nils-herrmann/clean_scirepeval huggingface dataset:
Comparison among three analytical methods for knowledge communities group-decision analysis

Knowledge management can greatly facilitate an organization’s learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), ‘‘Technique for Order Preference by Similarity to an Ideal Solution’’ (TOPSIS) and ‘‘VlseKriterijumska Optimizacija I Kompromisno Resenje’’ (VIKOR), are based on an aggregating function representing ‘‘closeness to the ideal point’’. The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems, provide excellent results close to reality, and grant superior analysis.
Sugar: Secure GPU Acceleration in Web Browsers

Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.
Full-body illusions and minimal phenomenal selfhood

We highlight the latest research on body perception and self-consciousness, but argue that despite these achievements, central aspects have remained unexplored, namely, global aspects of bodily self-consciousness. Researchers investigated central representations of body parts and actions involving these, but neglected the global and unitary character of self-consciousness, the 'I' of experience and behaviour. We ask, what are the minimally sufficient conditions for the appearance of a phenomenal self, that is, the fundamental conscious experience of being someone? What are necessary conditions for self-consciousness in any type of system? We offer conceptual clarifications, discuss recent empirical evidence from neurology and cognitive science and argue that these findings offer a new entry point for the systematic study of global and more fundamental aspects of self-consciousness.
An efficient algorithm for pothole detection using stereo vision

In this paper, a stereo vision based pothole detection system is proposed. Using the disparity map generated from an efficient disparity calculation algorithm, potholes can be detected by their distance from the fitted quadratic road surface. The system produces the size, volume and position of the potholes which allows the pothole repair to be prioritised according to its severity. The quadratic road surface model allows for camera orientation variation, road drainage and up/down hill gradients. Experimental results show robust detection in various scenarios.
Immersive Analytics for Clinical Neuroscience

This paper introduces NeuroCave, an open-source immersive analytics software tool for exploring and making sense of both structural and functional connectomes describing the interconnectivity between brain regions. The software supports visual analysis tasks for clinical neuroscientists, facilitating explorations of connectome datasets used in comparative group studies. Researchers can enter and leave virtual reality as desired in order to visualize and reason about connectome datasets from a range of different perspectives.
'Silk Road', the virtual drug marketplace: a single case study of user experiences.

The online promotion of 'drug shopping' and user information networks is of increasing public health and law enforcement concern. An online drug marketplace called 'Silk Road' has been operating on the 'Deep Web' since February 2011 and was designed to revolutionise contemporary drug consumerism.



A single case study approach explored a 'Silk Road' user's motives for online drug purchasing, experiences of accessing and using the website, drug information sourcing, decision making and purchasing, outcomes and settings for use, and perspectives around security. The participant was recruited following a lengthy relationship building phase on the 'Silk Road' chat forum.



The male participant described his motives, experiences of purchasing processes and drugs used from 'Silk Road'. Consumer experiences on 'Silk Road' were described as 'euphoric' due to the wide choice of drugs available, relatively easy once navigating the Tor Browser (encryption software) and using 'Bitcoins' for transactions, and perceived as safer than negotiating illicit drug markets. Online researching of drug outcomes, particularly for new psychoactive substances was reported. Relationships between vendors and consumers were described as based on cyber levels of trust and professionalism, and supported by 'stealth modes', user feedback and resolution modes. The reality of his drug use was described as covert and solitary with psychonautic characteristics, which contrasted with his membership, participation and feelings of safety within the 'Silk Road' community.



'Silk Road' as online drug marketplace presents an interesting displacement away from 'traditional' online and street sources of drug supply. Member support and harm reduction ethos within this virtual community maximises consumer decision-making and positive drug experiences, and minimises potential harms and consumer perceived risks. Future research is necessary to explore experiences and backgrounds of other users.
A Single-Stage LED Driver Based on Interleaved Buck–Boost Circuit and LLC Resonant Converter

A single-stage LED driver based on an interleaved buck-boost circuit and an LLC resonant converter is proposed. The buck-boost circuit and the LLC resonant converter are integrated by sharing switches, which can decrease the system cost and improve the system efficiency. The input voltage of the buck-boost circuit is half of the rectified voltage, and two buck-boost circuits are formed with the two half-bridge switches and corresponding diodes. The two buck-boost circuits work in interleaved mode and the inductor current is in discontinuous conduction mode, both helping to achieve the power factor (PF) correction. A half-bridge LLC resonant converter is adopted here, and the soft switching characteristic of the LLC resonant converter is not changed by the switch integration. The primary-side switches still work in zero voltage switching (ZVS) mode, and the secondary diodes still work in ZCS mode, which both reduce the switching losses and improve the efficiency of the system. The design process is described in detail this paper, and a 100-W LED street lighting prototype is proposed, with a PF of 0.995, a total harmonic distortion of 5.7%, and an efficiency of 91.6% at full load.
1 The Goal-Gradient Hypothesis Resurrected : Purchase Acceleration , Illusionary Goal Progress , and Customer Retention

