Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of rowing on a stationary and dynamic ergometer

The pqpose of this study was to examine the differences between rowing on a stationary (SE) and dynamic (DE) ergometer. Volunteers from Ithaca College Crew (26 women, 20 men) rowed 1000 meters at race pace for each ergometer condition (counterbalanced). A 2x2 rebeated measure's ANOVA (cr:0.05) compared stroke rate (SR), skoke ratio, impulse, peak force (PF), time to peak force, heart rate (HR), rating of perceived exertion (total body and lower extremity), respiratory exchange ratio (RER), absolute and relative oxygen consumption (VOz), and economy (Power.Vor-t) by condition and sex. HR was significantly higher on the DE by l.l%. PF, stroke ratio, and RER were significantly lowbr on the DE by 10.80 ,16.2yo, and l.60/o,. ,r"espectively. [nteractions occurred for SR, impulse, VOz, and economy; sdparate depeirdent f-tests 1cr:O.OS; for each sex were conducted for these variables. SR was iilfrificantly higher 1n the DE by 10.1% for women and 16.4% for men. Impulse was significantly lower on the DE'by 7.5o/o for women and 11 . 9o/o for men. Absolute and relative VO2 were both significantly higher on the DE by 5'. 1% for men. Economy was significdntly lower on the DE by 4'. 9%o for men. Neither VOz nor economy was significantly ilifferent betwdeh'conditions for.women. It was concluded that the DE presents decreased musculoskeletal stress biit increased cardiovascular stress relative to the SE, and that these effects are more pronounced in malO rowers. Future research should separate ergometer design and stroke rate effectS. ' +cmiowr,BodeMENTs. ; The advice and criticism of Dr. Thorhas Swensen and Dr. Deborah King were instrumental in the successful executiori o'f this project. Their discussion during the revision process led to a superior paper. Special thanks are extended to Dr. Julianne Abendroth-Smith, Associate Professor of Exercise Science at Willamette University in Salem, Oregon. Credit is due her for the use of the tension load cell'as well as for assistance with collection and analysis of the force data. The support of Becky aird Dan Robinson, head rowing coaches at Ithaca College, was also most welcome. Finally, the many subjects who volunteered must be praised.
Multi-task memory networks for category-specific aspect and opinion terms co-extraction

In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization. However, each task by itself only provides partial information to end users. To generate more detailed and structured opinion analysis, we propose a finer-grained problem, which we call category-specific aspect and opinion terms extraction. This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms. To this end, we propose an end-to-end multi-task attention model, where each task corresponds to aspect/opinion terms extraction for a specific category. Our model benefits from exploring the commonalities and relationships among different tasks to address the data sparsity issue. We demonstrate its state-of-the-art performance on three benchmark datasets.
Comparison and Analysis of the Open-Source Frameworks for Deep Learning

Deep Learning is the hottest trend now in AI and Machine Learning. The paper introduces four mainstream open-source frameworks for deep learning, including Caffe, TensorFlow, CNTK, Torchnet. And the open-source frameworks for deep learning are analyzed and compared from the aspects of network and model capability, interface, model deployment, performance, cross-platform and distributed. Finally, four open-source frameworks for deep learning are simulated on the dataset, and the advantages and disadvantages of each framework and its applicability are summarized.
PV System Analysis Under Partial Shading Using a Sine Model

A photovoltaic panel exhibits highly non-linear P-V & I-V characteristics, further, its Maximum Power Point Tracking (MPPT) operation becomes difficult during partial shading for high performance. During partial shading condition, the P-V array characteristics have more than one maxima point and the PV array panel models employed to locate for Global Maximum Power Point Tracking (GMPPT) analysis becomes complex. In this paper, the effects on PV system characteristics under possible shading conditions are analyzed. A 450 W, PV array system modeling is carried out for different shading conditions with sinusoidal functions. The problem is analyzed for PV panel connection to produce maximum power output. The results from the developed Sine model for the partial shading for GMPPT using Matlab/Simulink software platform is simulated. The effective GMPPT operation is done to validate the sine model.
Black–Scholes and Heston Models with Stochastic Interest Rates and Term Structure of Volatilities

This article considers the Black–Scholes and Heston models and generalize them to stochastic interest rates and maturity-dependent volatilities. In the Black–Scholes case, the author solves the extended model and provides a concrete form for the term structure of volatilities. In the Heston case, he proves that, under some conditions, the generalized model is equivalent to a hybrid model and finds semi-closed-form solutions in the Hull and White and CIR cases. TOPICS: Options, statistical methods, fixed income and structured finance
Late Breaking Results: Enabling interoperability between 3D formats through a generic architecture

