nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive caching for the future sensors in fog networking

In this paper, we propose a Cognitive Caching approach for the Future Fog (CCFF) that takes into consideration the value of the exchanged data in Information Centric Sensor Networks (ICSNs). Our approach depends on four functional parameters in ICSNs. These four main parameters are: age of the data, popularity of on-demand requests, delay to receive the requested information and data fidelity. These parameters are considered together to assign a value to the cached data while retaining the most valuable one in the cache for prolonged time periods. This CCFF approach provides significant availability for most valuable and difficult to retrieve data in the ICSNs. Extensive simulations and case studies have been examined in this research in order to compare to other dominant cache management frameworks in the literature under varying circumstances such as data popularity, cache size, data publisher load, and node connectivity degree. Formal fidelity and trust analysis has been applied as well to emphasize the effectiveness of CCFF in Fog paradigms, where edge devices can retrieve unsecured data from the authorized nodes in the cloud.
TouchML: A Machine Learning Toolkit for Modelling Spatial Touch Targeting Behaviour

Pointing tasks are commonly studied in HCI research, for example to evaluate and compare different interaction techniques or devices. A recent line of work has modelled user-specific touch behaviour with machine learning methods to reveal spatial targeting error patterns across the screen. These models can also be applied to improve accuracy of touchscreens and keyboards, and to recognise users and hand postures. However, no implementation of these techniques has been made publicly available yet, hindering broader use in research and practical deployments. Therefore, this paper presents a toolkit which implements such touch models for data analysis (Python), mobile applications (Java/Android), and the web (JavaScript). We demonstrate several applications, including hand posture recognition, on touch targeting data collected in a study with 24 participants. We consider different target types and hand postures, changing behaviour over time, and the influence of hand sizes.
An Improved DBSCAN Clustering Algorithm for Multi-density Datasets

In this paper, we proposed a DBSCAN-based clustering algorithm called NNDD-DBSCAN with the main focus of handling multi-density datasets and reducing parameter sensitivity. The NNDD-DBSCAN used a new distance measuring method called nearest neighbor density distance (NNDD) which makes the new algorithm can clustering properly in multi-density datasets. By analyzing the relationship between the threshold of nearest neighbor density distance and the threshold of nearest neighborcollection, we give a heuristic method to find the appropriate nearest neighbor density distance threshold and reducing parameter sensitivity. Experimental results show that the NNDD-DBSCAN has a good robustadaptation and can get the ideal clustering result both in single density datasets and multi-density datasets.
Effects of wheat-flour and oat mill fractions on jejunal flow, starch degradation and absorption of glucose over an isolated loop of jejunum in pigs.

The effect of cereal-based diets varying in dietary fibre (DF) on gastric emptying and glucose absorption over an isolated loop of jejunum was studied in four pigs fitted with two sets of re-entrant cannulas. The pigs were fed on either a wheat-flour diet or three diets based on oat flour (endosperm), rolled oats or oat bran containing different amounts of soluble DF. Mean transit time (MTT) of liquid estimated from the output from the first jejunal cannula was significantly higher with the two diets having the highest DF content, but MTT of dry matter (DM), starch, xylose and neutral non-starch polysaccharides (nNSP) was not correlated directly to the DF content of the diet. DF had a stimulatory effect on secretion of gastrointestinal juices, but the effect was not linearly correlated with the DF content of the diet. Starch was significantly degraded in digesta collected within 30 min after feeding with malto-oligosaccharides accounting for 140-147 g/kg total starch. The degradation was more extensive with higher DF and lower starch content of the diet. However, taking into account the differences in jejunal flow, the amount of malto-oligosaccharides available for absorption in the first 0.5 h decreased with higher levels of DF in the oat-based diets. The absorption of glucose from the isolated loop was 18-34 g/m intestine over an 8 h period with no significant differences between diets. This corresponded to a non-significant decrease in recovery of starch from 0.91 to 0.82 with increasing levels of DF and decreasing levels of starch in the diet. This suggests that the capacity for absorption of large doses of starch entering the proximal small intestine after ingestion of a carbohydrate-rich cereal-based diet has a major influence on the absorption at this site. Consequently any effect of DF on glucose absorption may be exerted either through the rate of gastric emptying or by impaired rate of absorption more distal in the small intestine and not by displacement of the site for starch absorption.
Bubbles , Financial Crises , and Systemic Risk ∗

