nils-herrmann/clean_scirepeval huggingface dataset:
Mining e-Learning Domain Concept Map from Academic Articles

Recent research has demonstrated the important of ontology and its applications. For example, while designing adaptive learning materials, designers need to refer to the ontology of a subject domain. Moreover, ontology can show the whole picture and core knowledge about a subject domain. Research from literature also suggested that graphical representation of ontology can reduce the problems of information overload and learning disorientation for learners. However, ontology constructions used to rely on domain experts in the past; it is a time consuming and high cost task. Ontology creation for emerging new domains like e-learning is even more challenging. The aim of this paper is to construct e-learning domain concept maps, an alternative form of ontology, from academic articles. We adopt some relevant journal articles and conferences papers in e-learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to e-leaning field, to study related issues, for teachers to design adaptive courses, and for learners to understand the whole picture of e-learning domain knowledge
Automated Detection and Classification of Glaucoma from Eye Fundus Images : A Survey

the identification of eye diseases in retinal image is the subject of several researches in the field of medical image processing. this research proposed a computerized aid diagnosis system to help specialists by displaying useful information such as the location of abnormalities in fundus images This paper is a survey of techniques for Automatic detection of retina image now a days it is plying a vital role in screening tool. This procedure helps to detect various kind of risks and diseases of eyes. Identification of Glaucoma using fundus images involves the measurement of the size, shape of the Optic cup and Neuroretinal rim One of the most common diseases which cause blindness is glaucoma. Early detection of this disease is essential to prevent the permanent blindness. Screenings of glaucoma based on digital images of the retina have been performed in the past few years. Several techniques are there to detect the anomaly of retina due to glaucoma. The key image processing techniques are image registration, image fusion, image segmentation, feature extraction, image enhancement, morphology, pattern matching, image classification, analysis and statistical measurements. The main idea behind this paper is to illustrate a system which is mainly based on image processing and classification techniques for automatic detection of glaucoma by comparing and measuring different parameters of fundus images of glaucoma patients and normal patients. Glaucoma, Cup to disk ratio, Neuroretinalrim Fundus Image, ISNT (inferior, superior, nasal, temporal quadrants), ANN, K-Means Clustering, Thresholding, CDR, ISNT, SVM, Bayesian.
Age differences in the enjoyment of incongruity-resolution and nonsense humor during adulthood.

This study tested a model of the development of incongruity-resolution and nonsense humor during adulthood. Subjects were 4,292 14- to 66-year-old Germans. Twenty jokes and cartoons representing structure-based humor categories of incongruity resolution and nonsense were rated for funniness and aversiveness. Humor structure preferences were also assessed with a direct comparison task. The results generally confirmed the hypotheses. Incongruity-resolution humor increased in funniness and nonsense humor decreased in funniness among progressively older subjects after the late teens. Aversiveness of both forms of humor generally decreased over the ages sampled. Age differences in humor appreciation were strongly correlated with age differences in conservatism. An especially strong parallel was found between age differences in appreciation of incongruity-resolution humor and age differences in conservatism.
Engineering of minds: the synchronicity between artificial intelligence and the management of knowledge in collaborative networked organizations

The present work is focused on the systematization of a process of knowledge acquisition for its use in intelligent management systems. The result was the construction of a computational structure for use inside the institutions (Intranet) as well as out of them (Internet). This structure was called Knowledge Engineering Suite an ontological engineering tool to support the construction of antologies in a collaborative setting and was based on observations made at Semantic Web, UNL (Universal Networking Language) and WorldNet. We use a knowledge representation technique called DCKR to organize knowledge and psychoanalytic studies, focused mainly on Lacan and his language theory to develop a methodology called Engineering of Minds to improve the synchronicity between knowledge engineers and specialist in a particular knowledge domain.
A Novel Error Detection and Correction Technique for RNS Based FIR Filters

