nils-herrmann/clean_scirepeval huggingface dataset:
Uncovering Clusters in Crowded Parallel Coordinates Visualizations

The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the user's ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in parallel coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets
Electroactive polymer-based devices for e-textiles in biomedicine

This paper describes the early conception and latest developments of electroactive polymer (EAP)-based sensors, actuators, electronic components, and power sources, implemented as wearable devices for smart electronic textiles (e-textiles). Such textiles, functioning as multifunctional wearable human interfaces, are today considered relevant promoters of progress and useful tools in several biomedical fields, such as biomonitoring, rehabilitation, and telemedicine. After a brief outline on ongoing research and the first products on e-textiles under commercial development, this paper presents the most highly performing EAP-based devices developed by our lab and other research groups for sensing, actuation, electronics, and energy generation/storage, with reference to their already demonstrated or potential applicability to electronic textiles
TokDoc: a self-healing web application firewall

The growing amount of web-based attacks poses a severe threat to the security of web applications. Signature-based detection techniques increasingly fail to cope with the variety and complexity of novel attack instances. As a remedy, we introduce a protocol-aware reverse HTTP proxy TokDoc (the token doctor), which intercepts requests and decides on a per-token basis whether a token requires automatic "healing". In particular, we propose an intelligent mangling technique, which, based on the decision of previously trained anomaly detectors, replaces suspicious parts in requests by benign data the system has seen in the past. Evaluation of our system in terms of accuracy is performed on two real-world data sets and a large variety of recent attacks. In comparison to state-of-the-art anomaly detectors, TokDoc is not only capable of detecting most attacks, but also significantly outperforms the other methods in terms of false positives. Runtime measurements show that our implementation can be deployed as an inline intrusion prevention system.
500-MS/s 5-bit ADC in 65-nm CMOS With Split Capacitor Array DAC

A 500-MS/s 5-bit ADC for UWB applications has been fabricated in a 65-nm CMOS technology using no analog-specific processing options. The time-interleaved successive approximation register (SAR) architecture has been chosen due to its simplicity versus flash and its amenability to scaled technologies versus pipelined, which relies on operational amplifiers. Six time-interleaved channels are used, sharing a single clock operating at the composite sampling rate. Each channel has a split capacitor array that reduces switching energy, increases speed, and has similar INL and decreased DNL, as compared to a conventional binary-weighted array. A variable delay line adjusts the instant of latch strobing to reduce preamplifier currents. The ADC achieves Nyquist performance, with an SNDR of 27.8 and 26.1 dB for 3.3and 239 MHz inputs, respectively. The total active area is 0.9mm2, and the ADC consumes 6 mW from a 1.2-V supply
Top-down Tree Long Short-Term Memory Networks

Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks. In this paper we develop Tree Long Short-Term Memory (TREELSTM), a neural network model based on LSTM, which is designed to predict a tree rather than a linear sequence. TREELSTM defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated subtree. We further enhance the modeling power of TREELSTM by explicitly representing the correlations between left and right dependents. Application of our model to the MSR sentence completion challenge achieves results beyond the current state of the art. We also report results on dependency parsing reranking achieving competitive performance.
A software development process model for artificial neural networks in critical applications

Using Artificial Neural Networh (A".) in critical applications can be challenging due to the often experimental nature of A" construction and the "black box" label that is fiequently attached to A".. Wellaccepted process models exist for algorithmic sofhyare development which facilitate software validation and acceptance. The sojiware development process model presented herein is targeted specifically toward artificial neural networks in crik-al appliicationr. 7% model is not unwieldy, and could easily be used on projects without critical aspects. This should be of particular interest to organizations that use AMVs and need to maintain or achieve a Capability Maturity Model (CM&?I or IS0 sofhyare development rating. Further, while this model is aimed directly at neural network development, with minor moda&ations, the model could be applied to any technique wherein knowledge is extractedfiom existing &ka, such as other numeric approaches or knowledge-based systems.
Sentiment Analysis of Citations using Sentence Structure-Based Features

Sentiment analysis of citations in scientific papers and articles is a new and interesting problem due to the many linguistic differences between scientific texts and other genres. In this paper, we focus on the problem of automatic identification of positive and negative sentiment polarity in citations to scientific papers. Using a newly constructed annotated citation sentiment corpus, we explore the effectiveness of existing and novel features, including n-grams, specialised science-specific lexical features, dependency relations, sentence splitting and negation features. Our results show that 3-grams and dependencies perform best in this task; they outperform the sentence splitting, science lexicon and negation based features.
Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing

Due to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models. The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products.
RGB-D flow: Dense 3-D motion estimation using color and depth

