nils-herrmann/clean_scirepeval huggingface dataset:
BPSO Adaboost KNN ensemble learning algorithm for multi class imbalanced data classification

This paper proposes an ensemble algorithm named of BPSO Adaboost KNN to cope with multi class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What's more, we utilize a novel evaluation metric called AUCarea which is especially for multi class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state of the art algorithms. In statistical analyses, we apply Bland Altman analysis to show the consistencies between AUCarea and other popular metrics like average G mean, average F value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil bearing of reservoir recognition. The classification precision is up to 99% in oilsk81 oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers.
Nonlinear dimension reduction for EEG based epileptic seizure detection
Venue: 2016 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Approximately 0.1 percent of epileptic patients die from unexpected deaths. In general, for intractable seizures, it is crucial to have an algorithm to accurately and automatically detect the seizures and notify care givers to assist patients. EEG signals are known as definitive diagnosis of seizure events. In this work, we utilize the frequency domain features (normalized in band power spectral density) for the EEG channels. We applied a nonlinear data embedding technique based on stochastic neighbor distance metric to capture the relationships among data elements in high dimension and improve the accuracy of seizure detection. This proposed data embedding technique not only makes it possible to visualize data in two or three dimensions, but also tackles the inherent difficulties regarding high dimensional data classification such as time complexity and memory requirement. We also applied a patient specific KNN classification to detect seizure and non seizure events. The results indicate that our nonlinear technique provides significantly better visualization and classification efficiency (F measure greater than 87% compared to conventional dimension reduction approaches.
Optimization Models for Feature Selection of Decomposed Nearest Neighbor
Venue: IEEE Transactions on Systems, Man, and Cybernetics: Systems

The traditional k nearest neighbor (KNN) method classifies an object by the majority vote of its neighbors, and only one parameter k is to be optimized. We propose a decomposed version of the KKN (DKNN) method, which classifies an unseen object based on the distances to the centroids of the KNNs. DKNN has a training process to learn the local optimafree distance metric by solving a convex optimization problem. The optimization problem not only learns a metric that minimizes classification errors and maximizes the margin between intraclass and interclass distances. In addition, it also selects important features and removes irrelevant features when L1 regularization is incorporated in the optimization model. The feature selection component is extremely useful for high dimensional classification problems. Tested on public datasets, the proposed DKNN is competitive and often outperforms traditional machine learning algorithms.
Optimal batch quantity in a cleaner multi stage lean production system with random defective rate

Optimal batch size reduces variability in the lean manufacturing process with minimized system costs. It simplifies the scheduling, enhances the quality, reduces inventories, and improves the production process continuously. On account of this, determining optimal batch size is the area of interest for the researchers with the objective of reducing inventories and related system costs. Products of the textile sector are produced generally in a multi stage production setup, and it consists of random defective rate within the long run production systems. In this context, this paper revisits an economic production quantity (EPQ) model with an imperfect multi stage production system and analyzes an inventory model by considering a random defective rate in a cleaner multi stage lean manufacturing system. These defective items are reworked and converted into perfect quality items by incurring additional processing costs. The proportion of defective items can be reduced through continuous improvements in the production process reliability by performing various lean manufacturing techniques, enlisting the total productive maintenance at the highest rank. A mathematical model is developed using a familiar beta distribution density function for the random defective rate. Then, the total cost of the system is minimized through analytical technique, where the batch size is a decision variable. Outcomes through analytical and numerical study confirm that optimum batch size is obtained by this model and it has a direct relationship with number of production stages.
Lean principles, practices, and impacts: a study on small and medium sized enterprises (SMEs)
Venue: Ann. Oper. Res.