This chapter surveys the literature on bubbles, financial crises, and systemic risk. The first part of the chapter provides a brief historical account of bubbles and financial crisis. The second part of the chapter gives a structured overview of the literature on financial bubbles. The third part of the chapter discusses the literatures on financial crises and systemic risk, with particular emphasis on amplification and propagation mechanisms during financial crises, and the measurement of systemic risk. Finally, we point toward some questions for future
Demonstration of quantum error correction and universal gate set on a binomial bosonic logical qubit

L. Hu,1, ∗ Y. Ma,1, ∗ W. Cai,1 X. Mu,1 Y. Xu,1 W. Wang,1 Y. Wu,2 H. Wang,1 Y. P. Song,1 C.-L. Zou,3, † S. M. Girvin,4 L-M. Duan,1, 2 and L. Sun1, ‡ 1Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China 2Department of Physics, University of Michigan,  4Departments of Applied Physics and Physics, Yale University, New Haven, CT 06511, USA
A Comparative Study of Recent Wireless Sensor Network Simulators

Over recent years, the continuous interest in wireless sensor networks (WSNs) has led to the appearance of new modeling methods and simulation environments for WSN applications. A broad variety of different simulation tools have been designed to explore and validate WSN systems before actual implementation and real-world deployment. These tools address different design aspects and offer various simulation abstractions to represent and model real-world behavior. In this article, we present a comprehensive comparative study of mainstream open-source simulation tools for WSNs. Two benchmark applications are designed to evaluate the frameworks with respect to the simulation runtime performance, network throughput, communication medium modeling, packet reception rate, network latency, and power consumption estimation accuracy. Such metrics are also evaluated against measurements on physical prototypes. Our experiments show that the tools produce equivalent results from a functional point of view and capacity to model communication phenomena, while the ability to model details of the execution platform significantly impacts the runtime simulation performance and the power estimation accuracy. The benchmark applications are also made available in the public domain for further studies.
Stylizing animation by example

Skilled artists, using traditional media or modern computer painting tools, can create a variety of expressive styles that are very appealing in still images, but have been unsuitable for animation. The key difficulty is that existing techniques lack adequate temporal coherence to animate these styles effectively. Here we augment the range of practical animation styles by extending the guided texture synthesis method of Image Analogies [Hertzmann et al. 2001] to create temporally coherent animation sequences. To make the method art directable, we allow artists to paint portions of keyframes that are used as constraints. The in-betweens calculated by our method maintain stylistic continuity and yet change no more than necessary over time.
A Survey on Customer Churn Prediction in Telecom Industry : Datasets , Methods and Metrics

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In this competitive world, business is becoming highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore, it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. In the past decade, several data mining techniques have been proposed in the literature for predicting the churners using heterogeneous customer records. This paper reviews the different categories of customer data available in open datasets, predictive models and performance metrics used in the literature for churn prediction in telecom industry.
How to build an educational bridge.

Spin–orbit torque, a torque brought about by in-plane current via the spin–orbit interactions in heavy-metal/ferromagnet nanostructures, provides a new pathway to switch the magnetization direction. Although there are many recent studies, they all build on one of two structures that have the easy axis of a nanomagnet lying orthogonal to the current, that is, along the z or y axes. Here, we present a new structure with the third geometry, that is, with the easy axis collinear with the current (along the x axis). We fabricate a three-terminal device with a Ta/CoFeB/MgO-based stack and demonstrate the switching operation driven by the spin–orbit torque due to Ta with a negative spin Hall angle. Comparisons with different geometries highlight the previously unknown mechanisms of spin–orbit torque switching. Our work offers a new avenue for exploring the physics of spin–orbit torque switching and its application to spintronics devices.
Virginal Breast Hypertrophy and Symptomatic Treatment: A Case Report.

