nils-herrmann/clean_scirepeval huggingface dataset:
Effective Next-Items Recommendation via Personalized Sequential Pattern Mining

Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.
Approximate Thin Plate Spline Mappings

The thin plate spline (TPS) is an e ective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inversion of a p p matrix, where p is the number of points in the data set, thus making it impractical for large scale applications. As it turns out, a surprisingly good approximate solution is often possible using only a small subset of corresponding points. We begin by discussing the obvious approach of using the subsampled set to estimate a transformation that is then applied to all the points, and we show the drawbacks of this method. We then proceed to borrow a technique from the machine learning community for function approximation using radial basis functions (RBFs) and adapt it to the task at hand. Using this method, we demonstrate a signi cant improvement over the naive method. One drawback of this method, however, is that is does not allow for principal warp analysis, a technique for studying shape deformations introduced by Bookstein based on the eigenvectors of the p p bending energy matrix. To address this, we describe a third approximation method based on a classic matrix completion technique that allows for principal warp analysis as a by-product. By means of experiments on real and synthetic data, we demonstrate the pros and cons of these di erent approximations so as to allow the reader to make an informed decision suited to his or her application.
Tools and approaches for topic detection from Twitter streams: survey

Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time.
Circular piezoelectric accelerometer for high band width application

An uniaxial bulk-micromachined piezoelectric MEMS accelerometer intended for high bandwidth application is fabricated and characterized. A circular seismic mass (radius = 1200 ¿m) is suspended by a 20 ¿m thick annular silicon membrane (radius = 1800 ¿m). A 24 ¿m PZT screen printed thick film is used as the sensing material on top of the silicon membrane. Accelerations in the out of plane direction induce a force on the seismic mass bending the membrane and a potential difference is measured in the out of plane direction of the stressed PZT. A resonance frequency of 23.50 kHz, a charge sensitivity of 0.23 pC/g and a voltage sensitivity of 0.24 mV/g are measured.
High performance XML parsing using parallel bit stream technology

Parabix (parallel bit streams for XML) is an open-source XML parser that employs the SIMD (single-instruction multiple-data) capabilities of modern-day commodity processors to deliver dramatic performance improvements over traditional byte-at-a-time parsing technology. Byte-oriented character data is first transformed to a set of 8 parallel bit streams, each stream comprising one bit per character code unit. Character validation, transcoding and lexical item stream formation are all then carried out in parallel using bitwise logic and shifting operations. Byte-at-a-time scanning loops in the parser are replaced by bit scan loops that can advance by as many as 64 positions with a single instruction.
 A performance study comparing Parabix with the open-source Expat and Xerces parsers is carried out using the PAPI toolkit. Total CPU cycle counts, level 2 data cache misses and branch mispredictions are measured and compared for each parser. The performance of Parabix is further studied with a breakdown of the cycle counts across the core components of the parser. Prospects for further performance improvements are also outlined, with a particular emphasis on leveraging the intraregister parallelism of SIMD processing to enable intrachip parallelism on multicore architectures.
Keyword Search Over Probabilistic RDF Graphs

In many real applications, RDF (Resource Description Framework) has been widely used as a W3C standard to describe data in the Semantic Web. In practice, RDF data may often suffer from the unreliability of their data sources, and exhibit errors or inconsistencies. In this paper, we model such unreliable RDF data by probabilistic RDF graphs, and study an important problem, keyword search query over probabilistic RDF graphs (namely, the pg-KWS query). To retrieve meaningful keyword search answers, we design the score rankings for subgraph answers specific for RDF data. Furthermore, we propose effective pruning methods (via offline pre-computed score bounds and probabilistic threshold) to quickly filter out false alarms. We construct an index over the pre-computed data for RDF, and present an efficient query answering approach through the index. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposed approaches.
A methodological approach to apply security tactics in software architecture design

Architectural tactics are decisions to efficiently solve quality attributes in software architecture. Security is a complex quality property due to its strong dependence on the application domain. However, the selection of security tactics in the definition of software architecture is guided informally and depends on the experience of the architect. This study presents a methodological approach to address and specify the quality attribute of security in architecture design applying security tactics. The approach is illustrated with a case study about a Tsunami Early Warning System.
A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization

Motor imagery (MI) based brain-computer interface (BCI) plays a crucial role in various scenarios ranging from post-traumatic rehabilitation to control prosthetics. Computer-aided interpretation of MI has augmented prior mentioned scenarios since decades but failed to address interpersonal variability. Such variability further escalates in case of multi-class MI, which is currently a common practice. The failures due to interpersonal variability can be attributed to handcrafted features as they failed to extract more generalized features. The proposed approach employs convolution neural network (CNN) based model with both filtering (through axis shuffling) and feature extraction to avail end-to-end training. Axis shuffling is performed adopted in initial blocks of the model for 1D preprocessing and reduce the parameters required. Such practice has avoided the overfitting which resulted in an improved generalized model. Publicly available BCI Competition-IV 2a dataset is considered to evaluate the proposed model. The proposed model has demonstrated the capability to identify subject-specific frequency band with an average and highest accuracy of 70.5% and S3.6% respectively. Proposed CNN model can classify in real time without relying on accelerated computing device like GPU.
Equivalent circuit model of the IC-Stripline coupling to IC package

