nils-herrmann/clean_scirepeval huggingface dataset:
Multi-scale CNN Stereo and Pattern Removal Technique for Underwater Active Stereo System

Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reