nils-herrmann/clean_scirepeval huggingface dataset:
The impact of participation in virtual brand communities on consumer trust and loyalty: The case of free software

The importance of virtual brand communities is growing day by day as a result of consumers increasingly using online tools to contact fellow consumers in order to get information on which to base their decisions. For this reason, this work aims to explore some of the effects of participation in a virtual brand community on consumer behaviour.  The paper proposes the positive effects of participation in a virtual community on both consumer trust and loyalty to the product, brand or organisation around which the community is developed. In addition, it also proposes a positive effect of trust on consumer loyalty. After the validations of measurement scales, the hypotheses are contrasted through structural modelling.  The data, obtained through a web survey using members of several free software virtual communities, show that participation in the activities carried out in a virtual community may foster consumer trust and loyalty to the mutual interest of the community (the free software in this case). In addition, the study also found a positive and significant effect of consumer trust on loyalty.  Data were collected thanks to a web survey using Spanish-speaking subjects.  The high costs every company has to face in order to get new customers make it increasingly necessary to reinforce the ties established with customers. In this respect, this study has shown that managers may foster consumer trust and loyalty by developing virtual brand communities and promoting consumers’ participation in them.  Most of the works that are focused on virtual communities have been conducted at the conceptual level. Thus, with the aim of moving on this topic, this study analyses empirically the effects of participation in a virtual brand community on consumer behaviour.
Beyond the hype: Big data concepts, methods, and analytics

Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper’s primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
Class-D Audio Amplifiers in Mobile Applications

A comparative system-level overview is given of alternative class-D amplifier architectures. The theory behind pulsewidth modulation and different modulation schemes is discussed. Topological alternatives such as open-loop versus feedback and fixed-carrier versus self-oscillating are analyzed and compared in terms of relevant characteristics, such as distortion, power supply rejection, efficiency, and electromagnetic interference. The combination of digital-to-analog conversion and class-D amplifiers is discussed. Experimental results of an integrated circuit based on a simple architecture that combines the benefits of digital input with an analog feedback loop are presented.
Intent Mining for the Good , Bad & Ugly Use of Social Web : Concepts , Methods , and Challenges

The social web has empowered us to easily share information, express opinions, and engage in discussions on events around the world. While users of social media platforms often o↵er help and emotional support to others (the good), they also spam (the bad) and harass others as well as even manipulate others via fake news (the ugly). In order to both leverage the positive e↵ects and mitigate the negative e↵ects of using social media, intent mining provides a computational approach to proactively analyze social media data. This chapter introduces an intent taxonomy of social media usage with examples and describes methods and future challenges to mine the intentional uses of social media.
Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets

Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the expressiveness. This task becomes even more difficult when people use figurative language (e.g. irony, sarcasm and metaphors) because it causes a mismatch between the literal meaning and the actual expressed sentiment. In this paper, we describe a sentiment analysis system designed for handling ironic and sarcastic tweets. Features grounded on several linguistic levels are proposed and used to classify the tweets in a 11-scale range, using a decision tree. The system is evaluated on the dataset released by the organizers of the SemEval 2015, task 11. The results show that our method largely outperforms the systems proposed by the participants of the task on ironic and sarcastic tweets.
X-Tag: A Fiducial Tag for Flexible and Accurate Bundle Adjustment

In this paper we design a novel planar 2D fiducial marker and develop fast detection algorithm aiming easy camera calibration and precise 3D reconstruction at the marker locations via the bundle adjustment. Even though an abundance of planar fiducial markers have been made and used in various tasks, none of them has properties necessary to solve the aforementioned tasks. Our marker, X-tag, enjoys a novel design, coupled with very efficient and robust detection scheme, resulting in a reduced number of false positives. This is achieved by constructing markers with random circular features in the image domain and encoding them using two true perspective invariants: cross-ratios and intersection preservation constraints. To detect the markers, we developed an effective search scheme, similar to Geometric Hashing and Hough Voting, in which the marker decoding is cast as a retrieval problem. We apply our system to the task of camera calibration and bundle adjustment. With qualitative and quantitative experiments, we demonstrate the robustness and accuracy of X-tag in spite of blur, noise, perspective and radial distortions, and showcase camera calibration, bundle adjustment and 3d fusion of depth data from precise extrinsic camera poses.
Wideband Circularly Polarized Patch Antenna Using Broadband Baluns

