nils-herrmann/clean_scirepeval huggingface dataset:
METRICS DRIVEN CONTINUAL SERVICE IMPROVEMENT USING AGILE CONCEPTS

Strategic alignment of business with the IT is a mandate for success in an enterprise. The services provided by the IT need to align with the needs of the business. However, focus of organizations tends to get lob sized with the day to day operations side of the services and lose focus on the alignment to the strategy and hence the business needs. Industry best practice frameworks emphasize on a measurement driven Continual Service Improvement to overcome this issue and to maintain the feedback loop from operations to strategy and vice versa. A robust metrics framework and measurement system forms the backbone of any Continual Service Improvement initiative. This paper proposes an agile approach to setup a metrics framework for continual service improvement for IT Services Organization. This research paper addresses the practical difficulties in implementing the metrics framework for a services environment in the IT space and an agile solution that is practical and realistic to overcome these difficulties. The implementation of this framework involves innovative techniques towards execution phase. Agile concepts and Quality Management Systems are proven techniques for software development solutions and have yielded great results. This paper looks to extend these concepts towards strategic domains such as metrics and hence Continual Service Improvements in the execution phase.
A low-power high-speed class-AB buffer amplifier for flat-panel-display application

A low-power, high-speed, but with a large input dynamic range and output swing class-AB output buffer circuit, which is suitable for the flat-panel display application, is proposed. The circuit employs an elegant comparator to sense the transients of the input to turn on charging/discharging transistors, thus draws little current during static, but has an improved driving capability during transients. It is demonstrated in a 0.6 m CMOS technology.
A passive UHF tag for RFID-based train axle temperature measurement system

A fully integrated passive UHF RFID tag with embedded temperature sensor, compatible with the ISO/IEC 18000 type 6C protocol, is developed in a standard 0.18µm CMOS process, which is designed to measure the axle temperature of a running train. The consumption of RF/analog front-end circuits is 1.556µA@1.0V, and power dissipation of digital part is 5µA@1.0V. The CMOS temperature sensor exhibits a conversion time under 2 ms, less than 7 µW power dissipation, resolution of 0.31°C/LSB and error of +2.3/−1.1°C with a 1.8 V power supply for range from −35°C to 105°C. Measured sensitivity of tag is −5dBm at room temperature.
Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors

A multi-objective optimization strategy to find optimal designs of composite multi-rim flywheel rotors is presented. Flywheel energy storage systems have been expanding into applications such as rail and automotive transportation, where the construction volume is limited. Common flywheel rotor optimization approaches for these applications are single-objective, aiming to increase the stored energy or stored energy density. The proposed multi-objective optimization offers more information for decision-makers optimizing three objectives separately: stored energy, cost and productivity. A novel approach to model the manufacturing of multi-rim composite rotors facilitates the consideration of manufacturing cost and time within the optimization. An analytical stress calculation for multi-rim rotors is used, which also takes interference fits and residual stresses into account. Constrained by a failure prediction based on the Maximum Strength, Maximum Strain and Tsai-Wu criterion, the discrete and nonlinear optimization was solved. A hybrid optimization strategy is presented that combines a genetic algorithm with a local improvement executed by a sequential quadratic program. The problem was solved for two rotor geometries used for light rail transit applications showing similar design results as in industry.
Named Entity Recognition and Classification in Kannada Language

Named Entity Recognition and classification (NERC) is an essential and challenging task in (NLP). Kann ada is a highly inflectional and agglutinating language prov iding one of the richest and most challenging sets of linguistic and statistical features resulting in long and complex word forms, which is large in number. It is primarily a suffixi ng Language and inflected word starts with a root and may have several suffix es added to the right. It is also a Freeword order Language. Like other Indian languages, it is a resource poor language. Annotate d corpora, name dictionaries, good morphological an lyzers, Parts of Speech (POS) taggers etc. are not yet available in the req ui d measure and not many works are reported for t his language. The work related to NERC in Kannada is not yet reported. In recent years, automatic named entity recognition an d extraction systems have become one of the popular research areas. Building NERC for Kannada is challenging. It seeks to classi fy words which represent names in text into predefined categories like perso n name, location, organization, date, time etc. Thi s paper deals with some attempts in this direction. This work starts with e xp riments in building Semi-Automated Statistical M achine learning NLP Models based on Noun Taggers. In this paper we have de loped an algorithm based on supervised learnin g techniques that include Hidden Markov Model (HMM). Some sample resu lts are reported.
DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks

