nils-herrmann/clean_scirepeval huggingface dataset:
CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns

Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.
Comparison of PubMed and Google Scholar literature searches.

Literature searches are essential to evidence-based respiratory care. To conduct literature searches, respiratory therapists rely on search engines to retrieve information, but there is a dearth of literature on the comparative efficiencies of search engines for researching clinical questions in respiratory care.



To compare PubMed and Google Scholar search results for clinical topics in respiratory care to that of a benchmark.



We performed literature searches with PubMed and Google Scholar, on 3 clinical topics. In PubMed we used the Clinical Queries search filter. In Google Scholar we used the search filters in the Advanced Scholar Search option. We used the reference list of a related Cochrane Collaboration evidence-based systematic review as the benchmark for each of the search results. We calculated recall (sensitivity) and precision (positive predictive value) with 2 x 2 contingency tables. We compared the results with the chi-square test of independence and Fisher's exact test.



PubMed and Google Scholar had similar recall for both overall search results (71% vs 69%) and full-text results (43% vs 51%). PubMed had better precision than Google Scholar for both overall search results (13% vs 0.07%, P < .001) and full-text results (8% vs 0.05%, P < .001).



Our results suggest that PubMed searches with the Clinical Queries filter are more precise than with the Advanced Scholar Search in Google Scholar for respiratory care topics. PubMed appears to be more practical to conduct efficient, valid searches for informing evidence-based patient-care protocols, for guiding the care of individual patients, and for educational purposes.
Digital government evolution: From transformation to contextualization

The Digital Government landscape is continuously changing to reflect how governments are trying to find innovative digital solutions to social, economic, political and other pressures, and how they transform themselves in the process. Understanding and predicting such changes is important for policymakers, government executives, researchers and all those who prepare, make, implement or evaluate Digital Government decisions. This article argues that the concept of Digital Government evolves towards more complexity and greater contextualization and specialization, similar to evolution-like processes that lead to changes in cultures and societies. To this end, the article presents a four-stage Digital Government Evolution Model comprising Digitization (Technology in Government), Transformation (Electronic Government), Engagement (Electronic Governance) and Contextualization (Policy-Driven Electronic Governance) stages; provides some evidence in support of this model drawing upon the study of the Digital Government literature published in Government Information Quarterly between 1992 and 2014; and presents a Digital Government Stage Analysis Framework to explain the evolution. As the article consolidates a representative body of the Digital Government literature, it could be also used for defining and integrating future research in the area.
The impact of pretend play on children's development: a review of the evidence.

Pretend play has been claimed to be crucial to children's healthy development. Here we examine evidence for this position versus 2 alternatives: Pretend play is 1 of many routes to positive developments (equifinality), and pretend play is an epiphenomenon of other factors that drive development. Evidence from several domains is considered. For language, narrative, and emotion regulation, the research conducted to date is consistent with all 3 positions but insufficient to draw conclusions. For executive function and social skills, existing research leans against the crucial causal position but is insufficient to differentiate the other 2. For reasoning, equifinality is definitely supported, ruling out a crucially causal position but still leaving open the possibility that pretend play is epiphenomenal. For problem solving, there is no compelling evidence that pretend play helps or is even a correlate. For creativity, intelligence, conservation, and theory of mind, inconsistent correlational results from sound studies and nonreplication with masked experimenters are problematic for a causal position, and some good studies favor an epiphenomenon position in which child, adult, and environment characteristics that go along with play are the true causal agents. We end by considering epiphenomenalism more deeply and discussing implications for preschool settings and further research in this domain. Our take-away message is that existing evidence does not support strong causal claims about the unique importance of pretend play for development and that much more and better research is essential for clarifying its possible role.
PMLAB: An Scripting Environment for Process Mining

In a decade of process mining research, several algorithms have been proposed to solve particular process mining tasks. At the same pace, tools have appeared both in the academic and the commercial domains. These tools have enabled the use of process mining practices to a rather limited extent. In this paper we advocate for a change in the mentality: process mining may be an exploratory discipline, and PMLAB – a Python-based scripting environment supporting this – is proposed. This demo presents the main features of the PMLAB environment
Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control

A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design
Benchmarking Single-Image Dehazing and Beyond

We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.
These aren't the droids you're looking for: retrofitting android to protect data from imperious applications

