nils-herrmann/clean_scirepeval huggingface dataset:
CLASSIFICATION OF DIFFERENT FOREST TYPES wITH MACHINE LEARNING ALGORITHMS

In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning repository database has been classified using different machine learning algorithms including Multilayer Perceptron, k-NN, J48, Naïve Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in the classification of forest type with 27 spectral features.
of Class Attendance With Grades and Student Characteristics Class Attendance in College : A Meta-Analytic Review of the Relationship

A meta-analysis of the relationship between class attendance in college and college grades reveals that attendance has strong relationships with both class grades (k = 69, N = 21,195, r = .44) and GPA (k = 33, N = 9,243, r = .41). These relationships make class attendance a better predictor of college grades than any other known predictor of academic performance, including scores on standardized admissions tests such as the SAT, high school GPA, study habits, and study skills. Results also show that class attendance explains large amounts of unique variance in college grades because of its relative independence from SAT scores and high school GPA and weak relationship with student characteristics such as conscientiousness and motivation. Mandatory attendance policies appear to have a small positive impact on average grades (k = 3, N = 1,421, d = .21). Implications for theoretical frameworks of student academic performance and educational policy are discussed. Many college instructors exhort their students to attend class as frequently as possible, arguing that high levels of class attendance are likely to increase learning and improve student grades. Such arguments may hold intuitive appeal and are supported by findings linking class attendance to both learning (e.g., Jenne, 1973) and better grades (e.g., Moore et al., 2003), but both students and some educational researchers appear to be somewhat skeptical of the importance of class attendance. This skepticism is reflected in high class absenteeism rates ranging from 18. This article aims to help resolve the debate regarding the importance of class attendance by providing a quantitative review of the literature investigating the relationship of class attendance with both college grades and student characteristics that may influence attendance. 273 At a theoretical level class attendance fits well into frameworks that emphasize the joint role of cognitive ability and motivation in determining learning and work performance (e.g., Kanfer & Ackerman, 1989). Specifically, cognitive ability and motivation influence academic outcomes via two largely distinct mechanisms— one mechanism related to information processing and the other mechanism being behavioral in nature. Cognitive ability influences the degree to which students are able to process, integrate, and remember material presented to them (Humphreys, 1979), a mechanism that explains the substantial predictive validity of SAT scores for college grades (e. & Ervin, 2000). Noncognitive attributes such as conscientiousness and achievement motivation are thought to influence grades via their influence on behaviors that facilitate the understanding and …
A backward pass through a CNN using a generative model of its activations

Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural network (CNN) mapping an image into one of a thousand object labels is approaching human performance in this particular task. However the mapping (neural network) does not automatically lend itself to other forms of queries, for example, to detect/reconstruct object instances, to enforce top-down signal on ambiguous inputs, or to recover object instances from occlusion. One way to address these queries is a backward pass through the network that fuses top-down and bottom-up information. In this paper, we show a way of building such a backward pass by defining a generative model of the neural network’s activations. Approximate inference of the model would naturally take the form of a backward pass through the CNN layers, and it addresses the aforementioned queries in a unified framework.
Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Short-term traffic forecasting based on deep learning methods, especially long-term short memory (LSTM) neural networks, received much attention in recent years. However, the potential of deep learning methods is far from being fully exploited in terms of the depth of the architecture, the spatial scale of the prediction area, and the prediction power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU-LSTM) neural network is proposed, which considers both forward and backward dependencies of time series data, to predict the networkwide traffic speed. A bidirectional LSTM (BDLSM) layer is exploited to capture spatial features and bidirectional temporal dependencies from historical data. To the best of our knowledge, this is the first time that BDLSTM is applied as building blocks for a deep architecture model to measure the backward dependency of traffic data for prediction. A comparison with other classical and state-of-the-art models indicates that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.
Undergraduate nursing students' attitudes and use of research and evidence-based practice - an integrative literature review.

This integrative review of the literature addresses undergraduate nursing students' attitudes towards and use of research and evidence-based practice, and factors influencing this. Current use of research and evidence within practice, and the influences and perceptions of students in using these tools in the clinical setting are explored.



Evidence-based practice is an increasingly critical aspect of quality health care delivery, with nurses requiring skills in sourcing relevant information to guide the care they provide. Yet, barriers to engaging in evidence-based practice remain. To increase nurses' use of evidence-based practice within healthcare settings, the concepts and skills required must be introduced early in their career. To date, however, there is little evidence to show if and how this inclusion makes a difference.



Integrative literature review.



ProQuest, Summon, Science Direct, Ovid, CIAP, Google scholar and SAGE databases were searched, and Snowball search strategies used. One hundred and eighty-one articles were reviewed. Articles were then discarded for irrelevance. Nine articles discussed student attitudes and utilisation of research and evidence-based practice.



Factors surrounding the attitudes and use of research and evidence-based practice were identified, and included the students' capability beliefs, the students' attitudes, and the attitudes and support capabilities of wards/preceptors.



