nils-herrmann/clean_scirepeval huggingface dataset:
An Auditory Scene Analysis Approach to Monaural Speech Segregation

A human listener has the remarkable ability to segregate an acoustic mixture and attend to a target sound. This perceptual process is called auditory scene analysis (ASA). Moreover, the listener can accomplish much of auditory scene analysis with only one ear. Research in ASA has inspired many studies in computational auditory scene analysis (CASA) for sound segregation. In this chapter we introduce a CASA approach to monaural speech segregation. After a brief overview of CASA, we present in detail a CASA system that segregates both voiced and unvoiced speech. Our description covers the major stages of CASA, including feature extraction, auditory segmentation, and grouping.
Efficient Document Similarity Detection Using Weighted Phrase Indexing

Document similarity techniques mostly rely on single term analysis of the document in the data set. To improve the efficiency and effectiveness of the process of document similarity detection, more informative feature terms have been developed and presented by many researchers. In this paper, we present phrase weight index, which indexes documents in the data set based on important phrases. Phrasal indexing aims to reduce the ambiguity inherent to the words considered in isolation, and then improve the effectiveness in document similarity computation. The method we are presenting here in this paper inherit the term tf-idf weighting scheme in computing important phrases in the collection. It computes the weight of phrases in the document collection and according to a given threshold; the important phrases are identified and are indexed. The data dimensionality which hinders the performance of document similarity for different methods is solved by an offline index creation of important phrases for every document. The evaluation experiments indicate that the presented method is very effective on document similarity detection and its quality surpasses the traditional phrase-based approach in which the reduction of dimensionality is ignored and other methods which use single-word tf-idf.
Proteomic insights into extracellular vesicle biology - defining exosomes and shed microvesicles.

Extracellular vesicles (EVs) are critical mediators of intercellular communication, capable of regulating the transcriptional landscape of target cells through horizontal transmission of biological information, such as proteins, lipids, and RNA species. This capability highlights their potential as novel targets for disease intervention.  This review focuses on the emerging importance of discovery proteomics (high-throughput, unbiased quantitative protein identification) and targeted proteomics (hypothesis-driven quantitative protein subset analysis) mass spectrometry (MS)-based strategies in EV biology, especially exosomes and shed microvesicles.  Recent advances in MS hardware, workflows, and informatics provide comprehensive, quantitative protein profiling of EVs and EV-treated target cells. This information is seminal to understanding the role of EV subtypes in cellular crosstalk, especially when integrated with other 'omics disciplines, such as RNA analysis (e.g., mRNA, ncRNA). Moreover, high-throughput MS-based proteomics promises to provide new avenues in identifying novel markers for detection, monitoring, and therapeutic intervention of disease.
Sentiment analysis for Arabic language: A brief survey of approaches and techniques

With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers.
Combining DNA damaging therapeutics with immunotherapy: more haste, less speed

The idea that chemotherapy can be used in combination with immunotherapy may seem somewhat counterproductive, as it can theoretically eliminate the immune cells needed for antitumour immunity. However, much preclinical work has now demonstrated that in addition to direct cytotoxic effects on cancer cells, a proportion of DNA damaging agents may actually promote immunogenic cell death, alter the inflammatory milieu of the tumour microenvironment and/or stimulate neoantigen production, thereby activating an antitumour immune response. Some notable combinations have now moved forward into the clinic, showing promise in phase I–III trials, whereas others have proven toxic, and challenging to deliver. In this review, we discuss the emerging data of how DNA damaging agents can enhance the immunogenic properties of malignant cells, focussing especially on immunogenic cell death, and the expansion of neoantigen repertoires. We discuss how best to strategically combine DNA damaging therapeutics with immunotherapy, and the challenges of successfully delivering these combination regimens to patients. With an overwhelming number of chemotherapy/immunotherapy combination trials in process, clear hypothesis-driven trials are needed to refine the choice of combinations, and determine the timing and sequencing of agents in order to stimulate antitumour immunological memory and improve maintained durable response rates, with minimal toxicity.
Different strategies for cancer treatment: Mathematical modelling