Virginal breast hypertrophy is a rare benign disease. It is characterized by rapid and excessive growth of one or two breasts during peripubertal period. There is no specific treatment algorithm, subcutaneous mastectomy and prosthesis replacement, reduction mammoplasty, medical treatment with particularly tamoxifen are all recommended in the literature. Unfortunately, all treatment methods have some disadvantages in this patient group who have not completed their sexual and physical maturation. Although these treatments are usually required, it should be noted that spontaneous remission could rarely be seen in virginal hypertrophy. We aimed to present a case of virginal hypertrophy, in whom symptomatic treatment has been used and breast growth regressed spontaneously.
A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

 This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).
Under what conditions does theory obstruct research progress?

Researchers display confirmation bias when they persevere by revising procedures until obtaining a theory-predicted result. This strategy produces findings that are overgeneralized in avoidable ways, and this in turn hinders successful applications. (The 40-year history of an attitude-change phenomenon, the sleeper effect, stands as a case in point.) Confirmation bias is an expectable product of theorycentered research strategies, including both the puzzle-solving activity of T. S. Kuhn's "normal science" and, more surprisingly, K. R. Popper's recommended method of falsification seeking. The alternative strategies of condition seeking (identifying limiting conditions for a known finding) and design (discovering conditions that can produce a previously unobtained result) are result centered; they are directed at producing specified patterns of data rather than at the logically impossible goals of establishing either the truth or falsity of a theory. Result-centered methods are by no means atheoretical. Rather, they oblige resourcefulness in using existing theory and can stimulate novel development of theory.
Optimal Power Control in Green Wireless Sensor Networks With Wireless Energy Harvesting, Wake-Up Radio and Transmission Control

Wireless sensor networks (WSNs) are autonomous networks of spatially distributed sensor nodes that are capable of wirelessly communicating with each other in a multihop fashion. Among different metrics, network lifetime and utility, and energy consumption in terms of carbon footprint are key parameters that determine the performance of such a network and entail a sophisticated design at different abstraction levels. In this paper, wireless energy harvesting (WEH), wake-up radio (WUR) scheme, and error control coding (ECC) are investigated as enabling solutions to enhance the performance of WSNs while reducing its carbon footprint. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR, and ECC, is formulated and solved using distributed dual subgradient algorithm based on the Lagrange multiplier method. Discussion and verification through simulation results show how the proposed solutions improve network utility, prolong the lifetime, and pave the way for a greener WSN by reducing its carbon footprint.
Tumor Necrosis Factor Alpha: A Link between Neuroinflammation and Excitotoxicity

Tumor necrosis factor alpha (TNF- α) is a proinflammatory cytokine that exerts both homeostatic and pathophysiological roles in the central nervous system. In pathological conditions, microglia release large amounts of TNF-α; this de novo production of TNF-α is an important component of the so-called neuroinflammatory response that is associated with several neurological disorders. In addition, TNF-α can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca(+2) permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons. Thus, the net effect of TNF-α is to alter the balance of excitation and inhibition resulting in a higher synaptic excitatory/inhibitory ratio. This review summarizes the current knowledge of the cellular and molecular mechanisms by which TNF-α links the neuroinflammatory and excitotoxic processes that occur in several neurodegenerative diseases, but with a special emphasis on amyotrophic lateral sclerosis (ALS). As microglial activation and upregulation of TNF-α expression is a common feature of several CNS diseases, as well as chronic opioid exposure and neuropathic pain, modulating TNF-α signaling may represent a valuable target for intervention.
Effect of nutrient intake and Helicobacter pylori infection on gastric cancer in Korea: a case-control study.

To examine the effects of dietary factor and Helicobacter pylori (H. pylori) infection with emphasis on vitamin intake on the risk of gastric cancer (GC), we conducted a case-control study in South Korea, a high-risk area for GC. Trained dietitians interviewed 136 cases histologically diagnosed with GC. An equal number of hospital controls was selected by matching sex and age. High dietary intakes of vegetable fat [odds ratio (OR) = 0.35; 95% confidence interval (CI) = 0.15-0.83], folate (OR = 0.35; 95% CI = 0.13-0.96), and antioxidants, such as vitamin A (OR = 0.34; 95% CI = 0.13-0.83), beta-carotene (OR = 0.33; 95% CI = 0.13-0.82), vitamin C (OR = 0.26; 95% CI = 0.09-0.72), and vitamin E (OR = 0.41; 95% CI = 0.17-1.01), were shown to have a protective effect on GC risk using a multivariate model adjusting for foods significantly related to GC in our previous study (charcoal grilled beef, spinach, garlic, mushroom, and a number of types of kimchi) and supplement use. When stratified according to H. pylori infection, high intakes of vitamin C (OR = 0.10; 95% CI = 0.02-0.63) and vitamin E (OR = 0.16; 95% CI = 0.03-0.83) exhibited highly significant inverse associations with GC among the H. pylori-infected subjects compared with noninfected individuals. GC risk was significantly decreased only when consumption levels for two of these vitamins were high. Our findings suggest that high intake of antioxidant vitamins contribute to the reduction of GC risk and that GC risk in Korea may be decreased by encouraging those with H. pylori infection to increase their intake of antioxidant vitamins.
Fuzzy fault tree analysis: a review of concept and application