In this paper a novel technique for detecting and correcting errors in the RNS representation is presented. It is based on the selection of a particular subset of the legitimate range of the RNS representation characterized by the property that each element is a multiple of a suitable integer number m. This method allows to detect and correct any single error in the modular processors of the RNS based computational unit. This subset of the legitimate range can be used to perform addition and multiplication in the RNS domain allowing the design of complex arithmetic structures like FIR filters. In the paper, the architecture of a FIR filter with error detection and correction capabilities is presented showing the advantages with respect to filters in which the error detection and correction are obtained by using the traditional RNS technique.
NF-κB: linking inflammation and immunity to cancer development and progression

There has been much effort recently to probe the long-recognized relationship between the pathological processes of infection, inflammation and cancer. For example, epidemiological studies have shown that ∼15% of human deaths from cancer are associated with chronic viral or bacterial infections. This Review focuses on the molecular mechanisms that connect infection, inflammation and cancer, and it puts forward the hypothesis that activation of nuclear factor-κB (NF-κB) by the classical, IKK-β (inhibitor-of-NF-κB kinase-β)-dependent pathway is a crucial mediator of inflammation-induced tumour growth and progression, as well as an important modulator of tumour surveillance and rejection.
Statistics on Adoption in the United States.

Adoption is a topic of crucial importance both to those directly involved and to society. Yet, at this writing, the federal government collects no comprehensive national statistics on adoption. The purpose of this article is to address what we do know, what we do not know, and what we need to know about the statistics on adoption. The article provides an overview of adoption and describes data available regarding adoption arrangements and the characteristics of parents who relinquish children, of children who are adopted or in substitute care, and of adults who seek to adopt. Recommendations for future data collection are offered, including the establishment of a national data collection system for adoption statistics. doption is an issue of vital importance for all persons involved in Kathy S. Stolley, M.A., is an instructor in the A the adoption triangle: the child, the adoptive parents, and the Department of Sociology birthparents. According to national estimates, one million children in the United States live with adoptive parents, and from 2% to and Criminal Justice at Old Dominion Univer4% of American families include an adopted child. sity, Norfolk, VA. Adoption is most important for infertile couples seeking children and children in need of parents. Yet adoption issues also have consequences for the larger society in such areas as public welfare and mental health. Additionally, adoption can be framed as a public health issue, particularly in light of increasing numbers of pediatric AIDS cases and concerns regarding drug-exposed infants, and “boarder” babies available for adoption. Adoption is also often supported as an alternative to abortion.  Despite the importance of adoption to many groups, it remains an underresearched area and a topic on which the data are incomplete. Indeed, at this writing, no comprehensive national data on adoption are collected by the federal government. Through the Children’s Bureau and later the National Center for Social Statistics (NCSS), the federal government collected adoption data periodically between 1944 and 1957, then annually from 1957 to 1975. States voluntarily reported summary statistics on all types of finalized adoptions using data primarily drawn from court records. The number of states and territories participating in this reporting system varied from year to year, ranging from a low of 22 in 1944 to a high of 52 during the early 1960s.4 This data collection effort ended in 1975 with the dissolution of the NCSS. The Future of Children ADOPTION Vol. 3 • No. 1 Spring 1993
The Lactobacillus plantarum ftsH gene is a novel member of the CtsR stress response regulon.