We formulate and analyze a mathematical model describin g tumor-immune dynamics under the influence of immunotherapy and chemotherapy and their combinations as well as vaccine treatments. For low-immunogenic tumor, neither immuno therapy nor chemotherapy is found to cause tumor regression to a small size, which would be b elow the clinically detectable threshold. The effect of vaccine therapy is considered as a paramet ric p rturbation of the model. Numerical simulations show that the efficiency of vaccine thera py depends on both the tumor size and the condition of immune system as well as on the response f the organism to vaccination. In particular, we found that vaccine therapy becomes more ef fective when used without time delay from a prescribed date of vaccination after surgery an d is ineffective without preliminary treatment. Our study of successive chemo/immuno, immuno/c hemo and concurrent immuno/chemo therapy shows that the chemo/immunother apy sequence is more effective while concurrent chemo/immunotherapy is more sparing.
FlashBlox: Achieving Both Performance Isolation and Uniform Lifetime for Virtualized SSDs

A longstanding goal of SSD virtualization has been to provide performance isolation between multiple tenants sharing the device. Virtualizing SSDs, however, has traditionally been a challenge because of the fundamental tussle between resource isolation and the lifetime of the device – existing SSDs aim to uniformly age all the regions of flash and this hurts isolation. We propose utilizing flash parallelism to improve isolation between virtual SSDs by running them on dedicated channels and dies. Furthermore, we offer a complete solution by also managing the wear. We propose allowing the wear of different channels and dies to diverge at fine time granularities in favor of isolation and adjusting that imbalance at a coarse time granularity in a principled manner. Our experiments show that the new SSD wears uniformly while the 99th percentile latencies of storage operations in a variety of multi-tenant settings are reduced by up to 3.1x compared to software isolated virtual SSDs.
DeltaPhish: Detecting Phishing Webpages in Compromised Websites

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system. ∗Preprint version of the work accepted for publication at ESORICS 2017.
Habit Formation in Consumption and Its Implications for Monetary Policy Models

This paper explores a monetary policy model with habit formation for consumers, in which consumers’ utility depends in part on current consumption relative to past consumption. The empirical tests developed in the paper show that one can reject the hypothesis of no habit formation with tremendous confidence, largely because the habit formation model captures the gradual hump-shaped response of real spending to various shocks. The paper then embeds the habit consumption specification in a monetary policy model and finds that the responses of both spending and inflation to monetary policy actions are significantly improved by this modification.  This implies that the model be closely tied to the underlying objectives of consumers and firms, hence the emphasis on optimization-based models. A critical test for whether a model reflects underlying objectives is its ability to accurately reflect the dominant dynamic interactions in the data. A number of recent papers (see, for example, Robert G. King and Alexander L. Wolman (1996), Bennett T. McCallum and Edward Nelson (1999a, 1999b); Julio R. Rotemberg and Michael Woodford (1997)) have developed models that incorporate explicit expectations, optimizing behavior, and frictions that allow monetary policy to have real effects. This paper continues in that line of research by documenting the empirical importance of a key feature of aggregate data: the “hump-shaped,” gradual response of spending and inflation to shocks. It then develops a monetary policy model that can capture this feature, as well as all of the features (e.g. the real effects of monetary policy, the persistence of inflation and output) embodied in earlier models. The key to the model’s success on the spending side is the inclusion of habit formation in the consumer’s utility function.
“How do ethnic minority patients experience the intercultural care encounter in hospitals? a systematic review of qualitative research”

In our globalizing world, caregivers are increasingly being confronted with the challenges of providing intercultural healthcare, trying to find a dignified answer to the vulnerable situation of ethnic minority patients. Until now, international literature lacks insight in the intercultural care process as experienced by the ethnic minority patients themselves. We aim to fill this gap by analysing qualitative literature on the intercultural care encounter in the hospital setting, as experienced by ethnic minority patients.



A systematic search was conducted for papers published between 2000 and 2015. Analysis and synthesis were guided by the critical interpretive synthesis approach.



Fifty one articles were included. Four dimensions emerged, describing the intercultural care encounter as (1) a meeting of two different cultural contexts of care, (2) in a dynamic and circular process of (3) balancing between the two cultural contexts, which is (4) influenced by mediators as concepts of being human, communication, family members and the hospital's organizational culture.



