nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
A fast sweeping method for Eikonal equations

In this paper a fast sweeping method for computing the numerical solution of Eikonal equations on a rectangular grid is presented. The method is an iterative method which uses upwind difference for discretization and uses Gauss-Seidel iterations with alternating sweeping ordering to solve the discretized system. The crucial idea is that each sweeping ordering follows a family of characteristics of the corresponding Eikonal equation in a certain direction simultaneously. The method has an optimal complexity of O(N) for N grid points and is extremely simple to implement in any number of dimensions. Monotonicity and stability properties of the fast sweeping algorithm are proven. Convergence and error estimates of the algorithm for computing the distance function is studied in detail. It is shown that 2n Gauss-Seidel iterations is enough for the distance function in n dimensions. An estimation of the number of iterations for general Eikonal equations is also studied. Numerical examples are used to verify the analysis.
The plastic human brain cortex.

Plasticity is an intrinsic property of the human brain and represents evolution's invention to enable the nervous system to escape the restrictions of its own genome and thus adapt to environmental pressures, physiologic changes, and experiences. Dynamic shifts in the strength of preexisting connections across distributed neural networks, changes in task-related cortico-cortical and cortico-subcortical coherence and modifications of the mapping between behavior and neural activity take place in response to changes in afferent input or efferent demand. Such rapid, ongoing changes may be followed by the establishment of new connections through dendritic growth and arborization. However, they harbor the danger that the evolving pattern of neural activation may in itself lead to abnormal behavior. Plasticity is the mechanism for development and learning, as much as a cause of pathology. The challenge we face is to learn enough about the mechanisms of plasticity to modulate them to achieve the best behavioral outcome for a given subject.
Charismatic leadership and user acceptance of information technology

12 June 2006  May 2007  22 July 2007  Although there is widespread agreement that leadership has important effects on information technology (IT) acceptance and use, relatively little empirical research to date has explored this phenomenon in detail. This paper integrates the unified theory of acceptance and use of technology (UTAUT) with charismatic leadership theory, and examines the role of project champions influencing user adoption. PLS analysis of survey data collected from 209 employees in seven organizations that had engaged in a large-scale IT implementation revealed that project champion charisma was positively associated with increased performance expectancy, effort expectancy, social influence and facilitating condition perceptions of users. Theoretical and managerial implications are discussed, and suggestions for future research in this area are provided.  (2007) 16, 494–510.
Pilot: Passive Device-Free Indoor Localization Using Channel State Information

Many emerging applications such as intruder detection and border protection drive the fast increasing development of device-free passive (DfP) localization techniques. In this paper, we present Pilot, a Channel State Information (CSI)-based DfP indoor localization system in WLAN. Pilot design is motivated by the observations that PHY layer CSI is capable of capturing the environment variance due to frequency diversity of wideband channel, such that the position where the entity located can be uniquely identified by monitoring the CSI feature pattern shift. Therefore, a ``passive'' radio map is constructed as prerequisite which include fingerprints for entity located in some crucial reference positions, as well as clear environment. Unlike device-based approaches that directly percepts the current state of entities, the first challenge for DfP localization is to detect their appearance in the area of interest. To this end, we design an essential anomaly detection block as the localization trigger relying on the CSI feature shift when entity emerges. Afterwards, a probabilistic algorithm is proposed to match the abnormal CSI to the fingerprint database to estimate the positions of potential existing entities. Finally, a data fusion block is developed to address the multiple entities localization challenge. We have implemented Pilot system with commercial IEEE 802.11n NICs and evaluated the performance in two typical indoor scenarios. It is shown that our Pilot system can greatly outperform the corresponding best RSS-based scheme in terms of anomaly detection and localization accuracy.
A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data

Patients' medical conditions often evolve in complex and seemingly unpredictable ways. Even within a relatively narrow and well-defined episode of care, variations between patients in both their progression and eventual outcome can be dramatic. Understanding the patterns of events observed within a population that most correlate with differences in outcome is therefore an important task in many types of studies using retrospective electronic health data. In this paper, we present a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data. Our approach combines (1) visual query capabilities to interactively specify episode definitions, (2) pattern mining techniques to help discover important intermediate events within an episode, and (3) interactive visualization techniques that help uncover event patterns that most impact outcome and how those associations change over time. In addition to presenting our methodology, we describe a prototype implementation and present use cases highlighting the types of insights or hypotheses that our approach can help uncover.
Design of a Multi-Segmented Magnetic Robot for Hull Inspection

