nils-herrmann/clean_scirepeval huggingface dataset:
Applying Winnow to Context-Sensitive Spelling Correction

Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved.  First printing, TR96-07, April 1996
Dynamic question generation system for web-based testing using particle swarm optimization

One aim of testing is to identify weaknesses in students’ knowledge. Computerized tests are now one of the most important ways to judge learning, and, selecting tailored questions for each learner is a significant part of such tests. Therefore, one current trend is that computerized adaptive tests (CATs) not only assist teachers in estimating the learning performance of students, but also facilitate understanding of problems in their learning process. These tests, must effectively and efficiently select questions from a large-scale item bank, and to cope with this problem we propose a dynamic question generation system for web-based tests using the novel approach of particle swarm optimization (PSO). The dynamic question generation system is built to select tailored questions for each learner from the item bank to satisfy multiple assessment requirements. Furthermore, the proposed approach is able to efficiently generate near-optimal questions that satisfy multiple assessment criteria. With a series of experiments, we compare the efficiency and efficacy of the PSO approach with other approaches. The experimental results show that the PSO approach is suitable for the selection of near-optimal questions from large-scale item banks.
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing largeamounts of data require overcoming challenges in multiple areas:managing large-scale data distribution, co-placement andscheduling of data with compute resources, and storing and transferringlarge volumes of data. We analyze the ecosystems of thetwo prominent paradigms for data-intensive applications, hereafterreferred to as the high-performance computing and theApache-Hadoop paradigm. We propose a basis, common terminologyand functional factors upon which to analyze the two approachesof both paradigms. We discuss the concept of "Big DataOgres" and their facets as means of understanding and characterizingthe most common application workloads found acrossthe two paradigms. We then discuss the salient features of thetwo paradigms, and compare and contrast the two approaches.Specifically, we examine common implementation/approaches ofthese paradigms, shed light upon the reasons for their current"architecture" and discuss some typical workloads that utilizethem. In spite of the significant software distinctions, we believethere is architectural similarity. We discuss the potential integrationof different implementations, across the different levelsand components. Our comparison progresses from a fully qualitativeexamination of the two paradigms, to a semi-quantitativemethodology. We use a simple and broadly used Ogre (K-meansclustering), characterize its performance on a range of representativeplatforms, covering several implementations from bothparadigms. Our experiments provide an insight into the relativestrengths of the two paradigms. We propose that the set of Ogreswill serve as a benchmark to evaluate the two paradigms alongdifferent dimensions.
Causal Generative Domain Adaptation Networks

An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.
Glomosim: a scalable network simulation environment

Large-scale hybrid networks that include wireless, wired, and satellite based communications are becoming common in both military and commercial situations. This paper describes a scalable simulation environment called GloMoSim (for Global Mobile Information System Simulator) that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. The paper also presents a set of case studies that evaluate the performance of large wireless networks with thousands of nodes and compares the impact of different lower layer protocols on the performance of typical applications.
Design for variety : developing standardized and modularized product platform architectures

Developing a robust, product platform architecture brings an important competitive advantage to a company. The major benefits are reduced design effort and time-to-market for future generations of the product. This paper describes a step-by-step method that aids companies in developing such product platform architectures. Using the concept of specification ‘‘flows’’ within a product development project, the design for variety (DFV) method develops two indices to measure a product’s architecture. The first index is the generational variety index (GVI), a measure for the amount of redesign effort required for future designs of the product. The second index is the coupling index (CI), a measure of the coupling among the product components. The design team uses these two indices to develop a decoupled architecture that requires less design effort for follow-on products. This paper describes the DFV method and uses a water cooler example to illustrate the method.
MatrixWave: Visual Comparison of Event Sequence Data

Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.
QPSK Modulator and Demodulator Using FPGA for SDR

A software-defined radio (SDR) allows for digital communication systems to simply accept more complicated coding and modulation technologies, which is enormously vital in meeting the ever-increasing demands of the wireless communication industry. An SDR has been constructed, using the Simulink tool, and implemented on the SPARTEN-3E Field Programmable Gate Array (FPGA) development kit. The modulation scheme used in the system is Quadrature Phase-Shift Keying (QPSK). In the first step to realize the whole modulation and demodulation schemes using MATLAB Simulink. The format of a VHDL program is built around the concept of BLOCKS which are the basic building units of a VHDL design. The results showed that the proposed method can greatly improve the developing efficiency, shorten developing period and reduce costs.
Knowledge Management Enabler Factors and Firm Performance : An empirical research of the Greek medium and large firms