This review provides in-depth insight in the dynamic process of establishing intercultural care relationships in the hospital. We call for a broader perspective towards cultural sensitive care in which patients are cared for in a holistic and dignity-enhancing way.
Downloading Hidden Web Content

An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the Hidden Web or the Deep Web. Since there are no static links to the Hidden Web pages, search engines cannot discover and index such pages and thus do not return them in the results. However, according to recent studies, the content provided by many Hidden Web sites is often of very high quality and can be extremely valuable to many users. In this paper, we study how we can build an effective Hidden Web crawler that can autonomously discover and download pages from the Hidden Web. Since the only “entry point” to a Hidden Web site is a query interface, the main challenge that a Hidden Web crawler has to face is how to automatically generate meaningful queries to issue to the site. Here, we provide a theoretical framework to investigate the query generation problem for the Hidden Web and we propose effective policies for generating queries automatically. Our policies proceed iteratively, issuing a different query in every iteration. We experimentally evaluate the effectiveness of these policies on 4 real Hidden Web sites and our results are very promising. For instance, in one experiment, one of our policies downloaded more than 90% of a Hidden Web site (that contains 14 million documents) after issuing fewer than 100 queries.
Toward Automatic Time-Series Forecasting Using Neural Networks

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.
Mobile application development process: A practical experience

Nowadays there is an increasing interest in mobile application development. However, developers often disregard, or at least significantly adapt, existing software development processes to suit their purpose, given the existing specific constraints. Such adjustments can introduce variations and new trends in existing processes that in many occasions are not shared with the scientific community since there is no official documentation, thus justifying further research. In this paper, we present a study and characterization of current mobile application development processes based on a practical experience. We consider a set of real case studies to investigate the current development processes for mobile applications used by software development companies, as well as by independent developers. The result of the present study is the identification of mobile software development processes, namely agile approaches, and also of shortcomings in current methodologies applied in industry and academy, namely the lack of informed and experienced resources to develop mobile apps.
Learning from Demonstrations: Is It Worth Estimating a Reward Function?

This poster provides a comparative study between Inverse Reinforcement Learning (IRL) and Apprenticeship Learning (AL). IRL and AL are two frameworks, using Markov Decision Processes (MDP), which are used for the imitation learning problem where an agent tries to learn from demonstrations of an expert. In the AL framework, the agent tries to learn the expert policy whereas in the IRL framework, the agent tries to learn a reward which can explain the behavior of the expert. This reward is then optimized to imitate the expert. One can wonder if it is worth estimating such a reward, or if estimating a policy is sufficient. This quite natural question has not really been addressed in the literature right now. We provide partial answers, both from a theoretical and empirical point of view.
Sinhala Speech Recognition for Interactive Voice Response Systems Accessed Through Mobile Phones

This paper presents the development of a Sinhala Speech Recognition System to be deployed in an Interactive Voice Response (IVR) system of a telecommunication service provider. The main objectives are to recognize Sinhala digits and names of Sinhala songs to be set up as ringback tones. Sinhala being a phonetic language, its features are studied to develop a list of 47 phonemes. A continuous speech recognition system is developed based on Hidden Markov Model (HMM). The acoustic model is trained using the voice through mobile phone. The outcome is a speaker independent speech recognition system which is capable of recognizing 10 digits and 50 Sinhala songs. A word error rate (WER) of 11.2% using a speech corpus of 0.862 hours and a sentence error rate (SER) of 5.7% using a speech corpus of 1.388 hours are achieved for digits and songs respectively.
A Click Chemistry‐Based Proteomic Approach Reveals that 1,2,4‐Trioxolane and Artemisinin Antimalarials Share a Common Protein Alkylation Profile