The use of a hull-climbing robot is proposed to assist hull surveyors in their inspection tasks, reducing cost and risk to personnel. A novel multisegmented hull-climbing robot with magnetic wheels is introduced where multiple two-wheeled modular segments are adjoined by flexible linkages. Compared to traditional rigid-body tracked magnetic robots that tend to detach easily in the presence of surface discontinuities, the segmented design adapts to such discontinuities with improved adhesion to the ferrous surface. Coordinated mobility is achieved with the use of a motion-control algorithm that estimates robot pose through position sensors located in each segment and linkage in order to optimally command each of the drive motors of the system. Self-powered segments and an onboard radio allow for wireless transmission of video and control data between the robot and its operator control unit. The modular-design approach of the system is highly suited for upgrading or adding segments as needed. For example, enhancing the system with a segment that supports an ultrasonic measurement device used to measure hull-thickness of corroded sites can help minimize the number of areas that a surveyor must personally visit for further inspection and repair. Future development efforts may lead to the design of autonomy segments that accept high-level commands from the operator and automatically execute wide-area inspections. It is also foreseeable that with several multi-segmented robots, a coordinated inspection task can take place in parallel, significantly reducing inspection time and cost. aaron.burmeister@navy.mil The focus of this paper is on the development efforts of the prototype system that has taken place since 2012. Specifically, the tradeoffs of the magnetic-wheel and linkage designs are discussed and the motion-control algorithm presented. Overall system-performance results obtained from various tests and demonstrations are also reported.
A Comparative Evaluation of SiC Power Devices for High-Performance Domestic Induction Heating

This paper presents a comparative evaluation of silicon carbide power devices for the domestic induction heating (IH) application, which currently has a major industrial, economic and social impact. The compared technologies include MOSFETs, normally on and normally off JFETs, as well as BJTs. These devices have been compared according to different figure-of-merit evaluating conduction and switching performance, efficiency, impact of temperature, as well as other driving and protection issues. To perform the proposed evaluation, a versatile test platform has been developed. As a result of this study, several differential features are identified and discussed, taking into account the pursued induction heating application.
Dataset-driven research for improving recommender systems for learning

In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or Each-Movie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for learning. We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms.
Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning

We propose to use a biologically motivated learning rule based on neural intrinsic plasticity to optimize reservoirs of analog neurons. This rule is based on an information maximization principle, it is local in time and space and thus computationally efficient. We show experimentally that it can drive the neurons' output activities to approximate exponential distributions. Thereby it implements sparse codes in the reservoir. Because of its incremental nature, the intrinsic plasticity learning is well suited for joint application with the online backpropagation-decorrelation or the least mean squares reservoir learning, whose performance can be strongly improved. We further show that classical echo state regression can also benefit from reservoirs, which are pre-trained on the given input signal with the implicit plasticity rule.
Learning Human Activities and Object Affordances from RGB-D Videos

Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural support vector machine (SSVM) approach, where labelings over various alternate temporal segmentations are considered as latent variables. We tested our method on a challenging dataset comprising 120 activity videos collected from 4 subjects, and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and 75.0% for high-level activity labeling. We then demonstrate the use of such descriptive labeling in performing assistive tasks by a PR2 robot.
Seeing is believing: The effect of brain images on judgments of scientific reasoning

Brain images are believed to have a particularly persuasive influence on the public perception of research on cognition. Three experiments are reported showing that presenting brain images with articles summarizing cognitive neuroscience research resulted in higher ratings of scientific reasoning for arguments made in those articles, as compared to articles accompanied by bar graphs, a topographical map of brain activation, or no image. These data lend support to the notion that part of the fascination, and the credibility, of brain imaging research lies in the persuasive power of the actual brain images themselves. We argue that brain images are influential because they provide a physical basis for abstract cognitive processes, appealing to people's affinity for reductionistic explanations of cognitive phenomena.
Exact Regenerating Codes for Distributed Storage

Erasure coding techniques are used to increase the reliability of distributed storage systems while minimizing storage overhead. The bandwidth required to repair the system after a node failure also plays a crucial role in the system performance. In  authors have shown that a tradeoff exists between storage and repair bandwidth. They also have introduced the scheme of regenerating codes which meet this tradeoff. In this paper, a scheme of Exact Regenerating Codes is introduced, which are regenerating codes with an additional property of regenerating back the same node upon failure. For the minimum bandwidth point, which is suitable for applications like distributed mail servers, explicit construction for exact regenerating codes is provided. A subspace approach is provided, using which the necessary and sufficient conditions for a linear code to be an exact regenerating code are derived. This leads to the uniqueness of our construction. For the minimum storage point which suits applications such as storage in peer-to-peer systems, an explicit construction of regenerating codes for certain suitable parameters is provided. This code supports variable number of nodes and can handle multiple simultaneous node failures. The constructions given for both the points require a low field size and have low complexity.
Beyond the convenience of the internet of things: Security and privacy concerns

The significant growth of the Internet of Things (IoT) is revolutionizing the way people live by transforming everyday Internet-enabled objects into an interconnected ecosystem of digital and personal information accessible anytime and anywhere. As more objects become Internet-enabled, the security and privacy of the personal information generated, processed and stored by IoT devices become complex and challenging to manage. This paper details the current security and privacy challenges presented by the increasing use of the IoT. Furthermore, investigate and analyze the limitations of the existing solutions with regard to addressing security and privacy challenges in IoT and propose a possible solution to address these challenges. The results of this proposed solution could be implemented during the IoT design, building, testing and deployment phases in the real-life environments to minimize the security and privacy challenges associated with IoT.
Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules

We propose an adaption of the explanation-generating system LIME. While LIME relies on sparse linear models, we explore how richer explanations can be generated. As application domain we use images which consist of a coarse representation of ancient graves. The graves are divided into two classes and can be characterised by meaningful features and relations. This domain was generated in analogy to a classic concept acquisition domain researched in psychology. Like LIME, our approach draws samples around a simplified representation of the instance to be explained. The samples are labelled by a generator – simulating a black-box classifier trained on the images. In contrast to LIME, we feed this information to the ILP system Aleph. We customised Aleph’s evaluation function to take into account the similarity of instances. We applied our approach to generate explanations for different variants of the ancient graves domain. We show that our explanations can involve richer knowledge thereby going beyond the expressiveness of sparse linear models.
GIS-based multicriteria decision analysis: a survey of the literature

The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so. This paper surveys the GISbased multicriteria decision analysis (GIS-MCDA) approaches using a literature review and classification of articles from 1990 to 2004. An electronic search indicated that over 300 articles appeared in refereed journals. The paper provides taxonomy of those articles and identifies trends and developments in GISMCDA.
3D cell nuclei segmentation based on gradient flow tracking

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding. Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%. The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.
Has My Algorithm Succeeded? An Evaluator for Human Pose Estimators

Example classifications (test set) [And09] Andriluka et al. Pictorial structures revisited: People detection and articulated pose estimation. In CVPR, 2009 [Eic09] Eichner et al. Articulated Human Pose Estimation and Search in (Almost) Unconstrained Still Images. In IJCV, 2012 [Sap10] Sapp et al. Cascaded models for articulated pose estimation. In ECCV, 2010 [Yan11] Yang and Ramanan. Articulated pose estimation with flexible mixturesof-parts. In CVPR, 2011.
Structural and functional brain development and its relation to cognitive development

Despite significant gains in the fields of pediatric neuroimaging and developmental neurobiology, surprisingly little is known about the developing human brain or the neural bases of cognitive development. This paper addresses MRI studies of structural and functional changes in the developing human brain and their relation to changes in cognitive processes over the first few decades of human life. Based on post-mortem and pediatric neuroimaging studies published to date, the prefrontal cortex appears to be one of the last brain regions to mature. Given the prolonged physiological development and organization of the prefrontal cortex during childhood, tasks believed to involve this region are ideal for investigating the neural bases of cognitive development. A number of normative pediatric fMRI studies examining prefrontal cortical activity in children during memory and attention tasks are reported. These studies, while largely limited to the domain of prefrontal functioning and its development, lend support for continued development of attention and memory both behaviorally and physiologically throughout childhood and adolescence. Specifically, the magnitude of activity observed in these studies was greater and more diffuse in children relative to adults. These findings are consistent with the view that increasing cognitive capacity during childhood may coincide with a gradual loss rather than formation of new synapses and presumably a strengthening of remaining synaptic connections. It is clear that innovative methods like fMRI together with MRI-based morphometry and nonhuman primate studies will transform our current understanding of human brain development and its relation to behavioral development.
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus

The literature contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product review sentiment classification. In this paper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review sentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment classification based on deep neural networks has been limited by the lack of a large-scale corpus; we sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from Amazon product reviews. Our proposed framework exhibits the dramatic transferability of deep neural networks for cross-domain product review sentiment classification and achieves state-of-the-art performance. The framework also outperforms complex engineered features used with a non-deep neural network method. The experiments demonstrate that introducing large-scale data from similar domains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the multi-source related domain dataset outperformed the one trained on a single similar domain.