3-D motion estimation is a fundamental problem that has far-reaching implications in robotics. A scene flow formulation is attractive as it makes no assumptions about scene complexity, object rigidity, or camera motion. RGB-D cameras provide new information useful for computing dense 3-D flow in challenging scenes. In this work we show how to generalize two-frame variational 2-D flow algorithms to 3-D. We show that scene flow can be reliably computed using RGB-D data, overcoming depth noise and outperforming previous results on a variety of scenes. We apply dense 3-D flow to rigid motion segmentation.
Exploring event correlation for failure prediction in coalitions of clusters

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure prediction is a crucial technique for self-managing resource burdens. Failure events in coalition systems exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. We further utilize the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPREFECTs), which explores correlations among failures and forecasts the time-between-failure of future instances. We evaluate the performance of hPREFECTs in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. Experimental results show the system achieves more than 76% accuracy in offline prediction and more than 70% accuracy in online prediction during the time from May 2006 to April 2007.
Learning User Behaviors for Advertisements Click Prediction

Predicting potential advertisement clicks of users are important for advertisement recommendation, advertisement placement, presentation pricing, and so on. In this paper, several machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), decision tree (DT) and backpropagation neural networks (BPN) are developed to learn user’s click behaviors from advertisement search and click logs. In addition, four levels of features are extracted to represent user search and click intents. Given a user’s search session and a query, machine learning algorithms along with different features are proposed to predict if the user will click advertisements displayed for the query. We further study the impact of feature selection algorithms on the prediction models. Random subspace (RS), F-score (FS) and information gain (IG) are employed to search for a predictive subset of features. The experiments show that CRF model with the random subspace feature selection algorithm achieves the best performance.
Ethnicity, Insurgency, and Civil War

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Stakeholder Excellence ? Framing the evolution and complexity of a stakeholder perspective of the firm

Analysing and dealing with the needs and demands of stakeholders is a major concern of modern business. This paper attempts to frame, systematise and conceptualise a number of the underlying issues related to the phenomenon. As a result, a vast array of unaddressed and unanswered questions emerges. What unites these is the implicit acceptance of an emerging (contemporary) stakeholder theory of the firm. This largely normative theory provides some understanding of what constitutes a stakeholder, what stakes they seek to protect and the way in which transactions between the stakeholder and the organisations are handled. While the brevity of the contribution necessitates a somewhat superficial treatment of the issues, the paper leads inevitably to the conclusion that the demonstrated complexity of really engaging with stakeholders means that both theory-development and practice still have a long way to go.
Impact of packetization and functional split on C-RAN fronthaul performance

Cloud-RAN (CRAN) is considered as one key enabler for beyond 4G networks, offering multiplexing gains, and advanced cooperation and coordinated signal processing. However, a key obstacle in the adoption of the CRAN architecture is that it requires very high capacity and low latency fronthaul (FH) links to carry raw I/Q samples between remote radio heads (RRH) and the baseband units (BBUs). These capacity requirements could be reduced by a more flexible split of baseband processing between BBUs and RRHs. Nevertheless, while moving some of the processing back into the RRH is expected to reduce FH rates, the amount of reduction mainly depends on the split, cell load, scenario and it might also introduce some delays. To this end, this paper studies the impact of different functional splits on the FH capacity for representative scenarios. Furthermore, we propose the use of a packet-based fronthaul network and study the joint impact of different packetization methods and RRH-BBU functional splits on the FH rate and latency. Based on this study, we provide some insights on the feasibility and optimality of different combinations, and the potential multiplexing benefits in terms of numbers of RRHs one could support over a single Ethernet-based FH network.
Motivational factors that influence the acceptance of Moodle using TAM

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.06.011 * Corresponding author.  fax E-mail addresses: rocio.arteaga@decd.uhu.es, r Sánchez), duarte@uhu.es (A.D. Hueros). Moodle and other virtual teaching platforms have bolstered the ability and motivation of universities to support distance learning. The aim of our study is to improve understanding of the motivational factors behind student satisfaction, or dissatisfaction, with the Web-based learning platform, Moodle. Our study extends the Technology Acceptance Model (TAM) to include technical support and perceived self-efficacy, with the expectation that they influence usage of Moodle. We surveyed 226 students of the Business Administration and Management (LADE) and Infant and Primary School Teaching degree courses of the University of Huelva. The data showed that technical support has a direct effect on perceived ease of use and perceived usefulness. Moodle usage was also directly influenced by perceived ease of use and attitude. The results reveal the importance of perceived ease of use and perceived usefulness on attitude.
Hypersensitivity to titanium: clinical and laboratory evidence.

This study was carried out to investigate the potential of titanium to induce hypersensitivity in patients chronically exposed to titanium-based dental or endoprosthetic implants.