FtsH proteins have dual chaperone-protease activities and are involved in protein quality control under stress conditions. Although the functional role of FtsH proteins has been clearly established, the regulatory mechanisms controlling ftsH expression in gram-positive bacteria remain largely unknown. Here we show that ftsH of Lactobacillus plantarum WCFS1 is transiently induced at the transcriptional level upon a temperature upshift. In addition, disruption of ftsH negatively affected the growth of L. plantarum at high temperatures. Sequence analysis and mapping of the ftsH transcriptional start site revealed a potential operator sequence for the CtsR repressor, partially overlapping the -35 sequence of the ftsH promoter. In order to verify whether CtsR is able to recognize and bind the ftsH promoter, CtsR proteins of Bacillus subtilis and L. plantarum were overproduced, purified, and used in DNA binding assays. CtsR from both species bound specifically to the ftsH promoter, generating a single protein-DNA complex, suggesting that CtsR may control the expression of L. plantarum ftsH. In order to confirm this hypothesis, a DeltactsR mutant strain of L. plantarum was generated. Expression of ftsH in the DeltactsR mutant strain was strongly upregulated, indicating that ftsH of L. plantarum is negatively controlled by CtsR. This is the first example of an ftsH gene controlled by the CtsR repressor, and the first of the low-G+C gram-positive bacteria where the regulatory mechanism has been identified.
Multilevel Thresholding Segmentation Based on Harmony Search Optimization

In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.
Automatic Test Generation for LUSTRE/SCADE Programs

Lustre is a declarative, data-flow language, which is devoted to the specification of synchronous and real-time applications. It ensures efficient code generation and provides formal specification and verification facilities. A graphical tool dedicated to the development of critical embedded systems and often used by industries and professionals is SCADE (Safety Critical Application Development Environment). SCADE is a graphical environment based on the LUSTRE language and it allows the hierarchical definition of the system components and the automatic code generation. This research work is partially concerned with Lutess, a testing environment which automatically transforms formal specifications into test data generators.
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization

Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose Adversarial Residual Dictionary Learning (ARDL) to supervise facial texture map recovering with only monocular images. Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves high-resolution frontalization appearances.
Guidelines for management of sodium hypochlorite extrusion injuries

Sodium hypochlorite (NaOCl) is the most common irrigant used in modern endodontics. It is highly effective at dissolving organic debris and disinfecting the root canal system due to the high pH. Extravasation of NaOCl into intra-oral and extra-oral tissues can lead to devastating outcomes leading to long-term functional and aesthetic deficits. Currently no clear guidelines are available which has caused confusion among the dental and oral and maxillofacial (OMFS) surgical community how best to manage these patients. Following a literature review and considering our own experience we have formulated clear and precise guidelines to manage patients with NaOCl injury.
Cromolyn administration (to block mast cell degranulation) reduces necrosis of dystrophic muscle in mdx mice

Duchenne muscular dystrophy is a lethal muscle wasting disorder, resulting from mutations in the gene encoding for the skeletal muscle protein dystrophin. The absence of functional dystrophin leaves the muscle membrane vulnerable to damage during contraction. Damage initially occurs as 'tears' in the membrane, this damage can be exacerbated by the inflammatory response leading to myofibre necrosis rather than repair. Mast cells resident within skeletal muscle represent an immediate source of pro-inflammatory cytokines. We hypothesise that blockade of mast cell degranulation would reduce the extent of myofibre necrosis in the mdx mouse. Daily cromolyn injections were performed on young and exercised adult mdx mice and histological analysis confirmed that mast cell degranulation contributes to myofibre necrosis. This research identified high biological variation between individual mdx mice in the severity of the dystrophic pathology, and supported a relationship between extent of muscle damage in adult mdx mice and their individual enthusiasm for voluntary wheel running.
Structured learning for spatial information extraction from biomedical text: bacteria biotopes

We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural language texts and putting this knowledge in databases for easy access by biologists. The task is challenging and the previous results are far below an acceptable level of performance, particularly for extraction of localization relationships. Therefore, we aim to design a new system for such extractions, using the framework of structured machine learning techniques. We design a new model for joint extraction of biomedical entities and the localization relationship. Our model is based on a spatial role labeling (SpRL) model designed for spatial understanding of unrestricted text. We extend SpRL to extract discourse level spatial relations in the biomedical domain and apply it on the BioNLP-ST 2013, BB-shared task. We highlight the main differences between general spatial language understanding and spatial information extraction from the scientific text which is the focus of this work. We exploit the text’s structure and discourse level global features. Our model and the designed features substantially improve on the previous systems, achieving an absolute improvement of approximately 57 percent over F1 measure of the best previous system for this task. Our experimental results indicate that a joint learning model over all entities and relationships in a document outperforms a model which extracts entities and relationships independently. Our global learning model significantly improves the state-of-the-art results on this task and has a high potential to be adopted in other natural language processing (NLP) tasks in the biomedical domain.
Dynamic Lithium-Ion Battery Model for System Simulation