Lean as a business strategy is used to improve quality and service, eliminate waste, reduce time and costs, and enhance overall organizational effectiveness. Heightening challenges in competition in recent years have prompted many small and medium sized enterprises (SMEs) to adopt lean to enhance firms' competitiveness. This paper attempts to present an all inclusive study and it examines various factors associated with the implementation of lean in SMEs in the U.S. The findings suggest that most of SMEs have a relatively accurate understanding of lean concept and philosophy. The primary reasons to implement lean are mainly internal, including reduce cost, improve profit margin, improve utilization of plant/facility, and maintain competitive position. A hierarchical cluster analysis was conducted to investigate lean status. It was discovered that both advanced adopters and beginners of lean are discovered. ANOVA test results show that there exist quite significant differences in terms of the degrees of lean implementation in SMEs. Varied lean tools and programs have been applied and they are positively related with firms' performance. Lastly, the paper provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know how.
Recent Advances on Fuzzy Model Based Nonlinear Networked Control Systems: A Survey
Venue: IEEE Transactions on Industrial Electronics

In recent years, the analysis and synthesis of fuzzy model based nonlinear networked control systems (NCSs) have received increasing attention from both scientific and industrial communities, and a number of significant results have been proposed. This paper gives a review on recent advances on the analysis and design of fuzzy model based nonlinear NCSs with various network induced limitations such as packet dropouts, time delays, and signal quantization. With these network induced constraints, the developments on various control and filtering design issues are surveyed in details, and some essential technical difficulties are mentioned. Then, some latest results on event triggered control and filtering design of fuzzy model based nonlinear NCSs are also summarized. Finally, some conclusions are drawn and several potential future research topics are highlighted.
Etiology and pathogenesis of dental erosion.
Venue: Quintessence international

The condition of dental erosion is defined as acid related loss of tooth structure which does not involve microorganisms. Depending on the origin of the acid, extrinsic (usually caused by acids in food) and intrinsic (caused by endogenous acid) erosion can be distinguished. The presence and severity of erosive defects depend on various parameters such as nutrition, saliva, general diseases, and mechanical stress by abrasion and attrition. As an example, dietary habits which involve frequent intake of acidic food and beverages, occupational acid exposure, as well as certain drugs or diseases that affect saliva flow rate are accompanied by an increased risk of erosive dental hard tissue defects. By a thorough clinical examination and an accurate anamnesis, various erosion related risk factors can be identified and strategies to reduce or eliminate these factors be identified.
An overview of multi modal medical image fusion
Venue: Neurocomputing

Multi modal medical image fusion is the process of merging multiple images from single or multiple imaging modalities to improve the imaging quality with preserving the specific features. Medical image fusion covers a broad number of hot topic areas, including image processing, computer vision, pattern recognition, machine learning and artificial intelligence. And medical image fusion has been widely used in clinical for physicians to comprehend the lesion by the fusion of different modalities medical images. In this review, methods in the field of medical image fusion are characterized by (1) image decomposition and image reconstruction, (2) image fusion rules, (3) image quality assessments, and (4) experiments on the benchmark dataset. In addition, this review provides a factual listing of scientific challenges faced in the field of multi modal medical image fusion.
A survey of IoT cloud platforms

Internet of Things (IoT) envisages overall merging of several "things" while utilizing internet as the backbone of the communication system to establish a smart interaction between people and surrounding objects. Cloud, being the crucial component of IoT, provides valuable application specific services in many application domains. A number of IoT cloud providers are currently emerging into the market to leverage suitable and specific IoT based services. In spite of huge possible involvement of these IoT clouds, no standard cum comparative analytical study has been found across the literature databases. This article surveys popular IoT cloud platforms in light of solving several service domains such as application development, device management, system management, heterogeneity management, data management, tools for analysis, deployment, monitoring, visualization, and research. A comparison is presented for overall dissemination of IoT clouds according to their applicability. Further, few challenges are also described that the researchers should take on in near future. Ultimately, the goal of this article is to provide detailed knowledge about the existing IoT cloud service providers and their pros and cons in concrete form.
Combining Car to Infrastructure Communication and Multi Agent Reinforcement Learning in Route Choice
Venue: ATT@IJCAI

Route choice is an important stage in transport planning and modeling. Most of the existing approaches do not consider that road users can nowadays consult new technologies to plan their routes. In this paper, we combine multi agent reinforcement learning (MARL) and car to infrastructure communication (C2I) to deal with route choice. The agents (road users) and the infrastructure interact with each other to exchange traffic information about the road network. The agents send the travel cost of the edges they crossed to the infrastructure. The infrastructure uses these costs to compute shortest paths, which are transmitted to the agents when requested. The agents use such received shortest path to update their knowledge base. The obtained results are compared against a classical MARL approach that does not use C2I communication. Experimental results show that our approach overcomes the compared method in terms of average travel cost.
Extreme Morphogenesis and Ecological Specialization among Cretaceous Basal Ants
Venue: Current Biology

Ants comprise one lineage of the triumvirate of eusocial insects and experienced their early diversification within the Cretaceous  Their ecological success is generally attributed to their remarkable social behavior. Not all ants cooperate in social hunting, however, and some of the most effective predatory ants are solitary hunters with powerful trap jaws [10] Recent evolutionary studies predict that the early branching lineages of extant ants formed small colonies of ground dwelling, solitary specialist predators [2, 5, 7, 11, 12] while some Cretaceous fossils suggest group recruitment and socially advanced behavior among stem group ants  We describe a trap jaw ant from 99 million year old Burmese amber with head structures that presumably functioned as a highly specialized trap for large bodied prey. These are a cephalic horn resulting from an extreme modification of the clypeus hitherto unseen among living and extinct ants and scythe like mandibles that extend high above the head, both demonstrating the presence of exaggerated morphogenesis early among stem group ants. The new ant belongs to the Haidomyrmecini, possibly the earliest ant lineage [9] and together these trap jaw ants suggest that at least some of the earliest Formicidae were solitary specialist predators. With their peculiar adaptations, haidomyrmecines had a refined ecology shortly following the advent of ants.
Colony colony interactions between highly invasive ants

Among invasive species, ants are a particularly prominent group with enormous impacts on native biodiversity and ecosystem functioning. Globalization and on going climate change are likely to increase the rate of ant invasions in the future, leading to simultaneous introductions of several highly invasive species within the same area, Here, we investigate pairwise interactions among four highly invasive species, Linepithema humile,Lashis neglectus, Pheidole megacephala and Wasmannia auropunctata, at the whole colony level, using a laboratory set up. :Each colony consisted of 300 workers and one queen. The number of surviving workers in the competing colonies was recorded daily over 7 weeks. We modelled the survival of each colony during pairwise colony interactions, using a nonlinear model characterizing the survival dynamics of each colony individually. The least dominant species was P. megacephala, which always went extinct. Interactions among the three other species showed more complex dynamics, rendering the outcome of the interactions less predictable. Overall, W auropunctata and L neglectus were the most dominant species. This study shows the importance of scaling up to the colony level in order to gain realism in predicting the outcome of multiple invasions.
Improving Management Students' Skills in Framing "Thought Provoking Questions" An Action Research

This paper describes how a teaching intervention in an action research (AR) environment improved the learning processes and higher order thinking skills among undergraduate management students. Action research has been gaining currency as a way of improving business and management education. The current study, conducted from October to December 2014, involved 70 students at a state university in the Philippines. As part of their class work in the Introduction to Management course, the students were assigned to teams, which took turns in presenting various management concepts to the class as a whole. Non presenting students were required to submit student generated "thought provoking questions" (TPQs) for class discussion. The teacher researcher's reflection on the initial results of the TPQs necessitated a teaching intervention to improve the quality of the students' TPQs. The lecture intervention improved the management students' TPQ writing skills and learning processes. This study's findings are supported by the triangulation method of (1) the teacher researcher's observations, (2) the students' self reports through a post intervention survey, and (3) a t test of mean differences in students' marks. The paired t test for mean differences between the pre and post lecture scores is significant and shows improved average scores after the lecture intervention. The study concludes that AR may be an effective mode of improving teaching methods in management education.
An empirical analysis of the service quality gap in business education: Evidence from higher education in Pakistan

This survey investigated the relationship and gap between students' expectations and perceptions of perceived service quality of education, emphasizing the least effective service quality dimension. The study was based on primary data, which turned out to be most significant. Data were collected from 349 of 405 students through a questionnaire (86.1% response rate) The research specified that there existed a gap (7.6% overall gap) between the expectations and perceptions of the students that was an indicator of improvement in quality of education being delivered by applying paired t tests. Perceived reliability was the most important service quality dimension found in the current study .001) and tangibility, the fifth dimension of service quality, appears to be insignificant 0.778) and was never found negative by any of the past researchers, indicating a null effect of physical structure of the classroom on quality of education. The work attempted to develop an understanding of the comparative assessments of two mindsets of the receivers. The results provide valuable feedback that could be used for identifying and improving weak service quality dimensions among institutes in Pakistan. No such study was carried out before at this level in Pakistan.
Testing Effect in Learning Digital Property and Cyber Ethics