Fault tree analysis (FTA) is a widely used method for analyzing a system’s failure logic and calculating overall reliability. However, application of conventional FTA has some shortcomings, e.g. in handling the uncertainties, allowing the use of linguistic variables, and integrating human error in failure logic model. Hence, Fuzzy set theory has been proposed to overcome the limitation of conventional FTA. Fuzzy logic provides a framework whereby basic notions such as similarity, uncertainty and preference can be modeled effectively. The aim of this paper is to present a review of the concept of fuzzy theory with fault tree analysis and their applications since 1981, to reflect the current status of Fuzzy fault tree analysis (FFTA) methodologies, their strengths, weaknesses, and their applications. This paper explains the fundamentals of fuzzy theory and describes application of fuzzy importance for using FFTA. The concept of the failure possibility and uncertainty analysis by using FFTA is discussed, and concludes with discussion on the application of FFTA in different fields. The review reveals the effectiveness of the FFTA in comparison with conventional FTA, when there is inadequate amount of accurate reliability oriented information.
Brain-Computer Interfacing

Recently, CNN reported on the future of brain-computer interfaces (BCIs) . Brain-computer interfaces are devices that process a user’s brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action . Historically, BCIs were developed with biomedical applications in mind, such as restoring communication in completely paralyzed individuals and replacing lost motor function. More recent applications have targeted non-disabled individuals by exploring the use of BCIs as a novel input device for entertainment and gaming.
OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds

Accurate and efficient foreground detection is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a very nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is difficult to apply for real-time system. To handle these challenges, this paper presents a robust foreground detection algorithm via Online Robust PCA (OR-PCA) using image decomposition along with continuous constraint such as Markov Random Field (MRF). OR-PCA with good initialization scheme using image decomposition approach improves the accuracy of foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets such as Wallflower, I2R, BMC 2012 and Change Detection 2014 dataset demonstrate that our proposed scheme significantly outperforms the state of the art approaches and works effectively on a wide range of complex background scenes.
Organizational culture and information systems implementation: A Structuration Theory perspective

The structure and culture of an organization does affect implementation of projects. In this paper we try to identify organizational factors that could affect the implementation efforts of an Integrated Financial Management Information System (IFMIS). The information system in question has taken overtly a long time and it's not complete yet. We set out to And out whether organizational issues are at play in this particular project. The project under study is a large-scale integrated information system which aims at strengthening and further developing Financial Management Information in the wider public service in Kenya. We borrow concepts from Structuration Theory (ST) as applied in sociology to understand the organizational perspective in the project. We use the theory to help explain some of the meanings, norms and issues of power experienced during the implementation of the IFMIS. Without ruling out problems of technological nature, the findings suggest that many of the problems in the IFMIS implementation may be attributed to organizational factors, and that certain issues are related to the existing organization culture within government.
Robust Steganography based on Matching Pixel Locations

Steganography consist of concealing secret information in a cover object to be sent over a public communication channel. It allows two parties to share hidden information in a way that no intruder can detect the presence of hidden information. This paper presents a novel steganography approach based on pixel location matching of the same cover image. Here the information is not directly embedded within the cover image but a sequence of 4 bits of secret data is compared to the 4 most significant bits (4MSB) of the cover image pixels. The locations of the matching pixels are taken to substitute the 2 least significant bits (2LSB) of the cover image pixels. Since the data are not directly hidden in cover image, the proposed approach is more secure and difficult to break. Intruders cannot intercept it by using common LSB techniques.