In spite of the recent increase in endoperoxide antimalarials under development, it remains unclear if all these chemotypes share a common mechanism of action. This is important since it will influence cross-resistance risks between the different classes. Here we investigate this proposition using novel clickable 1,2,4-trioxolane activity based protein-profiling probes (ABPPs). ABPPs with potent antimalarial activity were able to alkylate protein target(s) within the asexual erythrocytic stage of Plasmodium falciparum (3D7). Importantly, comparison of the alkylation fingerprint with that generated from an artemisinin ABPP equivalent confirms a highly conserved alkylation profile, with both endoperoxide classes targeting proteins in the glycolytic, hemoglobin degradation, antioxidant defence, protein synthesis and protein stress pathways, essential biological processes for plasmodial survival. The alkylation signatures of the two chemotypes show significant overlap (ca. 90 %) both qualitatively and semi-quantitatively, suggesting a common mechanism of action that raises concerns about potential cross-resistance liabilities.
APE: A Data-Driven, Behavioral Model-Based Anti-Poaching Engine

We consider the problem of protecting a set of animals such as rhinos and elephants in a game park using D drones and R ranger patrols (on the ground) with R ≥ D. Using two years of data about animal movements in a game park, we propose the probabilistic spatio-temporal graph (pSTG) model of animal movement behaviors and show how we can learn it from the movement data. Using 17 months of data about poacher behavior, we also learn the probability that a region in the game park will be targeted by poachers. We formalize the anti-poaching problem as that of finding a coordinated route for the drones and ranger patrols that maximize the expected number of animals that are protected, given these two models as input and show that it is NP-complete. Because of this, we fine tune classical local search and genetic algorithms to the case of anti-poaching by taking specific advantage of the nature of the anti-poaching problem and its objective function. We develop a measure of the quality of an algorithm to route the drones and ranger patrols called “improvement ratio.” We develop a dynamic programming based APE_Coord_Route algorithm and show that it performs very well in practice, achieving an improvement ratio over 90%.
New and Anticipated Therapies for Severe Asthma.

Asthma is frequently undertreated, resulting in a relatively high prevalence of patients with uncontrolled disease, characterized by the presence of symptoms and risk of adverse outcomes. Patients with uncontrolled asthma have a higher risk of morbidity and mortality, underscoring the importance of identifying uncontrolled disease and modifying management plans to improve control. Several assessment tools exist to evaluate control with various cutoff points and measures, but these tools do not reliably correlate with physiological measures and should be considered a supplement to physiological tests. When attempting to improve control in patients, nonpharmacological interventions should always be attempted before changing or adding pharmacotherapies. Among patients with severe, uncontrolled asthma, individualized treatment based on asthma phenotype and eosinophil presence should be considered. The efficacy of the anti-IgE antibody omalizumab has been well established for patients with allergic asthma, and novel biologic agents targeting IL-5, IL-13, IL-4, and other allergic pathways have been investigated for patients with allergic or eosinophilic asthma. Fevipiprant (a CRTH2 [chemokine receptor homologous molecule expressed on Th2 cells] antagonist) and imatinib (a tyrosine kinase inhibition) are examples of nonbiologic therapies that may be useful for patients with severe, uncontrolled asthma. Incorporation of new and emerging treatment into therapeutic strategies for patients with severe asthma may improve outcomes for this patient population.
Covariance based Fish Tracking in Real-life Underwater Environment

In this paper we present a covariance based tracking algorithm for intelligent video analysis to assist marine biologists in understanding the complex marine ecosystem in the Ken-Ding sub-tropical coral reef in Taiwan by processing underwater real-time videos recorded in open ocean. One of the most important aspects of marine biology research is the investigation of fish trajectories to identify events of interest such as fish preying, mating, schooling, etc. This task, of course, requires a reliable tracking algorithm able to deal with 1) the difficulties of following fish that have multiple degrees of freedom and 2) the possible varying conditions of the underwater environment. To accommodate these needs, we have developed a tracking algorithm that exploits covariance representation to describe the object’s appearance and statistical information and also to join different types of features such as location, color intensities, derivatives, etc. The accuracy of the algorithm was evaluated by using hand-labeled ground truth data on 30000 frames belonging to ten different videos, achieving an average performance of about 94%, estimated using multiple ratios that provide indication on how good is a tracking algorithm both globally (e.g. counting objects in a fixed range of time) and locally (e.g. in distinguish occlusions among objects).