The ever growing number of 3D formats and rendering engines makes difficult the loading of any 3D format into a rendering engine and nearly impossible the interoperability between various 3D formats. We thus propose a generic architecture able to decode and mix 3D formats whatever the rendering engine used by Virtual Reality platforms. This architecture relies on a scene graph adapter which defines two APIs to wrap any 3D format as well as various rendering engines and to make them communicate. This flexible architecture brings interoperability between 3D formats and reusability of format and renderer wrappers.
EVALITA 2007: the Part-of-Speech Tagging Task

SOMMARIO/ABSTRACT Questo contributo descrive il task relativo al PoS-tagging in EVALITA 2007, un’iniziativa per la valutazione di sistemi per l’annotazione automatica delle parti del discorso per la lingua italiana. Un numero rilevante di studiosi ha partecipato alla valutazione, sperimentando i vari sistemi sui dati forniti dagli organizzatori. I risultati sono molto interessanti e le prestazioni raggiunte da tali sistemi sono molto alte, specialmente se confrontate con quelle ottenute allo stato dell’arte relativamente alla lingua inglese. This paper reports on EVALITA 2007 PoS-tagging task, an initiative for the evaluation of automatic PoS-taggers for Italian. A noticeable number of scholars and teams across Europe participated experimenting their systems on the data provided by the task organisers. The results are very interesting and overall performances are very high, when compared with tagging accuracy for other more studied languages. In particular, the best scores are very close to the state-of-the-art performances obtained for English.
Seismic Study of Building with Roof Top Telecommunication Towers

Due to the boom in the telecommunication business, number of buildings carrying a roof top tower has been increased rapidly. Most of the building were not originally designed to carry a roof top tower, but later converted to carry roof top towers due to the changed requirements. In the present work an attempt has been made to study the behavior of buildings with roof top tower in the event of an Earth Quake using STAAD pro. A typical commercial building is considered for the analysis. Four towers with height 9m, 15m, 21m, and 27m is considered for the study. The building is analyzed by keeping these towers at four different locations at roof top to identify the most suitable location of the tower. A comparative seismic study between buildings with and without roof top towers is done . Study is carried out to identify the most vulnerable structural members and the relative location of such members in comparison with the tower location. The difference in the forces exerted on the structural members of the building when the tower mass is lumped roof top level and at tower joint is studied in order to find out whether lumping of the tower mass at the roof top will provide satisfactory design or not.
Investigation of Optimum Pattern Recognition Methods for Robust Myoelectric Control During Dynamic Limb Movement

The control of upper limb prostheses based on surface electromyogram (EMG) pattern recognition has long been the focus of many researchers as an important clinical option for amputees. More recently, it has been shown that changes induced during use, such as changes in limb position and performing dynamic activities, can have a substantial impact on the robustness of EMG pattern recognition. This work investigates whether there are alternative EMG features and classifiers which can outperform the commonly used time domain (TD) features and linear discriminant analysis (LDA) classifier in the context of limb positional changes and performing dynamic activities of daily living. A variety of EMG feature combinations and popular classifiers are compared in this study. The bases of comparison are classification accuracy and class separability. The results showed that adding Willison amplitude (WAMP) feature to the commonly used TD feature set combined with LDA classifier reduces the averaged absolute classification error by 1.4%.
Parsing the Resource Pie: Using STRTs to Measure Attention to Mediated Messages

This study directly tests the hypothesis that secondary task reaction time (STRTs) measured during television viewing index available resources rather than resources allocated by the viewer, resources required by the message, or resources remaining in the system. An initial test of the hypothesis did not support the theoretical interpretation of STRTs as either available or remaining resources. A subsequent secondary analysis introduced a new measure of television message complexity called information introduced. The stimuli were recoded using this measure and reanalyzed to test the same hypothesis. Results of the secondary analysis yielded a pattern of STRT responses supporting the prediction that STRTs are indexing available resources.
Trouble on the Road: Finding Reasons for Commuter Stress from Tweets

Intelligent Transportation Systems could benefit from harnessing social media content to get continuous feedback. In this work, we implement a system to identify reasons for stress in tweets related to traffic using a word vector strategy to select a reason from a predefined list generated by topic modeling and clustering. The proposed system, which performs better than standard machine learning algorithms, could provide inputs to warning systems for commuters in the area and feedback for the authorities.
Meta-Algoritme Adaptive Boosting untuk Meningkatkan Kinerja Metode Klasifikasi pada Prestasi Belajar Mahasiswa

