LLukas22/scidocs huggingface dataset:
CREDIT CARD FRAUD DETECTION USING NEURAL NETWORKS

Today, people are more used to online shopping and payment due to which frauds related to online payment is on the rise. The main modes of online payment are credits cards, debit cards, net banking. The fraud related to credit cards is increasing nowadays because people prefer using them as an easy mode of payment. In this paper, we have implemented a mechanism to detect credit card fraud using neural networks using Neuroph IDE. This paper presents the various parameters that were considered while training and testing the neural network. Also, we have presented the results and conclusions of our experiment. Keywords— Credit card frauds, Neural Networks, Multilayer Feed forward network, Back propagation, Neuroph
Endovenous laser ablation of varicose perforating veins with the 1470-nm diode laser using the radial fibre slim.

BACKGROUND
Endovenous Laser Ablation (EVLA) is one of the most accepted treatment options for varicose veins. The aim of this study was to investigate the efficacy and safety of the new radial fiber slim (ELVeS-radial-slim kit™) for the 1470 nm diode laser in perforator veins with a 1 month follow-up.


METHODS
Our prospective observational cohort study comprised 69 perforating veins in 55 patients. Ninety percent of all patients were in the CEAP-stage C3-C6. The radial fiber slim was used to occlude the perforating vein and the great or small saphenous vein in the same procedure. The primary efficacy endpoint of the study was ultrasonographically proven elimination of venous reflux in the perforating vein after at least one month. Secondary efficacy and further safety end points after one month were as follows: (1) sonographic exclusion of recanalization of the treated vein segments, (2) deep vein thrombosis (DVT), clinical pulmonary embolism (PE), or superficial vein thrombosis (SVT) as defined by objective testing, (3) death from any cause, (4) persistent clinical complaints such as pain and paresthesia.


RESULTS
Follow-up could be completed in all patients. In all treated perforating varicose veins, occlusion with elimination of reflux could be demonstrated immediately after the procedure. After one month 95.6% of the treated veins were still occluded (67/69). During follow-up, we did not diagnose any DVT, PE or SVT in the area related to the treated perforating vein. No patient died. One patient reported paresthesia distally of the puncture site.


CONCLUSION
Endovenous laser treatment of varicose perforating veins with 1470 nm diode laser using the radial fiber slim is effective and safe with low recanalization rates during 1-month follow-up.
Background sounds contribute to spectrotemporal plasticity in primary auditory cortex

The mammalian auditory system evolved to extract meaningful information from complex acoustic environments. Spectrotemporal selectivity of auditory neurons provides a potential mechanism to represent natural sounds. Experience-dependent plasticity mechanisms can remodel the spectrotemporal selectivity of neurons in primary auditory cortex (A1). Electrical stimulation of the cholinergic nucleus basalis (NB) enables plasticity in A1 that parallels natural learning and is specific to acoustic features associated with NB activity. In this study, we used NB stimulation to explore how cortical networks reorganize after experience with frequency-modulated (FM) sweeps, and how background stimuli contribute to spectrotemporal plasticity in rat auditory cortex. Pairing an 8–4 kHz FM sweep with NB stimulation 300 times per day for 20 days decreased tone thresholds, frequency selectivity, and response latency of A1 neurons in the region of the tonotopic map activated by the sound. In an attempt to modify neuronal response properties across all of A1 the same NB activation was paired in a second group of rats with five downward FM sweeps, each spanning a different octave. No changes in FM selectivity or receptive field (RF) structure were observed when the neural activation was distributed across the cortical surface. However, the addition of unpaired background sweeps of different rates or direction was sufficient to alter RF characteristics across the tonotopic map in a third group of rats. These results extend earlier observations that cortical neurons can develop stimulus specific plasticity and indicate that background conditions can strongly influence cortical plasticity
Fast Adaptation of Deep Neural Network Based on Discriminant Codes for Speech Recognition

