nils-herrmann/clean_scirepeval huggingface dataset:
A genetic model for colorectal tumorigenesis

Tumorigenesis has long been thought to be a multistep process (Foulds, 1958); however, only recently has it become possible to identify the molecular events that underlie the initiation and progression of human tumors (Weinberg, 1989; Bishop, 1987). Colorectal tumors provide an excellent system in which to search for and study the genetic alterations involved in the development of a common human neoplasm. Abundant clinical and histopathological data suggest that most, if not all, malignant colorectal tumors (carcinomas) arise from preexisting benign tumors (adenomas) (Sugarbaker et al., 1985). Tumors of various stages of development, from very small adenomas to large metastatic carcinomas, can be obtained for study, unlike the situation in most other common human tumor types. Furthermore, both hereditary and environmental factors contribute to the development of colorectal neoplasia, allowing for the study of both inherited and somatic genetic alterations. In this review we present a model for the genetic basis of colorectal neoplasia that includes the following salient features. First, colorectal tumors appear to arise as a result of the mutational activation of oncogenes coupled with the mutational inactivation of tumor suppressor genes; the latter changes predominate. Second, mutations in at least four to five genes are required for the formation of a malignant tumor. Fewer changes suffice for benign tumorigenesis. Third, although the genetic alterations often occur according to a preferred sequence, the total accumulation of changes, rather than their order with respect to one another, is responsible for determining the tumor’s biologic properties. Fourth, in some cases, mutant tumor suppressor genes appear to exert a phenotypic effect even when present in the heterozygous state; thus, some tumor suppressor genes may not be “recessive” at the cellular level. The general features of this model may be applicable to other common epithelial neoplasms, in which tumors of varying stage are more difficult to study.
3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN

Deep learning on point clouds has made a lot of progress recently. Many point cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have shown advantages in accuracy and speed comparing to those using traditional 3D convolution algorithms. However, nearly all of these methods face a challenge, since the coordinates of the point cloud are decided by the coordinate system, they cannot handle the problem of 3D transform invariance properly. In this paper, we propose a general framework for point cloud learning. We achieve transform invariance by learning inner 3D geometry feature based on local graph representation, and propose a feature extraction network based on graph convolution network. Through experiments on classification and segmentation tasks, our method achieves state-of-the-art performance in rotated 3D object classification, and achieve competitive performance with the state-of-the-art in classification and segmentation tasks with fixed coordinate value.
Performance comparison between low-speed axial-flux and radial-flux permanent-magnet machines including mechanical constraints

Performance comparison between different machine topologies is not a straightforward task since many variables exist if electromagnetic, thermal and mechanical aspects are taken into account. In this paper some methods to take into account relevant mechanical constraints in the performance comparison are proposed. A comparison study between low-speed axial-flux permanent-magnet machines and radial-flux permanent-magnet machines is provided with introduced mechanical constraints, respectively
Shape from shading for the digitization of curved documents

Document digitization is faster and more affordable using digital cameras than scanners. On the other hand, if we aim at extending the basic digital camera functionalities for such a purpose, post-processings will be of first importance, at least to improve the text legibility. In this paper, we address the specific problem of the virtual flattening of curved documents, as for example the pages of an opened book lying on its spine. In order to compute the document shape, we use the shape from shading technique and discuss why, in some cases, it is more suitable than other 3D single-view reconstruction techniques. We extend the seminal work by Wada et al. ( 1992) and consecutive papers, reformulating the problem in terms of perspective shape from shading. Finally, we design a complete post-processing algorithm and test it on real images. Even if the documents are much curved, it is shown that the restored images are almost identical to scanned images of the flattened documents.
Tracking Control of Ball and Plate System with a Double Feedback Loop Structure

This paper proposes a double feedback loop structure for tracking control of the ball and plate system on the experimental platform BPVS-JLU I. The ball and plate system BPVS-JLU I is introduced and its mathematical model is presented. A single input rule modules fuzzy logic controller with dynamic importance degree is utilized in the external loop, while a switching servo controller is used in the inner loop. Experiments have proved that tracking controller with a double feedback loop structure does stabilize the ball and plate system. When given trajectory is a circle with radius 150 (mm) and the desired ball tracking velocity is 3 (mm/s), average position tracking error is 10.761 (mm).
ModSpec: An open, flexible specification framework for multi-domain device modelling

We describe ModSpec, a MATLAB/Octave based specification format suitable for modelling devices across a wide variety of application domains, including circuits, optics, fluidics and biology. The ModSpec format and associated API are centered around describing the nonlinear differential equations at the core of any device model. The format is open, general and easy to use, and is supported by toolchains that translate and automatically differentiate models, set up equations for systems of interacting devices, and provide simulation facilities. We illustrate the use of ModSpec for modelling semiconductor, photovoltaic, fluidic and neuronal devices and systems.
Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments

Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS-denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here-presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS-denied environment and independently of any external artificial aids.
Discovering actionable patterns in event data

Applications such as those for systems management and intrusion detection employ an automated real-time operation system in which sensor data are collected and processed in real time. Although such a system effectively reduces the need for operation staff, it requires constructing and maintaining correlation rules. Currently, rule construction requires experts to identify problem patterns, a process that is timeconsuming and error-prone. In this paper, we propose reducing this burden by mining historical data that are readily available. Specifically, we first present efficient algorithms to mine three types of important patterns from historical event data: event bursts, periodic patterns, and mutually dependent patterns. We then discuss a framework for efficiently mining events that have multiple attributes. Last, we present Event Correlation Constructor—a tool that validates and extends correlation knowledge.
Health information systems: a survey of frameworks for developing countries.

The objective of this paper is to perform a survey of excellent research on health information systems (HIS) analysis and design, and their underlying theoretical frameworks. It classifies these frameworks along major themes, and analyzes the different approaches to HIS development that are practical in resource-constrained environments.



Literature review based on PubMed citations and conference proceedings, as well as Internet searches on information systems in general, and health information systems in particular.



The field of health information systems development has been studied extensively. Despite this, failed implementations are still common. Theoretical frameworks for HIS development are available that can guide implementers.



As awareness, acceptance, and demand for health information systems increase globally, the variety of approaches and strategies will also follow. For developing countries with scarce resources, a trial-and-error approach can be very costly. Lessons from the successes and failures of initial HIS implementations have been abstracted into theoretical frameworks. These frameworks organize complex HIS concepts into methodologies that standardize techniques in implementation. As globalization continues to impact healthcare in the developing world, demand for more responsive health systems will become urgent. More comprehensive frameworks and practical tools to guide HIS implementers will be imperative.
Using grid for accelerating density-based clustering

Clustering analysis is a primary method for data mining. The ever increasing volumes of data in different applications forces clustering algorithms to cope with it. DBSCAN is a well-known algorithm for density-based clustering. It is both effective so it can detect arbitrary shaped clusters of dense regions and efficient especially in existence of spatial indexes to perform the neighborhood queries efficiently. In this paper we introduce a new algorithm GriDBSCAN to enhance the performance of DBSCAN using grid partitioning and merging, yielding a high performance with the advantage of high degree of parallelism. We verified the correctness of the algorithm theoretically and experimentally, studied the performance theoretically and using experiments on both real and synthetic data. It proved to run much faster than original DBSCAN. We compared the algorithm with a similar algorithm, EnhancedDBSCAN, which is also an enhancement to DBSCAN using partitioning. Experiments showed the new algorithm's superiority in performance and degree of parallelism.
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.
ONTOLOGY ALIGNMENT USING MACHINE LEARNING TECHNIQUES

In semantic web, ontology plays an important role to provide formal definitions of concepts and relationships. Therefore, communicating similar ontologies becomes essential to provide ontologies interpretability and extendibility. Thus, it is inevitable to have similar but not the same ontologies in a particular domain, since there might be several definitions for a given concept. This paper presents a method to combine similarity measures of different categories without having ontology instances or any user feedbacks towards aligning two given ontologies. To align different ontologies efficiently, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT) and AdaBoost classifiers are investigated. Each classifier is optimized based on the lower cost and better classification rate. Experimental results demonstrate that the F-measure criterion improves to 99% using feature selection and combination of AdaBoost and DT classifiers, which is highly comparable, and outperforms the previous reported F-measures. Computer Engineering Department, Shahid Chamran University, Ahvaz, Iran.
Enhancing SDN security for IoT-related deployments through blockchain

The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
The association between psychological distance and construal level: evidence from an implicit association test.