Undergraduate nursing students are generally positive toward using research for evidence-based practice, but experience a lack of support and opportunity. These students face cultural and attitudinal disadvantage, and lack confidence to practice independently. Further research and collaboration between educational facilities and clinical settings may improve utilisation.


 TO CLINICAL PRACTICE
This paper adds further discussion to the topic from the perspective of and including influences surrounding undergraduate students and new graduate nurses.
Effects of Reader-to-Reader Interference on the UHF RFID Interrogation Range

In this paper, the effects of ultrahigh-frequency band passive radio-frequency identification (RFID) reader-to-reader interference are investigated in terms of the interrogation range. A reader-to-reader interference model based on interference statistics is proposed, and the nominal interrogation range of a desired reader is derived by this model. In order to evaluate the RFID reader-to-reader interference quantitatively, the interrogation-range-reduction ratio (IRRR) is also defined. For a simple case with only one interfering reader, the analytic and experimental results are in good agreement. Reader-reader distance achieving 0% IRRR, indicating no interference between the readers, is 1200 m for the multiple-reader environment. Additionally, IRRR values are calculated in various environments according to the North American and Korean regulations. The maximum number of allowable interfering readers is then calculated by the proposed interference model and a target IRRR.
Australian Forex Market Analysis Using Connectionist Models

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The forex market is difficult to understand by an average individual. However, once the market is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. This paper is an attempt to compare the performance of a TakagiSugeno type neuro-fuzzy system and a feed forward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. The exchange values of Australian dollar are considered with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pound. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that neuro-fuzzy technique performed better than the neural network.
Classification-Driven Watershed Segmentation

This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing
Shame , self-criticism , self-stigma , and compassion in Acceptance and Commitment Therapy

Within the past decade, empirical evidence has emerged supporting the use of Acceptance and Commitment Therapy (ACT) targeting shame and self-stigma. Little is known about the role of self-compassion in ACT, but evidence from other approaches indicates that self-compassion is a promising means of reducing shame and self-criticism. The ACT processes of defusion, acceptance, present moment, values, committed action, and self-as-context are to some degree inherently self-compassionate. However, it is not yet known whether the self-compassion inherent in the ACT approach explains ACT’s effectiveness in reducing shame and stigma, and/or whether focused self-compassion work may improve ACT outcomes for highly self-critical, shame-prone people. We discuss how ACT for shame and stigma may be enhanced by existing approaches specifically targeting self-compassion.
A cloud-based openflow firewall for mitigation against DDoS attacks in smart grid AMI networks

Recent architectures for the advanced metering infrastructure (AMI) have incorporated several back-end systems that handle billing and other smart grid control operations. The non-availability of metering data when needed or the untimely delivery of data needed for control operations will undermine the activities of these back-end systems. Unfortunately, there are concerns that cyber attacks such as distributed denial of service (DDoS) will manifest in magnitude and complexity in a smart grid AMI network. Such attacks will range from a delay in the availability of end user's metering data to complete denial in the case of a grounded network. This paper proposes a cloud-based (IaaS) firewall for the mitigation of DDoS attacks in a smart grid AMI network. The proposed firewall has the ability of not only mitigating the effects of DDoS attack but can prevent the attack before they are launched. Our proposed firewall system leverages on cloud computing technology which has an added advantage of reducing the burden of data computations and storage for smart grid AMI back-end systems. The openflow firewall proposed in this study is a better security solution with regards to the traditional on-premises DoS solutions which cannot cope with the wide range of new attacks targeting the smart grid AMI network infrastructure. Simulation results generated from the study show that our model can guarantee the availability of metering/control data and could be used to improve the QoS of the smart grid AMI network under a DDoS attack scenario.
Reordering Triple Patterns of SPARQL Queries Using Ant Colony Optimization

Semantic web is a paradigm that is proposed for configuring and controlling the overwhelming volumes of information on the web. One important challenge in semantic web is decreasing execution times of queries. Reordering triple patterns is an approach for decreasing execution times of queries. In this study, an ant colony optimization approach for optimizing SPARQL queries by reordering triple patterns is proposed. Contributions of this approach are optimizing order of triple patterns in SPARQL queries using ant colony optimization for lesser execution time and real time optimization without requiring any prior domain knowledge. This proposed novel method is implemented using ARQ query engine and it optimizes the queries for in-memory models of ontologies. Experiments show that proposed method reduces execution time considerably.
Deep learning for magnification independent breast cancer histopathology image classification

Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.
Monte Carlo Tree Search with Branch and Bound for Multi-Robot Task Allocation

Multi-robot teams are effective in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains have to perform tasks at given locations and specific times. Tasks have to be allocated to optimize given team objectives, such as minimizing the total distance traveled. We propose an efficient, satisficing and centralized Monte Carlo Tree Search (MCTS) based algorithm which exploits the branch and bound paradigm with a novel search parallelization method to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach maintains asymptotic convergence guarantees of MCTS and it has efficient anytime behavior. It finds near-optimal solutions for non-trivial problems in the Solomon data sets in an hour.
PoisonIvy: Safe speculation for secure memory

Encryption and integrity trees guard against physical attacks, but harm performance. Prior academic work has speculated around the latency of integrity verification, but has done so in an insecure manner. No industrial implementations of secure processors have included speculation. This work presents PoisonIvy, a mechanism which speculatively uses data before its integrity has been verified while preserving security and closing address-based side-channels. PoisonIvy reduces performance overheads from 40% to 20% for memory intensive workloads and down to 1.8%, on average.
Resolution phase of inflammation: novel endogenous anti-inflammatory and proresolving lipid mediators and pathways.

