nils-herrmann/clean_scirepeval huggingface dataset:
“ Characterization and Investigation of Mechanical Properties of Hybrid Natural Fiber Polymer Composite Materials Used As Orthopaedic Implants for Femur Bone Prosthesis ”

This Research Paper constitutes the study of Mechanical Properties of Tensile, Compression and Bending Strength of the 12%, 24% and 36% of Hybrid Fiber (Natural fiberSisal, Jute and Hemp) polymer composite material used as Bio-material. Characterization of 12%, 24% & 36% Hybrid Natural fiber polymer composite material with the low density, economical for prosthetic bone with respect to biocompatibility and the mechanical behavior of long human bones, such as Femur Bone. According to the ASTM Standards the specimen is fabricated by using the Epoxy resinLY556 as the matrix material and the Hardener-HY 951 with the 12%, 24% and 36% of the Natural Fibers (Sisal, Jute and Hemp) as the reinforcement material with fiber weight fraction, randomly continuous long fiber orientation. By using the Hand Layup fabrication technique the specimen are prepared. This Research work concentrated on study of Femur Bone and collection of the strength and other parameters of bone and compared the experimental results of the 12%, 24% and 36% Hybrid Natural fiber Polymer composite material with the Femur bone. This Research work suggesting the low weight, low Density and high strength, Bio-composite, Biocompatible material use or suggest to the orthopedic Implants Especially for Femur bone. From the Experimental results all the strengths of 12%, 24% &36% Hybrid Natural fiber Polymer composite materials will match the femur Bone Strengths and also it is found that by increasing the weight fraction of the fiber or percentage of fiber which will increase the Tensile, Compression, Bending strength and increases the density and mass of composite of the specimen. Finally we suggest the 36% Hybrid Natural fiber polymer composite material for Femur bone Prosthesis and hence it is a Natural Bio-compatible Hybrid Natural fiber polymer composite material.
Nonparametric General Reinforcement Learning

Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs). In this thesis we go beyond MDPs and consider reinforcement learning in environments that are non-Markovian, non-ergodic and only partially observable. Our focus is not on practical algorithms, but rather on the fundamental underlying problems: How do we balance exploration and exploitation? How do we explore optimally? When is an agent optimal? We follow the nonparametric realizable paradigm: we assume the data is drawn from an unknown source that belongs to a known countable class of candidates. First, we consider the passive (sequence prediction) setting, learning from data that is not independent and identically distributed. We collect results from artificial intelligence, algorithmic information theory, and game theory and put them in a reinforcement learning context: they demonstrate how an agent can learn the value of its own policy. Next, we establish negative results on Bayesian reinforcement learning agents, in particular AIXI. We show that unlucky or adversarial choices of the prior cause the agent to misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto optimality, which depend crucially on the choice of the prior, are entirely subjective. Moreover, in the class of all computable environments every policy is Pareto optimal. This undermines all existing optimality properties for AIXI. However, there are Bayesian approaches to general reinforcement learning that satisfy objective optimality guarantees: We prove that Thompson sampling is asymptotically optimal in stochastic environments in the sense that its value converges to the value of the optimal policy. We connect asymptotic optimality to regret given a recoverability assumption on the environment that allows the agent to recover from mistakes. Hence Thompson sampling achieves sublinear regret in these environments. AIXI is known to be incomputable. We quantify this using the arithmetical hierarchy, and establish upper and corresponding lower bounds for incomputability. Further, we show that AIXI is not limit computable, thus cannot be approximated using finite computation. However there are limit computable ε-optimal approximations to AIXI. We also derive computability bounds for knowledge-seeking agents, and give a limit computable weakly asymptotically optimal reinforcement learning agent. Finally, our results culminate in a formal solution to the grain of truth problem: A Bayesian agent acting in a multi-agent environment learns to predict the other agents’ policies if its prior assigns positive probability to them (the prior contains a grain of truth). We construct a large but limit computable class containing a grain of truth and show that agents based on Thompson sampling over this class converge to play ε-Nash equilibria in arbitrary unknown computable multi-agent environments.
Embedded System for Biometric Identiication

