nils-herrmann/clean_scirepeval huggingface dataset:
FUSION OF MEDICAL IMAGES USING MUTUAL INFORMATION AND INTENSITY BASED IMAGE REGISTRATION SCHEMES

Image fusion is the process of combining two or more images for providing more information. Medical image fusion refers to the fusion of medical images obtained from different modalities. Medical Image Fusion helps in medical diagnosis by way of improving the quality of the images. In diagnosis, images obtained from a single modality like MRI, CT etc, may not be able to provide all the required information. It is needed to combine information obtained from other modalities also to improve the information acquired. For example combination of information from MRI and CT modalities gives more information than the individual modalities separately. The aim is to provide a method for fusing the images from the individual modalities in such a way that the fusion results in an image that gives more information without any loss of the input information and without any redundancy or artifacts. In the fusion of medical images obtained from different modalities they might be in different coordinate systems and they have to be aligned properly for efficient fusion. The aligning of the input images before proceeding with the fusion is called image registration. The intensity based registration and Mutual information based image registration procedures are carried out before decomposing the images. The two imaging modalities CT and MRI are considered for this study. The results on CT and MR images demonstrate the performance of the fusion algorithms in comparison with registration schemes.
A New Algorithm for Cortical Bone Segmentation with Its Validation and Applications to In Vivo Imaging

Cortical bone supports and protects our skeletal functions and it plays an important in determining bone strength and fracture risks. Cortical bone segmentation is needed for quantitative analyses and the task is nontrivial for in vivo multi-row detector CT (MD-CT) imaging due to limited resolution and partial volume effects. An automated cortical bone segmentation algorithm for in vivo MD-CT imaging of distal tibia is presented. It utilizes larger contextual and topologic information of the bone using a modified fuzzy distance transform and connectivity analyses. An accuracy of 95.1% in terms of volume of agreement with true segmentations and a repeat MD-CT scan intra-class correlation of 98.2% were observed in a cadaveric study. An in vivo study involving 45 age-similar and height-matched pairs of male and female volunteers has shown that, on an average, male subjects have 16.3% thicker cortex and 4.7% increased porosity as compared to females.
A Reflective Symmetry Descriptor for 3D Models

Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vision and computational geometry. Most prior work has focused on finding the main axes of symmetry, or determining that none exists. In this paper we introduce a new reflective symmetry descriptor that represents a measure of reflective symmetry for an arbitrary 3D model for all planes through the model’s center of mass (even if they are not planes of symmetry). The main benefits of this new shape descriptor are that it is defined over a canonical parameterization (the sphere) and describes global properties of a 3D shape. We show how to obtain a voxel grid from arbitrary 3D shapes and, using Fourier methods, we present an algorithm computes the symmetry descriptor in O(N 4 log N) time for an N × N × N voxel grid and computes a multiresolution approximation in O(N 3 log N) time. In our initial experiments, we have found that the symmetry descriptor is insensitive to noise and stable under point sampling. We have also found that it performs well in shape matching tasks, providing a measure of shape similarity that is orthogonal to existing methods.
Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains. When this hidden representation is used for opinion target extraction, we find that it can outperform a number of strong baselines with a large margin.
Secreted Frizzled-Related Protein 4 Inhibits Glioma Stem-Like Cells by Reversing Epithelial to Mesenchymal Transition, Inducing Apoptosis and Decreasing Cancer Stem Cell Properties

The Wnt pathway is integrally involved in regulating self-renewal, proliferation, and maintenance of cancer stem cells (CSCs). We explored the effect of the Wnt antagonist, secreted frizzled-related protein 4 (sFRP4), in modulating epithelial to mesenchymal transition (EMT) in CSCs from human glioblastoma cells lines, U87 and U373. sFRP4 chemo-sensitized CSC-enriched cells to the most commonly used anti-glioblastoma drug, temozolomide (TMZ), by the reversal of EMT. Cell movement, colony formation, and invasion in vitro were suppressed by sFRP4+TMZ treatment, which correlated with the switch of expression of markers from mesenchymal (Twist, Snail, N-cadherin) to epithelial (E-cadherin). sFRP4 treatment elicited activation of the Wnt-Ca2(+) pathway, which antagonizes the Wnt/ß-catenin pathway. Significantly, the chemo-sensitization effect of sFRP4 was correlated with the reduction in the expression of drug resistance markers ABCG2, ABCC2, and ABCC4. The efficacy of sFRP4+TMZ treatment was demonstrated in vivo using nude mice, which showed minimum tumor engraftment using CSCs pretreated with sFRP4+TMZ. These studies indicate that sFRP4 treatment would help to improve response to commonly used chemotherapeutics in gliomas by modulating EMT via the Wnt/ß-catenin pathway. These findings could be exploited for designing better targeted strategies to improve chemo-response and eventually eliminate glioblastoma CSCs.
Using machine learning techniques to detect metamorphic relations for programs without test oracles

