nils-herrmann/clean_scirepeval huggingface dataset:
Logical-shapelets: an expressive primitive for time series classification

Time series shapelets are small, local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers and for certain visualization and summarization tasks. While shapelets were introduced only recently, they have already seen significant adoption and extension in the community. Despite their immense potential as a data mining primitive, there are two important limitations of shapelets. First, their expressiveness is limited to simple binary presence/absence questions. Second, even though shapelets are computed offline, the time taken to compute them is significant. In this work, we address the latter problem by introducing a novel algorithm that finds shapelets in less time than current methods by an order of magnitude. Our algorithm is based on intelligent caching and reuse of computations, and the admissible pruning of the search space. Because our algorithm is so fast, it creates an opportunity to consider more expressive shapelet queries. In particular, we show for the first time an augmented shapelet representation that distinguishes the data based on conjunctions or disjunctions of shapelets. We call our novel representation Logical-Shapelets. We demonstrate the efficiency of our approach on the classic benchmark datasets used for these problems, and show several case studies where logical shapelets significantly outperform the original shapelet representation and other time series classification techniques. We demonstrate the utility of our ideas in domains as diverse as gesture recognition, robotics, and biometrics.
Directional Resolution: The Davis-Putnam Procedure, Revisited

The paper presents algorithm directional resolution, a variation on the original DavisPutnam algorithm, and analyzes its worstcase behavior as a function of the topological structure of the theories. The notions of induced width and diversity are shown to play a key role in bounding the complexity of the procedure. The importance of our analysis lies in highlighting structure-based tractable classes of satis ability and in providing theoretical guarantees on the time and space complexity of the algorithm. Contrary to previous assessments, we show that for many theories directional resolution could be an e ective procedure. Our empirical tests con rm theoretical prediction, showing that on problems with special structures, like chains, directional resolution greatly outperforms one of the most e ective satis ability algorithm known to date, namely the popular DavisPutnam procedure.
Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet

Marcel Fritz, Christian Schlereth, Stefan Figge Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet The fair use flat rate is a promising tariff concept for the mobile telecommunication industry. Similar to classical flat rates it allows unlimited usage at a fixed monthly fee. Contrary to classical flat rates it limits the access speed once a certain usage threshold is exceeded. Due to the current global roll-out of the LTE (Long Term Evolution) technology and the related economic changes for telecommunication providers, the application of fair use flat rates needs a reassessment. We therefore propose a simulation model to evaluate different pricing strategies and their contribution margin impact. The key input element of the model is provided by socalled discrete choice experiments that allow the estimation of customer preferences. Based on this customer information and the simulation results, the article provides the following recommendations. Classical flat rates do not allow profitable provisioning of mobile Internet access. Instead, operators should apply fair use flat rates with a lower usage threshold of 1 or 3 GB which leads to an improved contribution margin. Bandwidth and speed are secondary and do merely impact customer preferences. The main motivation for new mobile technologies such as LTE should therefore be to improve the cost structure of an operator rather than using it to skim an assumed higher willingness to pay of mobile subscribers.
Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay

This paper outlines a new approach to the study of power, that of the sociology of translation. Starting from three principles, those of agnosticism (impartiality between actors engaged in controversy), generalised symmetry (the commitment to explain conflicting viewpoints in the same terms) and free association (the abandonment of all a priori distinctions between the natural and the social), the paper describes a scientific and economic controversy about the causes for the decline in the population of scallops in St. Brieuc Bay and the attempts by three marine biologists to develop a conservation strategy for that population. Four ‘moments’ of translation are discerned in the attempts by these researchers to impose themselves and their definition of the situation on others: (a) problematisation: the researchers sought to become indispensable to other actors in the drama by defining the nature and the problems of the latter and then suggesting that these would be resolved if the actors negotiated the ‘obligatory passage point’ of the researchers’ programme of investigation; (b) interessement: a series of processes by which the researchers sought to lock the other actors into the roles that had been proposed for them in that programme; (c) enrolment: a set of strategies in which the researchers sought to define and interrelate the various roles they had allocated to others; (d) mobilisation: a set of methods used by the researchers to ensure that supposed spokesmen for various relevant collectivities were properly able to represent those collectivities and not betrayed by the latter. In conclusion it is noted that translation is a process, never a completed accomplishment, and it may (as in the empirical case considered) fail.
A layered approach to cognitive radio network security: A survey