This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs). Since it is trained and deployed in an end-to-end manner, it infers poses directly from a sequence of raw RGB images (videos) without adopting any module in the conventional VO pipeline. Based on the RCNNs, it not only automatically learns effective feature representation for the VO problem through Convolutional Neural Networks, but also implicitly models sequential dynamics and relations using deep Recurrent Neural Networks. Extensive experiments on the KITTI VO dataset show competitive performance to state-of-the-art methods, verifying that the end-to-end Deep Learning technique can be a viable complement to the traditional VO systems.
Parametric analysis and design guidelines of RF-to-DC Dickson charge pumps for RFID energy harvesting

This paper presents the effects of design parameters on output efficiency and input impedance of RF-to-DC Dick-son charge pumps by varying input parameters in simulation. Diode parasitics and input impedance mismatch between the charge pump and antenna are found to significantly decrease the effectiveness of charge pumps, while stage capacitance size appears to have little effect on efficiency. Off-the-shelf diodes are also compared through simulation to find which diodes perform best at each ISM frequency band and various power levels. The investigation is summarized by guidelines to assist designers in developing efficient RF-to-DC Dickson charge pumps as well as concluding with a design methodology.
Detecting Abnormal Machine Characteristics in Cloud Infrastructures

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-per-use packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia\footnote{\url{ganglia.sourceforge.net/}} which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.
Data Mining: The Textbook

About the Book is textbook explores the di erent aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. e chapters of this book fall into one of three categories:
Affect and Engagement in Game-BasedLearning Environments

The link between affect and student learning has been the subject of increasing attention in recent years. Affective states such as flow and curiosity tend to have positive correlations with learning while negative states such as boredom and frustration have the opposite effect. Student engagement and motivation have also been shown to be critical in improving learning gains with computer-based learning environments. Consequently, it is a design goal of many computer-based learning environments to encourage positive affect and engagement while students are learning. Game-based learning environments offer significant potential for increasing student engagement and motivation. However, it is unclear how affect and engagement interact with learning in game-based learning environments. This work presents an in-depth analysis of how these phenomena occur in the game-based learning environment, Crystal Island. The findings demonstrate that game-based learning environments can simultaneously support learning and promote positive affect and engagement.
A simple gate drive for SiC MOSFET with switching transient improvement

This paper presents a new active gate drive for SiC MOSFETs switching. The proposed driver is based on feedforward control method. The switch is benefited from a simple and effective analog gate driver (GD). The main achievement of this GD is the transient enhancement with minimum undesirable effect on the switching efficiency. Also, the electromagnetic interference (EMI) as the main threat to the operation of SiC MOSFET is eliminated by this method. The proposed GD has been validated through the simulation and experimental tests. All the evaluation have been carried out in a hard switching condition with high-frequency switching.
Automatic Melodic Reduction Using a Supervised Probabilistic Context-Free Grammar

This research explores a Natural Language Processing technique utilized for the automatic reduction of melodies: the Probabilistic Context-Free Grammar (PCFG). Automatic melodic reduction was previously explored by means of a probabilistic grammar [11] . However, each of these methods used unsupervised learning to estimate the probabilities for the grammar rules, and thus a corpusbased evaluation was not performed. A dataset of analyses using the Generative Theory of Tonal Music (GTTM) exists [13], which contains 300 Western tonal melodies and their corresponding melodic reductions in tree format. In this work, supervised learning is used to train a PCFG for the task of melodic reduction, using the tree analyses provided by the GTTM dataset. The resulting model is evaluated on its ability to create accurate reduction trees, based on a node-by-node comparison with ground-truth trees. Multiple data representations are explored, and example output reductions are shown. Motivations for performing melodic reduction include melodic identification and similarity, efficient storage of melodies, automatic composition, variation matching, and automatic harmonic analysis.
Trust assessment and decision-making in dynamic multi-agent systems

