nils-herrmann/clean_scirepeval huggingface dataset:
Rendu-Osler-Weber disease: experience with 56 patients.

Rendu-Osler-Weber disease, or hereditary hemorrhagic telangiectasia (HHT), is an autosomal dominant disease characterized by systemic vascular dysplasia. The prevalence varies and ranges, according to region, from 1/3500 to 1/5000. Data concerning Italy are not available. The diagnosis is based on the following criteria: family history, epistaxis, telangiectases and visceral arteriovenous malformations. The diagnosis is to be considered definite if three criteria are present and suspected if two criteria are present. From September 2000 to March 2002, 100 patients (63 males, 37 females, mean age 45.5 +/- 17.3 years) potentially affected by HHT were evaluated in the HHT Center of the "Augusto Murri" Internal Medicine Section at the University of Bari (on a day-hospital or hospitalization basis). The diagnosis of HHT was confirmed in 56 patients and suspected in 10. Magnetic resonance imaging revealed cerebral arteriovenous malformations in 8.5% of patients. In 14.6% of patients contrast echocardiography revealed pulmonary arteriovenous malformations subsequently confirmed at multislice computed tomography in all cases but one. In 48.2% of subjects hepatic vascular malformations were revealed by echo color Doppler ultrasonography, whereas abdominal multislice computed tomography was positive in 63.8% of patients. In 64% of the 25 patients, who underwent endoscopy, gastric telangiectases were found. In 3 out of 6 patients presenting with pulmonary arteriovenous malformations, embolotherapy was performed with success. In our patients, the use of tranexamic acid caused a reduction in the frequency of epistaxis. The future objectives of the HHT Center of Bari are to increase knowledge of the disease, to cooperate with other centers with the aim of increasing the number of patients studied and to avoid the limits of therapeutic and diagnostic protocols of a rare disease such as HHT.
Physical layer security in wireless networks: a tutorial

Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics.
Concept Mask: Large-Scale Segmentation from Semantic Concepts

Existing works on semantic segmentation typically consider a small number of labels, ranging from tens to a few hundreds. With a large number of labels, training and evaluation of such task become extremely challenging due to correlation between labels and lack of datasets with complete annotations. We formulate semantic segmentation as a problem of image segmentation given a semantic concept, and propose a novel system which can potentially handle an unlimited number of concepts, including objects, parts, stuff, and attributes. We achieve this using a weakly and semi-supervised framework leveraging multiple datasets with different levels of supervision. We first train a deep neural network on a 6M stock image dataset with only image-level labels to learn visual-semantic embedding on 18K concepts. Then, we refine and extend the embedding network to predict an attention map, using a curated dataset with bounding box annotations on 750 concepts. Finally, we train an attention-driven class agnostic segmentation network using an 80-category fully annotated dataset. We perform extensive experiments to validate that the proposed system performs competitively to the state of the art on fully supervised concepts, and is capable of producing accurate segmentations for weakly learned and unseen concepts.
Adaptive Sampled Softmax with Kernel Based Sampling

Softmax is the most commonly used output function for multiclass problems and is widely used in areas such as vision, natural language processing, and recommendation. A softmax model has linear costs in the number of classes which makes it too expensive for many real-world problems. A common approach to speed up training involves sampling only some of the classes at each training step. It is known that this method is biased and that the bias increases the more the sampling distribution deviates from the output distribution. Nevertheless, almost all recent work uses simple sampling distributions that require a large sample size to mitigate the bias. In this work, we propose a new class of kernel based sampling methods and develop an efficient sampling algorithm. Kernel based sampling adapts to the model as it is trained, thus resulting in low bias. It can also be easily applied to many models because it relies only on the model’s last hidden layer. We empirically study the trade-off of bias, sampling distribution and sample size and show that kernel based sampling results in low bias with few samples.
Diversity and biotechnological potential of microorganisms associated with marine sponges

Marine sponges harbor diverse microbial communities, encompassing not only three domains of life including Bacteria, Archaea and eukaryotes, but also many different phyla within Bacteria. This diversity implies a rich source for biodiscovery of new natural products. Here, we review recent progress in our understanding of this genetic diversity, its retrieval via culture and genomic approaches, and its implications for chemical diversity and other biotechnology applications of sponge microorganisms and their genes.
Global strategy for asthma management and prevention: GINA executive summary.

