nils-herrmann/clean_scirepeval huggingface dataset:
Common tongue conditions in primary care.

Although easily examined, abnormalities of the tongue can present a diagnostic and therapeutic dilemma for physicians. Recognition and diagnosis require a thorough history, including onset and duration, antecedent symptoms, and tobacco and alcohol use. Examination of tongue morphology and a careful assessment for lymphadenopathy are also important. Geographic tongue, fissured tongue, and hairy tongue are the most common tongue problems and do not require treatment. Median rhomboid glossitis is usually associated with a candidal infection and responds to topical antifungals. Atrophic glossitis is often linked to an underlying nutritional deficiency of iron, folic acid, vitamin B12, riboflavin, or niacin and resolves with correction of the underlying condition. Oral hairy leukoplakia, which can be a marker for underlying immunodeficiency, is caused by the Epstein-Barr virus and is treated with oral antivirals. Tongue growths usually require biopsy to differentiate benign lesions (e.g., granular cell tumors, fibromas, lymphoepithelial cysts) from premalignant leukoplakia or squamous cell carcinoma. Burning mouth syndrome often involves the tongue and has responded to treatment with alpha-lipoic acid, clonazepam, and cognitive behavior therapy in controlled trials. Several trials have also confirmed the effectiveness of surgical division of tongue-tie (ankyloglossia), in the context of optimizing the success of breastfeeding compared with education alone. Tongue lesions of unclear etiology may require biopsy or referral to an oral and maxillofacial surgeon, head and neck surgeon, or a dentist experienced in oral pathology.
Off-policy Learning with Eligibility Traces: A Survey

In the framework of Markov Decision Processes, we consider linear off-policy learning, that is the problem of learning a linear approximation of the value function of some fixed policy from one trajectory possibly generated by some other policy. We briefly review on-policy learning algorithms of the literature (gradient-based and least-squares-based), adopting a unified algorithmic view. Then, we highlight a systematic approach for adapting them to off-policy learning with eligibility traces. This leads to some known algorithms— off-policy LSTD(λ), LSPE(λ), TD(λ), TDC/GQ(λ)—and suggests new extensions—offpolicy FPKF(λ), BRM(λ), gBRM(λ), GTD2(λ). We describe a comprehensive algorithmic derivation of all algorithms in a recursive and memory-efficent form, discuss their known convergence properties and illustrate their relative empirical behavior on Garnet problems. Our experiments suggest that the most standard algorithms on and off-policy LSTD(λ)/LSPE(λ)—and TD(λ) if the feature space dimension is too large for a leastsquares approach—perform the best.
MobiMine: Monitoring the Stock Market from a PDA

This paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held PDA. It presents the overall system architecture and the philosophy behind the design. It explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. This module works using data mining techniques. The paper describes the data mining component of the system that employs a novel Fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. The paper also discusses a quadratic programming-based personalization module that runs on the PDAs and the multi-media based user-interfaces. It reports experimental results using an ad hoc peer-to-peer IEEE 802.11 wireless network.
Walk detection and step counting on unconstrained smartphones

Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.
 This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.
Team assembly mechanisms determine collaboration network structure and team performance.

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.
Developing an Evaluation Framework for Blockchain in the Public Sector : The Example of the German Asylum Process

The public sector presents several promising applications for blockchain technology. Global organizations and innovative ministries in countries such as Dubai, Sweden, Finland, the Netherlands, and Germany have recognized these potentials and have initiated projects to evaluate the adoption of blockchain technology. As these projects can have a farreaching impact on crucial government services and processes, they should involve a particularly thorough evaluation. In this paper, we provide insights into the development of a framework to support such an evaluation for the German asylum process. We built this framework evolutionarily together with the Federal Office for Migration and Refugees. Its final version consists of three levels and eighteen categories of evaluation criteria across the technical, functional and legal domains and allows specifying use-case specific key performance indicators or knockout criteria.
BatchQC: interactive software for evaluating sample and batch effects in genomic data

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for '-omics' data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit.



BatchQC is available through Bioconductor: http://bioconductor.org/packages/BatchQC and GitHub: https://github.com/mani2012/BatchQC CONTACT: wej@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Datasets from Fifteen Years of Automated Requirements Traceability Research: Current State, Characteristics, and Quality

Software datasets play a crucial role in advancing automated software traceability research. They can be used by researchers in different ways to develop or validate new automated approaches. The diversity and quality of the datasets within a research community have a significant impact on the accuracy, generalizability, and reproducibility of the results and consequently on the usefulness and practicality of the techniques under study. Collecting and assessing the quality of such datasets are not trivial tasks and have been reported as an obstacle by many researchers in the domain of software engineering. This paper presents a first-of-its-kind study to review and assess the datasets that have been used in software traceability research over the last fifteen years. It presents and articulates the current status of these datasets, their characteristics, and their threats to validity. Furthermore, this paper introduces a Traceability-Dataset Quality Assessment (T-DQA) framework to categorize software traceability datasets and assist researchers to select appropriate datasets for their research based on different characteristics of the datasets and the context in which those datasets will be used.
Multi-modal human-machine communication for instructing robot grasping tasks

