nils-herrmann/clean_scirepeval huggingface dataset:
FDG PET-CT imaging of therapeutic response in granulomatous lymphocytic interstitial lung disease (GLILD) in common variable immunodeficiency (CVID).

Common variable immunodeficiency (CVID) is the most common severe adult primary immunodeficiency and is characterized by a failure to produce antibodies leading to recurrent predominantly sinopulmonary infections. Improvements in the prevention and treatment of infection with immunoglobulin replacement and antibiotics have resulted in malignancy, autoimmune, inflammatory and lymphoproliferative disorders emerging as major clinical challenges in the management of patients who have CVID. In a proportion of CVID patients, inflammation manifests as granulomas that frequently involve the lungs, lymph nodes, spleen and liver and may affect almost any organ. Granulomatous lymphocytic interstitial lung disease (GLILD) is associated with a worse outcome. Its underlying pathogenic mechanisms are poorly understood and there is limited evidence to inform how best to monitor, treat or select patients to treat. We describe the use of combined 2-[(18)F]-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography (FDG PET-CT) scanning for the assessment and monitoring of response to treatment in a patient with GLILD. This enabled a synergistic combination of functional and anatomical imaging in GLILD and demonstrated a widespread and high level of metabolic activity in the lungs and lymph nodes. Following treatment with rituximab and mycophenolate there was almost complete resolution of the previously identified high metabolic activity alongside significant normalization in lymph node size and lung architecture. The results support the view that GLILD represents one facet of a multi-systemic metabolically highly active lymphoproliferative disorder and suggests potential utility of this imaging modality in this subset of patients with CVID.
Over-Optimization of Academic Publishing Metrics: Observing Goodhart's Law in Action

The academic publishing world is changing significantly, with ever-growing numbers of publications each year and shifting publishing patterns. However, the metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades. Moreover, recent studies indicate that these metrics have become targets and follow Goodhart’s Law, according to which “when a measure becomes a target, it ceases to be a good measure.” In this study, we analyzed over 120 million papers to examine how the academic publishing world has evolved over the last century. Our study shows that the validity of citation-based measures is being compromised and their usefulness is lessening. In particular, the number of publications has ceased to be a good metric as a result of longer author lists, shorter papers, and surging publication numbers. Citation-based metrics, such citation number and h-index, are likewise affected by the flood of papers, self-citations, and lengthy reference lists. Measures such as a journal’s impact factor have also ceased to be good metrics due to the soaring numbers of papers that are published in top journals, particularly from the same pool of authors. Moreover, by analyzing properties of over 2600 research fields, we observed that citation-based metrics are not beneficial for comparing researchers in different fields, or even in the same department. Academic publishing has changed considerably; now we need to reconsider how we measure success. Multimedia Links I Interactive Data Visualization I Code Tutorials I Fields-of-Study Features Table
A novel planar four-quad antenna

So-called bi-quad antennas fabricated from a simple piece of wire have found their use in applications for terrestrial digital TV (DVB-T) and wireless LANs (WLAN). A realization of such antennas in planar form and the extension to a novel four-quad antenna are described in this contribution. A bandwidth of more than 40% and a gain of around 8 dB of this antenna are demonstrated; narrower beamwidths and a further increase of gain can be achieved using a reflector.
Visual screening for malignant melanoma: a cost-effectiveness analysis.

To evaluate the cost-effectiveness of various melanoma screening strategies proposed in the United States.



We developed a computer simulation Markov model to evaluate alternative melanoma screening strategies.



Hypothetical cohort of the general population and siblings of patients with melanoma.  We considered the following 4 strategies: background screening only, and screening 1 time, every 2 years, and annually, all beginning at age 50 years. Prevalence, incidence, and mortality data were taken from the Surveillance, Epidemiology, and End Results Program. Sibling risk, recurrence rates, and treatment costs were taken from the literature.



Outcomes included life expectancy, quality-adjusted life expectancy, and lifetime costs. Cost-effectiveness ratios were in dollars per quality-adjusted life year (US dollars/QALY) gained.



In the general population, screening 1 time, every 2 years, and annually saved 1.6, 4.4, and 5.2 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 10,100/QALY, US dollars 80,700/QALY, and US dollars 586,800/QALY, respectively. In siblings of patients with melanoma (relative risk, 2.24 compared with the general population), 1-time, every-2-years, and annual screenings saved 3.6, 9.8, and 11.4 QALYs per 1000 persons screened, with incremental cost-effectiveness ratios of US dollars 4000/QALY, US dollars 35,500/QALY, and US dollars 257,800/QALY, respectively. In higher risk siblings of patients with melanoma (relative risk, 5.56), screening was more cost-effective. Results were most sensitive to screening cost, melanoma progression rate, and specificity of visual screening.



One-time melanoma screening of the general population older than 50 years is very cost-effective compared with other cancer screening programs in the United States. Screening every 2 years in siblings of patients with melanoma is also cost-effective.
Physiology and genetics of procalcitonin.