Cognitive radios have been identified as a solution to the crowded spectrum issue. With the realization of cognitive radio networks came the recognition that both new and old security threats are relevant. The cognitive radio network is still vulnerable to many of the denial of service, wormhole, routing, and jamming attacks that plague other wireless technologies. In addition, the cognitive radio network is vulnerable to new attacks based on cognitive radio innovations, such as spectrum sharing, spectrum sensing, cognitive capability , and radio reconfigurability. The scope of this survey is to present an overview of security threats and challenges to the cognitive radio network, especially focusing on new solutions from 2012 and the first half of 2013. Included are prior mitigation techniques that are adaptive to the new technology, as well as new mitigation techniques specifically targeted at new cognitive radio vulnerabilities. The threats provided are organized according to the protocol layer at which the attack is targeted. It has been estimated that the people of the United States are now outnumbered by their wireless devices. The proliferation of wireless devices such as laptops, notebooks , cellular phones, smart phones, and tablets has caused the frequency spectrum used for transfer of information to become crowded [70]. Also, the expected growth in media-rich consumer applications and wireless data transfer will continue to further crowd the network, making additional spectrum throughput a priority. Currently in the United States spectrum is allotted to various services in three main categories: licensed, lightly licensed, and unlicensed . Licensed spectrum refers to the portions of the spectrum reserved by each country's equivalent of the Federal Communications Commission (FCC) for specific uses, such as military, public safety, and commercial uses. Lightly licensed spectrum refers to the bands that are generally regulated for licensed users, with regional or other exceptions. In the unlicensed band there are predefined technical rules for the hardware and radio technology intended to mitigate interference between the bands. The spectrum is available for network setup by any person or entity, public or private, to include commercial high speed internet, provided that it does not infringe upon the band's rules . In an effort to provide relief to the users of the overused spectrum, in 2010 the FCC allocated unused spectrum between television channels, or ''white spaces'' for unlicensed use. In addition, the FCC has proposed setting aside some low band spectrum, and possibly underutilized …
Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the documents, and distill information that is not possible to extract using contents only? In this paper, we propose a novel random process, referred to as the Dirichlet-Hawkes process, to take into account both information in a unified framework. A distinctive feature of the proposed model is that the preferential attachment of items to clusters according to cluster sizes, present in Dirichlet processes, is now driven according to the intensities of cluster-wise self-exciting temporal point processes, the Hawkes processes. This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever changing dynamics of the respective continuous arrival time. We conducted large-scale experiments on both synthetic and real world news articles, and show that Dirichlet-Hawkes processes can recover both meaningful topics and temporal dynamics, which leads to better predictive performance in terms of content perplexity and arrival time of future documents.
Learning 3D faces from 2D images via Stacked Contractive Autoencoder

3D face reconstruction from a 2D face image has been found important to various applications such as face detection and recognition because a 3D face provides more semantic information than 2D image. This paper proposes a deep learning framework for 3D face reconstruction. The framework is designed to compute subspace feature of arbitrary face image, then map the feature to its counterpart in another subspace learned with 3D faces, and reconstruct the 3D face using the counterpart feature. During the course of training, we learn 2D and 3D subspaces through Stacked Contractive Autoencoders (SCAE), use a one-layer fully connected neural network to learn the mapping, and use the pre-trained parameters of the SCAEs and the one-layer network to initialize a deep feedforward neural network whose input are face images and output are 3D faces. The network is optimized by gradient descent algorithm with back-propagation. Extensive experimental results on various data sets indicate the effectiveness of the proposed SCAE-based 3D face reconstruction method.
Predicting employee expertise for talent management in the enterprise

Strategic planning and talent management in large enterprises composed of knowledge workers requires complete, accurate, and up-to-date representation of the expertise of employees in a form that integrates with business processes. Like other similar organizations operating in dynamic environments, the IBM Corporation strives to maintain such current and correct information, specifically assessments of employees against job roles and skill sets from its expertise taxonomy. In this work, we deploy an analytics-driven solution that infers the expertise of employees through the mining of enterprise and social data that is not specifically generated and collected for expertise inference. We consider job role and specialty prediction and pose them as supervised classification problems. We evaluate a large number of feature sets, predictive models and postprocessing algorithms, and choose a combination for deployment. This expertise analytics system has been deployed for key employee population segments, yielding large reductions in manual effort and the ability to continually and consistently serve up-to-date and accurate data for several business functions. This expertise management system is in the process of being deployed throughout the corporation.
UIML: An Appliance-Independent XML User Interface Language

Today’s Internet appliances feature user interface technologies almost unknown a few years ago: touch screens, styli, handwriting and voice recognition, speech synthesis, tiny screens, and more. This richness creates problems. First, different appliances use different languages: WML for cell phones; SpeechML, JSML, and VoxML for voice enabled devices such as phones; HTML and XUL for desktop computers, and so on. Thus, developers must maintain multiple source code families to deploy interfaces to one information system on multiple appliances. Second, user interfaces differ dramatically in complexity (e.g, PC versus cell phone interfaces). Thus, developers must also manage interface content. Third, developers risk writing appliance-specific interfaces for an appliance that might not be on the market tomorrow. A solution is to build interfaces with a single, universal language free of assumptions about appliances and interface technology. This paper introduces such a language, the User Interface Markup Language (UIML), an XML-compliant language. UIML insulates the interface designer from the peculiarities of different appliances through style sheets. A measure of the power of UIML is that it can replace hand-coding of Java AWT or Swing user interfaces.
Sampling in Qualitative Research : Insights from an Overview of the Methods Literature

The methods literature regarding sampling in qualitative research is characterized by important inconsistencies and ambiguities, which can be problematic for students and researchers seeking a clear and coherent understanding. In this article we present insights about sampling in qualitative research derived from a systematic methods overview we conducted of the literature from three research traditions: grounded theory, phenomenology, and case study. We identified and selected influential methods literature from each tradition using a purposeful and transparent procedure, abstracted textual data using structured abstraction forms, and used a multistep approach for deriving conclusions from the data. We organize the findings from this review into eight topic sections corresponding to the major domains of sampling identified in the review process: definitions of sampling, usage of the term sampling strategy, purposeful sampling, theoretical sampling, sampling units, saturation, sample size, and the timing of sampling decisions. Within each section we summarize how the topic is characterized in the corresponding literature, present our comparative analysis of important differences among research traditions, and offer analytic comments on the findings for that topic. We identify several specific issues with the available guidance on certain topics, representing opportunities for future methods authors to improve our collective understanding.
Exposing native device APIs to web apps

A recent survey among developers revealed that half plan to use HTML5 for mobile apps in the future. An earlier survey showed that access to native device APIs is the biggest shortcoming of HTML5 compared to native apps. Several different approaches exist to overcome this limitation, among them cross-compilation and packaging the HTML5 as a native app. In this paper we propose a novel approach by using a device-local service that runs on the smartphone and that acts as a gateway to the native layer for HTML5-based apps running inside the standard browser. WebSockets are used for bi-directional communication between the web apps and the device-local service. The service approach is a generalization of the packaging solution. In this paper we describe our approach and compare it with other popular ways to grant web apps access to the native API layer of the operating system.
Fault-tolerant control with active fault diagnosis for four-wheel independently-driven electric ground vehicles