Fast adaptation of deep neural networks (DNN) is an important research topic in deep learning. In this paper, we have proposed a general adaptation scheme for DNN based on discriminant condition codes, which are directly fed to various layers of a pre-trained DNN through a new set of connection weights. Moreover, we present several training methods to learn connection weights from training data as well as the corresponding adaptation methods to learn new condition code from adaptation data for each new test condition. In this work, the fast adaptation scheme is applied to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion. We have proposed three different ways to apply this adaptation scheme based on the so-called speaker codes: i) Nonlinear feature normalization in feature space; ii) Direct model adaptation of DNN based on speaker codes; iii) Joint speaker adaptive training with speaker codes. We have evaluated the proposed adaptation methods in two standard speech recognition tasks, namely TIMIT phone recognition and large vocabulary speech recognition in the Switchboard task. Experimental results have shown that all three methods are quite effective to adapt large DNN models using only a small amount of adaptation data. For example, the Switchboard results have shown that the proposed speaker-code-based adaptation methods may achieve up to 8-10% relative error reduction using only a few dozens of adaptation utterances per speaker. Finally, we have achieved very good performance in Switchboard (12.1% in WER) after speaker adaptation using sequence training criterion, which is very close to the best performance reported in this task ("Deep convolutional neural networks for LVCSR," T. N. Sainath et al., Proc. IEEE Acoust., Speech, Signal Process., 2013).
Automatic Music Genres Classification using Machine Learning

Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%. Keywords—K-nearest neighbor (k-NN); Support Vector Machine (SVM); music; genre; classification; features; Mel Frequency Cepstral Coefficients (MFCC); principal component analysis (PCA)
The Elgar Companion to Austrian Economics

This reference reflects areas where Austrian economists have made contributions, including technical economics, methodology of the social sciences, political theory and political science. It includes contributors whose thinking is similar to that of the Austrian school, but is not part of it.
Prophylaxis against atrial fibrillation after cardiac surgery: beneficial effect of perioperative metoprolol.

INTRODUCTION
Multiple agents have been investigated to prevent atrial fibrillation (AF) after cardiac surgery. Several studies have investigated the use of β-blockers such as metoprolol or amiodarone with promising results. We aimed to investigate perioperative pharmacologic prophylaxis against AF using metoprolol, and amiodarone in combination with metoprolol.


METHODS
We conducted a prospective, randomised, single-blind, controlled pilot study in patients undergoing elective cardiac surgery. Subjects were randomised pre-operatively to one of three treatment groups: standard therapy (control) or metoprolol (5 mg IV over 5 min on commencement of bypass then 5 mg IV qid for 24h then 25-50 mg tds orally until discharge) or amiodarone (300 mg over 1h starting shortly after the commencement of bypass, then 900 mg over 24h then 400 mg orally tds until discharge) plus metoprolol as above. Patients had ECG monitoring for the occurrence of AF for six days or until discharge.


RESULTS
Two hundred and fifteen patients were enrolled. Between-group differences in AF in an intention-to-treat analysis were not significant: control 34% (23-45%), metoprolol 35% (24-46%), combined 22% (12-33%) (p = 0.21). However 87 patients (40%) did not receive the assigned treatment mainly due to side effects, especially bradycardia. The remaining 128 patients were analysed on a per-protocol basis with the overall difference between the three groups bordering on significance: control 34% (23-45%), metoprolol 26% (9-43%), combined 11% (0-23%) (p = 0.06). Logistic regression analysis, correcting for age and gender, was used to separate the individual effects of metoprolol and amiodarone in the presence of metoprolol which showed that compared to control there was a significant effect of metroprolol on AF incidence (O.R. 0.31 (0.10-0.99), p = 0.048) but not of amiodarone (O.R. 0.97 (0.19-5.02), p = 0.97).


