nils-herrmann/clean_scirepeval huggingface dataset:
Emotional Gratification in Entertainment Experience . Why Viewers of Movies and TV Series Find it Rewarding to

This paper presents four studies designed to assess different types of gratifications that can be associated with the experience of emotions in movie and TV audiences. Exploratory and confirmatory factor analyses of a pool of statements derived from qualitative interviews revealed three factors that reflect rewarding feelings: 1) fun, 2) thrill, and 3) empathic sadness, and four factors that reflect the role of emotional media experiences within the broader context of individuals' social and cognitive needs: 4) contemplative emotional experiences, 5) emotional engagement with characters, 6) social sharing of emotions, and 7) vicarious release of emotions. Validation analyses showed that the scales developed to assess these factors are predicted by the experience of emotions and meta-emotions and served in turn to predict different aspects of positive content evaluation. Results are discussed with regard to theoretical issues including entertainment audiences' voluntary exposure to unpleasant feelings, and the role of entertainment in psychosocial need satisfaction and eudaimonic well-being.
Appearance contrast for fast, robust trail-following

We describe a framework for finding and tracking “trails” for autonomous outdoor robot navigation. Through a combination of visual cues and ladar-derived structural information, the algorithm is able to follow paths which pass through multiple zones of terrain smoothness, border vegetation, tread material, and illumination conditions. Our shape-based visual trail tracker assumes that the approaching trail region is approximately triangular under perspective. It generates region hypotheses from a learned distribution of expected trail width and curvature variation, and scores them using a robust measure of color and brightness contrast with flanking regions. The structural component analogously rewards hypotheses which correspond to empty or low-density regions in a groundstrike-filtered ladar obstacle map. Our system's performance is analyzed on several long sequences with diverse appearance and structural characteristics. Ground-truth segmentations are used to quantify performance where available, and several alternative algorithms are compared on the same data.
MeSH Up: effective MeSH text classification for improved document retrieval

Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeSH concepts have been proposed to replace manual annotation, but they are either limited to a small subset of MeSH or have only been compared with a limited number of other systems.



We compare the performance of six MeSH classification systems [MetaMap, EAGL, a language and a vector space model-based approach, a K-Nearest Neighbor (KNN) approach and MTI] in terms of reproducing and complementing manual MeSH annotations. A KNN system clearly outperforms the other published approaches and scales well with large amounts of text using the full MeSH thesaurus. Our measurements demonstrate to what extent manual MeSH annotations can be reproduced and how they can be complemented by automatic annotations. We also show that a statistically significant improvement can be obtained in information retrieval (IR) when the text of a user's query is automatically annotated with MeSH concepts, compared to using the original textual query alone.



The annotation of biomedical texts using controlled vocabularies such as MeSH can be automated to improve text-only IR. Furthermore, the automatic MeSH annotation system we propose is highly scalable and it generates improvements in IR comparable with those observed for manual annotations.
A 6.3∶1 bandwidth scanning Tightly Coupled Dipole Array with co-designed compact balun

We present a design for a Tightly Coupled Dipole Array (TCDA) with wide angle scanning and a wideband compact balun. Although TCDAs are low profile, capable of wide bandwidth, good scan performance, and low cross polarization, they require a balanced feeding structure whose realization introduces significant practical challenges. Among them is developing a balun that is both compact and wideband. In this paper, we demonstrate a design for a low cost balun that fits within the existing array volume and increases the overall bandwidth of the array. This is accomplished by exploiting the balun's reactance as part of the matching network for the aperture. The final array delivers 6.3 : 1 bandwidth, ±45° scanning in all planes, and <; -10dB cross-polarization. These capabilities are believed to be unique for a low profile array with a self-contained feed.
"Sleepiness" is serious in adolescence: Two surveys of 3235 Canadian students

Evidence is growing that sleep problems in adolescents are significant impediments to learning and negatively affect behaviour, attainment of social competence and quality of life. The objectives of the study were to determine the level of sleepiness among students in high school, to identify factors to explain it, and to determine the association between sleepiness and performance in both academic and extracurricular activities



A cross-sectional survey of 2201 high school students in the Hamilton Wentworth District School Board and the Near North District School Board in Ontario was conducted in 1998/9. A similar survey was done three years later involving 1034 students in the Grand Erie District School Board in the same Province. The Epworth Sleepiness Scale (ESS) was used to measure sleepiness and we also assessed the reliability of this tool for this population. Descriptive analysis of the cohort and information on various measures of performance and demographic data were included. Regression analysis, using the generalised estimating equation (GEE), was utilized to investigate factors associated with risk of sleepiness (ESS>10).



Seventy per cent of the students had less than 8.5 hours weeknight sleep. Bedtime habits such as a consistent bedtime routine, staying up late or drinking caffeinated beverages before bed were statistically significantly associated with ESS, as were weeknight sleep quantity and gender. As ESS increased there was an increase in the proportion of students who felt their grades had dropped because of sleepiness, were late for school, were often extremely sleepy at school, and were involved in fewer extracurricular activities. These performance measures were statistically significantly associated with ESS. Twenty-three percent of the students felt their grades had dropped because of sleepiness. Most students (58-68%) reported that they were "really sleepy" between 8 and 10 A.M.