Knowledge has become one of the most important driving forces for business success. Knowledge management helps organizations to find, select, organise, distribute, and transfer vital information. Through a successful knowledge management (KM) organizations improve their effectiveness and gain competitive advantage. The development of KM has led to the need of identifying its critical success factors. This study identifies and discusses the critical success factors or enablers that determine the KM effectiveness within organizations, which in turn influence the total performance of the firm. Based on existing frameworks and models, this study outlines the five most important factors that are believed to be critical for an effective KM implementation. This paper also investigates the effect of knowledge management effectiveness on firm performance. The proposed research model is tested via an online survey sent to 280 medium and large sized enterprises, randomly selected, all over Greece; from those only 109 answered the questionnaire correctly. The results of the study will help organizations to understand the impact that different enablers have on the KM successful implementation and how the effectiveness of KM affect firm performance. 1 Contact author: Kavala Institute of Technology, School of Business & Economics, Department of Business Administration, Agios Loukas, 65404 Kavala, Tel.:0030-2510-462371, email: ntheriou@teikav.edu.gr
Patient satisfaction and medication adherence assessment amongst patients at the diabetes medication therapy adherence clinic.

To determine the satisfaction and current adherence status of patients with diabetes mellitus at the diabetes Medication Therapy Adherence Clinic and the relationship between patient satisfaction and adherence.



This cross-sectional descriptive study was carried out at three government hospitals in the state of Johor, Malaysia. Patient's satisfaction was measured using the Patient Satisfaction with Pharmaceutical Care Questionnaire; medication adherence was measured using the eight-item Morisky Medication Adherence Scale.



Of n=165 patients, 87.0% of patients were satisfied with DMTAC service (score 60-100) with mean scores of 76.8. On the basis of MMAS, 29.1% had a medium rate and 26.1% had a high rate of adherence. Females are 3.02 times more satisfied with the pharmaceutical service compared to males (OR 3.03, 95% CI 1.12-8.24, p<0.05) and non-Malays are less satisfied with pharmaceutical care provided during DMTAC compared to Malays (OR 0.32, 95% CI 0.12-0.85, p<0.05). Older patients age group ≥60 years were 3.29 times more likely to adhere to their medications (OR 3.29, 95% CI 1.10-9.86, p<0.05). Females were the most adherent compared to males (OR 2.33, 95%CI 1.10-4.93, p<0.05) and patients with secondary level of education were 2.72 times more adherent to their medications compared to those in primary school and no formal education (OR 2.72, 95%CI 1.13-6.55, p<0.05). There is a significant (p<0.01), positive fair correlation (r=0.377) between satisfaction and adherence.



Patients were highly satisfied with DMTAC service, while their adherence levels were low. There is an association between patient satisfaction and adherence.
Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.
Rank aggregation methods for the Web

We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. We develop a set of techniques for the rank aggregation problem and compare their performance to that of well-known methods. A primary goal of our work is to design rank aggregation techniques that can e ectively combat \spam," a serious problem in Web searches. Experiments show that our methods are simple, e cient, and e ective.
Dynamic relationship and event discovery

This paper studies the problem of dynamic relationship and event discovery. A large body of previous work on relation extraction focuses on discovering predefined and static relationships between entities. In contrast, we aim to identify temporally defined (e.g., co-bursting) relationships that are not predefined by an existing schema, and we identify the underlying time constrained events that lead to these relationships. The key challenges in identifying such events include discovering and verifying dynamic connections among entities, and consolidating binary dynamic connections into events consisting of a set of entities that are connected at a given time period. We formalize this problem and introduce an efficient end-to-end pipeline as a solution. In particular, we introduce two formal notions, global temporal constraint cluster and local temporal constraint cluster, for detecting dynamic events. We further design efficient algorithms for discovering such events from a large graph of dynamic relationships. Finally, detailed experiments on real data show the
 effectiveness of our proposed solution.
The so-called Hoffman's lymphangitis of the penis: Is it a lymphangitis or a phlebitis?