A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.
Genetic and Stress-Induced Loss of NG2 Glia Triggers Emergence of Depressive-like Behaviors through Reduced Secretion of FGF2

NG2-expressing glia (NG2 glia) are a uniformly distributed and mitotically active pool of cells in the central nervous system (CNS). In addition to serving as progenitors of myelinating oligodendrocytes, NG2 glia might also fulfill physiological roles in CNS homeostasis, although the mechanistic nature of such roles remains unclear. Here, we report that ablation of NG2 glia in the prefrontal cortex (PFC) of the adult brain causes deficits in excitatory glutamatergic neurotransmission and astrocytic extracellular glutamate uptake and induces depressive-like behaviors in mice. We show in parallel that chronic social stress causes NG2 glia density to decrease in areas critical to Major Depressive Disorder (MDD) pathophysiology at the time of symptom emergence in stress-susceptible mice. Finally, we demonstrate that loss of NG2 glial secretion of fibroblast growth factor 2 (FGF2) suffices to induce the same behavioral deficits. Our findings outline a pathway and role for NG2 glia in CNS homeostasis and mood disorders.
In Google We Trust: Users' Decisions on Rank, Position and Relevancy in Google We Trust: Users' Decisions on Rank, Position and Relevancy in Google We Trust: Users' Decisions on Rank, Position and Relevancy

This research intends to find out whether a user’s choice of a particular abstract in Google results pages was based on the rank of that abstract, the user’s evaluation of the relevancy of that abstract, or a combination of the two. An eye tracking experiment revealed that users have substantial trust in Google’s ability to rank results by their true relevance to the query. When users selected a link to follow from Google’s result pages, their decisions were strongly biased towards links higher in rank even if the abstracts themselves were less relevant. While users reacted to artificially reduced retrieval quality by greater scrutiny, they failed to achieve the same success rate. This demonstrated trust in Google has implications for the search engines’ tremendous potential influence on culture, society, and user traffic on the Web.
Markov Logic Networks for Optical Chemical Structure Recognition

Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.
A Novel Distributed SDN-Secured Architecture for the IoT

Due to their rapid evolution, mobile devices demand for more dynamic and flexible networking services. A major challenges of future mobile networks is the increased mobile traffic. With the recent upcoming technologies of network programmability like Software-Defined Network (SDN), it may be integrated to create a new communication platform for Internet of Things (IoT). In this work, we present how to determine the effectiveness of an approach to build a new secured network architecture based on SDN and clusters. Our proposed scheme is a starting point for some experiments providing perspective over SDN deployment in a cluster environment. With this aim in mind, we suggest a routing protocol that manages routing tasks over Cluster-SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system controlled by SDN. With our testbed, we are able to manage 500 things. We can analyze every OpenFlow messages and we have discovered that with a particular flow, the things can exchange information unlike the routing principle.
Analysis and Control of Multiphase Inductively Coupled Resonant Converter for Wireless Electric Vehicle Charger Applications

In this paper, an inductively coupled multiphase resonant converter is presented for wireless electric vehicle charging applications. As an alternative to the traditional frequency and phase shift control methods, a hybrid phase-frequency control strategy is implemented to improve the system efficiency. A theoretical analysis of the proposed system is carried out considering a wide battery state-of-charging range. In order to confirm the proposed converter and control technique, a laboratory prototype wireless charger is designed using 8-in air-gap coreless transformer and rectifier. The proposed control is compared with the conventional control methods for various load conditions at the different power levels. In comparison results, the proposed hybrid control methodology demonstrates the efficiency improvements of 1.1% at the heaviest load condition and 5.7% at the lightest load condition.
The link between childhood trauma and depression: Insights from HPA axis studies in humans

Childhood trauma is a potent risk factor for developing depression in adulthood, particularly in response to additional stress. We here summarize results from a series of clinical studies suggesting that childhood trauma in humans is associated with sensitization of the neuroendocrine stress response, glucocorticoid resistance, increased central corticotropin-releasing factor (CRF) activity, immune activation, and reduced hippocampal volume, closely paralleling several of the neuroendocrine features of depression. Neuroendocrine changes secondary to early-life stress likely reflect risk to develop depression in response to stress, potentially due to failure of a connected neural circuitry implicated in emotional, neuroendocrine and autonomic control to compensate in response to challenge. However, not all of depression is related to childhood trauma and our results suggest the existence of biologically distinguishable subtypes of depression as a function of childhood trauma that are also responsive to differential treatment. Other risk factors, such as female gender and genetic dispositions, interfere with components of the stress response and further increase vulnerability for depression. Similar associations apply to a spectrum of other psychiatric and medical disorders that frequently coincide with depression and are aggravated by stress. Taken together, this line of evidence demonstrates that psychoneuroendocrine research may ultimately promote optimized clinical care and help prevent the adverse outcomes of childhood trauma.
Ontology Learning and Population: Bridging the Gap between Text and Knowledge

