nils-herrmann/clean_scirepeval huggingface dataset:
The Semantic Middleware for Networked Embedded Systems Applied in the Internet of Things and Services Domain

The paper presents the LinkSmart middleware platform that addresses the Internet of Things and Services approach. The platform was designed to support the interoperability and seamless integration of various external devices, sensors, and services into the mainstream enterprise systems. The design and development of LinkSmart goes across two integrated European research projects, namely the FP6 IST project Hydra and the FP7 ICT project EBBITS. Modular architecture and functionality of LinkSmart prototype, developed by combining the service-oriented architecture, peer-to-peer networking, and semantic web services technologies, is described with focus on semantic binding of networked devices by means of underlying ontologies and knowledgebased inference mechanisms. Extensions of the solution towards the service orchestration, complex event handling, business process modelling and workflow processing are discussed and described on a mechanism of context-aware processing of sensor data.
Is zirconia a viable alternative to titanium for oral implant? A critical review.

Titanium based implant systems, though considered as the gold standard for rehabilitation of edentulous spaces, have been criticized for many inherent flaws. The onset of hypersensitivity reactions, biocompatibility issues, and an unaesthetic gray hue have raised demands for more aesthetic and tissue compatible material for implant fabrication. Zirconia is emerging as a promising alternative to conventional Titanium based implant systems for oral rehabilitation with superior biological, aesthetics, mechanical and optical properties. This review aims to critically analyze and review the credibility of Zirconia implants as an alternative to Titanium for prosthetic rehabilitation.



The literature search for articles written in the English language in PubMed and Cochrane Library database from 1990 till December 2016. The following search terms were utilized for data search: "zirconia implants" NOT "abutment", "zirconia implants" AND "titanium implants" AND "osseointegration", "zirconia implants" AND compatibility.



The number of potential relevant articles selected were 47. All the human in vivo clinical, in vitro, animals' studies were included and discussed under the following subheadings: Chemical composition, structure and phases; Physical and mechanical properties; Aesthetic and optical properties; Osseointegration and biocompatibility; Surface modifications; Peri-implant tissue compatibility, inflammation and soft tissue healing, and long-term prognosis.



Zirconia implants are a promising alternative to titanium with a superior soft-tissue response, biocompatibility, and aesthetics with comparable osseointegration. However, further long-term longitudinal and comparative clinical trials are required to validate zirconia as a viable alternative to the titanium implant.
A gap analysis of Internet-of-Things platforms

We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today’s solutions to improve their integration to tomorrow’s ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.
Effects of fatty acids on meat quality: a review.

Interest in meat fatty acid composition stems mainly from the need to find ways to produce healthier meat, i.e. with a higher ratio of polyunsaturated (PUFA) to saturated fatty acids and a more favourable balance between n-6 and n-3 PUFA. In pigs, the drive has been to increase n-3 PUFA in meat and this can be achieved by feeding sources such as linseed in the diet. Only when concentrations of α-linolenic acid (18:3) approach 3% of neutral lipids or phospholipids are there any adverse effects on meat quality, defined in terms of shelf life (lipid and myoglobin oxidation) and flavour. Ruminant meats are a relatively good source of n-3 PUFA due to the presence of 18:3 in grass. Further increases can be achieved with animals fed grain-based diets by including whole linseed or linseed oil, especially if this is "protected" from rumen biohydrogenation. Long-chain (C20-C22) n-3 PUFA are synthesised from 18:3 in the animal although docosahexaenoic acid (DHA, 22:6) is not increased when diets are supplemented with 18:3. DHA can be increased by feeding sources such as fish oil although too-high levels cause adverse flavour and colour changes. Grass-fed beef and lamb have naturally high levels of 18:3 and long chain n-3 PUFA. These impact on flavour to produce a 'grass fed' taste in which other components of grass are also involved. Grazing also provides antioxidants including vitamin E which maintain PUFA levels in meat and prevent quality deterioration during processing and display. In pork, beef and lamb the melting point of lipid and the firmness/hardness of carcass fat is closely related to the concentration of stearic acid (18:0).
What Do I Annotate Next? An Empirical Study of Active Learning for Action Localization

Despite tremendous progress achieved in temporal action localization, state-of-the-art methods still struggle to train accurate models when annotated data is scarce. In this paper, we introduce a novel active learning framework for temporal localization that aims to mitigate this data dependency issue. We equip our framework with active selection functions that can reuse knowledge from previously annotated datasets. We study the performance of two state-of-the-art active selection functions as well as two widely used active learning baselines. To validate the effectiveness of each one of these selection functions, we conduct simulated experiments on ActivityNet. We find that using previously acquired knowledge as a bootstrapping source is crucial for active learners aiming to localize actions. When equipped with the right selection function, our proposed framework exhibits significantly better performance than standard active learning strategies, such as uncertainty sampling. Finally, we employ our framework to augment the newly compiled Kinetics action dataset with ground-truth temporal annotations. As a result, we collect Kinetics-Localization, a novel large-scale dataset for temporal action localization, which contains more than 15K YouTube videos.
N-gram-based text categorization

Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in email, and character recognition errors in documents that come through OCR. Text categorization must work reliably on all input, and thus must tolerate some level of these kinds of problems. We describe here an N-gram-based approach to text categorization that is tolerant of textual errors. The system is small, fast and robust. This system worked very well for language classification, achieving in one test a 99.8% correct classification rate on Usenet newsgroup articles written in different languages. The system also worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject, achieving as high as an 80% correct classification rate. There are also several obvious directions for improving the system’s classification performance in those cases where it did not do as well. The system is based on calculating and comparing profiles of N-gram frequencies. First, we use the system to compute profiles on training set data that represent the various categories, e.g., language samples or newsgroup content samples. Then the system computes a profile for a particular document that is to be classified. Finally, the system computes a distance measure between the document’s profile and each of the category profiles. The system selects the category whose profile has the smallest distance to the document’s profile. The profiles involved are quite small, typically 10K bytes for a category training set, and less than 4K bytes for an individual document. Using N-gram frequency profiles provides a simple and reliable way to categorize documents in a wide range of classification tasks.
SO-Net: Self-Organizing Network for Point Cloud Analysis

This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted by conducting point-to-node k nearest neighbor search. In recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our proposed network demonstrates performance that is similar with or better than state-of-the-art approaches. In addition, the training speed is significantly faster than existing point cloud recognition networks because of the parallelizability and simplicity of the proposed architecture. Our code is available at the project
Does culture influence what and how we think? Effects of priming individualism and collectivism.

Do differences in individualism and collectivism influence values, self-concept content, relational assumptions, and cognitive style? On the one hand, the cross-national literature provides an impressively consistent picture of the predicted systematic differences; on the other hand, the nature of the evidence is inconclusive. Cross-national evidence is insufficient to argue for a causal process, and comparative data cannot specify if effects are due to both individualism and collectivism, only individualism, only collectivism, or other factors (including other aspects of culture). To address these issues, the authors conducted a meta-analysis of the individualism and collectivism priming literature, with follow-up moderator analyses. Effect sizes were moderate for relationality and cognition, small for self-concept and values, robust across priming methods and dependent variables, and consistent in direction and size with cross-national effects. Results lend support to a situated model of culture in which cross-national differences are not static but dynamically consistent due to the chronic and moment-to-moment salience of individualism and collectivism. Examination of the unique effects of individualism and collectivism versus other cultural factors (e.g., honor, power) awaits the availability of research that primes these factors.
Blockchain-based Personal Health Data Sharing System Using Cloud Storage

With the advent of rapid development of wearable technology and mobile computing, huge amount of personal health-related data is being generated and accumulated on continuous basis at every moment. These personal datasets contain valuable information and they belong to and asset of the individual users, hence should be owned and controlled by themselves. Currently most of such datasets are stored and controlled by different service providers and this centralised data storage brings challenges of data security and hinders the data sharing. These personal health data are valuable resources for healthcare research and commercial projects. In this research work, we propose a conceptual design for sharing personal continuous-dynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner. Besides, we also introduce a data quality inspection module based on machine learning techniques to have control over data quality. The primary goal of the proposed system is to enable users to own, control and share their personal health data securely, in a General Data Protection Regulation (GDPR) compliant way to get benefit from their personal datasets. It also provides an efficient way for researchers and commercial data consumers to collect high quality personal health data for research and commercial purposes.
On Becoming a Strategic Partner : The Role of Human Resources in Gaining Competitive Advantage by

Although managers cite human resources as a firm's most important asset, many organizational decisions do not reflect this belief. This paper uses the VRIO (value, rareness, imitability, and organization) framework to examine the role that the Human Resource (HR) function plays in developing a sustainable competitive advantage. We discuss why some popularly cited sources of sustainable competitive advantage are not, and what aspects of a firm's human resources can provide a source of sustainable competitive advantage. We also examine the role of the HR executive as a strategic partner in developing and maintaining competitive advantage within the firm.
An Efficient DSP-FPGA-Based Real-Time Implementation Method of SVM Algorithms for an Indirect Matrix Converter

This paper proposes a real-time DSP- and FPGA-based implementation method of a space vector modulation (SVM) algorithm for an indirect matrix converter (IMC). Therefore, low-cost and compact control platform is built using a 32-bit fixed-point DSP (TMS320F2812) operating at 150 MHz and a SPARTAN 3E FPGA operating at 50 MHz. The method consists in using the event-manager modules of the DSP to build specified pulses at its PWM output peripherals, which are fed to the digital input ports of a FPGA. Moreover, a simple logical processing and delay times are thereafter implemented in the FPGA so as to synthesize the suitable gate pulse patterns for the semiconductor-controlled devices. It is shown that the proposed implementation method enables high switching frequency operation with high pulse resolution as well as a negligible propagation time for the generation of the gating pulses. Experimental results from an IMC prototype confirm the practical feasibility of the proposed technique.
Persistent Navigation and Mapping using a Biologically Inspired SLAM System