According to construal level theory (N. Liberman, Y. Trope, & E. Stephan, in press; Y. Trope & N. Liberman, 2003), people use a more abstract, high construal level when judging, perceiving, and predicting more psychologically distal targets, and they judge more abstract targets as being more psychologically distal. The present research demonstrated that associations between more distance and higher level of construal also exist on a pure conceptual level. Eight experiments used the Implicit Association Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998) to demonstrate an association between words related to construal level (low vs. high) and words related to four dimensions of distance (proximal vs. distal): temporal distance, spatial distance, social distance, and hypotheticality. In addition to demonstrating an association between level of construal and psychological distance, these findings also corroborate the assumption that all 4 dimensions of psychological distance are related to level of construal in a similar way and support the notion that they all are forms of psychological distance.
Audio fingerprinting based on normalized spectral subband moments

The performance of a fingerprinting system, which is often measured in terms of reliability and robustness, is directly related to the features that the system uses. In this letter, we present a new audio-fingerprinting method based on the normalized spectral subband moments. A threshold used to reliably determine a fingerprint match is obtained by modeling the features as a stationary process. The robustness of the normalized moments was evaluated experimentally and compared with that of the spectral flatness measure. Among the considered subband features, the first-order normalized moment showed the best performance for fingerprinting.
The POSTECH face database (PF07) and performance evaluation

We constructed a face database POSTECH face database (PF07). PF07 contains the true-color face images of 200 people, 100 men and 100 women, representing 320 various images (5 pose variations times 4 expression variations times 16 illumination variations) per person. All of the people in the database are Korean. We also present the results of face recognition experiments under various conditions using three baseline face recognition algorithms in order to provide an example evaluation protocol on the database. The database is expected to be used to evaluate the algorithm of face recognition for Korean people or for people with systematic variations.
Active learning for on-road vehicle detection: a comparative study

In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.
A column store engine for real-time streaming analytics

This paper describes novel aspects of the column store implemented in the MemSQL database engine and describes the design choices made to support real-time streaming workloads. Column stores have traditionally been restricted to data warehouse scenarios where low latency queries are a secondary goal, and where restricting data ingestion to be offline, batched, append-only, or some combination thereof is acceptable. In contrast, the MemSQL column store implementation treats low latency queries and ongoing writes as first class citizens, with a focus on avoiding interference between read, ingest, update, and storage optimization workloads through the use of fragmented snapshot transactions and optimistic storage reordering. This implementation broadens the range of serviceable column store workloads to include those with more stringent demands on query and data latency, such as those backing operational systems used by adtech, financial services, fraud detection and other real-time or data streaming applications.
SIFT Features Tracking for Video Stabilization

This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.
Probabilistic neural network and its application

Nuclear marine apparatus is a huge complicated system, most of which equipments are of nonlin-earity, time varying, coupling and inexactness. Neuralnet is widely applied in nuclear fault diagnosis for its approaching any kinds of nonlinearity mapping. At present, BP neural net is used more widely, but the layers of the net and the neurones on each layer can not be delimited easily; such net may fall into the minimum point in the course of training. In this essay, PNN proves effective in diagnosing faults on nuclearmarine apparatus.
Bitcoin movement prediction with text mining

In the last few years, Bitcoin is one of the most discussed and popular topic in financial system. This article aims to predict Bitcoin movement by using Machine Learning and Text Mining models. Many models have been used to this end, including the most popular models in financial prediction; Artifical Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). In addition to this, in order to examine the effect of daily news on Bitcoin movement, the text mining models are involved into the prediction system. This paper focuses on applying Machine Learning models on a integrated dataset, which contains both historical Bitcoin values and features from daily news text. Overall, our model can estimate the direction of Bitcoin with a high success.