Determining the right class on student achievement is important in an evaluation process, because placing students in the right class helps lecturer in reflecting the successfullness of learning process. This problem relates to the performance of classification method which is measured by the classifier metrics. High performance is indicated by the optimality of these classifier's metrics. Besides, meta-algorithm adaptive boosting has been proven to be able to improve the performance of classifier in various fields. Therefore, this paper employs adaptive boosting to reduce the number of incorrect student placement in a class. The experimental results of implementing adaptive boosting in classifying student achievement shows that there is an increase of performance of all classification metrics, i.e., Kappa, Precision, Recall, F-Measure, ROC, and MAE. In terms of accuracy, J-48 is able to rise about 3.09%, which means this method reduces three misclassified students. Additionally, decisionStump increases 12.37% of accuracy. This also means this method is able to decrease 12 misclassified students. Finally, Simple Cart reaches the highest accuracy of about 23.71%, while the number of misclassified students is reduced to 24 students. However, there is no improvement in Random Forest method by using this adaptive boosting.
Three-dimensional study on the interconnection and shape of crystals in a graphic granite by X-ray CT and image analysis

The technique of investigating 3-dimensional interconnections and the shapes of crystals in a rock by X-ray computerized tomography (CT) and image analysis was developed using a graphic granite specimen as an example. Fifty 2-dimensional tomographic images (slices) of the graphic granite were obtained ‘non-destructively’ using a medical X-ray CT scanner. Since a CT value of the specimen was decreased with increasing cross-sectional sample area by the effect of beam-hardening, the CT value was corrected using the area of each slice. Binary images of the slices were made comparing one of them with a thin-section of the slice. Using the binary images, connection analysis of quartz rods in the graphic granite specimen was performed on the basis of percolation theory (cluster labelling). This analysis showed that at least 89.9% of the quartz rods were connected in three dimensions. Furthermore, the 3-dimensional shape of the quartz rods was analysed using the 2-point correlation function calculated from the binary images. The average shape of the quartz rods was obtained by fitting an ellipsoid to the high-value region of the 2-point correlation function. The elongation axis of the ellipsoid agreed well with the crystallographic c-axes of the quartz rods.
Challenges and facilitators practices for knowledge management in large-scale scrum teams

ABSTRACT Sharing and managing knowledge is an essential resource for the success of a Scrum team. However, widely known knowledge management practices typically address only team level collaboration, which cannot be easily scaled to multiple Scrum teams. In this sense, this paper conducts an empirical study at one of the largest information technology companies operating in Portugal, in which multiple Scrum teams working locally and in remote offices develops e-commerce solutions for the African market. Several facilitators’ knowledge practices are identified and discussed, and also the main challenges and difficulties of scaling Scrum practices for large-scale teams are addressed.
An Improved Orthogonal Iterative Algorithm for Monocular Camera Pose Estimation

In this paper, we focus on the classical perspective-n-point(PnP) problem in monocular camera pose estimation and propose an improved orthogonal iterative algorithm. The key idea is to integrate the steps in each iteration, and extract the repetitive calculation of each iteration. The repetitive computation in each iteration can be abstracted and done before iteration process. The computational complexity of each iteration is reduced from toO(n) to O(1) . So that, more iterations can be done in a short time and the accuracy is improved as well. The simulation results and real experiments results show the efficiency and accuracy of our entry accepted iterative algorithms, our algorithm can maintain high pose estimation accuracy. This algorithm is more likely to converge to correct pose in a short run time, which makes it more applicable for real time applications.
Stochastic Modeling and Optimization of Stragglers in Mapreduce Framework

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This thesis analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.
SnO2 Nanostructure as Pollutant Gas Sensors: Synthesis, Sensing Performances, and Mechanism

A significant amount of pollutants is produced from factories and motor vehicles in the form of gas. Their negative impact on the environment is well known; therefore detection with effective gas sensors is important as part of pollution prevention efforts. Gas sensors use a metal oxide semiconductor, specifically SnO2 nanostructures. This semiconductor is interesting and worthy of further investigation because of its many uses, for example, as lithium battery electrode, energy storage, catalyst, and transistor, and has potential as a gas sensor. In addition, there has to be a discussion of the use of SnO2 as a pollutant gas sensor especially for waste products such as CO, CO2, SO2, and NOx. In this paper, the development of the fabrication of SnO2 nanostructures synthesis will be described as it relates to the performances as pollutant gas sensors. In addition, the functionalization of SnO2 as a gas sensor is extensively discussed with respect to the theory of gas adsorption, the surface features of SnO2, the band gap theory, and electron transfer.
An Application of Probabilistic Grammars to Efficient Machne Translation