Hoffman's plastic lymphangitis of the penis is a benign, uncommon entity whose aetiology is still unknown. Microscopically there is a fibrous thickening of the involved lymph vessels but the primary localization of the lesion—lymphatic or venous—is still debated. Anatomo-clinical and histological findings suggest a primary involvement of the lymphatic system of the penis, probably related to a prolonged period of sexual excitement.
A comparison between several NoSQL databases with comments and notes

This paper is trying to comment on the various NoSQL (Not only Structured Query Language) systems and to make a comparison (using multiple criteria) between them. The NoSQL databases were created as a mean to offer high performance (both in terms of speed and size) and high availability at the price of loosing the ACID (Atomic, Consistent, Isolated, Durable) trait of the traditional databases in exchange with keeping a weaker BASE (Basic Availability, Soft state, Eventual consistency) feature. Remains to be seen which of the multiple solutions created since the official appearance of the NoSQL concept (which was defined in 1998 and reintroduced in 2009, around which moment several NoSQL solutions emerged; at the present moment there are known over 120 such solutions) are really delivering on these promises of higher performance (although several of them are already used with very good results).
Extracting Semantic Information from Visual Data: A Survey

The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.
Shared Fun Is Doubled Fun: Player Enjoyment as a Function of Social Setting

Although the social relevance of digital gaming has been discussed and investigated in cultural and sociological readings, social context has been largely neglected in terms of in-game player experience. In this paper we present a study in which player experience is empirically tested comprehensively, with the focus on the interplay between player enjoyment and social context. Self reports on the Game Experience Questionnaire and an aggression state questionnaire were explored for three play configurations: virtual, mediated, and co-located co-play. Additionally, effects of familiarity and winning vs. losing were tested. Results indicate that a co-located co-player significantly adds to the fun, challenge, and perceived competence in the game. Aggression was influenced by familiarity and performance. The effect of social context was mediated by the level of social presence. These findings illustrate that social context is an important determinant of player enjoyment and should be incorporated in models of player experience.
Identifying Design Problems in the Source Code

The prevalence of design problems may cause re-engineering or even discontinuation of the system. Due to missing, informal or outdated design documentation, developers often have to rely on the source code to identify design problems. Therefore, developers have to analyze different symptoms that manifest in several code elements, which may quickly turn into a complex task. Although researchers have been investigating techniques to help developers in identifying design problems, there is little knowledge on how developers actually proceed to identify design problems. In order to tackle this problem, we conducted a multi-trial industrial experiment with professionals from 5 software companies to build a grounded theory. The resulting theory offers explanations on how developers identify design problems in practice. For instance, it reveals the characteristics of symptoms that developers consider helpful. Moreover, developers often combine different types of symptoms to identify a single design problem. This knowledge serves as a basis to further understand the phenomena and advance towards more effective identification techniques.
Pornography, sexual socialization, and satisfaction among young men.

In spite of a growing presence of pornography in contemporary life, little is known about its potential effects on young people's sexual socialization and sexual satisfaction. In this article, we present a theoretical model of the effects of sexually explicit materials (SEM) mediated by sexual scripting and moderated by the type of SEM used. An on-line survey dataset that included 650 young Croatian men aged 18-25 years was used to explore empirically the model. Descriptive findings pointed to significant differences between mainstream and paraphilic SEM users in frequency of SEM use at the age of 14, current SEM use, frequency of masturbation, sexual boredom, acceptance of sex myths, and sexual compulsiveness. In testing the model, a novel instrument was used, the Sexual Scripts Overlap Scale, designed to measure the influence of SEM on sexual socialization. Structural equation analyses suggested that negative effects of early exposure to SEM on young men's sexual satisfaction, albeit small, could be stronger than positive effects. Both positive and negative effects-the latter being expressed through suppression of intimacy-were observed only among users of paraphilic SEM. No effect of early exposure to SEM was found among the mainstream SEM users. To counterbalance moral panic but also glamorization of pornography, sex education programs should incorporate contents that would increase media literacy and assist young people in critical interpretation of pornographic imagery.
Learning Hierarchical Semantic Image Manipulation through Structured Representations

Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation on natural image manifold through color strokes, keypoints, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ structured semantic layout as our intermediate representation for manipulation. Initialized with coarse-level bounding boxes, our structure generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation.