nils-herrmann/clean_scirepeval huggingface dataset:
Dwarfism with gloomy face: a new syndrome with features of 3-M syndrome.

Nine children with primordial dwarfism are described and a new syndrome is delineated. The significant features of this syndrome include facial dysmorphism with gloomy face and very short stature, but no radiological abnormality or hormone deficiency. Mental development is normal. The mode of inheritance seems to be autosomal recessive because of consanguinity in three of the four sibships. Some overlap with the 3-M syndrome is discussed but the autonomy of the gloomy face syndrome seems to be real.
A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis

Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. An essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ 1 penality. An application to the analysis of gene expression data of patients with bladder cancer is finally proposed.
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination

This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.
Dynamic adaptive streaming over HTTP -: standards and design principles

In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and examples are provided.
Simulating Cellular Communications in Vehicular Networks: Making SimuLTE Interoperable with Veins

The evolution of cellular technologies toward 5G progressively enables efficient and ubiquitous communications in an increasing number of fields. Among these, vehicular networks are being considered as one of the most promising and challenging applications, requiring support for communications in high-speed mobility and delay-constrained information exchange in proximity. In this context, simulation frameworks under the OMNeT++ umbrella are already available: SimuLTE and Veins for cellular and vehicular systems, respectively. In this paper, we describe the modifications that make SimuLTE interoperable with Veins and INET, which leverage the OMNeT++ paradigm, and allow us to achieve our goal without any modification to either of the latter two. We discuss the limitations of the previous solution, namely VeinsLTE, which integrates all three in a single framework, thus preventing independent evolution and upgrades of each building block.  SimuLTE, Veins, vehicular networks, cellular
A wearable system to assist walking of Parkinson s disease patients.

About 50% of the patients with advanced Parkinson's disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest.



The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides assistance based on pre-determined specifications.



We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient.



Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation.



With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients' and physiotherapists' perspectives on wearability and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.
A Service-Oriented Architecture for Virtualizing Robots in Robot-as-a-Service Clouds

Exposing software and hardware computing resources as services through a cloud is increasingly emerging in the recent years. This comes as a result of extending the service-oriented architecture (SOA) paradigm to virtualize computing resources. In this paper, we extend the paradigm of the SOA approach to virtualize robotic hardware and software resources to expose them as services through the Web. This allows non-technical users to access, interact and manipulate robots simply through a Web browser. The proposed RoboWeb system is based on a SOAP-based Web service middleware that binds robots computing resources as services and publish them to the end-users. We consider robots that operates with the Robotic Operating System (ROS), as it provides hardware abstraction that makes easier applications development. We describe the implementation of RoboWeb and demonstrate how researchers can use it to interact remotely with the robots. We believe that this work consistently contributes to enabling remote robotic labs using the cloud paradigm.
A Reconfigurable Smart Sensor Interface for Industrial WSN in IoT Environment

A sensor interface device is essential for sensor data collection of industrial wireless sensor networks (WSN) in IoT environments. However, the current connect number, sampling rate, and signal types of sensors are generally restricted by the device. Meanwhile, in the Internet of Things (IoT) environment, each sensor connected to the device is required to write complicated and cumbersome data collection program code. In this paper, to solve these problems, a new method is proposed to design a reconfigurable smart sensor interface for industrial WSN in IoT environment, in which complex programmable logic device (CPLD) is adopted as the core controller. Thus, it can read data in parallel and in real time with high speed on multiple different sensor data. The standard of IEEE1451.2 intelligent sensor interface specification is adopted for this design. It comprehensively stipulates the smart sensor hardware and software design framework and relevant interface protocol to realize the intelligent acquisition for common sensors. A new solution is provided for the traditional sensor data acquisitions. The device is combined with the newest CPLD programmable technology and the standard of IEEE1451.2 intelligent sensor specification. Performance of the proposed system is verified and good effects are achieved in practical application of IoT to water environment monitoring.
A Deep Network with Visual Text Composition Behavior

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.
High-Accuracy Tracking Control of Hydraulic Rotary Actuators With Modeling Uncertainties