CONCLUSIONS
(1) Perioperative metoprolol but not amiodarone itself in combination with metoprolol is associated with a significant reduction in postoperative AF. (2) Perioperative administration of metoprolol and combination of metoprolol with amiodarone is associated with a high incidence of side effects, especially bradycardia. (3) Further studies are indicated to confirm these preliminary findings but in the meantime it would not be unreasonable to implement the use of perioperative metoprolol for routine prophylaxis of AF.
Effect of reservoir on electromigration of short interconnects

As the interconnect cross-sections are ever scaled down, a particular care must be taken on the tradeoff between increase of current density in the back end of line and reliability to prevent electromigration (EM). Some lever exists as the well-known Blech effect [1]. One can take advantage of the EM induced backflow flux that counters the EM flux. As a consequence, the total net flux in the line is reduced and additional current density in designs can be allowed in short lines. However, the immortality condition is most of the time addressed with a standard test structures ended by two vias [2]–[3]. Designs present complex configurations far from this typical case and the Blech product (jL)c can be deteriorated or enhanced [4]. In the present paper, we present our study of EM performances of short lines ended by an inactive end of line (EOL) at one end of the test structure. Significant differences on the median time to failure (MTF) are observed with respect to the current direction, from a quasi deletion of failure to a significant reduction of the Blech effect. Based on the resistance saturation, a method is proposed to determine effective lengths of inactive EOL configurations corresponding to the standard case.
Separate dimensions of anxiety differentially predict alcohol use among male juvenile offenders.

INTRODUCTION
Although research has documented robust prospective relationships between externalizing symptomatology and subsequent adolescent alcohol use, the extent to which internalizing symptoms such as anxiety may increase risk for alcohol consumption remains controversial. Recent evidence suggests that one possible reason for mixed findings is that separate dimensions of anxiety differentially confer risk for alcohol use. The present study tested two dimensions of anxiety, worry and physiological anxiety symptoms, as predictors of alcohol use and misuse in a longitudinal sample of juvenile offenders.


METHODS
Participants were 818 male juvenile offenders drawn from a larger multi-site, longitudinal study. Zero-inflated Poisson regression models estimated the influence of anxiety symptoms on typical drinking quantity, frequency of binge drinking, and alcohol dependence symptoms.


RESULTS
Results indicate that physiological anxiety and worry symptoms showed differential relations with alcohol use risk. Physiological anxiety was positively associated with increased risk for typical alcohol involvement, frequency of binge drinking, and alcohol dependence symptoms, whereas worry was negatively associated with all alcohol use outcomes.


CONCLUSIONS
Current findings underscore the importance of considering anxiety as a multidimensional construct when examining the prospective relation between anxiety and adolescent alcohol use risk.
Is there More? A Dialogue Between Sight and Insight

A travers un dialogue sur la question y-a-t-il plus? l'A. emet des suppositions sur le scientisme et le transcendantalisme
Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

OBJECTIVE
Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research.


APPROACH
A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop.


MAIN RESULTS
Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories.


SIGNIFICANCE
Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.
Active Text for Structuring and Understanding Source Code

2 SUMMARY Programs are not read sequentially like a book but rather selectively like an encyclopedia. Instead of linear text we therefore suggest non-linear active text as a medium for structuring and browsing source code. While common program editors are mainly character based we believe that non-textual information such as pictures, links, folds, and annotations can improve the readability of programs. In this paper we show how a text editor can be extended to include active text elements and give examples for how to apply these facilities to source code. Our approach is different from that of syntax-oriented editors in that our structuring is not based on the syntax of the edited texts. The implementation was done in the Oberon system which offers powerful mechanisms for extending software in an object-oriented way.
Rain Bar: Robust Application-Driven Visual Communication Using Color Barcodes