Sleep deprivation and excessive daytime sleepiness were common in two samples of Ontario high school students and were associated with a decrease in academic achievement and extracurricular activity. There is a need to increase awareness of this problem in the education and health communities and to translate knowledge already available to strategies to address it.
An Effective High-Performance Multiway Spatial Join Algorithm with Spark

Multiway spatial join plays an important role in GIS (Geographic Information Systems) and their applications. With the increase in spatial data volumes, the performance of multiway spatial join has encountered a computation bottleneck in the context of big data. Parallel or distributed computing platforms, such as MapReduce and Spark, are promising for resolving the intensive computing issue. Previous approaches have focused on developing single-threaded join algorithms as an optimizing and partition strategy for parallel computing. In this paper, we present an effective high-performance multiway spatial join algorithm with Spark (MSJS) to overcome the multiway spatial join bottleneck. MSJS handles the problem through cascaded pairwise join. Using the power of Spark, the formerly inefficient cascaded pairwise spatial join is transformed into a high-performance approach. Experiments using massive real-world data sets prove that MSJS outperforms existing parallel approaches of multiway spatial join that have been described in the literature.
Know your personalization: learning topic level personalization in online services

Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it.
 Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs.
 We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google.
Cost-Optimization of the IPv4 Zeroconf Protocol

We develop a stochastic model of a simple protocol for the self-configuration of IP network interfaces. We describe the mean cost that incurs during a selfconfiguration phase and describe a trade-off between reliability and speed. We derive a cost function which we use to derive optimal parameters. We show that optimal cost and optimal reliability are qualities that cannot be achieved at the same time. Embedded control software; IP; zeroconf protocol; cost optimisation
Overview of PAN 2018 - Author Identification, Author Profiling, and Author Obfuscation

PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics. More specifically, this edition of PAN introduces a shared task in cross-domain authorship attribution, where texts of known and unknown authorship belong to distinct domains, and another task in style change detection that distinguishes between single-author and multi-author texts. In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender. Finally, the author obfuscation task studies how a text by a certain author can be paraphrased so that existing author identification tools are confused and cannot recognize the similarity with other texts of the same author. New corpora have been built to support these shared tasks. A relatively large number of software submissions (41 in total) was received and evaluated. Best paradigms are highlighted while baselines indicate the pros and cons of submitted approaches.
SMICloud: A Framework for Comparing and Ranking Cloud Services

With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).
Adaptive Multi-Resource Allocation for Cloudlet-Based Mobile Cloud Computing System

Mobile cloud computing utilizing cloudlet is an emerging technology to improve the quality of mobile services. In this paper, to better overcome the main bottlenecks of the computation capability of cloudlet and the wireless bandwidth between mobile devices and cloudlet, we consider the multi-resource allocation problem for the cloudlet environment with resource-intensive and latency-sensitive mobile applications. The proposed multi-resource allocation strategy enhances the quality of mobile cloud service, in terms of the system throughput (the number of admitted mobile applications) and the service latency. We formulate the resource allocation model as a semi-Markov decision process under the average cost criterion, and solve the optimization problem using linear programming technology. Through maximizing the long-term reward while meeting the system requirements of the request blocking probability and service time latency, an optimal resource allocation policy is calculated. From simulation result, it is indicated that the system adaptively adjusts the allocation policy about how much resource to allocate and whether to utilize the distant cloud according to the traffic of mobile service requests and the availability of the resource in the system. Our algorithm outperforms greedy admission control over a broad range of environments.
A comparative study of fairness-enhancing interventions in machine learning

Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions that require investigation for these algorithms to receive broad adoption.
 We present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures and existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits) and to different forms of preprocessing, indicating that fairness interventions might be more brittle than previously thought.
Fuzzy group decision making for selection among computer integrated manufacturing systems

A decision to invest in new manufacturing enabling technologies supporting computer integrated manufacturing (CIM) must include non-quantifiable, intangible benefits to the organization in meeting its strategic goals. Therefore, use of tactical level, purely economic, evaluation methods normally result in the rejection of strategically vital automation proposals. This paper includes four different fuzzy multi-attribute group decision-making methods. The first one is a fuzzy model of group decision proposed by Blin. The second is fuzzy synthetic evaluation, the third is Yager’s weighted goals method, and the last one is fuzzy analytic hierarchy process. These methods are extended to select the best computer integrated manufacturing system by taking into account both intangible and tangible factors. A computer software for these approaches is developed and finally some numerical applications of these methods are given to compare the results of all methods.
Conditional CycleGAN for Attribute Guided Face Image Generation