A novel 90deg broadband balun comprising a broadband 90deg Schiffman phase shifter is introduced as a means of enhancing the wideband circular polarization performance of dual-fed type microstrip antennas. The proposed 90deg broadband balun delivers good impedance matching, balanced power splitting and consistent 90deg (plusmn5deg) phase shifting, across a wide bandwidth (~57.5%). A circular patch antenna utilizing the proposed 90deg broadband balun is shown to attain measured impedance(S11< -10 dB) and axial ratio (AR < 3 dB) bandwidths of 60.24% and 37.7%, respectively, for the dual L-probe case; and 71.28% and 81.6% respectively, for the quadruple L-probe case.
A flexible vehicle surround view camera system by central-around coordinate mapping model

The surround view camera system is an emerging driving assistant technology that can assist drivers in parking by providing top-down view of surrounding situations. Such a system usually consists of four wide-angle or fish-eye cameras that mounted around the vehicle, and a bird-eye view is synthesized from images of these cameras. Commonly there are two fundamental problems for the surround view synthesis, geometric alignment and image synthesis. Geometric alignment performs fish-eye calibration and computes the image perspective transformation between the bird-eye view and images from the surrounding cameras. Image synthesis technique dedicates to seamless stitch between adjacent views and color balancing. In this paper, we propose a flexible central-around coordinate mapping (CACM) model for vehicle surround view synthesis. The CACM model calculates perspective transformation between a top-view central camera coordinate and the around camera coordinates by a marker point based method. With the transformation matrices, we could generate the pixel point mapping relationship between the bird-eye view and images of the surrounding cameras. After geometric alignment, an image fusion method based on distance weighting is adopted for seamless stitch, and an effective overlapping region brightness optimization method is proposed for color balancing. Both the seamless stitch and color balancing can be easily operated by using two types of weight coefficient under the framework of the CACM model. Experimental results show that the proposed approaches could provide a high-performance surround view camera system.
Pinpointing Vulnerabilities

Memory-based vulnerabilities are a major source of attack vectors. They allow attackers to gain unauthorized access to computers and their data. Previous research has made significant progress in detecting attacks. However, developers still need to locate and fix these vulnerabilities, a mostly manual and time-consuming process. They face a number of challenges. Particularly, the manifestation of an attack does not always coincide with the exploited vulnerabilities, and many attacks are hard to reproduce in the lab environment, leaving developers with limited information to locate them. In this paper, we propose Ravel, an architectural approach to pinpoint vulnerabilities from attacks. Ravel consists of an online attack detector and an offline vulnerability locator linked by a record & replay mechanism. Specifically, Ravel records the execution of a production system and simultaneously monitors it for attacks. If an attack is detected, the execution is replayed to reveal the targeted vulnerabilities by analyzing the program's memory access patterns under attack. We have built a prototype of Ravel based on the open-source FreeBSD operating system. The evaluation results in security and performance demonstrate that Ravel can effectively pinpoint various types of memory vulnerabilities and has low performance overhead.
Quark-X: An Efficient Top-K Processing Framework for RDF Quad Stores

There is a growing trend towards enriching the RDF content from its classical Subject-Predicate-Object triple form to an annotated representation which can model richer relationships such as including fact provenance, fact confidence, higher-order relationships and so on. One of the recommended ways to achieve this is to use reification and represent it as N-Quads "or simply quads" where an additional identifier is associated with the entire RDF statement which can then be used to add further annotations. A typical use of such annotations is to have quantifiable confidence values to be attached to facts. In such settings, it is important to support efficient top-k queries, typically over user-defined ranking functions containing sentence level confidence values in addition to other quantifiable values in the database. In this paper, we present Quark-X, an RDF-store and SPARQL processing system for reified RDF data represented in the form of quads. This paper presents the overall architecture of our system -- illustrating the modifications which need to be made to a native quad store for it to process top-k queries. In Quark-X, we propose indexing and query processing techniques for making top-k querying efficient. In addition, we present the results of a comprehensive empirical evaluation of our system over Yago2S and DBpedia datasets. Our performance study shows that the proposed method achieves one to two order of magnitude speed-up over baseline solutions.
Nostalgia and the emotional tone and content of song lyrics.

