nils-herrmann/clean_scirepeval huggingface dataset:
Predicting football results using Bayesian nets and other machine learning techniques

Bayesian networks (BNs) provide a means for representing, displaying, and making available in a usable form the knowledge of experts in a given Weld. In this paper, we look at the performance of an expert constructed BN compared with other machine learning (ML) techniques for predicting the outcome (win, lose, or draw) of matches played by Tottenham Hotspur Football Club. The period under study was 1995–1997 – the expert BN was constructed at the start of that period, based almost exclusively on subjective judgement. Our objective was to determine retrospectively the comparative accuracy of the expert BN compared to some alternative ML models that were built using data from the two-year period. The additional ML techniques considered were: MC4, a decision tree learner; Naive Bayesian learner; Data Driven Bayesian (a BN whose structure and node probability tables are learnt entirely from data); and a K-nearest neighbour learner. The results show that the expert BN is generally superior to the other techniques for this domain in predictive accuracy. The results are even more impressive for BNs given that, in a number of key respects, the study assumptions place them at a disadvantage. For example, we have assumed that the BN prediction is ‘incorrect’ if a BN predicts more than one outcome as equally most likely (whereas, in fact, such a prediction would prove valuable to somebody who could place an ‘each way’ bet on the outcome). Although the expert BN has now long been irrelevant (since it contains variables relating to key players who have retired or left the club) the results here tend to conWrm the excellent potential of BNs when they are built by a reliable domain expert. The ability to provide accurate predictions without requiring much learning data are an obvious bonus in any domain where data are scarce. Moreover, the BN was relatively simple for the expert to build and its structure could be used again in this and similar types of problems.
Cluster-based routing protocols in wireless sensor networks: A survey

Owing to the limited resources of the sensor nodes, designing energy-efficient routing mechanism to prolong the overall network lifetime becomes one of the most important technologies in wireless sensor networks (WSNs). As an active branch of routing technology, cluster-based routing protocols have proven to be effective in network topology management, energy minimization, data aggregation and so on. In this paper, we present a survey of state-of-the-art routing techniques in WSNs. We first outline the clustering architecture in WSNs, and classify the proposed approaches based on their objectives and design principles. Furthermore, we highlight the challenges in clustering WSNs, including rotating the role of cluster heads, optimization of cluster size and communication mode, followed by a comprehensive survey of routing techniques. Finally, the paper concludes with possible future research areas.
How Generative Adversarial Networks and Their Variants Work: An Overview

Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.
Feature-based comparison and selection of Software Defined Networking (SDN) controllers

Software Defined Networking (SDN) is seen as one way to solve some problems of the Internet including security, managing complexity, multi-casting, load balancing, and energy efficiency. SDN is an architectural paradigm that separates the control plane of a networking device (e.g., a switch / router) from its data plane, making it feasible to control, monitor, and manage a network from a centralized node (the SDN controller). However, today there exists many SDN controllers including POX, FloodLight, and OpenDaylight. The question is, which of the controllers is to be selected and used? To find out the answer to this question, a decision making template is proposed in this paper to help researchers choose the SDN controller that best fits their needs. The method works as follows; first, several existing open-source controllers are analyzed to collect their properties. For selecting the suitable controller based on the derived requirements (for example, a “Java” interface must be provided by the controller), a matching mechanism is used to compare the properties of the controllers with the requirements. Additionally, for selecting the best controller based on optional requirements (for example, GUI will be extremely preferred over the age of the controller), a Multi-Criteria Decision Making (MCDM) method named Analytic Hierarchy Process (AHP) has been adapted by a monotonic interpolation / extrapolation mechanism which maps the values of the properties to a value in a pre-defined scale. By using the adapted AHP, the topmost five controllers have been compared and “Ryu” is selected to be the best controller based on our requirements.
An Optimization Framework for Online Ride-Sharing Markets

Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.
Passive properties of human skeletal muscle during stretch maneuvers. A review.