Biometrics refers to automatic identification of a person based on his or her physiological or behavioral characteristics which provide a reliable and secure user authentication for the increased security requirements of our personal information compared to traditional identification methods such as passwords and PINs (Jain et al., 2000). Organizations are looking to automate identity authentication systems to improve customer satisfaction and operating efficiency as well as to save critical resources due to the fact that identity fraud in welfare disbursements, credit card transactions, cellular phone calls, and ATM withdrawals totals over $6 billion each year (Jain et al., 1998). Furthermore, as people become more connected electronically, the ability to achieve a highly accurate automatic personal identification system is substantially more critical. Enormous change has occurred in the world of embedded systems driven by the advancement on the integrated circuit technology and the availability of open source. This has opened new challenges and development of advanced embedded system. This scenario is manifested in the appearance of sophisticated new products such as PDAs and cell phones and by the continual increase in the amount of resources that can be packed into a small form factor which requires significant high end skills and knowledge. More people are gearing up to acquire advanced skills and knowledge to keep abreast of the technologies to build advanced embedded system using available Single Board Computer (SBC) with 32 bit architectures.
Fostering Knowledge Exchange in Online Communities: A Social Capital Building Approach

Prior studies on knowledge contribution started with the motivating role of social capital to predict knowledge contribution but did not specifically examine how they can be built in the first place. Our research addresses this gap by highlighting the role technology plays in supporting the development of social capital and eventual knowledge sharing intention. Herein, we propose four technology-based social capital builders – identity profiling, sub-community building, feedback mechanism, and regulatory practice – and theorize that individuals’ use of these IT artifacts determine the formation of social capital, which in turn, motivate knowledge contribution in online communities. Data collected from 253 online community users provide support for the proposed structural model. The results show that use of IT artifacts facilitates the formation of social capital (network ties, shared language, identification, trust in online community, and norms of cooperation) and their effects on knowledge contribution operate indirectly through social capital.
Why Measure Performance ? Different Purposes Require Different Measures

measures are worthless. To use a performance measure—to extract information from it—a manager needs a specific, comparative gauge, plus an understanding of the relevant context. A truck has been driven 6.0 million. Six million what? Six million miles? That’s impressive. Six million feet? That’s only 1,136 miles. Six million inches? That’s not even 95 miles. Big deal—unless those 95 miles were driven in two hours along a dirt road on a very rainy night. To use performance measures to achieve any of these eight purposes, the public manager needs some kind of standard with which the measure can be compared. 1. To use a measure to evaluate performance, public managers need some kind of desired result with which to compare the data, and thus judge performance. 2. To use a measure of performance to control behavior, public managers need first to establish the desired behavioral or input standard from which to gauge individual or collective deviance. 3. To use efficiency measures to budget, public managers need an idea of what is a good, acceptable, or poor level of efficiency. 4. To use performance measures to motivate people, public managers need some sense of what are reasonable and significant targets. 5. To use performance measures to promote an agency’s competence, public managers need to understand what the public cares about. 6. To use performance measures to celebrate, public managers need to discern the kinds of achievements that employees and collaborators think are worth celebrating. 7. To use performance measures to learn, public managers need to be able to detect unexpected (and significant) developments and anticipate a wide variety of common organizational, human, and societal behaviors. 8. To use performance measures to improve, public managers need an understanding (or prediction) of how their actions affect the inside-the-black-box behavior of the people who contribute to their desired outputs and outcomes. All of the eight purposes require (explicitly or implicitly) a baseline with which the measure can be compared. And, of course, the appropriate baseline depends on the context. The standard against which to compare current performance can come from a variety of sources—each with its own advantages and liabilities. The agency may use its historical record as a baseline, looking to see how much it has improved. It may use comparative information from similar organizations, such as the data collected by the Comparative Performance Measurement Consortium organized by the International City/County Management Association (1999), or the effort to measure and compare the performance of local jurisdictions in North Carolina organized by the University of North Carolina (Rivenbark and Few 2000). Of course, comparative data also may come from dissimilar organizations; citizens may compare—implicitly or quite explicitly—the ease of navigating a government Web site with the ease of navigating those created by private businesses. Or the standard may be an explicit performance target established by the legislature, by political executives, or by career managers.
Helios: Web-based Open-Audit Voting

Voting with cryptographic auditing, sometimes called open-audit voting, has remained, for the most part, a theoretical endeavor. In spite of dozens of fascinating protocols and recent ground-breaking advances in the field, there exist only a handful of specialized implementations that few people have experienced directly. As a result, the benefits of cryptographically audited elections have remained elusive. We present Helios, the first web-based, open-audit voting system. Helios is publicly accessible today: anyone can create and run an election, and any willing observer can audit the entire process. Helios is ideal for online software communities, local clubs, student government, and other environments where trustworthy, secretballot elections are required but coercion is not a serious concern. With Helios, we hope to expose many to the power of open-audit elections.
Effekt: extensible algebraic effects in Scala (short paper)

Algebraic effects are an interesting way to structure effectful programs and offer new modularity properties. We present the Scala library Effekt, which is implemented in terms of a monad for multi-prompt delimited continuations and centered around capability passing. This makes the newly proposed feature of implicit function types a perfect fit for the syntax of our library. Basing the library design on capability passing and a polymorphic embedding of effect handlers furthermore opens up interesting dimensions of extensibility. Preliminary benchmarks comparing Effekt with an established library suggest significant speedups.
Banking on Blockchain: Costs Savings Thanks to the Blockchain Technology

This paper looks at the challenges and opportunities of implementing blockchain technology across banking, providing food for thought about the potentialities of this disruptive technology. The blockchain technology can optimize the global financial infrastructure, achieving sustainable development, using more efficient systems than at present. In fact, many banks are currently focusing on blockchain technology to promote economic growth and accelerate the development of green technologies. In order to understand the potential of blockchain technology to support the financial system, we studied the actual performance of the Bitcoin system, also highlighting its major limitations, such as the significant energy consumption due to the high computing power required, and the high cost of hardware. We estimated the electrical power and the hash rate of the Bitcoin network, over time, and, in order to evaluate the efficiency of the Bitcoin system in its actual operation, we defined three quantities: “economic efficiency”, “operational efficiency”, and “efficient service”. The obtained results show that by overcoming the disadvantages of the Bitcoin system, and therefore of blockchain technology, we could be able to handle financial processes in a more efficient way than under the current system.
The gut microbiome of kittens is affected by dietary protein:carbohydrate ratio and associated with blood metabolite and hormone concentrations.

High-protein, low-carbohydrate (HPLC) diets are common in cats, but their effect on the gut microbiome has been ignored. The present study was conducted to test the effects of dietary protein:carbohydrate ratio on the gut microbiota of growing kittens. Male domestic shorthair kittens were raised by mothers fed moderate-protein, moderate-carbohydrate (MPMC; n 7) or HPLC (n 7) diets, and then weaned at 8 weeks onto the same diet. Fresh faeces were collected at 8, 12 and 16 weeks; DNA was extracted, followed by amplification of the V4–V6 region of the 16S rRNA gene using 454 pyrosequencing. A total of 384 588 sequences (average of 9374 per sample) were generated. Dual hierarchical clustering indicated distinct clustering based on the protein:carbohydrate ratio regardless of age. The protein:carbohydrate ratio affected faecal bacteria. Faecal Actinobacteria were greater (P< 0·05) and Fusobacteria were lower (P< 0·05) in MPMC-fed kittens. Faecal Clostridium, Faecalibacterium, Ruminococcus, Blautia and Eubacterium were greater (P< 0·05) in HPLC-fed kittens, while Dialister, Acidaminococcus, Bifidobacterium, Megasphaera and Mitsuokella were greater (P< 0·05) in MPMC-fed kittens. Principal component analysis of faecal bacteria and blood metabolites and hormones resulted in distinct clusters. Of particular interest was the clustering of blood TAG with faecal Clostridiaceae, Eubacteriaceae, Ruminococcaceae, Fusobacteriaceae and Lachnospiraceae; blood ghrelin with faecal Coriobacteriaceae, Bifidobacteriaceae and Veillonellaceae; and blood glucose, cholesterol and leptin with faecal Lactobacillaceae. The present results demonstrate that the protein:carbohydrate ratio affects the faecal microbiome, and highlight the associations between faecal microbes and circulating hormones and metabolites that may be important in terms of satiety and host metabolism.
Learning and example selection for object and pattern detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach rst builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an active learning formulation for function approximation, and show for three speci c approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.  This research is sponsored by grants from the O ce of Naval Research under contracts N00014-91-J-1270 and N00014-92-J-1879; by a grant from the National Science Foundation under contract ASC-9217041. Support for the A.I. Laboratory's arti cial intelligence research is provided by ONR contract N00014-91-J-4038. Learning and Example Selection for Object and Pattern Detection
Pricing Digital Marketing : Information , Risk Sharing and Performance