The concept of trust in multi-agent systems (MASs) has received significant attention in recent years, and a number of approaches have been proposed to enable agents to form, maintain and use trust relationships in their dealings with others. However, current approaches do not adequately address highly dynamic multi-agent systems, where the population and structure changes frequently. For example, agents may frequently join and leave, and ad-hoc structures may form in response to emerging situations. In these highly unstable environments, trust can be difficult or impossible to build with existing techniques. Trust matters most when risk is involved, but in situations of extreme uncertainty, the risk may be too great to permit any interactions, resulting in a breakdown of the system. In this thesis, we propose a general approach for trust evaluation and decision-making in highly dynamic multi-agent systems. First, we present a model of stereotypes, which allows agents to build tentative trust relationships with others on the basis of visible features. We show that this approach can help agents to form trust relationships, despite a high degree of social dynamicity. We present a method of selecting providers of trust evidence, when those providers may be stereotypically biased. Secondly, we present a trust decision-making model which employs controls, as well as trust evaluations and stereotypes, in order to facilitate initial interactions when trust is low or absent, and bootstrap dynamic societies. We show that control can be used initially to enable interactions. As trust builds, control can be reduced. Our approach is general and applicable to existing models of trust in MASs. We evaluate our model within a simulated multi-agent environment characterised by high degrees of dynamicity and structural change.
How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions.

The secretion of glucocorticoids (GCs) is a classic endocrine response to stress. Despite that, it remains controversial as to what purpose GCs serve at such times. One view, stretching back to the time of Hans Selye, posits that GCs help mediate the ongoing or pending stress response, either via basal levels of GCs permitting other facets of the stress response to emerge efficaciously, and/or by stress levels of GCs actively stimulating the stress response. In contrast, a revisionist viewpoint posits that GCs suppress the stress response, preventing it from being pathologically overactivated. In this review, we consider recent findings regarding GC action and, based on them, generate criteria for determining whether a particular GC action permits, stimulates, or suppresses an ongoing stress-response or, as an additional category, is preparative for a subsequent stressor. We apply these GC actions to the realms of cardiovascular function, fluid volume and hemorrhage, immunity and inflammation, metabolism, neurobiology, and reproductive physiology. We find that GC actions fall into markedly different categories, depending on the physiological endpoint in question, with evidence for mediating effects in some cases, and suppressive or preparative in others. We then attempt to assimilate these heterogeneous GC actions into a physiological whole.
Gaussian mixture models and the EM algorithm

These notes give a short introduction to Gaussian mixture models (GMMs) and the Expectation-Maximization (EM) algorithm, first for the specific case of GMMs, and then more generally. These notes assume you’re familiar with basic probability and basic calculus. If you’re interested in the full derivation (Section 3), some familiarity with entropy and KL divergence is useful but not strictly required. The notation here is borrowed from Introduction to Probability by Bertsekas & Tsitsiklis: random variables are represented with capital letters, values they take are represented with lowercase letters, pX represents a probability distribution for random variable X, and pX(x) represents the probability of value x (according to pX). We’ll also use the shorthand notation X 1 to represent the sequence X1, X2, . . . , Xn, and similarly x n 1 to represent x1, x2, . . . , xn. These notes follow a development somewhat similar to the one in Pattern Recognition and Machine Learning by Bishop.
Robust and sparse estimation of tensor decompositions

We propose novel tensor decomposition methods that advocate both properties of sparsity and robustness to outliers. The sparsity enables us to extract some essential features from a big data that are easily interpretable. The robustness ensures the resistance to outliers that appear commonly in high-dimensional data. We first propose a method that generalizes the ridge regression in M-estimation framework for tensor decompositions. The other approach we propose combines the least absolute deviation (LAD) regression and the least absolute shrinkage operator (LASSO) for the CANDECOMP/PARAFAC (CP) tensor decompositions. We also formulate various robust tensor decomposition methods using different loss functions. The simulation study shows that our robust-sparse methods outperform other general tensor decomposition methods in the presence of outliers.
A comparison of feature selection approach between greedy, IG-ratio, Chi-square, and mRMR in educational mining