Procalcitonin (PCT), a protein of 116 amino-acids with molecular weight of 13 kDa, was discovered 25 years ago as a prohormone of calcitonin produced by C-cells of the thyroid gland and intracellularly cleaved by proteolytic enzymes into the active hormone. Circulating levels of PCT in healthy subjects are below detection limit. Since 1993 when its elevated level was found in patients with bacterial infection, PCT became an important protein in the detection and differential diagnostics of inflammatory states. The production of PCT during inflammation is linked with a bacterial endotoxin and with inflammatory cytokines (TNF, IL-6). PCT detectable in the plasma during inflammation is not produced in C-cells of the thyroid. The probable site of PCT production during inflammation are the neuroendocrine cells in the lungs or intestine. There is no evidence of plasma PCT binding to cellular receptors of calcitonin, and the role of PCT in calcium and phosphate metabolism during sepsis is still not clear. Other hypothetical roles of PCT (cytokine network regulation, PCT as an endogenous non-steroid antiinflammatory drug) are being considered.
Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set.
ISO 9000 practices and financial performance : A technology coherence perspective

Attention to processes has increased, as thousands of organizations have adopted process-focused programs such as TQM and ISO 9000. Proponents of such programs stress the promise of improved efficiency and profitability. But research has not consistently borne out these prospects. Moreover, the expectation of universal benefits is not consistent with research highlighting the important role of firm-specific capabilities in sustaining competitive advantage. In this paper, we use longitudinal panel data on ISO 9000 practices for firms in the auto supplier industry to study two new issues related to the adoption of process management practices. First, we find that, as the majority of firms within an industry adopt ISO 9000, late adopters no longer gain financial benefits from these practices. Second, we explore how firms’ technological coherence moderates the performance advantages of ISO 9000 practices. We find that firms that have a very narrow or very broad technological focus have fewer opportunities for complementary interactions that arise from process management practices and thus benefit less than those with limited breadth in technologically related activities.
Android malware detection from Google Play meta-data: Selection of important features

Android malware has emerged in the last decade as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features to identify patterns often observed in malware applications. We show that modern Machine Learning techniques applied to collected metadata from Google Play can provide a first approach towards the detection of malware applications, and we further identify which features have the highest predictive power among the total.
A general framework for never-ending learning from time series streams

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.
How do the brain’s time and space mediate consciousness and its different dimensions? Temporo-spatial theory of consciousness (TTC)

Time and space are the basic building blocks of nature. As a unique existent in nature, our brain exists in time and takes up space. The brain's activity itself also constitutes and spreads in its own (intrinsic) time and space that is crucial for consciousness. Consciousness is a complex phenomenon including different dimensions: level/state, content/form, phenomenal aspects, and cognitive features. We propose a Temporo-spatial Theory of Consciousness (TTC) focusing primarily on the temporal and spatial features of the brain activity. We postulate four different neuronal mechanisms accounting for the different dimensions of consciousness: (i) "temporo-spatial nestedness" of the spontaneous activity accounts for the level/state of consciousness as neural predisposition of consciousness (NPC); (ii) "temporo-spatial alignment" of the pre-stimulus activity accounts for the content/form of consciousness as neural prerequisite of consciousness (preNCC); (iii) "temporo-spatial expansion" of early stimulus-induced activity accounts for phenomenal consciousness as neural correlates of consciousness (NCC); (iv) "temporo-spatial globalization" of late stimulus-induced activity accounts for the cognitive features of consciousness as neural consequence of consciousness (NCCcon).
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization

Nonsmooth nonnegative matrix factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data. However, nsNMF as well as other existing NMF methods are incompetent to learn hierarchical features of complex data due to its shallow structure. To fill this gap, we propose a deep nsNMF method coined by the fact that it possesses a deeper architecture compared with standard nsNMF. The deep nsNMF not only gives part-based features due to the nonnegativity constraints but also creates higher level, more abstract features by combing lower level ones. The in-depth description of how deep architecture can help to efficiently discover abstract features in dnsNMF is presented, suggesting that the proposed model inherits the major advantages from both deep learning and NMF. Extensive experiments demonstrate the standout performance of the proposed method in clustering analysis.
On the Usefulness of Compression Models for Authorship Verification

Compression models represent an interesting approach for different classification tasks and have been used widely across many research fields. We adapt compression models to the field of authorship verification (AV), a branch of digital text forensics. The task in AV is to verify if a questioned document and a reference document of a known author are written by the same person. We propose an intrinsic AV method, which yields competitive results compared to a number of current state-of-the-art approaches, based on support vector machines or neural networks. However, in contrast to these approaches our method does not make use of machine learning algorithms, natural language processing techniques, feature engineering, hyperparameter optimization or external documents (a common strategy to transform AV from a one-class to a multi-class classification problem). Instead, the only three key components of our method are a compressing algorithm, a dissimilarity measure and a threshold, needed to accept or reject the authorship of the questioned document. Due to its compactness, our method performs very fast and can be reimplemented with minimal effort. In addition, the method can handle complicated AV cases where both, the questioned and the reference document, are not related to each other in terms of topic or genre. We evaluated our approach against publicly available datasets, which were used in three international AV competitions. Furthermore, we constructed our own corpora, where we evaluated our method against state-of-the-art approaches and achieved, in both cases, promising results.
Wireless Sensor Based Remote Controlled Agriculture Monitoring System using ZigBee