Learning is up to now dominated by techniques which use text as input. There are only few methods which use a different data source. The techniques which use highly structured data as input have the disadvantage that such data sources are rare. On the other side, there are enormous amounts of Web content present today. We present the XTREEM (Xhtml TREE Mining) methods which enable Ontology Learning from Web Documents. Those methods rely on the semi-structure of Web Documents. The added value of Web document markup is exploited by the XTREEM methods. We show methods for the acquisition of terms, synonyms and semantic relations. The XTREEM techniques are based on the structure of Web documents; they are domain and language independent. There is no need for NLP software nor for training. They do not rely on domain or document collection specific resources or background knowledge, such as patterns, rules or other heuristics; nor do they rely on manually assembling a document collection.
Supply Chain Optimization within Aviation MRO

Maintenance Repair and Operations (MRO) supply chain not only provides one of the best opportunities to reduce cost of operation and increase productivity but also offers complex challenges such as end to end integrated planning, increased availability of assets, inventory optimization and effective spend management. This paper gives an overview of the challenges faced in MRO supply chains and highlights some of the approaches to meet these challenges, drive efficiencies and optimize the entire supply chain. KeywordsERP; Supply chain, MRO, Aviation,
Complex Linguistic Features for Text Classification: A Comprehensive Study

Previous researches on advanced representations for document retrieval have shown that statistical state-of-the-art models are not improved by a variety of different linguistic representations. Phrases, word senses and syntactic relations derived by Natural Language Processing (NLP) techniques were observed ineffective to increase retrieval accuracy. For Text Categorization (TC) are available fewer and less definitive studies on the use of advanced document representations as it is a relatively new research area (compared to document retrieval). In this paper, advanced document representations have been investigated. Extensive experimentation on representative classifiers, Rocchio and SVM, as well as a careful analysis of the literature have been carried out to study how some NLP techniques used for indexing impact TC. Cross validation over 4 different corpora in two languages allowed us to gather an overwhelming evidence that complex nominals, proper nouns and word senses are not adequate to improve TC accuracy.
Towards Generalizable Sentence Embeddings

In this work, we evaluate different sentence encoders with emphasis on examining their embedding spaces. Specifically, we hypothesize that a “high-quality” embedding aids in generalization, promoting transfer learning as well as zero-shot and one-shot learning. To investigate this, we modify Skipthought vectors to learn a more generalizable space by exploiting a small amount of supervision. The aim is to introduce an additional notion of similarity in the embeddings, rendering the vectors informative for different tasks requiring less adaptation. Our embeddings capture human intuition on similarity favorably than competing models, while we also show positive indications of transfer from the task of natural language inference to paraphrase detection and paraphrase ranking. Further, our model’s behaviour on paraphrase detection when trained with an increasing amount of labelled data is indicative of a generalizable model. Finally, we support our hypothesis on generalizability of our embeddings through inspection of their statistics.
Designing structurally-sound ornamental curve networks

We present a computational tool for designing ornamental curve networks---structurally-sound physical surfaces with user-controlled aesthetics. In contrast to approaches that leverage texture synthesis for creating decorative surface patterns, our method relies on user-defined spline curves as central design primitives. More specifically, we build on the physically-inspired metaphor of an embedded elastic curve that can move on a smooth surface, deform, and connect with other curves. We formalize this idea as a globally coupled energy-minimization problem, discretized with piece-wise linear curves that are optimized in the parametric space of a smooth surface. Building on this technical core, we propose a set of interactive design and editing tools that we demonstrate on manually-created layouts and semi-automated deformable packings. In order to prevent excessive compliance, we furthermore propose a structural analysis tool that uses eigenanalysis to identify potentially large deformations between geodesically-close curves and guide the user in strengthening the corresponding regions. We used our approach to create a variety of designs in simulation, validated with a set of 3D-printed physical prototypes.
Plan-Based Dialogue Management in a Physics Tutor

This paper describes an application of APE (the Atlas Planning Engine), an integrated planning and execution system at the heart of the Atlas dialogue management system. APE controls a mixedinitiative dialogue between a human user and a host system, where turns in the ‘conversation’ may include graphical actions and/or written text. APE has full unification and can handle arbitrarily nested discourse constructs, making it more powerful than dialogue managers based on finitestate machines. We illustrate this work by describing Atlas-Andes, an intelligent tutoring system built using APE with the Andes physics tutor as the host.