Structured and unstructured uncertainties are the main obstacles in the development of advanced controllers for high-accuracy tracking control of hydraulic servo systems. For the structured uncertainties, nonlinear adaptive control can be employed to achieve asymptotic tracking performance. But modeling errors, such as nonlinear frictions, always exist in physical hydraulic systems and degrade the tracking accuracy. In this paper, a robust integral of the sign of the error controller and an adaptive controller are synthesized via backstepping method for motion control of a hydraulic rotary actuator. In addition, an experimental internal leakage model of the actuator is built for precise model compensation. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties), but also the unstructured uncertainties (i.e., nonlinear frictions). Furthermore, the controller theoretically guarantees asymptotic tracking performance in the presence of various uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive comparative experimental results are obtained to verify the high-accuracy tracking performance of the proposed control strategy.
Heat, vibration, dust, salt spray, weather - taking wireless positioning to the extreme

There are lots of local positioning systems available for indoor applications, partly even for outdoor use. Many of them rely on a rather clean environment which does not interfere in their measurements too much. Industrial environments, in- or outdoors, often do not meet these requirements. The positioning applications here are surrounded by dirt, dust and vibrations. The number of local positioning systems being able to perform properly here at all times is limited to such not dependent on optical or sonic measurements. Local secondary radar systems proved to be a reliable source of distance and position measurements, even in harsh environments. This paper examines a few use cases and depicts the performance of local positioning radar systems there.
A NEW PERSPECTIVE ON PORT SUPPLY CHAIN MANAGEMENT ACCORDING TO THE SERVICE DOMINANT LOGIC

The increasing role of ports in the whole logistics system invites a more detailed examination of port supply chain management (Christopher, 1992; Meersman and Van de Voorde, 1996; Coyle et al., 2003; De Martino and Morvillo, 2009). The purpose of this paper is to provide a new perspective on port supply chain management according to the Service Dominant logic (S-D logic) perspective (Lusch et al., 2010; Lusch, 2011). Thus, the concepts of service, value co-creation, value propositions, operant resources, networks, service ecosystems and information technology are explored through a case study approach.  A systematic analysis of the current supply chain (SCM) and port-related literature is carried out through the lens of the value co-creation process (actors, resources, actions for practice/service development and value). Drawing from the Service Dominant Logic perspective (Vargo and Lusch, 2008; Vargo, 2008; Gummesson et al., 2010) and the Service Science, this paper offers a more complete understanding of SCM in an innovative way which potentially lead to co-create value. The study takes a qualitative approach adopting a case study (Yin, 2003) to understand and explain the role of S-D Logic in the conceptualization of port supply chain management.  The adoption of S-D logic perspective to port supply chain management enables identifying a system of value co-creation and competitiveness development, individualized according to the key stakeholders involved and the purpose it is created for. As this paper demonstrates, the port supply chain is re-conceptualized as a network of service systems each representing distinct (mostly operant) resource according to the service-dominant logic perspective (Lusch and Vargo, 2006).  Hence, the main practical implication of this paper is the definition of a value co-creation process as a suitable perspective to develop ports’ competitiveness. The use of the model of S-D logic enables to design a comprehensive framework which allows planning the creation of competitive supply chains.  The study represents a first attempt to analyse relations between port supply chain management according to the S-D logic perspective. This enables a more rigorous and comprehensive approach to understand ports’ competitiveness development. TYPE OF PAPER – Case study paper/Research paper.
Clustering in the Presence of Background Noise

We address the problem of noise management in clustering algorithms. Namely, issues that arise when on top of some cluster structure the data also contains an unstructured set of points. We consider how clustering algorithms can be “robustified” so that they recover the cluster structure in spite of the unstructured part of the input. We introduce some quantitative measures of such robustness that take into account the strength of the embedded cluster structure as well as the mildness of the noise subset. We propose a simple and efficient method to turn any centroid-based clustering algorithm into a noiserobust one, and prove robustness guarantees for our method with respect to these measures. We also prove that more straightforward ways of “robustifying” clustering algorithms fail to achieve similar guarantees.
Comparator Networks

The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair – this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage highquality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.
Natural history and evolutionary principles of gene duplication in fungi

Gene duplication and loss is a powerful source of functional innovation. However, the general principles that govern this process are still largely unknown. With the growing number of sequenced genomes, it is now possible to examine these events in a comprehensive and unbiased manner. Here, we develop a procedure that resolves the evolutionary history of all genes in a large group of species. We apply our procedure to seventeen fungal genomes to create a genome-wide catalogue of gene trees that determine precise orthology and paralogy relations across these species. We show that gene duplication and loss is highly constrained by the functional properties and interacting partners of genes. In particular, stress-related genes exhibit many duplications and losses, whereas growth-related genes show selection against such changes. Whole-genome duplication circumvents this constraint and relaxes the dichotomy, resulting in an expanded functional scope of gene duplication. By characterizing the functional fate of duplicate genes we show that duplicated genes rarely diverge with respect to biochemical function, but typically diverge with respect to regulatory control. Surprisingly, paralogous modules of genes rarely arise, even after whole-genome duplication. Rather, gene duplication may drive the modularization of functional networks through specialization, thereby disentangling cellular systems.
Differentiation of classical music requires little learning but rhythm