We examine two privacy controls for Android smartphones that empower users to run permission-hungry applications while protecting private data from being exfiltrated: (1) covertly substituting shadow data in place of data that the user wants to keep private, and (2) blocking network transmissions that contain data the user made available to the application for on-device use only. We retrofit the Android operating system to implement these two controls for use with unmodified applications. A key challenge of imposing shadowing and exfiltration blocking on existing applications is that these controls could cause side effects that interfere with user-desired functionality. To measure the impact of side effects, we develop an automated testing methodology that records screenshots of application executions both with and without privacy controls, then automatically highlights the visual differences between the different executions. We evaluate our privacy controls on 50 applications from the Android Market, selected from those that were both popular and permission-hungry. We find that our privacy controls can successfully reduce the effective permissions of the application without causing side effects for 66% of the tested applications. The remaining 34% of applications implemented user-desired functionality that required violating the privacy requirements our controls were designed to enforce; there was an unavoidable choice between privacy and user-desired functionality.
Towards the Semantic Web: Collaborative Tag Suggestions

Content organization over the Internet went through several interesting phases of evolution: from structured di rectories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step toward s the semantic web vision. Tagging allows ranking and dat a organization to directly utilize inputs from end us er , enabling machine processing of Web content. Since tags are c reated by individual users in a free form, one important prob lem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set o f general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popu larity to ensure tag quality. We propose a collaborative tag su gestion algorithm using these criteria to spot high-quality ags. The proposed algorithm employs a goodness measure for t ags derived from collective user authorities to combat spam. Th e goodness measure is iteratively adjusted by a reward-penalty lgorithm, which also incorporates other sources of tags, e.g. , content-based auto-generated tags. Our experiments based on My We b 2.0 show that the algorithm is effective.
A Linear Programming Approach for Multiple Object Tracking

We propose a linear programming relaxation scheme for the class of multiple object tracking problems where the inter-object interaction metric is convex and the intra-object term quantifying object state continuity may use any metric. The proposed scheme models object tracking as a multi-path searching problem. It explicitly models track interaction, such as object spatial layout consistency or mutual occlusion, and optimizes multiple object tracks simultaneously. The proposed scheme does not rely on track initialization and complex heuristics. It has much less average complexity than previous efficient exhaustive search methods such as extended dynamic programming and is found to be able to find the global optimum with high probability. We have successfully applied the proposed method to multiple object tracking in video streams.
Modular switched capacitor voltage multiplier topology for pulsed power supply

Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, based on conventional voltage multiplier and by using power electronics switches a new topology in high voltage pulsed power application is proposed. This topology is a modular circuit that can generate a high output voltage from a relatively low input voltage with fast rise time and adjustable frequency, pulse width, and voltage levels using a series connection of switched capacitor cells. A comparative analysis is carried out to show the advantages of proposed topology. Experimental and simulation results are presented to confirm the analysis.
Developing fully functional E-government: A four stage model

Literature reports the experiences with e-government initiatives as chaotic and unmanageable, despite recent numerous initiatives at different levels of government and academic and practitionersâ€TM conferences on e-government. E-government presents a number of challenges for public administrators. To help public administrators think about e-government and their organizations, this article describes different stages of egovernment development and proposes a â€ ̃stages of growthâ€TM model for fully functional e-government. Various government websites and related e-government initiatives help to ground and explain this model. These stages outline the multiperspective transformation within government structures and functions as they make transitions to e-government through each stage. Technological and organizational challenges for each stage accompany these descriptions. At the same time, this paper describes how the e-government becomes amalgamated with traditional public administrative structure. a b Purchase Export
Matchbox: large scale online bayesian recommendations

We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional `trait space' in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences. Here we present three alternatives: direct observation of an absolute rating each user gives to some items, observation of a binary preference (like/ don't like) and observation of a set of ordinal ratings on a user-specific scale. Efficient inference is achieved by approximate message passing involving a combination of Expectation Propagation (EP) and Variational Message Passing. We also include a dynamics model which allows an item's popularity, a user's taste or a user's personal rating scale to drift over time. By using Assumed-Density Filtering (ADF) for training, the model requires only a single pass through the training data. This is an on-line learning algorithm capable of incrementally taking account of new data so the system can immediately reflect the latest user preferences. We evaluate the performance of the algorithm on the MovieLens and Netflix data sets consisting of approximately 1,000,000 and 100,000,000 ratings respectively. This demonstrates that training the model using the on-line ADF approach yields state-of-the-art performance with the option of improving performance further if computational resources are available by performing multiple EP passes over the training data.
Multivariate LSTM-FCNs for Time Series Classification

Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance. Our proposed models outperform most of the state of the art models while requiring minimum preprocessing. The proposed models work efficiently on various complex multivariate time series classification tasks such as activity recognition or action recognition. Furthermore, the proposed models are highly efficient at test time and small enough to deploy on memory constrained systems. Convolutional neural network, long short term memory, recurrent neural network, multivariate time series classification
Practical Implementations of Arithmetic Coding 1

We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-e cient, approximate arithmetic coder with only minimal loss of compression e ciency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme. Index terms : Data compression, arithmetic coding, adaptive modeling, analysis of algorithms, data structures, low precision arithmetic. A similar version of this paper appears in Image and Text Compression, James A. Storer, ed., Kluwer Academic Publishers, Norwell, MA, 1992, 85{112. A shortened version of this paper appears in the proceedings of the International Conference on Advances in Communication and Control (COMCON 3), Victoria, British Columbia, Canada, October 16{18, 1991. Support was provided in part by NASA Graduate Student Researchers Program grant NGT{ 50420 and by a National Science Foundation Presidential Young Investigators Award grant with matching funds from IBM. Additional support was provided by a Universities Space Research Association/CESDIS associate membership. Support was provided in part by National Science Foundation Presidential Young Investigator Award CCR{9047466 with matching funds from IBM, by NSF research grant CCR{9007851, by Army Research O ce grant DAAL03{91{G{0035, and by the O ce of Naval Research and the Defense Advanced Research Projects Agency under contract N00014{91{J{4052ARPA  Additional support was provided by a Universities Space Research Association/CESDIS associate membership.
Honeypots : The Need of Network Security

Network forensics is basically used to detect attackers activity and to analyze their behavior. Data collection is the major task of network forensics and honeypots are used in network forensics to collect useful data. Honeypot is an exciting new technology with enormous potential for security communities. This review paper is based upon the introduction to honeypots, their importance in network security, types of honeypots, their advantages disadvantages and legal issues related with them. Research Paper also discuss about the shortcomings of intrusion detection system in a network security and how honeypots improve the security architecture of the organizational network. Furthermore, paper reveals about the different kind of honeypots their level of interaction and risks associated with them. honeyd; honeypots; nmap; network forensics
A new method for pulse oximetry possessing inherent insensitivity to artifact

A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.
Attacks on the Pairing Protocol of Bluetooth v 2 . 1

The Bluetooth protocol for close-range wireless communication has been a huge success. It is a widely adopted standard and is used for a wide range of devices, from cellphones to PDAs to laptops and more. Due to its ubiquity and importance, its security has become a critical issue. In the new version 2.1 released in July 2007, a complete overhaul of the pairing procedure was carried out with the express aim of making it more secure. In this paper we show that the Bluetooth pairing protocol in passkey entry mode completely leaks the password. In addition, we show that it is possible to pair with a device that uses a fixed (but unknown) password, even when the password is random and reasonably long. Our attacks demonstrate that passkey entry mode can only be used with a different random password each time. Unfortunately this is not possible for devices that use a fixed password (like many hands-free car kits). In addition, due to human behavior, this is unlikely to be the case when the user enters the password into two devices in order to pair them. Thus, devices who leave it to the user to enter a password (instead of randomly generating it on one of the devices) will be vulnerable to attack.
High quality lip-sync animation for 3D photo-realistic talking head

We propose a new 3D photo-realistic talking head with high quality, lip-sync animation. It extends our prior high-quality 2D photo-realistic talking head to 3D. An a/v recording of a person speaking a set of prompted sentences with good phonetic coverage for ~20-minutes is first made. We then use a 2D-to-3D reconstruction algorithm to automatically adapt a general 3D head mesh model to the person. In training, super feature vectors consisting of 3D geometry, texture and speech are augmented together to train a statistical, multi-streamed, Hidden Markov Model (HMM). The HMM is then used to synthesize both the trajectories of head motion animation and the corresponding dynamics of texture. The resultant 3D talking head animation can be controlled by the model predicted geometric trajectory while the articulator movements, e.g., lips, are rendered with dynamic 2D texture image sequences. Head motions and facial expression can also be separately controlled by manipulating corresponding parameters. In a real-time demonstration, the life-like 3D talking head can take any input text, convert it into speech and render lip-synced speech animation photo-realistically.
Recognizing Bird Species in Audio Recordings using Deep Convolutional Neural Networks

This paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural networks. The approach is evaluated in the context of the LifeCLEF 2016 bird identification task an open challenge conducted on a dataset containing 34 128 audio recordings representing 999 bird species from South America. Three different network architectures and a simple ensemble model are considered for this task, with the ensemble submission achieving a mean average precision of 41.2% (official score) and 52.9% for foreground species.