Much software lacks test oracles, which limits automated testing. Metamorphic testing is one proposed method for automating the testing process for programs without test oracles. Unfortunately, finding the appropriate metamorphic relations required for use in metamorphic testing remains a labor intensive task, which is generally performed by a domain expert or a programmer. In this work we present a novel approach for automatically predicting metamorphic relations using machine learning techniques. Our approach uses a set of features developed using the control flow graph of a function for predicting likely metamorphic relations. We show the effectiveness of our method using a set of real world functions often used in scientific applications.
The past makes the present meaningful: nostalgia as an existential resource.

The present research tested the proposition that nostalgia serves an existential function by bolstering a sense of meaning in life. Study 1 found that nostalgia was positively associated with a sense of meaning in life. Study 2 experimentally demonstrated that nostalgia increases a sense of meaning in life. In both studies, the link between nostalgia and increased meaning in life was mediated by feelings of social connectedness. Study 3 evidenced that threatened meaning increases nostalgia. Study 4 illustrated that nostalgia, in turn, reduces defensiveness following a meaning threat. Finally, Studies 5 and 6 showed that nostalgia disrupts the link between meaning deficits and compromised psychological well-being. Collectively, these findings indicate that the provision of existential meaning is a pivotal function of nostalgia.
Priority capabilities in LTE supporting national security and emergency preparedness next generation network priority services

Various priority services exist today in the public networks to support key personnel in their critical communications during a National Security and Emergency Preparedness (NS/EP) condition. These priority services will evolve as the public networks evolve to Next Generation Network (NGN) Internet Protocol (IP)-based infrastructures, including the 4G wireless network called Long Term Evolution (LTE). In addition to evolving the legacy priority Voice services to Voice over IP (VoIP), it is expected that NS/EP NGN Priority Services (NGN-PS) will include priority data and priority video services. NS/EP NGN-PS, and associated network capabilities, have been specified by the Office of Emergency Communications (OEC) and are based on industry Standards. During an NS/EP condition, the public networks can get overloaded due to heavy user traffic and possible failures. NS/EP NGN-PS provides the mechanisms in the access and core networks to facilitate the needed priority communications. In particular, the wireless access networks such as LTE may become severely overloaded in an area, affecting call admissions and Quality of Service (QoS). This paper describes the NS/EP priority mechanisms in LTE that address and mitigate the various potential LTE congestion points, to then facilitate the high probability of call and data admission and completion. These NS/EP priority mechanisms provide capabilities to access, invoke and maintain NS/EP NGN-PS, i.e., NS/EP voice, data and video priority services, for end-to-end user-to-user communications. These mechanisms will result in priority during (a) user attachment, (b) node signaling and processing, (c) device paging, (d) handover, (e) media bearer establishment, (f) radio resource admission, (g) packet transport, and (h) overload controls.
Integration of Medical and Dental Care and Patient Data

In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line integration of medical and dental care and patient data book, as the world window, as many people suggest.
Towards consistent visual-inertial navigation

Visual-inertial navigation systems (VINS) have prevailed in various applications, in part because of the complementary sensing capabilities and decreasing costs as well as sizes. While many of the current VINS algorithms undergo inconsistent estimation, in this paper we introduce a new extended Kalman filter (EKF)-based approach towards consistent estimates. To this end, we impose both state-transition and obervability constraints in computing EKF Jacobians so that the resulting linearized system can best approximate the underlying nonlinear system. Specifically, we enforce the propagation Jacobian to obey the semigroup property, thus being an appropriate state-transition matrix. This is achieved by parametrizing the orientation error state in the global, instead of local, frame of reference, and then evaluating the Jacobian at the propagated, instead of the updated, state estimates. Moreover, the EKF linearized system ensures correct observability by projecting the most-accurate measurement Jacobian onto the observable subspace so that no spurious information is gained. The proposed algorithm is validated by both Monte-Carlo simulation and real-world experimental tests.
Decentralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids Using Resistive Output Impedance

