nils-herrmann/clean_scirepeval huggingface dataset:
Pulsed radiofrequency treatment in interventional pain management: mechanisms and potential indications—a review

The objective of this review is to evaluate the efficacy of Pulsed Radiofrequency (PRF) treatment in chronic pain management in randomized clinical trials (RCTs) and well-designed observational studies. The physics, mechanisms of action, and biological effects are discussed to provide the scientific basis for this promising modality. We systematically searched for clinical studies on PRF. We searched the MEDLINE (PubMed) and EMBASE database, using the free text terms: pulsed radiofrequency, radio frequency, radiation, isothermal radiofrequency, and combination of these. We classified the information in two tables, one focusing only on RCTs, and another, containing prospective studies. Date of last electronic search was 30 May 2010. The methodological quality of the presented reports was scored using the original criteria proposed by Jadad et al. We found six RCTs that evaluated the efficacy of PRF, one against corticosteroid injection, one against sham intervention, and the rest against conventional RF thermocoagulation. Two trials were conducted in patients with lower back pain due to lumbar zygapophyseal joint pain, one in cervical radicular pain, one in lumbosacral radicular pain, one in trigeminal neuralgia, and another in chronic shoulder pain. From the available evidence, the use of PRF to the dorsal root ganglion in cervical radicular pain is compelling. With regards to its lumbosacral counterpart, the use of PRF cannot be similarly advocated in view of the methodological quality of the included study. PRF application to the supracapular nerve was found to be as efficacious as intra-articular corticosteroid in patients with chronic shoulder pain. The use of PRF in lumbar facet arthropathy and trigeminal neuralgia was found to be less effective than conventional RF thermocoagulation techniques.
PLDA for speaker verification with utterances of arbitrary duration

The duration of speech segments has traditionally been controlled in the NIST speaker recognition evaluations so that researchers working in this framework have been relieved of the responsibility of dealing with the duration variability that arises in practical applications. The fixed dimensional i-vector representation of speech utterances is ideal for working under such controlled conditions and ignoring the fact that i-vectors extracted from short utterances are less reliable than those extracted from long utterances leads to a very simple formulation of the speaker recognition problem. However a more realistic approach seems to be needed to handle duration variability properly. In this paper, we show how to quantify the uncertainty associated with the i-vector extraction process and propagate it into a PLDA classifier. We evaluated this approach using test sets derived from the NIST 2010 core and extended core conditions by randomly truncating the utterances in the female, telephone speech trials so that the durations of all enrollment and test utterances lay in the range 3-60 seconds and we found that it led to substantial improvements in accuracy. Although the likelihood ratio computation for speaker verification is more computationally expensive than in the standard i-vector/PLDA classifier, it is still quite modest as it reduces to computing the probability density functions of two full covariance Gaussians (irrespective of the number of the number of utterances used to enroll a speaker).
Virtual try-on of eyeglasses using 3D model of the head

This work presents a system for virtual try-on of eyeglasses using a 3D model of user's face and head. The 3D head model is reconstructed using only one image of the user. The 3D glasses model are then fitted onto this head model, and the user's head movement is tracked in real-time to rotate the 3D head model with glasses accordingly.
Visual-Inertial Monocular SLAM With Map Reuse

In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of $1\%$ and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.
A Neurolinguistic Model of Grammatical Construction Processing

One of the functions of everyday human language is to communicate meaning. Thus, when one hears or reads the sentence, John gave a book to Mary, some aspect of an event concerning the transfer of possession of a book from John to Mary is (hopefully) transmitted. One theoretical approach to language referred to as construction grammar emphasizes this link between sentence structure and meaning in the form of grammatical constructions. The objective of the current research is to (1) outline a functional description of grammatical construction processing based on principles of psycholinguistics, (2) develop a model of how these functions can be implemented in human neurophysiology, and then (3) demonstrate the feasibility of the resulting model in processing languages of typologically diverse natures, that is, English, French, and Japanese. In this context, particular interest will be directed toward the processing of novel compositional structure of relative phrases. The simulation results are discussed in the context of recent neurophysiological studies of language processing.
A scalable deduplication and garbage collection engine for incremental backup

Very large block-level data backup systems need scalable data deduplication and garbage collection techniques to make efficient use of the storage space and to minimize the performance overhead of doing so. Although the deduplication and garbage collection logic is conceptually straight-forward, their implementations pose a significant technical challenge because only a small portion of their associated data structures could fit into memory. In this paper, we describe the design, implementation and evaluation of a data deduplication and garbage collection engine called Sungem that is designed to remove duplicate blocks in incremental data backup streams. Sungem features three novel techniques to maximize the deduplication throughput without compromising the deduplication ratio. First, Sungem puts related fingerprint sequences, rather than fingerprints from the same backup stream, into the same container in order to increase the fingerprint prefetching efficiency. Second, to make the most of the memory space reserved for storing fingerprints, Sungem varies the sampling rates for fingerprint sequences based on their stability. Third, Sungem combines reference count and expiration time in a unique way to arrive at the first known incremental garbage collection algorithm whose bookkeeping overhead is proportional to the size of a disk volume's incremental backup snapshot rather than its full backup snapshot. We evaluated the Sungem prototype using a real-world data backup trace, and showed that the average throughput of Sungem is more than 200,000 fingerprint lookups per second on a standard X86 server, including the garbage collection cost.
Remote Vehicle Diagnostics over the Internet using the DoIP Protocol