A unique value proposition of Internet-based digital marketing is the availability of precise measures of the actual performance of individual campaigns, which makes performance-based advertising pricing schedules feasible. These pricing schemes are studied in the presence of competition, performance uncertainty and asymmetric information about the quality of the client’s content and the effectiveness of the publisher’s technology. The paper’s findings challenge the dominant practice of CPM-based pricing for digital marketing by establishing that performance-based pricing is always profit-improving for publishers, even when publishers are constrained to offer CPM-based pricing in parallel. It is shown that performance-based pricing cannot screen out clients with lower quality content/creative quality, and describe when to choose between low-end coverage and full coverage performance-based pricing. However, publishers can use performance-based pricing to credibly signal superior technological effectiveness. These results highlight the value of risk pooling for publishers of digital advertising, and the strategic role of pricing in signalling technological quality. Managerial guidelines are also provided for how to strategically respond to varying competitive intensity, client size and changes in outcome distributions.
Estimating Google's search engine ranking function from a search engine optimization perspective

This study aims to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective.  The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results.  The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. Our empirical results reveal that PageRank is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries. This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar.  The findings indicate that Web marketers should pay particular attention to a webpage’s PageRank, and then place the keyword in the page title, snippet, and URL.  There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort.
The Ecosystem of Software Platform: A Study of Asymmetric Cross-Side Network Effects and Platform Governance

In the context of software platforms, we examine how cross-side network effects (CNEs) on different platform sides (application-side and user-side) are temporally asymmetric, and how these CNEs are influenced by the platform’s governance policies. Informed by a perspective of value creation and capture, we theorize how the app-side and the userside react to each other with distinct value creation/capture processes, and how these processes are influenced by the platform’s governance policies on app review and platform updates. We use a time-series analysis to empirically investigate the platform ecosystem of a leading web browser. Our findings suggest that while the growth in platform usage results in long-term growth in both the number and variety of apps, the growth in the number of apps and the variety of apps only leads to short-term growth in platform usage. We also find that long app review time weakens the long-term CNE of the user-side on the app-side, but not the short-term CNE of the app-side on the user-side. Moreover, we find that frequent platform updates weaken the CNEs of both the user-side and the app-side on each other. These findings generate important implications regarding how a software platform may better govern its ecosystem with different participants.
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

Ensemble methods use multiple models to get better performance. Ensemble methods have been used in multiple research fields such as computational intelligence, statistics and machine learning. This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners. The ensemble methods are categorized into conventional ensemble methods such as bagging, boosting and random forest, decomposition methods, negative correlation learning methods, multi-objective optimization based ensemble methods, fuzzy ensemble methods, multiple kernel learning ensemble methods and deep learning based ensemble methods. Variations, improvements and typical applications are discussed. Finally this paper gives some recommendations for future research directions.
Integrating Computational Thinking with K-12 Science Education - A Theoretical Framework

Computational thinking (CT) draws on concepts that are fundamental to computing and computer science, however, as an approach, it includes practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction that are also central to modelling, reasoning, and problem solving in many scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating programming and CT with K-12 curricula. In this paper, we present a theoretical investigation of key issues that need to be considered for integrating CT with K-12 science. We identify the synergies between programming and CT on one hand, and scientific expertise on the other. We then present a critical review of literature on educational computing, and propose a set of guidelines for designing learning environments in science that can jointly foster the development of computational thinking with scientific expertise. Finally, we describe the design of a learning environment that supports CT through modelling and simulation to help middle school students learn physics and biology.