Asthma is a serious health problem throughout the world. During the past two decades, many scientific advances have improved our understanding of asthma and ability to manage and control it effectively. However, recommendations for asthma care need to be adapted to local conditions, resources and services. Since it was formed in 1993, the Global Initiative for Asthma, a network of individuals, organisations and public health officials, has played a leading role in disseminating information about the care of patients with asthma based on a process of continuous review of published scientific investigations. A comprehensive workshop report entitled "A Global Strategy for Asthma Management and Prevention", first published in 1995, has been widely adopted, translated and reproduced, and forms the basis for many national guidelines. The 2006 report contains important new themes. First, it asserts that "it is reasonable to expect that in most patients with asthma, control of the disease can and should be achieved and maintained," and recommends a change in approach to asthma management, with asthma control, rather than asthma severity, being the focus of treatment decisions. The importance of the patient-care giver partnership and guided self-management, along with setting goals for treatment, are also emphasised.
Summarization Algorithms for Record Linkage

Record linkage has received significant attention in recent years due to the plethora of data sources that have to be integrated to facilitate data analyses. In several cases, such an integration involves disparate data sources containing huge volumes of records and must be performed in near real-time in order to support critical applications. In this paper, we propose the first summarization algorithms for speeding up online record linkage tasks. Our first method, called SkipBloom, summarizes efficiently the participating data sets, using their blocking keys, to allow for very fast comparisons among them. The second method, called BlockSketch, summarizes a block to achieve a constant number of comparisons for a submitted query record, during the matching phase. Additionally, we extend BlockSketch to adapt its functionality to streaming data, where the objective is to use a constant amount of main memory to handle potentially unbounded data sets. Through extensive experimental evaluation, using three real-world data sets, we demonstrate the superiority of our methods against two state-of-the-art algorithms for online record linkage.
A unifying framework for detecting outliers and change points from time series

We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc. Although, in most previous work, outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them. In this framework, a probabilistic model of time series is incrementally learned using an online discounting learning algorithm, which can track a drifting data source adaptively by forgetting out-of-date statistics gradually. A score for any given data is calculated in terms of its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. By taking an average of the scores over a window of a fixed length and sliding the window, we may obtain a new time series consisting of moving-averaged scores. Change point detection is then reduced to the issue of detecting outliers in that time series. We compare the performance of our framework with those of conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security.
Enhancing Backchannel Prediction Using Word Embeddings

Backchannel responses like “uh-huh”, “yeah”, “right” are used by the listener in a social dialog as a way to provide feedback to the speaker. In the context of human-computer interaction, these responses can be used by an artificial agent to build rapport in conversations with users. In the past, multiple approaches have been proposed to detect backchannel cues and to predict the most natural timing to place those backchannel utterances. Most of these are based on manually optimized fixed rules, which may fail to generalize. Many systems rely on the location and duration of pauses and pitch slopes of specific lengths. In the past, we proposed an approach by training artificial neural networks on acoustic features such as pitch and power and also attempted to add word embeddings via word2vec. In this work, we refined this approach by evaluating different methods to add timed word embeddings via word2vec. Comparing the performance using various feature combinations, we could show that adding linguistic features improves the performance over a prediction system that only uses acoustic features.
Very Compact Fully Lumped Decoupling Network for a Coupled Two-Element Array

Without the need of any transmission line, a very compact decoupling network based on reactive lumped elements is presented for a two-element closely spaced array. The lumped network, consisting of two series and four shunt elements, can be analytically designed using the even-odd mode analysis. In the even mode, the half-circuit of the decoupling network is identical to an L-section matching network, while in the odd mode it is equivalent to a π-section one. The proposed decoupling network can deal with the matching conditions of the even and odd modes independently so as to simultaneously achieve good impedance matching and port isolation of the whole antenna array. The design principle, formulation, and experimental results including the radiation characteristics are introduced.
I/O-Cache: A Non-volatile Memory Based Buffer Cache Policy to Improve Storage Performance