Educational data mining is a widely interesting issue in data mining research field. One of the topics is feature selection method to reduce a feature set. The main purpose of this study is to compare feature selection methods for the efficiency of student performance prediction improvement. In this research, we proposed 4 feature selection methods: greedy algorithm, Information gain ratio, chi-square, and mRMR that combine with 4 classification models. The example data were 6,882 engineering students in Rajamangala University of Technology Thanyaburi, Thailand from year 2004 to 2010. The experiments demonstrate the effectiveness of the feature selection method in classification of student performance prediction. The result shows that greedy forward selection with neural network classification model presents the best efficiency couple with 91.16% accuracy.
Modeling and Simulation of an Unmanned Ground Vehicle Power System

Long-duration missions challenge ground robot systems with respect to energy storage and efficient conversion to power on demand. Ground robot systems can contain multiple power sources such as fuel cell, battery and/or ultra capacitor. This paper presents a hybrid systems framework for collectively modeling the dynamics and switching between these different power components. The hybrid system allows modeling power source on/off switching and different regimes of operation, together with continuous parameters such as state of charge, temperature, and power output. We apply this modeling framework to a fuel cell/battery power system applicable to unmanned ground vehicles such as Packbot or TALON. A simulation comparison of different control strategies is presented. These strategies are compared based on maximizing energy efficiency and meeting thermal constraints.
Microbiome and mucosal inflammation as extra-articular triggers for rheumatoid arthritis and autoimmunity.

Despite the progress toward understanding the molecular pathogenesis of rheumatoid arthritis (RA), its cause remains elusive. Genes are important but rather insufficient to explain the majority of RA cases. This review describes the novel data supporting the microbiome and its interactions with the human host as potential en('in')vironmental factors in RA pathogenesis.



Animal models of inflammatory arthritis have shown that the presence of bacteria in mucosal surfaces is sufficient to alter local and systemic host immune responses and elicit joint inflammation. Human RA studies have focused on three mucosal sites: the gut, the gingiva, and the respiratory tree. The oral microbiome, and specifically Porphyromonas gingivalis, has long been implicated. Novel sequencing technologies have allowed investigations into the role of the gut microbiome in the development of autoimmune arthritis. Most recently, the pulmonary parenchyma has also been described as yet another possible mucosal site of initiation of autoimmunity in RA.



Emerging data implicate the microbiome in RA pathogenesis. Mucosal sites exposed to a high load of bacterial antigens--such as the periodontium, lung, and gut--may represent the initial site of autoimmune generation. If validated, these findings could lead to the discovery of potential biomarkers and therapeutic approaches in the preclinical and clinical phases of RA.
Peripheral nerve hyperexcitability syndromes.

Peripheral nerve hyperexcitability (PNH) syndromes can be subclassified as primary and secondary. The main primary PNH syndromes are neuromyotonia, cramp-fasciculation syndrome (CFS), and Morvan's syndrome, which cause widespread symptoms and signs without the association of an evident peripheral nerve disease. Their major symptoms are muscle twitching and stiffness, which differ only in severity between neuromyotonia and CFS. Cramps, pseudomyotonia, hyperhidrosis, and some other autonomic abnormalities, as well as mild positive sensory phenomena, can be seen in several patients. Symptoms reflecting the involvement of the central nervous system occur in Morvan's syndrome. Secondary PNH syndromes are generally seen in patients with focal or diffuse diseases affecting the peripheral nervous system. The PNH-related symptoms and signs are generally found incidentally during clinical or electrodiagnostic examinations. The electrophysiological findings that are very useful in the diagnosis of PNH are myokymic and neuromyotonic discharges in needle electromyography along with some additional indicators of increased nerve fiber excitability. Based on clinicopathological and etiological associations, PNH syndromes can also be classified as immune mediated, genetic, and those caused by other miscellaneous factors. There has been an increasing awareness on the role of voltage-gated potassium channel complex autoimmunity in primary PNH pathogenesis. Then again, a long list of toxic compounds and genetic factors has also been implicated in development of PNH. The management of primary PNH syndromes comprises symptomatic treatment with anticonvulsant drugs, immune modulation if necessary, and treatment of possible associated dysimmune and/or malignant conditions.