Resolution of inflammation and the return of tissues to homeostasis are essential. Efforts to identify molecular events governing termination of self-limited inflammation uncovered pathways in resolving exudates that actively generate, from essential omega fatty acids, new families of local-acting mediators. These chemical mediator families, termed resolvins and protectins, are potent stereoselective agonists that control the duration and magnitude of inflammation, joining the lipoxins as signals in resolution. This review examines the mapping of these circuits and recent advances in our understanding of the biosynthesis and actions of these novel proresolving lipid mediators. Aspirin jump-starts resolution by triggering biosynthesis of specific epimers of these mediators. In addition to their origins in inflammation resolution, these compounds also display potent protective roles in neural systems, liver, lung, and eye. Given the potent actions of lipoxins, resolvins, and protectins in models of human disease, deficiencies in resolution pathways may contribute to many diseases and offer exciting new potential for therapeutic control via resolution.
Hybrid Deposition Manufacturing : Design Strategies for Multimaterial Mechanisms Via Three-Dimensional Printing and Material Deposition

This paper describes a novel fabrication technique called hybrid deposition manufacturing (HDM), which combines additive manufacturing (AM) processes such as fused deposition manufacturing (FDM) with material deposition and embedded components to produce multimaterial parts and systems for robotics, mechatronics, and articulated mechanism applications. AM techniques are used to print both permanent components and sacrificial molds for deposited resins and inserted parts. Design strategies and practical techniques for developing these structures and molds are described, taking into account considerations such as printer resolution, build direction, and printed material strength. The strengths of interfaces between printed and deposited materials commonly used in the authors’ implementation of the process are measured to characterize the robustness of the resulting parts. The process is compared to previously documented layered manufacturing methodologies, and the authors present examples of systems produced with the process, including robot fingers, a multimaterial airless tire, and an articulated camera probe. This effort works toward simplifying fabrication and assembly complexity over comparable techniques, leveraging the benefits of AM, and expanding the range of design options for robotic mechanisms.
A Simple Equivalent Circuit Model for Plasma Dipole Antenna

Plasma antenna is an emerging technology that utilizes ionized gas as a conducting medium instead of metal. It is often convenient to represent the input impedance of the antenna by a lumped-element-equivalent circuit. Input impedance of the plasma dipole antenna is deduced using finite integration technique. A five-lumped-element equivalent circuit for the plasma dipole antenna variation with plasma frequency is investigated and optimized using the genetic algorithm (GA). The effect of plasma frequency and collision frequency of the ionized gas on input impedance variations of the plasma dipole antenna is studied with the help of the equivalent circuit model. Another equivalent circuit is synthesized using a rational function and GA. The Cauer realization method is used to deduce a new lumped-element equivalent circuit.
Stator flux active damping by optimal rotor current injection for grid tied doubly-fed induction generator

Doubly-fed induction generator (DFIG) tied with grid and controlled in the stator flux oriented reference frame is known to have poor damping due to the large time constant of the stator circuit. Therefore, the stator flux exhibits sustained oscillation subjected to any disturbances. In this paper, we propose a state feedback control method to actively damp the oscillation by injecting optimal amount of rotor current. The controller is further modified to simplify the implementation of the control and to minimize the effect of current injection on the power converter rating. The modified controller can be termed as a suboptimal controller. The performance of the designed controllers are verified through simulation of the simplified model of DFIG with an arbitrary initial stator flux perturbation. The required rotor current injections are observed in each of the cases of design. Effect of current injection by different controller on the converter rating is also explained. The suboptimal control is implemented to damp the stator flux oscillation in a DFIG, used in wave energy conversion system (WECS). In WECS, the stator flux oscillation appears due to the phase sequence switching of the DFIG. Simulation results show that the active damping control improves the damping of the system.
An intelligent self-checkout system for smart retail

Most of current self-checkout systems rely on barcodes, RFID tags, or QR codes attached on items to distinguish products. This paper proposes an Intelligent Self-Checkout System (ISCOS) embedded with a single camera to detect multiple products without any labels in real-time performance. In addition, deep learning skill is applied to implement product detection, and data mining techniques construct the image database employed as training dataset. Product information gathered from a number of markets in Taiwan is utilized to make recommendation to customers. The bounding boxes are annotated by background subtraction with a fixed camera to avoid time-consuming process for each image. The contribution of this work is to combine deep learning and data mining approaches to real-time multi-object detection in image-based checkout system.