Color barcode-based visible light communication (VLC) over screen-camera links has attracted great research interest in recent years due to its many desirable properties, including free of charge, free of interference, free of complex network configuration and well-controlled communication security. To achieve high-throughput barcode streaming, previous systems separately address design challenges such as image blur, imperfect frame synchronization and error correction etc., without being investigated as an interrelated whole. This does not fully exploit the capacity of color barcode streaming, and these solutions all have their own limitations from a practical perspective. This paper proposes RainBar, a new and improved color barcode-based visual communication system, which features a carefully-designed high-capacity barcode layout design to allow flexible frame synchronization and accurate code extraction. A progressive code locator detection and localization scheme and a robust color recognition scheme are proposed to enhance system robustness and hence the decoding rate under various working conditions. An extensive experimental study is presented to demonstrate the effectiveness and flexibility of RainBar. Results on Android smartphones show that our system achieves higher average throughput than previous systems, under various working environments.
Automatic audiovisual behavior descriptors for psychological disorder analysis

☆ This paper has been recommended for acceptance by ⁎ Corresponding author at: University of Southern C Technologies 12015 Waterfront Dr. 90094, Playa Vista, CA E-mail addresses: scherer@ict.usc.edu (S. Scherer), Str Lucas@ict.usc.edu (G. Lucas), emmam3@cam.ac.uk (M. M (J. Boberg), gratch@ict.usc.edu (J. Gratch), rizzo@ict.usc.ed morency@ict.usc.edu (L.-P. Morency). 1 This work is an extension of the work in [4] originally the IEEE Automatic Face and Gesture Recognition Confere
Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of preand post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions. OPEN ACCESS Remote Sens. 2014, 6 471
Efficacy and costs of nutritional rehabilitation in muscle-wasted patients with chronic obstructive pulmonary disease in a community-based setting: a prespecified subgroup analysis of the INTERCOM trial.

RATIONALE
Limited data are available on effectiveness and costs of nutritional rehabilitation for patients with COPD in community care.


METHODS
In a 2-year RCT, 199 COPD patients (FEV(1)%pred. 60% [SD 16%]) and impaired exercise capacity were randomized to the interdisciplinary community-based COPD management program (INTERCOM) or usual care (UC). A prescheduled subgroup analysis was performed on 39 of 199 patients who were muscle wasted and received UC or nutritional therapy in combination with exercise training. Body composition, muscle strength, and exercise capacity were assessed at baseline and 4, 12, and 24 months.


RESULTS
Between group differences after 4 months in favor of the intervention group: fat free mass index (FFMI 0.9 kg/m(2) [SE = 0.2, P < .001]), body mass index (BMI 1.0 kg/m(2) [SE = 0.4, P = .009]), maximum inspiratory mouth pressure (Pimax 1.4 kPa [SE = 0.5, P = .011]), quadriceps average power (QAP 13.1 Watt [SE=5.8, P = .036]), 6-minute walking distance (6MWD 27 m, [SE = 11.5, P = .028]), cycle endurance time (CET 525 seconds [SE=195, P = .013]), and peak exercise capacity (Wmax 12 Watt [SE = 5, P = .036]). Between group difference over 24 months in favor of the intervention group: Pimax 1.7 kPa (SE = 0.53, P = .004), QAP 19 Watt (SE = 6, P = .005), 6MWD 57 (SE = 19, P = .006), and CET 485 seconds (SE = 159, P = .006). After 4 months total costs were Euro 1501 higher in the intervention group than in the UC group (P < .05), but not significantly different after 24 months. Hospital admission costs were significantly lower in the intervention group -euro 4724 (95% CI -7704, -1734).


CONCLUSION
This study in muscle-wasted COPD patients with moderate airflow obstruction shows a prolonged positive response to nutritional support integrated in a community-based rehabilitation program.
Multi-lingual Common Semantic Space Construction via Cluster-Consistent Word Embedding

We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space via which all available resources and knowledge can be shared across multiple languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features which are extracted from manually crafted lexical resources than state-of-the-art multi-lingual embedding learning methods do. Using low-resource language name tagging as a case study for extrinsic evaluation, our approach achieves up to 14.6% absolute F-score gain over the state of the art on cross-lingual direct transfer. Our approach is also shown to be robust even when the size of bilingual dictionary is small.1
IBM Research Report Minimum Up/Down Polytopes of the Unit Commitment Problem with Start-Up Costs

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