Detecting distinctions between the styles of classical music (e.g. Baroque and Romantic) is often viewed as the privilege of musicians. However, this elite perspective underestimates the abilities of non-musicians. We report that Western musicians and non-musicians, and non-Westerners (i.e. Chinese participants) rated pairs of excerpts presented auditorily as more similar as their compositional styles were closer in history. Moreover, the styles were considered by all participants as more different when presented in historical order, the older style preceding the more recent style (e.g. Baroque followed by Romantic), than the reverse (e.g. Romantic followed by Baroque). This historical distance effect appears related to rhythm (or temporal variability).
Children referred for possible sexual abuse: medical findings in 2384 children.

The goal of this study was to compare rates of positive medical findings in a 5-year prospective study of 2384 children, referred for evaluation of possible sexual abuse, with two decades of research. The prospective study summarizes demographic information, clinical history, relationship of perpetrators, nature of abuse, and clinical findings. The study reports on the results by patterns of referral and the medical examination.



There were 2384 children evaluated in a tertiary referral center between 1985 and 1990 for possible sexual abuse. Children were referred after they disclosed sexual abuse, because of behavioral changes or exposure to an abusive environment, and because of possible medical conditions. A total of 96.3% of all children referred for evaluation had a normal medical examination; 95.6% of children reporting abuse were normal, 99.8% who were referred for behavioral changes or exposure to abuse were also normal. Of the 182 children referred for evaluation of medical conditions, 92% were found to be normal at the time of examination by the Child Advocacy Center. The remaining 15/182 (8%) that were found to be abnormal were diagnosed with sexually transmitted diseases, acute or healed genital injuries, and were 17% (15/88) of the total cases found to have medical findings diagnostic of abuse. Interviews of the children indicated that 68% of the girls and 70% of the boys reported severe abuse, defined as penetration of vagina or anus. Penetration was associated with a higher percentage of abnormal findings in girls (6%) compared to 1% of the boys. The relationship of the abuser impacted on the severity of the abuse.



Research indicates that medical, social, and legal professionals have relied too heavily on the medical examination in diagnosing child sexual abuse. History from the child remains the single most important diagnostic feature in coming to the conclusion that a child has been sexually abused. Only 4% of all children referred for medical evaluation of sexual abuse have abnormal examinations at the time of evaluation. Even with a history of severe abuse such as vaginal or anal penetration, the rate of abnormal medical findings is only 5.5%. Biological parents are less likely to engage in severe abuse than parental substitutes, extended family members, or strangers.
List-only Entity Linking

Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simple and sparse as lists of names of the same type (e.g., lists of products). We call it as List-only Entity Linking problem. Fortunately, some mentions may have more cues for linking, which can be used as seed mentions to bridge other mentions and the uninformative entities. In this work, we select the most linkable mentions as seed mentions and disambiguate other mentions by comparing them with the seed mentions rather than directly with the entities. Our experiments on linking mentions to seven automatically mined lists show promising results and demonstrate the effectiveness of our
AIMED- A Personalized TV Recommendation System

Previous personalized DTV recommendation systems focus only on viewers’ historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information—AIMED. The AIMED data is fed into a neural network model to predict TV viewers’ program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.
Interactive Storytelling with Literary Feelings

In this paper, we describe the integration of Natural Language Processing (NLP) within an emotional planner to support Interactive Storytelling. Our emotional planner is based on a standard HSP planner, whose originality is drawn from altering the agents’ beliefs and emotional states. Each character is driven by its own planner, while characters are able to operate on their reciprocal feelings thus affecting each other. Our baseline story is constituted by a classic XIX century French novel from Gustave Flaubert in which characters feelings play a dominant role. This approach benefits from the fact that Flaubert has described a specific ontology for his characters feelings. The objective of NLP should be to uncover from natural language utterances the same kind of affective elements, which requires an integration between NLP and the planning component at the level of semantic content. This research is illustrated with examples from a first fully integrated prototype comprising NLP, emotional planning and real-time 3D animation.