In this paper we present one of the algorithms used to parse probabilistic context-free grammars: the A* parsing algorithm, which is based on the A* graph search method. We show an example of application of the algorithm in an existing machine translation system. The existing CYK-based parser used in the Translatica system was modified by applying the A* parsing algorithm in order to examine the possibilities of improving its performance. This paper presents the results of applying the A* algorithm with different heuristic functions and their impact on the performance of the parser.
A Database of Anechoic Microphone Array Measurements of Musical Instruments

Violin (classical) Violin_historical 430 Hz G3-C7 G3-C7 Normal of the soundboard aiming at Mic#25, Scroll aiming at between Mic#02 and Mic#03 Daniel Deuter 200905-18 14:15 15:15 Instrument: Neil Kristóv Értz (1998, rev. 2003), Copy of Guaneri; Bow: Violin Bow (50 g), René Groppe (Metz, 1997), Copy of baroque model (17th century); Strings: E String: Catgut (Damian Dlugolecki, 0.62 mm); A String: Catgut (Nicholas Baldock, Kathedrale Strings, 0.74 mm); D String: Catgut (+0.25 mm Silver, 1.12mm, Nicholas Baldock, LuxLine); G String: Catgut, Silver, diameter unknown
Bayesian Methods and Machine Learning for Processing Text and Image Data

BAYESIAN METHODS AND MACHINE LEARNING FOR PROCESSING TEXT AND IMAGE DATA by Yingying Gu The University of Wisconsin-Milwaukee, 2017 Under the Supervision of Professor Jun Zhang Classification/clustering is an important class of unstructured data processing problems. The classification (supervised, semi-supervised and unsupervised) aims to discover the clusters and group the similar data into categories for information organization and knowledge discovery. My work focuses on using the Bayesian methods and machine learning techniques to classify the free-text and image data, and address how to overcome the limitations of the traditional methods. The Bayesian approach provides a way to allow using more variations(numerical or categorical), and estimate the probabilities instead of explicit rules, which will benefit in the ambiguous cases. The MAP(maximum a posterior) estimation is used to deal with the local maximum problems which the ML(maximum likelihood) method gives inaccurate estimates. The EM(expectation-maximization) algorithm can be applied with MAP estimation for the incomplete/missing data problems. Our proposed framework can be used in both supervised and unsupervised classification. For natural language processing(NLP), we applied the machine learning techniques for sentence/text classification. For 3D CT image segmentation, MAP EM clustering approach is proposed to auto-detect the number of objects in the 3D CT luggage image, and the prior knowledge and constraints in MAP estimation are used to avoid/improve the local maximum problems. The algorithm can automatically determine the number of classes and find the optimal parameters for each class. As a result, it can automatically detect the number of objects ii and produce better segmentation for each object in the image. For segmented object recognition, we applied machine learning techniques to classify each object into targets or non-targets. We have achieved the good results with 90% PD(probability of detection) and 6% PFA(probability of false alarm). For image restoration, in X-ray imaging, scatter can produce noise, artifacts, and decreased contrast. In practice, hardware such as anti-scatter grids are often used to reduce scatter. However, the remaining scatter can still be significant and additional software-based correction are desirable. Furthermore, good software solutions can potentially reduce the amount of needed anti-scatter hardware, thereby reducing cost. In this work, the scatter correction is formulated as a Bayesian MAP (maximum a posteriori) problem with a non-local prior, which leads to better textural detail preservation in scatter reduction. The efficacy of our algorithm is demonstrated through experimental and simulation results.
Banks’ Audit Committees, Audit Firm Alumni, and Fees Paid to Audit Firm

Purpose This paper focuses on bank audit committees and examines whether audit committee members who are former auditors are associated with the acquisition of audit and non-audit services from their former employers. Design/methodology/approach The study empirically examines a sample of large banks that are included in the S&P Composite 1500. Findings The paper reports significantly lower audit fees and a higher proportion of non-audit fees to total fees when the audit committee chair is an alumnus of the incumbent audit firm. Moreover, additional analysis reveals that these findings are stronger for banks with more earnings management. Research limitations/implications Overall, the findings indicate that audit firms might consider banks using their alumni as audit committee chairs to be less risky or easier to audit, thus requiring relatively less effort from the auditors. The reduced effort required to audit clients with audit firm alumni on their audit committees then has the effect of reducing the audit fees charged. Alternatively, their auditing experience and cognitive proximity might influence the assessment of the need for auditing or the ability to negotiate lower audit fees on the part of audit firm alumni. Originality/value This paper provides empirical evidence of the association between audit firm alumni in influential positions on an audit committee and fees paid to those audit firms in the banking industry. The findings contribute to the literature by suggesting that banks with affiliated former auditors chairing their audit committees not only have significantly lower audit fees but also a higher proportion is spent on non-audit services.