Evaluating to what extent the students could acquire the targeted behaviours is of paramount importance for the quality of the education. In education, measurement and evaluation procedures are mainly carried out through exams and, in particular, test techniques. However, it is claimed that tests can be used for both evaluating whether the targeted behaviours are acquired and increasing the possibility of the learners to recall the information needed to be learned (Roediger Karpicke, 2006a) Increasing the existing performance through tests is called as testing effect (Roediger Karpicke, 2006a) The aim of this study is to examine whether the learning conditions (studying repeatedly or undergoing a test) are influential for preservice teachers to learn the topics of digital property and cyber ethics. These two topics were chosen since it was thought that the awareness, information and skill acquisitions of the teachers of the future were important so that they could set an example for their students with respect to compliance with the ethical rules emerging with the development of the information technologies. According to the results of the study, while the recall levels of the preservice teachers whose learning condition is studying repeatedly are found to be higher following the 1st test (5 minutes later) the preservice teachers whose learning condition is undergoing a test have higher recall levels in the retention test (1 week later) Although results were not statistically significant, the probable reasons of the situations were discussed on the basis of the opinions of the participants.  Test effect, Digital ownership, Cyber ethic
Mathematics education and language diversity the 21st ICMI study

Challenges and opportunities for second language learners in undergraduate mathematics. Mathematics in the hands of deaf learners and blind learners: visual gestural somatic means of doing and expressing. Challenging deficit perspectives: student agency in multilingual mathematics classrooms. Language diversity in mathematics teacher education: challenges across three countries. Impact of differing grammatical structures in mathematics teaching and learning. Addressing multi language diversity in mathematics teacher education programs. Language diversity and new media: issues of multimodality and performance. Making use of multiple (non shared) first languages: state and need of research and development in the European language context. Purposefully relating multilingual registers building theory and teaching strategies for bilingual learners based on an integration of three traditions. Trends in mathematics education in multilingual contexts for indigenous population: experiences from Latin America. Tensions in teaching mathematics in contexts of language diversity. Research rationalities and the construction of the deficient multilingual mathematics learner. Language diversity in research and its consequences. Using ICTs to facilitate multilingual mathematics teaching and learning. Mathematics teaching, language policy and the political role of language: perspectives from around the world. Reflections. Index.
Language Diversity in Research on Language Diversity in Mathematics Education

Research on educational phenomena, which always occur in cultural and societal contexts, cannot escape the trappings of language. This applies even to research on the teaching and learning of mathematics, despite the relative similarity of the topics taught around the world and the practicality of communicating internationally in a few common languages. In this chapter, we propose a framework which allows us to identify some intriguing effects of the need for shared natural languages to function in the work of researchers. We use this framework to examine some cases of research on language diversity in mathematics education, including research from the present ICMI study. The framework is essentially based on the anthropological theory of didactics, used as a "meta theory."
Ameliorating effects of individual and combined application of biomass ash, bone meal and alkaline slag on acid soils

Incubation experiments with biomass ash (BA) bone meal (BM) and alkaline slag (AS) applied alone and together were conducted to ameliorate acidity and increase nutrient contents of five Ultisols collected from four provinces of southern China. The results showed that the ameliorating effect of the combined application of three amendments on soil acidity was greater than that of BA BM or their single applications. The combined application of BA, BM and AS increased the pH by 0.63 1.37 for five Ultisols. The most significant decrease of soil exchangeable acidity was also observed in the treatments of BA BM AS by 80.1 96.9% for five Ultisols. Meanwhile, the combined application of the amendments greatly increased the exchangeable potassium, calcium and magnesium of the five soils, respectively, by 0.4 3.8, 1.9 10 and 1.7 9.7 times. The contents of available phosphorus of the five soils were also increased significantly by 0.6 184 times due to the application of BA HBM AS. In general, the application of the amendments did not increase available heavy metals in the soils in this short term incubation experiment, but the potential risk of the amendments applied should be further assessed under field conditions through long term experiments.