In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an AC-distributed system is proposed. The paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed taking into account the special nature of a low voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop control method, the proposed controller uses resistive output impedance, and as a result a different control law is obtained. The controller is implemented by using a DSP board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6 kVA inverters connected in parallel, showing the features of the proposed wireless control
Simulation results of ranging performance in two-ray multipath model

In this paper, we analyze the ranging performance of chirp spread spectrum (CSS) in two-ray multipath indoor environment. Reflected multipath signals will affect the received signal in the receiver deforming the correlation shape and thus will alter the real value of ranging. In indoor environment, conventional methods for tracking positions are unsuccessful due to the harsh multipath signals. Thus, IEEE 802.15.4a was defined for wireless local area network capable of local high speed communication and precise ranging. Here CSS is used as a physical layer because of their robustness to multipath indoor environment. We study the ranging error of CSS caused by multipath signals. We simulate the ranging error by Matlab. And we assume the two-ray multipath model as the indoor wireless channel and measure the ranging values using delay lock loop which utilizes null tracking strategy.
Ground leakage current suppression in a 50 kW 5-level T-type transformerless PV inverter

In this paper ground leakage current suppression in a 50 kW 5-level T-type transformerless PV inverter has been presented. Compared to a 3-level T-type PV inverter, this topology allows a simple modulation method such as carrier-based (CB) PWM can be used to suppress the leakage current without the penalty of the traditional methods. Phase disposition (PD) and phase opposition disposition (POD) based CB PWM method has been applied to this 5-level topology. The spectrum analysis has demonstrated that PD and POD will generate the same phase voltage spectrum. In addition, the common-mode (CM) voltage of a 5-level T-type PV inverter has been derived and the CM choke has been designed. The value of the CM choke is a 73% reduction compared to that of a 3-level T-type PV inverter. The simulation and experimental verification have been provided.
Accelerated Training for Massive Classification via Dynamic Class Selection

Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with substantial difficulties, e.g. excessive memory demand and computational cost, when applied to massive problems. We present a new method to tackle this problem. This method can efficiently and accurately identify a small number of “active classes” for each mini-batch, based on a set of dynamic class hierarchies constructed on the fly. We also develop an adaptive allocation scheme thereon, which leads to a better tradeoff between performance and cost. On several large-scale benchmarks, our method significantly reduces the training cost and memory demand, while maintaining competitive performance.
Reducing Combinatorics in GUI Testing of Android Applications

The rising popularity of Android and the GUI-driven nature of its apps have motivated the need for applicable automated GUI testing techniques. Although exhaustive testing of all possible combinations is the ideal upper bound in combinatorial testing, it is often infeasible, due to the combinatorial explosion of test cases. This paper presents TrimDroid, a framework for GUI testing of Android apps that uses a novel strategy to generate tests in a combinatorial, yet scalable, fashion. It is backed with automated program analysis and formally rigorous test generation engines. TrimDroid relies on program analysis to extract formal specifications. These specifications express the app's behavior (i.e., control flow between the various app screens) as well as the GUI elements and their dependencies. The dependencies among the GUI elements comprising the app are used to reduce the number of combinations with the help of a solver. Our experiments have corroborated TrimDroid's ability to achieve a comparable coverage as that possible under exhaustive GUI testing using significantly fewer test cases.
Semantic Representation of Cloud Patterns and Services with Automated Reasoning to Support Cloud Application Portability