Next generation vehicles will provide powerful connectivity and telematics services, enabling many new applications of vehicle communication. We will in this paper study the opportunities of performing remote vehicle diagnostics, where the diagnostic tool (test equipment) and the vehicle are separated by an internetwork, e.g., the Internet. The development of a prototype system for remote vehicle diagnostics, based on the emerging Diagnostics over IP (DoIP) ISO standard, is presented and early usage experiments with synchronous remote diagnostic read-out and control are described. A number of safety related issues are identified that will need closer study before a broad deployment of remote diagnostics services is feasible. Furthermore, a classification of vehicle diagnostics applications is provided, which is intended to elucidate the differences between synchronous (online) and asynchronous (offline) operation in local and distributed settings. vehicle diagnostics, vehicular communication
Learning Algorithms from Data

Statistical machine learning is concerned with learning models that describe observations. We train our models from data on tasks like machine translation or object recognition because we cannot explicitly write down programs to solve such problems. A statistical model is only useful when it generalizes to unseen data. Solomonoff114 has proved that one should choose the model that agrees with the observed data, while preferring the model that can be compressed the most, because such a choice guarantees the best possible generalization. The size of the best possible compression of the model is called the Kolmogorov complexity of the model. We define an algorithm as a function with small Kolmogorov complexity. This Ph.D. thesis outlines the problem of learning algorithms from data and shows several partial solutions to it. Our data model is mainly neural networks as they have proven to be successful in various domains like object recognition67,109,122, language modelling90, speech recognition48,39 and others. First, we examine empirical trainability limits for classical neural networks. Then, we extend them by providing interfaces, which provide a way to read memory, access the input, and postpone predictions. The model learns how to use them with reinforcement learning techniques like REINFORCE and Q-learning. Next, we ex-
New generation waveform approaches for 5G and beyond

For 5G and beyond cellular communication systems, new waveform development studies have been continuing in the whole world. Solutions for providing high throughput, high data rates, low latency, large number of terminals, supporting multiple antenna systems, and the related problems originated from these issues are playing an important role in the development of 5G and new waveform technologies. OFDM which has many important advantages continues to be included in various standards. However, because of its disadvantages, OFDM will not be sufficient for dynamic spectrum access and cognitive radio applications in new generation communication systems gradually. Additionally, OFDM is not suitable for asynchronous networks and radios which we expect to see commonly in the future. In this study, some potential candidate 5G waveforms are examined and various comparisons are carried out. Besides, assessments about future hybrid solutions are presented.
Analysis of packet loss and delay variation on QoE for H.264 andWebM/VP8 Codecs

The popularity of multimedia services over Internet has increased in the recent years. These services include Video on Demand (VoD) and mobile TV which are predominantly growing, and the user expectations towards the quality of videos are gradually increasing. Different video codecs are used for encoding and decoding. Recently Google has introduced the VP8 codec which is an open source compression format. It is introduced to compete with existing popular codec namely H.264/AVC developed by ITU-T Video Coding Expert Group (VCEG), as by 2016 there will be a license fee for H.264. In this work we compare the performance of H.264/AVC and WebM/VP8 in an emulated environment. NetEm is used as an emulator to introduce delay/delay variation and packet loss. We have evaluated the user perception of impaired videos using Mean Opinion Score (MOS) by following the International Telecommunication Union (ITU) Recommendations Absolute Category Rating (ACR) and analyzed the results using statistical methods. It was found that both video codecs exhibit similar performance in packet loss, But in case of delay variation H.264 codec shows better results when compared to WebM/VP8. Moreover along with the MOS ratings we also studied the effect of user feelings and online video watching experience impacts on their perception.
A Low-Profile Wideband Surface-Mountable Substrate-Integrated Waveguide Horn Antenna

A low-profile surface-mountable substrate-integrated waveguide (SIW) horn antenna with wideband performance is proposed in this letter. In order to reduce the serious impedance mismatch at the SIW horn aperture resulting from the thin substrate (0.12 λ0), a semi-open SIW horn is designed by gradually detaching upper broadwall while maintaining another one. Furthermore, in order to realize wide bandwidth for the SIW horn antenna, air-vias are drilled into the semi-open horn to realize smooth transition from semi-open horn to free space. Finally, a fabricated prototype is measured, and results indicate that a 35.7% impedance bandwidth ranging from 19.1 to 27.4 GHz and a 31.8% 3 dB gain bandwidth ranging from 19.3 to 26.6 GHz are obtained, respectively. Stable end fire radiation patterns are observed across the operating band.
Towards an observatory for mobile participatory sensing applications