Despite limited scientific knowledge, stretching of human skeletal muscle to improve flexibility is a widespread practice among athletes. This article reviews recent findings regarding passive properties of the hamstring muscle group during stretch based on a model that was developed which could synchronously and continuously measure passive hamstring resistance and electromyographic activity, while the velocity and angle of stretch was controlled. Resistance to stretch was defined as passive torque (Nm) offered by the hamstring muscle group during passive knee extension using an isokinetic dynamometer with a modified thigh pad. To simulate a clinical static stretch, the knee was passively extended to a pre-determined final position (0.0875 rad/s, dynamic phase) where it remained stationary for 90 s (static phase). Alternatively, the knee was extended to the point of discomfort (stretch tolerance). From the torque-angle curve of the dynamic phase of the static stretch, and in the stretch tolerance protocol, passive energy and stiffness were calculated. Torque decline in the static phase was considered to represent viscoelastic stress relaxation. Using the model, studies were conducted which demonstrated that a single static stretch resulted in a 30% viscoelastic stress relaxation. With repeated stretches muscle stiffness declined, but returned to baseline values within 1 h. Long-term stretching (3 weeks) increased joint range of motion as a result of a change in stretch tolerance rather than in the passive properties. Strength training resulted in increased muscle stiffness, which was unaffected by daily stretching. The effectiveness of different stretching techniques was attributed to a change in stretch tolerance rather than passive properties. Inflexible and older subjects have increased muscle stiffness, but a lower stretch tolerance compared to subjects with normal flexibility and younger subjects, respectively. Although far from all questions regarding the passive properties of humans skeletal muscle have been answered in these studies, the measurement technique permitted some initial important examinations of vicoelastic behavior of human skeletal muscle.
Toileting behavior and urinary tract symptoms among younger women

Irregular or infrequent voiding due to avoiding school toilets can contribute to a number of urinary problems among school children. There is, however, a lack of studies on younger women. The aim of this study was to investigate toileting behavior and the correlation to lower urinary tract symptoms (LUTS) among young women (age 18–25 years). A further aim was to validate the Swedish version of the Toileting Behavior scale (TB scale). Quantitative descriptive design was used with two questionnaires: the International Consultation on Incontinence Questionnaire Female Lower Urinary Tract Symptoms (ICIQ-FLUTS) and the TB scale, together with six background questions. The questionnaires were distributed in November 2014 to 550 women aged 18–25 years randomly selected from the population register in southern Sweden. A total of 173 (33%) women responded. Mean age was 21.6 years (range 18–25). The Swedish version of TB scale showed good construct validity and reliability, similar to the original. Most toileting behavior was significantly correlated with LUTS, which were common, as 34.2% reported urgency and 35.9% urine leakage at least sometimes or more often. LUTS were quite common in this group of young women. Toileting behaviors were also significantly related to urinary tract symptoms. Thus, TB scale was useful in this population, and the translated Swedish version showed good construct validity and reliability.
Information technology and systems justification: A review for research and applications

Information technology (IT) such as Electronic Data Interchange (EDI), Radio Frequency Identification Technology (RFID), wireless, the Internet and World Wide Web (WWW), and Information Systems (IS) such as Electronic Commerce (E-Commerce) systems and Enterprise Resource Planning (ERP) systems have had tremendous impact in education, healthcare, manufacturing, transportation, retailing, pure services, and even war. Many organizations turned to IT/IS to help them achieve their goals; however, many failed to achieve the full potential of IT/IS. These failures can be attributed at least in part to a weak link in the planning process. That weak link is the IT/IS justification process. The decision-making process has only grown more difficult in recent years with the increased complexity of business brought about by the rapid growth of supply chain management, the virtual enterprise and E-business. These are but three of the many changes in the business environment over the past 10–12 years. The complexities of this dynamic new business environment should be taken into account in IT/IS justification. We conducted a review of the current literature on IT/IS justification. The purpose of the literature review was to assemble meaningful information for the development of a framework for IT/IS evaluation that better reflects the new business environment. A suitable classification scheme has been proposed for organizing the literature reviewed. Directions for future research are indicated.
Changes in EEG alpha frequency and evoked response latency during solitary confinement.