During the past years the Cloud Computing offer has exponentially grown, with new Cloud providers, platforms and services being introduced in the IT market. The extreme variety of services, often providing non uniform and incompatible interfaces, makes it hard for customers to decide how to develop, or even worse to migrate, their own application into the Cloud. This situation can only get worse when customers want to exploit services from different providers, because of the portability and interoperability issues that often arise. In this paper we propose a uniform, integrated, machine-readable, semantic representation of cloud services, patterns, appliances and their compositions. Our approach aims at supporting the development of new applications for the Cloud environment, using semantic models and automatic reasoning to enhance potability and interoperability when multiple platforms are involved. In particular, the proposed reasoning procedure allows to: perform automatic discovery of Cloud services and Appliances; map between agnostic and vendor dependent Cloud Patterns and Services; automatically enrich the semantic knowledge base.
Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness

Computing the semantic similarity/relatedness between terms is an important research area for several disciplines, including artificial intelligence, cognitive science, linguistics, psychology, biomedicine and information retrieval. These measures exploit knowledge bases to express the semantics of concepts. Some approaches, such as the information theoretical approaches, rely on knowledge structure, while others, such as the gloss-based approaches, use knowledge content. Firstly, based on structure, we propose a new intrinsic Information Content (IC) computing method which is based on the quantification of the subgraph formed by the ancestors of the target concept. Taxonomic measures including the IC-based ones consume the topological parameters that must be extracted from taxonomies considered as Directed Acyclic Graphs (DAGs). Accordingly, we propose a routine of graph algorithms that are able to provide some basic parameters, such as depth, ancestors, descendents, Lowest Common Subsumer (LCS). The IC-computing method is assessed using several knowledge structures which are: the noun and verb WordNet “is a” taxonomies, Wikipedia Category Graph (WCG), and MeSH taxonomy. We also propose an aggregation schema that exploits the WordNet “is a” taxonomy and WCG in a complementary way through the IC-based measures to improve coverage capacity. Secondly, taking content into consideration, we propose a gloss-based semantic similarity measure that operates based on the noun weighting mechanism using our IC-computing method, as well as on the WordNet, Wiktionary and Wikipedia resources. Further evaluation is performed on various items, including nouns, verbs, multiword expressions and biomedical datasets, using well-recognized benchmarks. The results indicate an improvement in terms of similarity and relatedness assessment accuracy.
Curcumin, resveratrol and flavonoids as anti-inflammatory, cyto- and DNA-protective dietary compounds.

Numerous dietary compounds, ubiquitous in fruits, vegetables and spices have been isolated and evaluated during recent years for their therapeutic potential. These compounds include flavonoid and non-flavonoid polyphenols, which describe beneficial effects against a variety of ailments. The notion that these plant products have health promoting effects emerged because their intake was related to a reduced incidence of cancer, cardiovascular, neurological, respiratory, and age-related diseases. Exposure of the body to a stressful environment challenges cell survival and increases the risk of chronic disease developing. The polyphenols afford protection against various stress-induced toxicities through modulating intercellular cascades which inhibit inflammatory molecule synthesis, the formation of free radicals, nuclear damage and induce antioxidant enzyme expression. These responses have the potential to increase life expectancy. The present review article focuses on curcumin, resveratrol, and flavonoids and seeks to summarize their anti-inflammatory, cytoprotective and DNA-protective properties.
HexType: Efficient Detection of Type Confusion Errors for C++

Type confusion, often combined with use-after-free, is the main attack vector to compromise modern C++ software like browsers or virtual machines. Typecasting is a core principle that enables modularity in C++. For performance, most typecasts are only checked statically, i.e., the check only tests if a cast is allowed for the given type hierarchy, ignoring the actual runtime type of the object. Using an object of an incompatible base type instead of a derived type results in type confusion. Attackers abuse such type confusion issues to attack popular software products including Adobe Flash, PHP, Google Chrome, or Firefox. We propose to make all type checks explicit, replacing static checks with full runtime type checks. To minimize the performance impact of our mechanism HexType, we develop both low-overhead data structures and compiler optimizations. To maximize detection coverage, we handle specific object allocation patterns, e.g., placement new or reinterpret_cast which are not handled by other mechanisms. Our prototype results show that, compared to prior work, HexType has at least 1.1 -- 6.1 times higher coverage on Firefox benchmarks. For SPEC CPU2006 benchmarks with overhead, we show a 2 -- 33.4 times reduction in overhead. In addition, HexType discovered 4 new type confusion bugs in Qt and Apache Xerces-C++.