As the operating frequency of integrated circuit (IC) is rapidly increased, the importance of electromagnetic compatibility (EMC) performance test in the design stage of IC development process is becoming more significant. The new EMC test method, IC-Stripline enables fast and accurate EMC test with smaller form factor and lower cost compared with transverse electromagnetic (TEM) cell which is the most widely used for radiated electromagnetic interference test at the moment. This paper presents the equivalent lumped-element circuit model to capture capacitive and inductive coupling between open version of IC-Stripline and an IC package. Also, the equations to calculate the circuit components with geometrical structure of IC-Stripline are proposed. The S-parameter results from presented circuit models are compared against those obtained by 3D EM solver. The presented models are very accurate up to 3 GHz which is the upper limit of effective frequency range of IC-Stripline described in IEC standards , . Therefore, the proposed IC-Stripline models can be effectively used to predict radiated emission and immunity of ICs using circuit level simulation tools.
Preoperative But Not Postoperative Flurbiprofen Axetil Alleviates Remifentanil-induced Hyperalgesia After Laparoscopic Gynecological Surgery: A Prospective, Randomized, Double-blinded, Trial.

Acute remifentanil exposure during intraoperative analgesia might enhance sensitivity to noxious stimuli and nociceptive responses to innocuous irritation. Cyclooxygenase inhibition was demonstrated to attenuate experimental remifentanil-induced hyperalgesia (RIH) in rodents and human volunteers. The study aimed to compare the effects of preoperative and postoperative flurbiprofen axetil (FA) on RIH after surgery.



Ninety patients undergoing elective laparoscopic gynecologic surgery were randomly assigned to receive either intravenous placebo before anesthesia induction (Group C); or intravenous FA (1.0 mg/kg) before anesthesia induction (Group F1) or before skin closure (Group F2). Anesthesia consisted off sevoflurane and remifentanil (0.30 μg/kg/min). Postoperative pain was managed by sufentanil titration in the postanesthetic care unit, followed by sufentanil infusion via patient-controlled analgesia. Mechanical pain threshold (primary outcome), pain scores, sufentanil consumption, and side-effects were documented for 24 hours postoperatively.



Postoperative pain score in Group F1 was lower than Group C. Time of first postoperative sufentanil titration was prolonged in Group F1 than Group C (P=0.021). Cumulative sufentanil consumption in Group F1 was lower than Group C (P<0.001), with a mean difference of 8.75 (95% confidence interval, 5.21-12.29) μg. Mechanical pain threshold on the dominant inner forearm was more elevated in Group F1 than Group C (P=0.005), with a mean difference of 17.7 (95% confidence interval, 5.4-30.0) g. Normalized hyperalgesia area was decreased in Group F1 compared to Group C (P=0.007). No statistically significant difference was observed between Group F2 and Group C.



Preoperative FA reduces postoperative RIH in patients undergoing laparoscopic gynecologic surgery under sevoflurane-remifentanil anesthesia.
An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The G 0 distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.
Ideal Facial Relationships and Goals.

Analysis of the face is an essential component of facial plastic surgery. In training, we are taught standards and ideals based on neoclassical models of beauty from Greek and Roman art and architecture. In practice, we encounter a wide range of variation in patient desires and perceptions of beauty. Our goals seem to be ever shifting, yet our education has provided us with a foundation from which to draw ideals of beauty. Plastic surgeons must synthesize classical ideas of beauty with patient desires, cultural nuances, and ethnic considerations all the while maintaining a natural appearance and result. This article gives an overview of classical models of facial proportions and relationships, while also discussing unique ethnic and cultural considerations which may influence the goal for the individual patient.
Achieving Controllability of Electric Loads

This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.
Neural information retrieval: introduction to the special issue

The applications of neural network models, shallow or deep, to information retrieval (IR) tasks falls under the purview of neural IR. Over the years, machine learning methods— including neural networks—have been popularly employed in IR, such as in learning-torank (LTR) frameworks (Liu 2009). Recently, neural representation learning and neural models with deep architectures have demonstrated significant improvements in speech recognition, machine translation, and computer vision tasks (LeCun et al. 2015). Similar methods are now being explored by the IR community that may lead to new models and performance breakthroughs for retrieval scenarios. In text retrieval, neural approaches may refer to the application of pre-trained term embeddings for ad-hoc retrieval (Ai et al. 2016; Diaz et al. 2016; Ganguly et al. 2015; Guo et al. 2016b; Kenter et al. 2016; Mitra et al. 2016; Roy et al. 2016; Vulić and Moens 2015; Zamani and Croft 2016a, b), or training deep neural networks end-to-end for the ranking task (Cohen and Croft 2016; Guo et al. 2016a; Huang et al. 2013; Mitra et al. 2017; Nanni et al. 2017; Pang et al. 2016a, b; Severyn and Moschitti 2015). But neural IR may also encompass the use of neural networks in proactive recommendations (Luukkonen et al. 2016; Van Gysel et al. 2017), query formulation and suggestions (Mitra 2015; Mitra and Craswell 2015; Sordoni et al. 2015), modelling user behaviour (Borisov et al. 2016a, b),
Which battery charger components should be included in power system stability studies of controllable load strategies?