A fault-tolerant (FT) control approach for four-wheel independently-driven (4WID) electric vehicles is presented. An adaptive control based passive fault-tolerant controller is designed to ensure the system stability when an in-wheel motor/motor driver fault happens. As an over-actuated system, it is challenging to isolate the faulty wheel and accurately estimate the control gain of the faulty in-wheel motor for 4WID electric vehicles. An active fault diagnosis approach is thus proposed to isolate and evaluate the fault. Based on the estimated control gain of the faulty in-wheel motor, the control efforts of all the four wheels are redistributed to relieve the torque demand on the faulty wheel. Simulations using a high-fidelity, CarSim, full-vehicle model show the effectiveness of the proposed in-wheel motor/motor driver fault diagnosis and fault-tolerant control approach.
Corpus-based Question Answering for why-Questions

This paper proposes a corpus-based approach for answering why-questions. Conventional systems use hand-crafted patterns to extract and evaluate answer candidates. However, such hand-crafted patterns are likely to have low coverage of causal expressions, and it is also difficult to assign suitable weights to the patterns by hand. In our approach, causal expressions are automatically collected from corpora tagged with semantic relations. From the collected expressions, features are created to train an answer candidate ranker that maximizes the QA performance with regards to the corpus of why-questions and answers. NAZEQA, a Japanese why-QA system based on our approach, clearly outperforms a baseline that uses hand-crafted patterns with a Mean Reciprocal Rank (top-5) of 0.305, making it presumably the best-performing fully implemented why-QA system.
Using Vital Sensors in Mobile Healthcare Business Applications - Challenges, Examples, Lessons Learned

Today, sensors are increasingly used for data collection. In the medical domain, for example, vital signs (e.g., pulse or oxygen saturation) of patients can be measured with sensors and used for further processing. In this paper, different types of applications will be discussed whether sensors might be used in the context of these applications and their suitability for applying external sensors to them. Furthermore, a system architecture for adding sensor technology to respective applications is presented. For this purpose, a real-world business application scenario in the field of well-being and fitness is presented. In particular, we integrated two different sensors in our fitness application. We report on the lessons learned from the implementation and use of this application, e.g., in respect to connection and data structure. They mainly deal with problems relating to the connection and communication between the smart mobile device and the external sensors, as well as the selection of the appropriate type of application. Finally, a robust sensor framework, arising from this fitness application is presented. This framework provides basic features for connecting sensors. Particularly, in the medical domain, it is crucial to provide an easy to use toolset to relieve medical staff.
An Approach to Vehicle Recognition Using Supervised Learning

To enhance safety and traffic efficiency, a driver assistance system and an autonomous vehicle system are being developed. A preceding vehicle recognition method is important to develop such systems. In this paper, the vision-based preceding vehicle recognition method, based on supervised learning from sample images is proposed. And the improvement for Modified Quadratic Discriminant Function (MQDF) classifier that is used in the proposed method is also shown. In the case of the road environment recognition including the preceding vehicle recognition, there are many reports, in which evaluations are done with a few images, but a quantitative evaluation with large number of images has rarely been done. We prepare over 1,000 sample images for passenger vehicles, which are recorded on a highway at daytime, and evaluate the proposed method with those images. The evaluation result shows that the performance in a low order case is improved from the ordinary MQDF. The feasibility of the proposed method is proved, due to the result that the proposed method indicates over 98% as classification rate.
Delta TFIDF: An Improved Feature Space for Sentiment Analysis

Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets.
A novel refinement approach for text categorization

In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to successively refine the classification model of a base classifier. We describe how it is applied to generate two new classification algorithms; a Refined Centroid Classifier and a Refined Naïve Bayes Classifier. We present an extensive experimental evaluation of both algorithms on three English collections and one Chinese corpus. The results indicate that in each case, the refined classifiers achieve significant performance improvement over the base classifiers used. Furthermore, the performance of the Refined Centroid Classifier implemented is comparable, if not better, to that of state-of-the-art support vector machine (SVM)-based classifier, but offers a much lower computational cost.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification

We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data. We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction. Experiments are carried out on DMOZ and Wikipedia collections with 10,000 to 100,000 classes where we show the efficiency of the proposed approach in terms of training and prediction time, memory consumption, and predictive performance with respect to state-of-the-art approaches.