Fifty-six patients who had developed clinical symptoms after receiving titanium-based implants were tested in the optimized lymphocyte transformation test MELISA against 10 metals including titanium. Out of 56 patients, 54 were patch-tested with titanium as well as with other metals. The implants were removed in 54 patients (2 declined explantation), and 15 patients were retested in MELISA.



Of the 56 patients tested in MELISA, 21 (37.5%) were positive, 16 (28.6%) ambiguous, and 19 (33.9%) negative to titanium. In the latter group, 11 (57.9%) showed lymphocyte reactivity to other metals, including nickel. All 54 patch-tested patients were negative to titanium. Following removal of the implants, all 54 patients showed remarkable clinical improvement. In the 15 retested patients, this clinical improvement correlated with normalization in MELISA reactivity.



These data clearly demonstrate that titanium can induce clinically-relevant hypersensitivity in a subgroup of patients chronically exposed via dental or endoprosthetic implants.
Fast and Robust Ellipse Extraction from Complicated Images

-In this paper, we present a fast and robust ellipse extraction method. The proposed method can extract ellipses with high accuracy and speed from images with complicated backgrounds. It consists of two parts. First, we extract arc segments from an ellipse approximated by short straight lines that are extracted by a fast line extraction algorithm. Second, the arc segments are used to calculate accurate ellipse parameters with a least squares ellipse fitting algorithm. Tests performed on synthetic and real-world images show that the method quickly and accurately extracts circles from complicated images.
Classifying call profiles in large-scale mobile traffic datasets

Cellular communications are undergoing significant evolutions in order to accommodate the load generated by increasingly pervasive smart mobile devices. Dynamic access network adaptation to customers' demands is one of the most promising paths taken by network operators. To that end, one must be able to process large amount of mobile traffic data and outline the network utilization in an automated manner. In this paper, we propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. We evaluate our framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months. We show how our approach allows to classify similar network usage profiles and to tell apart normal and outlying call behaviors.
Software Engineering for Self-Adaptive Systems: A Research Roadmap

The goal of this roadmap paper is to summarize the state-ofthe-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on “Software Engineering for Self-Adaptive Systems, ” which took place
Amoeba: A Shape changing Storage System for Big Data

Data partitioning significantly improves the query performance in distributed database systems. A large number of techniques have been proposed to efficiently partition a dataset for a given query workload. However, many modern analytic applications involve ad-hoc or exploratory analysis where users do not have a representative query workload upfront. Furthermore, workloads change over time as businesses evolve or as analysts gain better understanding of their data. Static workload-based data partitioning techniques are therefore not suitable for such settings. In this paper, we describe the demonstration of Amoeba, a distributed storage system which uses adaptive multi-attribute data partitioning to efficiently support ad-hoc as well as recurring queries. Amoeba applies a robust partitioning algorithm such that ad-hoc queries on all attributes have similar performance gains. Thereafter, Amoeba adaptively repartitions the data based on the observed query sequence, i.e., the system improves over time. All along Amoeba offers both adaptivity (i.e., adjustments according to workload changes) as well as robustness (i.e., avoiding performance spikes due to workload changes). We propose to demonstrate Amoeba on scenarios from an internet-ofthings startup that tracks user driving patterns. We invite the audience to interactively fire fast ad-hoc queries, observe multi-dimensional adaptivity, and play with a robust/reactive knob in Amoeba. The web front end displays the layout changes, runtime costs, and compares it to Spark with both default and workload-aware partitioning.
Design space exploration for 3D architectures

As technology scales, interconnects have become a major performance bottleneck and a major source of power consumption for microprocessors. Increasing interconnect costs make it necessary to consider alternate ways of building modern microprocessors. One promising option is 3D architectures where a stack of multiple device layers with direct vertical tunneling through them are put together on the same chip. As fabrication of 3D integrated circuits has become viable, developing CAD tools and architectural techniques is imperative to explore the design space to 3D microarchitectures. In this article, we give a brief introduction to 3D integration technology, discuss the EDA design tools that can enable the adoption of 3D ICs, and present the implementation of various microprocessor components using 3D technology. An industrial case study is presented as an initial attempt to design 3D microarchitectures.
Machine Classification and Analysis of Suicide-Related Communication on Twitter

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation.
Spatial memory: how egocentric and allocentric combine

Recent experiments indicate the need for revision of a model of spatial memory consisting of viewpoint-specific representations, egocentric spatial updating and a geometric module for reorientation. Instead, it appears that both egocentric and allocentric representations exist in parallel, and combine to support behavior according to the task. Current research indicates complementary roles for these representations, with increasing dependence on allocentric representations with the amount of movement between presentation and retrieval, the number of objects remembered, and the size, familiarity and intrinsic structure of the environment. Identifying the neuronal mechanisms and functional roles of each type of representation, and of their interactions, promises to provide a framework for investigation of the organization of human memory more generally.