The main objective of the present paper is to develop a smart wireless sensor network (WSN) for an agricultural environment. Monitoring agricultural environment for various factors such as temperature and humidity along with other factors can be of significance. The advanced development in wireless sensor networks can be used in monitoring various parameters in agriculture. Due to uneven natural distribution of rain water it is very difficult for farmers to monitor and control the distribution of water to agriculture field in the whole farm or as per the requirement of the crop. There is no ideal irrigation method for all weather conditions, soil structure and variety of crops cultures. Farmers suffer large financial losses because of wrong prediction of weather and incorrect irrigation methods. Sensors are the essential device for precision agricultural applications. In this paper we have detailed about how to utilize the sensors in crop field area and explained about Wireless Sensor Network (WSN), Zigbee network, Protocol stack, zigbee Applications and the results are given, when implemented the zigbee network experimentally in real time environment.
Evidence supporting the match/mismatch hypothesis of psychiatric disorders

Chronic stress is one of the predominant environmental risk factors for a number of psychiatric disorders, particularly for major depression. Different hypotheses have been formulated to address the interaction between early and adult chronic stress in psychiatric disease vulnerability. The match/mismatch hypothesis of psychiatric disease states that the early life environment shapes coping strategies in a manner that enables individuals to optimally face similar environments later in life. We tested this hypothesis in female Balb/c mice that underwent either stress or enrichment early in life and were in adulthood further subdivided in single or group housed, in order to provide aversive or positive adult environments, respectively. We studied the effects of the environmental manipulation on anxiety-like, depressive-like and sociability behaviors and gene expression profiles. We show that continuous exposure to adverse environments (matched condition) is not necessarily resulting in an opposite phenotype compared to a continuous supportive environment (matched condition). Rather, animals with mismatched environmental conditions behaved differently from animals with matched environments on anxious, social and depressive like phenotypes. These results further support the match/mismatch hypothesis and illustrate how mild or moderate aversive conditions during development can shape an individual to be optimally adapted to similar conditions later in life.
Codebook hardware implementation on FPGA for background subtraction

Object detection and tracking are main tasks in video surveillance systems. Extracting the background is an intensive task with high computational cost. This work proposes a hardware computing engine to perform background subtraction on low-cost field programmable gate arrays (FPGAs), focused on resource-limited environments. Our approach is based on the codebook algorithm and offers very low accuracy degradation. We have analyzed resource consumption and performance trade-offs in Spartan-3 FPGAs by Xilinx. In addition, an accuracy evaluation with standard benchmark sequences has been performed, obtaining better results than previous hardware approaches. The implementation is able to segment objects in sequences with resolution $$768\times 576$$ at 50 fps using a robust and accurate approach, and an estimated power consumption of 5.13 W.
Positive influence maximization in signed social networks based on simulated annealing

Current studies of influence maximization focus almost exclusively on unsigned social networks ignoring the polarities of the relationships between users. Influence maximization in signed social networks containing both positive relationships (e.g., friend or like) and negative relationships (e.g., enemy or dislike) is still a challenging problem which remains much open. A few studies made use of greedy algorithms to solve the problem of positive influence or negative influence maximization in signed social networks. Although greedy algorithm is able to achieve a good approximation, it is computational expensive and not efficient enough. Aiming at this drawback, we propose an alternative method based on Simulated Annealing (SA) for the positive influence maximization problem in this paper. Additionally, we also propose two heuristics to speed up the convergence process of the proposed method. Comprehensive experiments results on three signed social network datasets, Epinions, Slashdot and Wikipedia, demonstrate that our method can yield similar or better performance than the greedy algorithms in terms of positive influence spread but run faster.
Interactive High-Relief Reconstruction for Organic and Double-Sided Objects from a Photo

We introduce an interactive user-driven method to reconstruct high-relief 3D geometry from a single photo. Particularly, we consider two novel but challenging reconstruction issues: i) common non-rigid objects whose shapes are organic rather than polyhedral/symmetric, and ii) double-sided structures, where front and back sides of some curvy object parts are revealed simultaneously on image. To address these issues, we develop a three-stage computational pipeline. First, we construct a 2.5D model from the input image by user-driven segmentation, automatic layering, and region completion, handling three common types of occlusion. Second, users can interactively mark-up slope and curvature cues on the image to guide our constrained optimization model to inflate and lift up the image layers. We provide real-time preview of the inflated geometry to allow interactive editing. Third, we stitch and optimize the inflated layers to produce a high-relief 3D model. Compared to previous work, we can generate high-relief geometry with large viewing angles, handle complex organic objects with multiple occluded regions and varying shape profiles, and reconstruct objects with double-sided structures. Lastly, we demonstrate the applicability of our method on a wide variety of input images with human, animals, flowers, etc.
Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.