The goal-gradient hypothesis (Hull 1934) denotes the classic finding from behaviorism that animals expend more effort as they approach a reward (e.g., hungry rats run faster as they near cheese). Building on this hypothesis, we generate new propositions for the human psychology of rewards. We test these propositions using a variety of methods, data, and modeling approaches, including field experiments, paper-and-pencil problems, and secondary customer data as well as hazard rate, Tobit, and logit models. Some of the key findings indicate that: (a) participants in a real café reward program (“buy ten coffees, get one free”) purchase coffees more frequently the closer they are to earning a free coffee; (b) Internet users who rate songs in return for reward certificates visit the rating website more often, rate more songs per visit, and persist longer in the rating effort as they approach the reward goal; (c) the illusion of progress toward the goal likewise induces purchase acceleration; for example, customers who receive a “12-stamp” coffee card with two pre-existing “bonus” stamps complete the 10 required purchases faster than customers who receive a “regular” 10-stamp card; and (d) a stronger tendency to accelerate toward the reward predicts greater retention and faster reengagement in the program. Our conceptualization and empirical findings are captured by a parsimonious goal-distance model, in which effort investment is a function of the proportion of original distance remaining to the goal (i.e., psychological distance). In addition, using statistical and experimental controls, we show that the observed goalgradients cannot be explained by habituation, expiration concerns, other time-trend effects, or heterogeneity bias. We discuss the theoretical implications of the findings and their managerial implications for incentive systems, promotions, and customer retention.
Cloud Docs: Secure Scalable Document Sharing on Public Clouds

Secure cloud storage solutions such as Trust Store, Sec Cloud, HPI Secure, and Twin Cloud have primarily focused on securing persistent data while storing it in public cloud services. Though data sharing has been recognized as an important security feature, these storage solutions mostly focus on three key properties: confidentiality, integrity and availability. Modern enterprise applications demand data is able to be shared within or across organizations. The challenge is how to securely share data in public clouds without increasing data movement and computation costs. This problem has been addressed in recent times by utilizing or developing new data encryption techniques such as identity-based encryption, attribute-based encryption and proxy-re-encryption. However, these techniques suffer from scalability and flexibility problems when dealing with big data and support for dynamic access control rules. This paper presents a novel architecture and corresponding protocols to provide secure sharing of documents on public cloud services: Cloud Docs. This system uses AES for data encryption to achieve scalability and supports identity based access control rules using private-public key pairs to provide flexibility.
BER Analysis of MIMO-OFDM System using Alamouti STBC and MRC Diversity Scheme over Rayleigh Multipath Channel

Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Global Journal of Researches in Engineering Electrical and Electronics Engineering Volume 13 Issue 13 Version 1.0 Year 2013  Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4596 &
Crawling AJAX by Inferring User Interface State Changes

AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.
Spanning Tree Methods for Discriminative Training of Dependency Parsers

Untyped dependency parsing can be viewed as the problem of finding maximum spanning trees (MSTs) in directed graphs. Using this representation, the Eisner (1996) parsing algorithm is sufficient for searching the space of projective trees. More importantly, the representation is extended naturally to non-projective parsing using Chu-Liu-Edmonds (Chu and Liu, 1965; Edmonds, 1967) MST algorithm. These efficient parse search methods support large-margin discriminative training methods for learning dependency parsers. We evaluate these methods experimentally on the English and Czech treebanks. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-06-11. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/55 Spanning Tree Methods for Discriminative Training of Dependency Parsers Ryan McDonald Koby Crammer Fernando Pereira Department of Computer and Information Science University of Pennsylvania Philadelphia, PA, 19107
Towards reliable traffic sign recognition

The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS . In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.
Wireless communication applications of the continuous transverse stub (CTS) array at microwave and millimeter wave frequencies

The continuous transverse stub (CTS) array represents a unique class of array antenna that exploits the low-loss, low-dispersion, dimensional robustness, and design flexibility of an open parallel-plate structure as both its transmission-line and radiator basis. Relative advantages of this array type include unusually high aperture efficiencies; precision sidelobe control; benign active impedance dependence on both frequency and scan angle; and inherently low cost This paper describes and illustrate recent wideband, dual-polarized and phased-array developments of the CTS array applied to fixed and mobile terrestrial- and satellite-based broadband access applications at both microwave and millimeter-wave frequencies.
Optimized Cost per Click in Taobao Display Advertising

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.
A Dual Coordinate Descent Algorithm for SVMs Combined with Rational Kernels

This paper presents a novel application of automata algorithms to machine learning. It introduces the first optimization solution for support vector machines used with sequence kernels that is purely based on weighted automata and transducer algorithms, without requiring any specific solver. The algorithms presented apply to a family of kernels covering all those commonly used in text and speech processing or computational biology. We show that these algorithms have significantly better computational complexity than previous ones and report the results of large-scale experiments demonstrating a dramatic reduction of the training time, typically by several orders of magnitude.
Impact of high-throughput screening in biomedical research

High-throughput screening (HTS) has been postulated in several quarters to be a contributory factor to the decline in productivity in the pharmaceutical industry. Moreover, it has been blamed for stifling the creativity that drug discovery demands. In this article, we aim to dispel these myths and present the case for the use of HTS as part of a proven scientific tool kit, the wider use of which is essential for the discovery of new chemotypes.
Thin skin elastodynamics

We present a novel approach for simulating thin hyperelastic skin. Real human skin is only a few millimeters thick. It can stretch and slide over underlying body structures such as muscles, bones, and tendons, revealing rich details of a moving character. Simulating such skin is challenging because it is in close contact with the body and shares its geometry. Despite major advances in simulating elastodynamics of cloth and soft bodies for computer graphics, such methods are difficult to use for simulating thin skin due to the need to deal with non-conforming meshes, collision detection, and contact response. We propose a novel Eulerian representation of skin that avoids all the difficulties of constraining the skin to lie on the body surface by working directly on the surface itself. Skin is modeled as a 2D hyperelastic membrane with arbitrary topology, which makes it easy to cover an entire character or object. Unlike most Eulerian simulations, we do not require a regular grid and can use triangular meshes to model body and skin geometry. The method is easy to implement, and can use low resolution meshes to animate high-resolution details stored in texture-like maps. Skin movement is driven by the animation of body shape prescribed by an artist or by another simulation, and so it can be easily added as a post-processing stage to an existing animation pipeline. We provide several examples simulating human and animal skin, and skin-tight clothes.
Alterations of the human gut microbiome in liver cirrhosis

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.