Emotion and topic were manipulated in original song lyrics. Participants completed Batcho's and Holbrook's nostalgia surveys and rated 6 sets of lyrics for happiness, sadness, anger, nostalgia, meaning, liking, and relevance. Nostalgic lyrics were characterized by bittersweet reverie, loss of the past, identity, and meaning. Contrary to theories linking nostalgia to pathology, participants who scored high on Batcho's measure of personal nostalgia preferred happy lyrics, found them more meaningful, and related more closely to them. Consistent with theories relating nostalgia to social connectedness, high-nostalgia respondents preferred other-directed to solitary themes. Historical nostalgia was associated with relating more closely to sad lyrics.
High current planar transformer for very high efficiency isolated boost dc-dc converters

This paper presents a design and optimization of a high current planar transformer for very high efficiency dc-dc isolated boost converters. The analysis considers different winding arrangements, including very high copper thickness windings. The analysis is focused on the winding ac-resistance and transformer leakage inductance. Design and optimization procedures are validated based on an experimental prototype of a 6 kW dc-dc isolated full bridge boost converter developed on fully planar magnetics. The prototype is rated at 30-80 V 0-80 A on the low voltage side and 700-800 V on the high voltage side with a peak efficiency of 97.8% at 80 V 3.5 kW. Results highlights that thick copper windings can provide good performance at low switching frequencies due to the high transformer filling factor. PCB windings can also provide very high efficiency if stacked in parallel utilizing the transformer winding window in an optimal way.
Health Care Information Systems: Architectural Models and Governance

The adoption of ICT within health care has been characterized by a series of phases evolving since the 1960s (Khoumbati et al., 2009). Health informatics adoption started mainly from financial systems, providing support to the organization’s billing, payroll, accounting and reporting systems. Clinical departments launched a major initiative during the 1970s that supported such internal activities as radiology, laboratory and pharmacy (Wickramasinghe & Geisler, 2008), where machinery could support high-volume operations with the implementation of standardized procedures. Financial systems once again became prominent in the 1980s, with major investments in cost accounting and materials management systems (Grimson, 2001). During the 1990s, attention turned towards enterprise-wide clinical systems, including clinical data repositories and visions of a fully computerized Electronic Medical Record (EMR) (Bates, 2005).
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community

This research draws on longitudinal network data from an online community to examine patterns of users’ behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users’ gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world”characterized by the emergence, in its early stages,of a hub-dominated structure with heterogeneity in users’ behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first-mover advantages underpinning users’ability to rise to system prominence, and uncover gender differences in users’ gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems.
Vehicle license plate detection in images

Frequently, there is a need to identify vehicle license plates (VLP) in images taken from a camera that is far away from the vehicle for security. The extracted information from vehicle license plates is used for enforcement, access-control, and flow management, e.g. to keep a time record for automatic payment calculations or to fight against crime. That's make license plates detection crucial and inevitable in the vehicle license plate recognition system. This paper aims to present a new robust method to detect and localize license plates in images. Especially we focus on the Moroccan's VLP. The proposed approach is based on edge features and characteristics of license plate's characters. Various images including Moroccan's VLP taken from different distances and under different angles were used to evaluate the proposed method. The experimental results show that our system can efficiently detect and localize the Moroccan's VLP in the images. Indeed, the recall/precision curve of the proposed method proves that 95% precision rate is obtained for recall rate value equals to 81%. In addition, the standard measure of quality is equal to 87.44 %.
Workload characterization of JVM languages

Being developed with a single language in mind, namely Java, the Java Virtual Machine (JVM) nowadays is targeted by numerous programming languages. Automatic memory management, Just-In-Time (JIT) compilation, and adaptive optimizations provided by the JVM make it an attractive target for different language implementations. Even though being targeted by so many languages, the JVM has been tuned with respect to characteristics of Java programs only – different heuristics for the garbage collector or compiler optimizations are focused more on Java programs. In this dissertation, we aim at contributing to the understanding of the workloads imposed on the JVM by both dynamically-typed and statically-typed JVM languages. We introduce a new set of dynamic metrics and an easy-to-use toolchain for collecting the latter. We apply our toolchain to applications written in six JVM languages – Java, Scala, Clojure, Jython, JRuby, and JavaScript. We identify differences and commonalities between the examined languages and discuss their implications. Moreover, we have a close look at one of the most efficient compiler optimizations – method inlining. We present the decision tree of the HotSpot JVM’s JIT compiler and analyze how well the JVM performs in inlining the workloads written in different JVM languages.
VIKOR method using interval type two fuzzy sets

One of the frequently used multi-attribute decision making (MADM) methods is VIKOR, which provides a compromise solution with maximum group utility and minimum regret of decision makers with non-commensurable and conflicting attributes. In the literature, VIKOR method has been extended to the fuzzy case using type-1 fuzzy sets. Type-2 fuzzy sets let the fuzzy membership functions to be defined for providing us more design degrees of freedom and flexibility. In this paper, we developed an interval type-2 fuzzy VIKOR method including a fuzzy ranking method for interval type-2 fuzzy sets. Interval type-2 fuzzy weights for decision makers’ strategies have also been used in the proposed method. A sensitivity analysis has been applied to see the behavior of our decision with respect to the changing weights of maximum group utility strategies. We showed the applicability of the proposed method through an existent problem in the literature.
Algorithms for Delta Compression and Remote File Synchronization

Delta compression and remote file synchronization techniques are concerned with efficient file transfer over a slow communication link in the case where the receiving party already has a similar file (or files). This problem arises naturally, e.g., when distributing updated versions of software over a network or synchronizing personal files between different accounts and devices. More generally, the problem is becoming increasingly common in many networkbased applications where files and content are widely replicated, frequently modified, and cut and reassembled in different contexts and packagings. In this chapter, we survey techniques, software tools, and applications for delta compression, remote file synchronization, and closely related problems. We first focus on delta compression, where the sender knows all the similar files that are held by the receiver. In the second part, we survey work on the related, but in many ways quite different, problem of remote file synchronization, where the sender does not have a copy of the files held by the receiver. Work supported by NSF CAREER Award NSF CCR-0093400 and by Intel Corporation.
Textual Spatial Cosine Similarity

When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprisewide search environments. To address these real-time constraints, we developed a new measure of document similarity called Textual Spatial Cosine Similarity, which is able to detect similitude at the semantic level using word placement information contained in the document. We will see in this paper that two degenerate cases exist for this model, which coincide with Cosine Similarity on one side and with a paraphrasing detection model to the other.
Patterns of Play: Play-Personas in User-Centred Game Development

In recent years certain trends from User-Centered design have been seeping into the practice of designing computer games. The balance of power between game designers and players is being renegotiated in order to find a more active role for players and provide them with control in shaping the experiences that games are meant to evoke. A growing player agency can turn both into an increased sense of player immersion and potentially improve the chances of critical acclaim. This paper presents a possible solution to the challenge of involving the user in the design of interactive entertainment by adopting and adapting the "persona" framework introduced by Alan Cooper in the field of Human Computer Interaction. The original method is improved by complementing the traditional ethnographic descriptions of personas with parametric, quantitative, data-oriented models of patterns of user behaviour for computer games.
A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications.

Crossbar arrays based on two-terminal resistive switches have been proposed as a leading candidate for future memory and logic applications. Here we demonstrate a high-density, fully operational hybrid crossbar/CMOS system composed of a transistor- and diode-less memristor crossbar array vertically integrated on top of a CMOS chip by taking advantage of the intrinsic nonlinear characteristics of the memristor element. The hybrid crossbar/CMOS system can reliably store complex binary and multilevel 1600 pixel bitmap images using a new programming scheme.