Most computer systems currently consist of DRAM as main memory and hard disk drives (HDDs) as storage devices. Due to the volatile nature of DRAM, the main memory may suffer from data loss in the event of power failures or system crashes. With rapid development of new types of non-volatile memory (NVRAM), such as PCM, Memristor, and STT-RAM, it becomes likely that one of these technologies will replace DRAM as main memory in the not-too-distant future. In an NVRAM based buffer cache, any updated pages can be kept longer without the urgency to be flushed to HDDs. This opens opportunities for designing new buffer cache policies that can achieve better storage performance. However, it is challenging to design a policy that can also increase the cache hit ratio. In this paper, we propose a buffer cache policy, named I/O-Cache, that regroups and synchronizes long sets of consecutive dirty pages to take advantage of HDDs' fast sequential access speed and the non-volatile property of NVRAM. In addition, our new policy can dynamically separate the whole cache into a dirty cache and a clean cache, according to the characteristics of the workload, to decrease storage writes. We evaluate our scheme with various traces. The experimental results show that I/O-Cache shortens I/O completion time, decreases the number of I/O requests, and improves the cache hit ratio compared with existing cache policies.
Drawing into the AR-CANVAS : Designing Embedded Visualizations for Augmented Reality

With AR-CANVAS we introduce the notion of the augmented reality canvas for information visualization. This is beyond the traditional empty (white), rectangular, and flat-dimensional canvas seen in traditional information visualization. Instead, the AR-CANVAS describes the part of a viewer’s field-of-view where information visualization is rendered in-situ with respect to visible and potentially invisible real-world objects. The visual and spatial complexity of the canvas requires rethinking how to design visualizations for augmented reality. Based on an example of a library exploration scenario, we describe the essential aspects of the AR-CANVAS as well as dimensions for visualization design. We conclude with a brief discussion of challenges in designing visualizations into such a canvas.
Processing and Rendering Massive 3 D Geospatial Environments using WebGL – The examples of OpenWebGlobe and SmartMobileMapping

Generating and visualizing rich and interactive (geospatial) 3D contents over the World Wide Web (WWW) using state of the art web technologies such as WebGL as a native component of modern web browsers is a continuously growing approach to modern geospatial data exploitation. In this paper we are introducing OpenWebGlobe and 3DCityTV as two showcase applications to demonstrate the capabilities for exploiting massive 3d geospatial environments with current web technologies without the extensive use of third-party technologies such as browser plugins or extensions. Real time rendering of massive 3d virtual worlds using WebGL as well as the parallel processing and storage within common cloud computing services of large scale datasets will be discussed. Using an imaged-based approach to 3d modeling on vast stereo-vision based mobile mapping data will be introduced using an entirely web based 3d exploitation solution.
Determinants of usage intention in mobile social media: A structural equation modeling test

This study aims to explore the determinants influencing usage intention in mobile social media from the user motivation and the Theory of Planned Behavior (TPB) perspectives. Based on TPB, this study added three motivations, namely entertainment, sociality, and information, into the TPB model, and further examined the moderating effect of posters and lurkers in the relationships of the proposed model. A structural equation modeling was used and 468 LINE users in Taiwan were investigated. The results revealed that entertainment, sociality, and information are positively associated with behavioral attitude. Moreover, behavioral attitude, subjective norms, and perceived behavioral control are positively associated with usage intention. Furthermore, posters likely post messages on the LINE because of entertainment, sociality, and information, but they are not significantly subject to subjective norms. In contrast, lurkers tend to read, not write messages on the LINE because of entertainment and information rather than sociality and perceived behavioral control.
Massively Parallel Hyperparameter Tuning

Modern learning models are characterized by large hyperparameter spaces. In order to adequately explore these large spaces, we must evaluate a large number of configurations, typically orders of magnitude more configurations than available parallel workers. Given the growing costs of model training, we would ideally like to perform this search in roughly the same wall-clock time needed to train a single model. In this work, we tackle this challenge by introducing ASHA, a simple and robust hyperparameter tuning algorithm with solid theoretical underpinnings that exploits parallelism and aggressive early-stopping. Our extensive empirical results show that ASHA outperforms state-of-the-art hyperparameter tuning methods; scales linearly with the number of workers in distributed settings; converges to a high quality configuration in half the time taken by Vizier (Google’s internal hyperparameter tuning service) in an experiment with 500 workers; and beats the published result for a near state-of-the-art LSTM architecture in under 2× the time to train a single model.
Materiable: Rendering Dynamic Material Properties in Response to Direct Physical Touch with Shape Changing Interfaces

