge diagnosis of possible ischemic event.


CONCLUSIONS
Our results suggest that in patients referred for suspicion of transient ischemic attack, automated TMax is more sensitive than MTT, and both ASL and TMax increase the yield of MRI for the detection of ischemic lesions.
A hybrid clustering approach to recognition of protein families in 114 microbial genomes

Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function). Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families. We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters. Hybrid (Markov followed by single-linkage) clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains) and single-linkage clustering (preservation of topological information as a function of threshold). Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient approach to the automated recognition of protein families for phylogenomic analysis.
Access to ICT education for girls and women in rural South Africa: A case study

This paper describes the impact of socio-economic f actors on girls and women’s access to Information and Communication Technology (ICT) education and training in a rural South African environment and recommends strategies for improved access to ICT education and training. The study was undert aken through a case study conducted in the Mootse village in the Elandsdoorn area of Mpumalanga Province,
Prospective analysis of creatine kinase muscle-brain fraction and comparison with troponin T to predict cardiac risk and benefit of an invasive strategy in patients with non-ST-elevation acute coronary syndromes.

OBJECTIVE
We sought to determine whether elevation of plasma creatine kinase muscle-brain fraction (CK-MB) would be useful to triage patients with acute coronary syndromes (ACS) to early angiography/revascularization.


BACKGROUND
It is unknown whether the measurement of CK-MB is effective for triage to an aggressive management strategy.


METHOD
Patients in the Treat Angina With Aggrastat and Determine Cost of Therapy With an Invasive or Conservative Strategy (TACTICS-TIMI) 18 study received aspirin, heparin, and tirofiban for treatment of ACS, were randomized to an invasive or a conservative strategy (angiography/revascularization between 4 and 48 h), and were followed up for a composite end point of death, myocardial infarction, or rehospitalization for ACS. Of 2,220 patients, CK-MB was elevated in 826 (37%). Of the patients with negative CK-MB, troponin T was elevated in 361 (31.2%). Event rates at 30 and 180 days were twice as high in patients with elevated CK-MB than in patients without elevated CK-MB. Both groups had similar benefit from an invasive strategy; there was no evidence of interaction between CK-MB elevation and strategy on the composite end point at 30 or 180 days. When patients were stratified according to both CK-MB and troponin status, there was evidence of a benefit in the invasive strategy among patients who were CK-negative but troponin-positive (odds ratios [95% confidence interval]: 0.13 [0.04 to 0.39] at 30 days and 0.29 [0.16 to 0.52] at 180 days).


CONCLUSION
Patients with minimal amounts of recent onset myonecrosis but elevated risk as indicated by CK-MB and troponin, respectively, benefit most from invasive management. Determination of troponin levels yielded significant information regarding triage to an invasive strategy, particularly in CK-MB-negative patients.
Yard crane dispatching based on real time data driven simulation for container terminals

This paper studies the problem of real time yard crane dispatching in container terminals. Many technologies, including transponders, RFID and GPS have been used in the container terminal setting for real-time tracking of terminal equipment. A judicious integration of real-time data into the yard crane management system will allow better utilization of terminal resources to improve overall terminal productivity. We propose a yard crane dispatching algorithm based on real time data driven simulation to solve the problem of yard crane job sequencing to minimize average vehicle waiting time. The algorithm will produce optimal operation sequence for each planning window. Several policies to select jobs to form the planning window are also proposed. Our simulation results show that dispatching yard crane based on real time data driven simulation is of great value in improving yard crane performance in 3 scenarios with different vehicle arriving patterns and our results are 10% worse off a loosely estimated overall optimal performance result.
Efficacy of peer support interventions for depression: a meta-analysis.

OBJECTIVE
To assess the efficacy of peer support for reducing symptoms of depression.


METHODS
Medline, PsycINFO, CINAHL and CENTRAL databases were searched for clinical trials published as of April 2010 using Medical Subject Headings and free text terms related to depression and peer support. Two independent reviewers selected randomized controlled trials (RCTs) that compared a peer support intervention for depression to usual care or a psychotherapy control condition. Meta-analyses were conducted to generate pooled standardized mean differences (SMD) in the change in depressive symptoms between study conditions.


RESULTS
Seven RCTs of peer support vs. usual care for depression involving 869 participants were identified. Peer support interventions were superior to usual care in reducing depressive symptoms, with a pooled SMD of -0.59 (95% CI, -0.98 to -0.21; P=.002). Seven RCTs with 301 total participants compared peer support to group cognitive behavioral therapy (CBT). There was no statistically significant difference between group CBT and peer interventions, with a pooled SMD of 0.10 (95% CI, -0.20 to 0.39, P=.53).


CONCLUSION
Based on the available evidence, peer support interventions help reduce symptoms of depression. Additional studies are needed to determine effectiveness in primary care and other settings with limited mental health resources.
Combining Concurrent Think-Aloud Protocols and Eye-Tracking Observations: An Analysis of Verbalizations and Silences

Research problem: Concurrent think-aloud (CTA) protocols are one of the dominant approaches of usability testing. However, there is still debate about the validity of the method, partly focusing on the usefulness and exhaustiveness of participants' verbalizations. The rise of eye-tracking technology sheds new light on this discussion, as participants' working processes can now be observed in more detail. Research questions: (1) What kinds of verbalizations do participants produce, and how do they relate to the information that can be directly observed using eye tracking? (2) What do eye movements reveal about cognitive processes at times when participants stop verbalizing? Literature review: Our study replicates an earlier study by Cooke (2010), who used a combination of CTA protocols and eye tracking in a small sample with experienced and highly educated participants to investigate the validity of CTA. Cooke's results suggest that the additional value of participants' verbalizations is limited: at least 77% of the verbalizations referred to things that could be easily observed with eye tracking. Methodology: We conducted a study in which 60 participants with different characteristics performed tasks on informational websites. During their task performance, they verbalized their thoughts, and simultaneously their eye movements were measured. The resulting think-aloud protocols were divided in verbalization units, which were coded into content types. Silences were registered, and eye movements during these silences were analyzed. Results and discussion: We found a different distribution of verbalization types than Cooke (2010) reported, with far more verbalizations where participants formulated doubts, judgments on the website, or expressions of frustration. In our study, verbalizations provided a substantial contribution in addition to the directly observable user problems. We measured a rather high percentage of silences (27%), during which participants most often were scanning pages for information. During these silences, interesting observations could be made about users' processes and obstacles on the website. The implication of our study is that we now have a better understanding of the types of verbalizations that a CTA evaluation might generate. Further, we know that relevant usability observations can be made during silences. A limitation is that we do not know yet the influence of specific characteristics of the evaluation setting on the types of verbalizations and silences. Future research should focus on the influence of evaluation settings on the outcomes of an evaluation, in particular, the influence of characteristics of the participants who are involved in the study.