One week of solitary confinement of prison inmates produced significant changes in their EEG frequency and visual evoked potentials (VEP) that parallel those reported in laboratory studies of sensory deprivation. EEG frequency declined in a nonlinear manner over the period. VEP latency, which decreased with continued solitary confinement, was shorter for these 5s than for control 5s whose VEP latency did not change over the same period. Experimental 5s who had been in prison longer had shorter VEP latencies than relative newcomers to the prison.
Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers

Abst ract Qualit at ive case st udy met hodology provides t ools f or researchers t o st udy complex phenomena wit hin t heir cont ext s. When t he approach is applied correct ly, it becomes a valuable met hod f or healt h science research t o develop t heory, evaluat e programs, and develop int ervent ions. T he purpose of t his paper is t o guide t he novice researcher in ident if ying t he key element s f or designing and implement ing qualit at ive case st udy research project s. An overview of t he t ypes of case st udy designs is provided along wit h general recommendat ions f or writ ing t he research quest ions, developing proposit ions, det ermining t he “case” under st udy, binding t he case and a discussion of dat a sources and t riangulat ion. T o f acilit at e applicat ion of t hese principles, clear examples of research quest ions, st udy proposit ions and t he dif f erent t ypes of case st udy designs are provided Keywo rds Case St udy and Qualit at ive Met hod Publicat io n Dat e 12-1-2008 Creat ive Co mmo ns License Journal Home About T his Journal Aims & Scope Edit orial Board Policies Open Access
Transcranial Doppler versus angiography in patients with vasospasm due to a ruptured cerebral aneurysm: A systematic review.

Transcranial Doppler (TCD) is used for diagnosis of vasospasm in patients with subarachnoid hemorrhage due to a ruptured aneurysm. Our aim was to evaluate both the accuracy of TCD compared with angiography and its usefulness as a screening method in this setting.



A search (MEDLINE, EMBASE, Cochrane Library, bibliographies, hand searching, any language, through January 31, 2001) was performed for studies comparing TCD with angiography. Data were critically appraised using a modified published 10-point score and were combined using a random-effects model.



Twenty-six reports compared TCD with angiography. Median validity score was 4.5 (range 1 to 8). Meta-analyses could be performed with data from 7 trials. For the middle cerebral artery (5 trials, 317 tests), sensitivity was 67% (95% CI 48% to 87%), specificity was 99% (98% to 100%), positive predictive value (PPV) was 97% (95% to 98%), and negative predictive value (NPV) was 78% (65% to 91%). For the anterior cerebral artery (3 trials, 171 tests), sensitivity was 42% (11% to 72%), specificity was 76% (53% to 100%), PPV was 56% (27% to 84%), and NPV was 69% (43% to 95%). Three of these 7 studies reported on the same patients, each on another artery, and for 4, data recycling could not be disproved. Other arteries were tested in only 1 trial each.



For the middle cerebral artery, TCD is not likely to indicate a spasm when angiography does not show one (high specificity), and TCD may be used to identify patients with a spasm (high PPV). For all other situations and arteries, there is either lack of evidence of accuracy or of any usefulness of TCD. Most of these data are of low methodological quality, bias cannot not be ruled out, and data reporting is often uncritical.
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet

This paper presents an adaptation of Lesk’s dictionary– based word sense disambiguation algorithm. Rather than using a standard dictionary as the source of glosses for our approach, the lexical database WordNet is employed. This provides a rich hierarchy of semantic relations that our algorithm can exploit. This method is evaluated using the English lexical sample data from the Senseval-2 word sense disambiguation exercise, and attains an overall accuracy of 32%. This represents a significant improvement over the 16% and 23% accuracy attained by variations of the Lesk algorithm used as benchmarks during the Senseval-2 comparative exercise among word sense disambiguation
Data warehouse design to support customer relationship management analyses

CRM is a strategy that integrates the concepts of Knowledge Management, Data Mining, and Data Warehousing in order to support the organization's decision-making process to retain long-term and profitable relationships with its customers. In this paper, we first present the design implications that CRM poses to data warehousing, and then propose a robust multidimensional starter model that supports CRM analyses. We then present sample CRM queries, test our starter model using those queries and define two measures (% success ratio and CRM suitability ratio) by which CRM models can be evaluated. We finally introduce a preliminary heuristic for designing data warehouses to support CRM analyses. Our study shows that our starter model can be used to analyze various profitability analyses such as customer profitability analysis, market profitability analysis, product profitability analysis, and channel profitability analysis.
Competition among memes in a world with limited attention

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.
Relief-based feature selection: Introduction and review

Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computationally efficient, yet sensitive to complex patterns of association, e.g. interactions, so that informative features are not mistakenly eliminated prior to downstream modeling. This paper focuses on Relief-based algorithms (RBAs), a unique family of filter-style feature selection algorithms that have gained appeal by striking an effective balance between these objectives while flexibly adapting to various data characteristics, e.g. classification vs. regression. First, this work broadly examines types of feature selection and defines RBAs within that context. Next, we introduce the original Relief algorithm and associated concepts, emphasizing the intuition behind how it works, how feature weights generated by the algorithm can be interpreted, and why it is sensitive to feature interactions without evaluating combinations of features. Lastly, we include an expansive review of RBA methodological research beyond Relief and its popular descendant, ReliefF. In particular, we characterize branches of RBA research, and provide comparative summaries of RBA algorithms including contributions, strategies, functionality, time complexity, adaptation to key data characteristics, and software availability.
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines. Unfortunately, the computational cost of scoring thousands of candidate documents by traversing large ensembles of trees is high. Thus, several works have investigated solutions aimed at improving the efficiency of document scoring by exploiting advanced features of modern CPUs and memory hierarchies. In this article, we present QuickScorer, a new algorithm that adopts a novel cache-efficient representation of a given tree ensemble, performs an interleaved traversal by means of fast bitwise operations, and supports ensembles of oblivious trees. An extensive and detailed test assessment is conducted on two standard Learning-to-Rank datasets and on a novel very large dataset we made publicly available for conducting significant efficiency tests. The experiments show unprecedented speedups over the best state-of-the-art baselines ranging from 1.9 × to 6.6 × . The analysis of low-level profiling traces shows that QuickScorer efficiency is due to its cache-aware approach in terms of both data layout and access patterns and to a control flow that entails very low branch mis-prediction rates.
Mental Toughness and Success in Sport : A Review and Prospect

Pivotal to athletic performance is the ability to more maintain desired athletic performance levels during particularly critical periods of competition , such as during pressurised situations that typically evoke elevated levels of anxiety (e.g., penalty kicks) or when exposed to unexpected adversities (e.g., unfavourable umpire calls on crucial points) . These kinds of situations become markedly important when athletes, who are separated by marginal physical and technical differences, are engaged in closely contested matches, games, or races [4]. It is within these competitive conditions, in particular, that athletes’ responses define their degree of success (or lack thereof); responses that are largely dependent on athletes’ psychological attributes . One of these attributes appears to be mental toughness (MT), which has often been classified as a critical success factor due to the role it plays in fostering adaptive responses to positively and negatively construed pressures, situations, and events [6 8]. However, as scholars have intensified
Exponentiated Gradient Exploration for Active Learning

Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named EG − Active that can improve any Active learning algorithm by an optimal random exploration. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.