State-of-the-art techniques in Generative Adversarial Networks (GANs) such as cycleGAN is able to learn the mapping of one image domain X to another image domain Y using unpaired image data. We extend the cycleGAN to Conditional cycleGAN such that the mapping from X to Y is subjected to attribute condition Z. Using face image generation as an application example, where X is a low resolution face image, Y is a high resolution face image, and Z is a set of attributes related to facial appearance (e.g. gender, hair color, smile), we present our method to incorporate Z into the network, such that the hallucinated high resolution face image Y ′ not only satisfies the low resolution constrain inherent in X , but also the attribute condition prescribed by Z. Using face feature vector extracted from face verification network as Z, we demonstrate the efficacy of our approach on identitypreserving face image super-resolution. Our approach is general and applicable to high-quality face image generation where specific facial attributes can be controlled easily in the automatically generated results.
RFID-Based 3-D Positioning Schemes

This research focuses on RFID-based 3-D positioning schemes, aiming to locate an object in a 3-dimensional space, with reference to a predetermined arbitrary coordinates system, by using RFID tags and readers. More specifically, we consider a hexahedron which may be a shipping container, a storage room, or other hexahedral shape spaces. A number of RFID tags and/or readers with known locations are deployed as reference nodes. We propose two positioning schemes, namely, the active scheme and the passive scheme. The former scheme locates an RFID reader. For example, it may be employed to locate a mobile person who is equipped with an RFID reader or an object that is approached by an RFID reader. The passive scheme locates an RFID tag, which is attached to the target object. Both approaches are based on a Nelder-Mead nonlinear optimization method that minimizes the error objective functions. We have carried out analyses and extensive simulations to evaluate the proposed schemes. Our results show that both schemes can locate the targets with acceptable accuracy. The active scheme usually results in smaller errors and has a lower hardware cost compared to its passive counterpart. On the other hand, the passive scheme is more efficient when locating multiple targets simultaneously. The effectiveness of our proposed approaches is verified experimentally using the IDENTEC RFID kits.
Distributed Firewalls

Conventional firewalls rely on the notions of restricted topology and controlled entry points to function. More precisely, they rely on the assumption that everyone on one side of the entry point—the firewall—is to be trusted, and that anyone on the other side is, at least potentially, an enemy. The vastly expanded Internet connectivity in recent years has called that assumption into question. We propose a “distributed firewall”, using IPSEC, a policy language, and system management tools. A distributed firewall preserves central control of access policy, while reducing or eliminating any dependency on topology.
Kernel Based Detection of Mislabeled Training Examples

The problem of identifying mislabeled training examples has been examined in several studies, with a variety of approaches developed for editing the training data to obtain better classifiers. Many of these approaches involve applying an individual or an ensemble of classifiers to the trainin g set and filtering the mislabeled examples based on their consistency with respect to the classifier’s outputs. In this st udy, we formulate mislabeled detection as an optimization problem and introduce a kernel-based approach for filtering the mislabeled examples. Experimental results using a variety of data sets from the UCI data repository demonstrate the effectiveness of our proposed method, compared to existing nearest-neighbor and ensemble-based filtering schemes.
A systematic, tool-supported method for conducting literature reviews in information systems

While the importance of literature studies in the IS discipline is well recognized, little attention has been paid to the underlying structure and method of conducting effective literature reviews. Despite the fact that literature is often used to refine the research context and direct the pathways for successful research outcomes, there is very little evidence of the use of resource management tools to support the literature review process. In this paper we want to contribute to advancing the way in which literature studies in Information Systems are conducted, by proposing a systematic, pre-defined and tool-supported method to extract, analyse and report literature. This paper presents how to best identify relevant IS papers to review within a feasible and justifiable scope, how to extract relevant content from identified papers, how to synthesise and analyse the findings of a literature review and what are ways to effectively write and present the results of a literature review. The paper is specifically targeted towards novice IS researchers, who would seek to conduct a systematic detailed literature review in a focused domain. Specific contributions of our method are extensive tool support, the identification of appropriate papers including primary and secondary paper sets and a precodification scheme. We use a literature study on shared services as an illustrative example to present the proposed approach.
Coupled-Fed Shorted Monopole With a Radiating Feed Structure for Eight-Band LTE/WWAN Operation in the Laptop Computer

A coupled-fed shorted monopole with its feed structure as an effective radiator for eight-band LTE/WWAN (LTE700/GSM850/900/1800/ 1900/UMTS/LTE2300/2500) operation in the laptop computer is presented. The radiating feed structure capacitively excites the shorted monopole. The feed structure mainly comprises a long feeding strip and a loop feed therein. The loop feed is formed at the front section of the feeding strip and connected to a 50-Ω mini-cable to feed the antenna. Both the feeding strip and loop feed contribute wideband resonant modes to combine with those generated by the shorted monopole for the desired eight-band operation. The antenna size above the top shielding metal wall of the laptop display is 4 × 10 × 80 mm3 and is suitable to be embedded inside the casing of the laptop computer. The proposed antenna is fabricated and tested, and good radiation performances of the fabricated antenna are obtained.
HYDRA: large-scale social identity linkage via heterogeneous behavior modeling

We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID linkage information and the cross-platform structure consistency maximization. Extensive experiments on 10 million users across seven popular social network platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.