The impact of converter-based loads on the power system is evaluated based on stability studies. This requires adequate load models. This paper deals with a time-domain model of an electric vehicle battery charger with grid support functionality (so-called controllable load). The dynamic properties of the components of a unidirectional 1,2 kW battery charger with input diode rectifier are discussed with respect to transients and oscillatory modes in power systems. Simulation results are shown for the battery charger model for a low-frequency oscillation. These results are compared to an induction motor load, which is directly connected to the mains.
Deriving decision rules to locate export containers in container yards

A methodology is proposed to determine the storage location of an arriving export container considering its weight. We consider the con®guration of the container stack and the weight distribution of containers in the yard-bay. A dynamic programming model is formulated to determine the storage location to minimize the number of relocation movements expected for the loading operation. We also develop a decision tree from the set of the optimal solutions to support real time decisions. The performance of the decision tree is evaluated by the number of decisions that are dierent from the optimal solutions of the slower dynamic programming solution method.
Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

Computational techniques are highly used in medical image analysis to aid the medical professionals. Glaucoma is a sight threatening retinal disease that needs attention at its early stages, though it does not reveal any symptoms. Glaucoma is identified usually through cup to disc ratio and ISNT rule. This work involves segmentation of blood vessels, segmentation of optic disc through proposed maximum voting of three segmentation algorithms (K-Means, Wavelet and Histogram based), segmentation of optic cup through intensity thresholding, feature extraction from these segmented structures, feature selection to identify siginificant features, hybrid model involving Naive Bayes to remove noise in data followed by ensemble classification of Reduced Error Pruning Tree. Optic disc segmentation methodology attains an average accuracy of 99.33%. Glaucoma detection accuracy reaches a maximum of 96.42%.
Imperforate hymen and urinary retention in a newborn girl.

Urinary retention is relatively rare in infants,especially in girls. Imperforate hymen is the most frequent congenital malformation of the female genital tract and is usually asymptomatic until puberty. Mucocolpos with an abdominal mass in neonatal age is extremely rare. We report a case of a 20-day-old newborn girl with acute urinary retention due to isolated imperforate hymen and mucocolpos.
Measuring information quality of web sites: development of an instrument

Web sites have been extensively used to provide information to consumers. While practitioners and researchers have proposed different criteria for effective Web site design based on common sense, intuition, and rules-ofthumb, effective Web site design focusing on the quality of the information it provides has rarely been studied. In this research, we propose a framework and develop an instrument to measure the information quality of individual or personal Web sites. The theoretical foundation of this research is the information quality framework. The proposed framework and instrument were tested in an individual or personal Web site context.
Evaluating user experience in moodle learning management systems

Learning management systems are becoming widespread technology adopted at educational systems. Moodle system is used to record the user experience in e-learning environment. In Moodle system, the user experience is recorded and must be studied and analyzed to provide the optimal solution to meet pedagogical requirements of both students and teachers in all areas. In Moodle Learning management systems can offer a large variety of workspaces to smooth the progress of information sharing and communication among participants in e-learning courses. This study evaluates an open source Moodle learning management system to help academicians create effective online learning communities using data mining techniques.
Enhanced Security for Cloud Storage using File Encryption

Cloud computing is a term coined to a network that offers incredible processing power, a wide array of storage space and unbelievable speed of computation. Social media channels, corporate structures and individual consumers are all switching to the magnificent world of cloud computing. The flip side to this coin is that with cloud storage emerges the security issues of confidentiality, data integrity and data availability. Since the “cloud” is a mere collection of tangible super computers spread across the world, authentication and authorization for data access is more than a necessity. Our work attempts to overcome these security threats. The proposed methodology suggests the encryption of the files to be uploaded on the cloud. The integrity and confidentiality of the data uploaded by the user is ensured doubly by not only encrypting it but also providing access to the data only on successful authentication. KeywordsCloud computing, security, encryption, password based AES algorithm
Van Gogh ’ s brushstrokes : marks of authenticity ?

The process of attributing paintings relies partly upon recognition of an artist’s hand. Around the middle of the last century, Maurits M. van Dantzig (1903–1960) attempted to define the ‘characteristic touch’ of the painter, Vincent van Gogh (1853–1890). His broader aim was to develop a flexible yet precise method to measure the features of both spontaneity and inhibition, evident in the style of brushwork for example. The underlying idea that these qualities can be used to separate a genuine work from a second-rate copy or forgery still plays a key role in attribution studies today. A recent initiative explores the potential of advanced computer image analysis techniques to help identify and quantify these properties at the scale of brushwork. This paper describes the basic principles of the method used, involving statistical analysis of different size wavelets present in digital images of paintings by Van Gogh or other artists.