Shape changing interfaces give physical shapes to digital data so that users can feel and manipulate data with their hands and bodies. However, physical objects in our daily life not only have shape but also various material properties. In this paper, we propose an interaction technique to represent material properties using shape changing interfaces. Specifically, by integrating the multi-modal sensation techniques of haptics, our approach builds a perceptive model for the properties of deformable materials in response to direct manipulation. As a proof-of-concept prototype, we developed preliminary physics algorithms running on pin-based shape displays. The system can create computationally variable properties of deformable materials that are visually and physically perceivable. In our experiments, users identify three deformable material properties (flexibility, elasticity and viscosity) through direct touch interaction with the shape display and its dynamic movements. In this paper, we describe interaction techniques, our implementation, future applications and evaluation on how users differentiate between specific properties of our system. Our research shows that shape changing interfaces can go beyond simply displaying shape allowing for rich embodied interaction and perceptions of rendered materials with the hands and body.
A real-time lane marking localization, tracking and communication system

In this paper, we present an in-vehicle computing system capable of localizing lane markings and communicating them to drivers. To the best of our knowledge, this is the first system that combines the Maximally Stable Extremal Region (MSER) technique with the Hough transform to detect and recognize lane markings (i.e., lines and pictograms). Our system begins by localizing the region of interest using the MSER technique. A three-stage refinement computing algorithm is then introduced to enhance the results of MSER and to filter out undesirable information such as trees and vehicles. To achieve the requirements of real-time systems, the Progressive Probabilistic Hough Transform (PPHT) is used in the detection stage to detect line markings. Next, the recognition of the color and the form of line markings is performed; this it is based on the results of the application of the MSER to left and right line markings. The recognition of High-Occupancy Vehicle pictograms is performed using a new algorithm, based on the results of MSER regions. In the tracking stage, Kalman filter is used to track both ends of each detected line marking. Several experiments are conducted to show the efficiency of our system.
An Indoor Navigation System for Visually Impaired People Using a Path Finding Algorithm and a Wearable Cap

This paper represents the design and implementation of an indoor based navigation system for visually impaired people using a path finding algorithm and a wearable cap. This development of the navigation system consists of two modules: a Wearable part and a schematic of the area where the navigation system works by guiding the user. The wearable segment consists of a cap designed with IR receivers, an Arduino Nano processor, a headphone and an ultrasonic sensor. The schematic segment plans for the movement directions inside a room by dividing the room area into cells with a predefined matrix containing location information. For navigating the user, sixteen IR transmitters which continuously monitor the user position are placed at equal interval in the XY (8 in X-plane and 8 in Y-plane) directions of the indoor environment. A Braille keypad is used by the user where he gave the cell number for determining destination position. A path finding algorithm has been developed for determining the position of the blind person and guide him/her to his/her destination. The developed algorithm detects the position of the user by receiving continuous data from transmitter and guide the user to his/her destination by voice command. The ultrasonic sensor mounted on the cap detects the obstacles along the pathway of the visually impaired person. This proposed navigation system does not require any complex infrastructure design or the necessity of holding any extra assistive device by the user (i.e. augmented cane, smartphone, cameras). In the proposed design, prerecorded voice command will provide movement guideline to every edge of the indoor environment according to the user's destination choice. This makes this navigation system relatively simple and user friendly for those who are not much familiar with the most advanced technology and people with physical disabilities. Moreover, this proposed navigation system does not need GPS or any telecommunication networks which makes it suitable for use in rural areas where there is no telecommunication network coverage. In conclusion, the proposed system is relatively cheaper to implement in comparison to other existing navigation system, which will contribute to the betterment of the visually impaired people's lifestyle of developing and under developed countries.
Association rules-based Ontology Enrichment

Among the most powerful tools for knowledge representation, we cite the ontology which allows knowledge structuring and sharing. In order to achieve efficient domain knowledge bases content, the latter has to establish well linked and knowledge between its components. In parallel, data mining techniques are used to discover hidden structures within large databases. In particular, association rules are used to discover co-occurrence relationships from past experiences. In this context, we propose, to develop a method to enrich existing ontologies with the identification of novel semantic relations between concepts in order to have a better coverage of the domain knowledge. The enrichment process is realized through discovered association rules. Nevertheless, this technique generates a large number of rules, where some of them, may be evident or already declared in the knowledge base. To this end, the generated association rules are categorized into three main classes: known knowledge, novel knowledge and unexpected rules. We demonstrate the applicability of this method using an existing mammographic ontology and patient’s records.