This paper presents an observatory for registering applications that use participatory sensing to collect data. Cataloging these applications will aid the scientific community to exchange more information, facilitating the comparison between different initiatives. Through an initial research, the applications are categorized in areas usually considered in the literature. We propose a survey to validate the platform and also discuss the taxonomies created as a result of this survey. The main contributions of this paper include the classification of crowdsensing applications in different ontological categories, as well as the proposal of a technology platform that enables the distributed and collaborative cataloging of crowdsensing initiatives.
Multi-label Text Categorization with Model Combination based on F1-score Maximization

Text categorization is a fundamental task in natural language processing, and is generally defined as a multi-label categorization problem, where each text document is assigned to one or more categories. We focus on providing good statistical classifiers with a generalization ability for multi-label categorization and present a classifier design method based on model combination and F1-score maximization. In our formulation, we first design multiple models for binary classification per category. Then, we combine these models to maximize the F1-score of a training dataset. Our experimental results confirmed that our proposed method was useful especially for datasets where there were many combinations of category labels.
Minerva: A Scalable OWL Ontology Storage and Inference System

With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems. In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases. It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance. The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference. Furthermore, it customizes the database schema based on inference requirements. User queries are answered by directly retrieving materialized results from the back-end database. The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time. Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.
A gene-coexpression network for global discovery of conserved genetic modules.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System

The exponentially growing healthcare costs coupled with the increasing interest of patients in receiving care in the comfort of their own homes have prompted a serious need to revolutionize healthcare systems. This has prompted active research in the development of solutions that enable healthcare providers to remotely monitor and evaluate the health of patients in the comfort of their residences. However, existing works lack flexibility, scalability, and energy efficiency. This article presents a pervasive patient health monitoring (PPHM) system infrastructure. PPHM is based on integrated cloud computing and Internet of Things technologies. In order to demonstrate the suitability of the proposed PPHM infrastructure, a case study for real-time monitoring of a patient suffering from congestive heart failure using ECG is presented. Experimental evaluation of the proposed PPHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system.
DeepGaze II: Reading fixations from deep features trained on object recognition

Here we present DeepGaze II, a model that predicts where people look in images. The model uses the features from the VGG-19 deep neural network trained to identify objects in images. Contrary to other saliency models that use deep features, here we use the VGG features for saliency prediction with no additional fine-tuning (rather, a few readout layers are trained on top of the VGG features to predict saliency). The model is therefore a strong test of transfer learning. After conservative crossvalidation, DeepGaze II explains about 87% of the explainable information gain in the patterns of fixations and achieves top performance in area under the curve metrics on the MIT300 hold-out benchmark. These results corroborate the finding from DeepGaze I (which explained 56% of the explainable information gain), that deep features trained on object recognition provide a versatile feature space for performing related visual tasks. We explore the factors that contribute to this success and present several informative image examples. A web service is available to compute model predictions at http://deepgaze.bethgelab.org.
Agent-based Simulation Platforms: Review and Development Recommendations

Five software platforms for scientific agent-based models (ABMs) were reviewed by implementing example models in each. NetLogo is the highest-level platform, providing a simple yet powerful programming language, built-in graphical interfaces, and comprehensive documentation. It is designed primarily for ABMs of mobile individuals with local interactions in a grid space, but not necessarily clumsy for others. NetLogo is highly recommended, even for prototyping complex models. MASON, Repast, and Swarm are “framework and library” platforms, providing a conceptual framework for organizing and designing ABMs and corresponding software libraries. MASON is least mature and designed with execution speed a high priority.  C seems more natural than Java for ABMs but weak error-handling and the lack of developer tools are drawbacks. Java Swarm allows Swarm’s Objective-C libraries to be called from Java; it does not seem to combine advantages of the two languages well. Repast provides Swarm-like functions in a Java library and is a good choice for many, but parts of its organization and design could be improved. A rough comparison of execution speed found MASON and Repast usually fastest (MASON 135% faster than Repast), Swarm (including Objective-C) fastest for simple models but slowest for complex ones, and NetLogo intermediate. Recommendations include completing the documentation (for all platforms except NetLogo), strengthening conceptual frameworks, providing better tools for statistical output and automating simulation experiments, simplifying common tasks, and researching technologies for understanding how simulation results arise.
CARDIAC: An Intelligent Conversational Assistant for Chronic Heart Failure Patient Heath Monitoring

We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The natural language interface allows patients to interact with CARDIAC without special training. The system is designed to understand information that arises spontaneously in the course of the interview. If the patient gives more detail than necessary for answering a question, the system updates the user model accordingly. CARDIAC is a first step towards developing cost-effective, customizable, automated in-home conversational assistants that help patients manage their care and monitor their health using natural language.
A data-driven approach to predict the success of bank telemarketing

We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.