We present here a complete dynamic model of a lithium ion battery that is suitable for virtual-prototyping of portable battery-powered systems. The model accounts for nonlinear equilibrium potentials, rateand temperature-dependencies, thermal effects and response to transient power demand. The model is based on publicly available data such as the manufacturers’ data sheets. The Sony US18650 is used as an example. The model output agrees both with manufacturer’s data and with experimental results. The model can be easily modified to fit data from different batteries and can be extended for wide dynamic ranges of different temperatures and current rates.
Violable Contracts and Governance for Blockchain Applications

We examine blockchain technologies, especially smart contracts, as a platform for decentralized applications. By providing a basis for consensus, blockchain promises to upend business models that presuppose a central authority. However, blockchain suffers from major shortcomings arising from an over-regimented way of organizing computation that limits its prospects. We propose a sociotechnical, yet computational, perspective that avoids those shortcomings. A centerpiece of our vision is the notion of a declarative, violable contract in contradistinction to smart contracts. This new way of thinking enables flexible governance, by formalizing organizational structures; verification of correctness without obstructing autonomy; and a meaningful basis for trust.
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bidirectional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.
Cloud-based cooperative navigation for mobile service robots in dynamic industrial environments

In this paper we address the demand for flexibility and economic efficiency in industrial autonomous guided vehicle (AGV) systems by the use of cloud computing. We propose a cloud-based architecture that moves parts of mapping, localization and path planning tasks to a cloud server. We use a cooperative longterm Simultaneous Localization and Mapping (SLAM) approach which merges environment perception of stationary sensors and mobile robots into a central Holistic Environment Model (HEM). Further, we deploy a hierarchical cooperative path planning approach using Conflict-Based Search (CBS) to find optimal sets of paths which are then provided to the mobile robots. For communication we utilize the Manufacturing Service Bus (MSB) which is a component of the manufacturing cloud platform Virtual Fort Knox (VFK). We demonstrate the feasibility of this approach in a real-life industrial scenario. Additionally, we evaluate the system's communication and the planner for various numbers of agents.
Impairments in spatial representations and rhythmic coordination of place cells in the 3xTg mouse model of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible and highly progressive neurodegenerative disease. Clinically, patients with AD display impairments in episodic and spatial memory. However, the underlying neuronal dysfunctions that result in these impairments remain poorly understood. The hippocampus is crucial for spatial and episodic memory, and thus we tested the hypothesis that abnormal neuronal representations of space in the hippocampus contribute to memory deficits in AD. To test this hypothesis, we recorded spikes from place cells in hippocampal subfield CA1, together with corresponding rhythmic activity in local field potentials, in the 3xTg AD mouse model. We observed disturbances in place cell firing patterns, many of which were consistent with place cell disturbances reported in other rodent models of AD. We found place cell representations of space to be unstable in 3xTg mice compared to control mice. Furthermore, coordination of place cell firing by hippocampal rhythms was disrupted in 3xTg mice. Specifically, a smaller proportion of place cells from 3xTg mice were significantly phase-locked to theta and slow gamma rhythms, and the theta and slow gamma phases at which spikes occurred were also altered. Remarkably, these disturbances were observed at an age before detectable Aβ pathology had developed. Consistencies between these findings in 3xTg mice and previous findings from other AD models suggest that disturbances in place cell firing and hippocampal rhythms are related to AD rather than reflecting peculiarities inherent to a particular transgenic model. Thus, disturbed rhythmic organization of place cell activity may contribute to unstable spatial representations, and related spatial memory deficits, in AD.