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times. The  No. 00, Xxxxxxxx 2009,  2009.  http://www.sagepub.co.uk/journalsPermissions.nav Figures 1–3, 6–12, 15–19, A.1 appear in color online: http://ijr.sagepub.com KEY WORDS—persistent navigation and mapping, SLAM, RatSLAM, biologically inspired
On the Future of Technological Forecasting

Technological forecasting is now poised to respond to the emerging needs of private and public sector organizations in the highly competitive global environment. The history of the subject and its variant forms, including impact assessment, national foresight studies, roadmapping, and competitive technological intelligence, shows how it has responded to changing institutional motivations. Renewed focus on innovation, attention to science-based opportunities, and broad social and political factors will bring renewed attention to technological forecasting in industry, government, and academia. Promising new tools are anticipated, borrowing variously from fields such as political science, computer science, scientometrics, innovation management, and complexity science.  Technological forecasting—its purpose, methods, terminology, and uses—will be shaped in the future, as in the past, by the needs of corporations and government  These have a continual pressing need to anticipate and cope with the direction and rate of technological change. The future of technological forecasting will also depend on the views of the public and their elected representatives about technological progress, economic competition, and the government’s role in technological development. In the context of this article, “technological forecasting” (TF) includes several new forms—for example, national foresight studies, roadmapping, and competitive technological intelligence—that have evolved to meet the changing demands of user institutions. It also encompasses technology assessment (TA) or social impact analysis, which emphasizes the downstream effects of technology’s invention, innovation, and evolution. VARY COATES is associated with the Institute for Technology Assessment, Washington, DC. MAHMUD FAROQUE is with George Mason University, Fairfax, VA. RICHARD KLAVANS is with CRP, Philadelphia, PA. KOTY LAPID is with Softblock, Beer Sheba, Israel. HAROLD LINSTONE is with Portland State University. CARL PISTORIUS is with the University of Pretoria, South Africa. ALAN PORTER is with the Georgia Institute of Technology, Atlanta, GA. We also thank Joseph Coates and Joseph Martino for helpful critiques. 1 The term “technological forecasting” is used in this article to apply to all purposeful and systematic attempts to anticipate and understand the potential direction, rate, characteristics, and effects of technological change, especially invention, innovation, adoption, and use. No distinction is intended between “technological forecasting” “technology forecasting,” or “technology foresight,” except as specifically described in the text. Technological Forecasting and Social Change 67, 1–17 (2001)  front matter 655 Avenue of the Americas, New York, NY 10010 PII S0040-1625(00)00122-0
E-Commerce Recommendation Applications

Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.
FairAccess: a new Blockchain-based access control framework for the Internet of Things

Security and privacy are huge challenges in Internet of Things (IoT) environments, but unfortunately, the harmonization of the IoT-related standards and protocols is hardly and slowly widespread. In this paper, we propose a new framework for access control in IoT based on the blockchain technology. Our first contribution consists in providing a reference model for our proposed framework within the Objectives, Models, Architecture and Mechanism specification in IoT. In addition, we introduce FairAccess as a fully decentralized pseudonymous and privacy preserving authorization management framework that enables users to own and control their data. To implement our model, we use and adapt the blockchain into a decentralized access control manager. Unlike financial bitcoin transactions, FairAccess introduces new types of transactions that are used to grant, get, delegate, and revoke access. As a proof of concept, we establish an initial implementation with a Raspberry PI device and local blockchain. Finally, we discuss some limitations and propose further opportunities.
Econometrics of Financial Markets The Econometrics of Financial Markets

Sometimes you just have to clench your teeth and go for the differential matrix algebra. And the central limit theorems. Together with the maximum likelihood techniques. And the static mean variance portfolio theory. Not forgetting the dynamic asset pricing models. And these are just the tools you need before you can start making empirical inferences in financial economics.” So wrote Ruben Lee, playfully, in a review of The Econometrics of Financial Markets, winner of TIAA-CREF’s  Paul A. Samuelson Award. In  economist Harry M. Markowitz, who in  won the Nobel Prize in Economics, published his landmark thesis “Portfolio Selection” as an article in the Journal of Finance, and financial economics was born. Over the subsequent decades, this young and burgeoning field saw many advances in theory but few in econometric technique or empirical results. Then, nearly four decades later, Campbell, Lo, and MacKinlay’s The Econometrics of Financial Markets made a bold leap forward by integrating theory and empirical work. The three economists combined their own pathbreaking research with a generation of foundational work in modern financial theory and research. The book includes treatment of topics from the predictability of asset returns to the capital asset pricing model and arbitrage pricing theory, from statistical fractals to chaos theory. Read widely in academe as well as in the business world, The Econometrics of Financial Markets has become a new landmark in financial economics, extending and enhancing the Nobel Prize– winning work established by the early trailblazers in this important field.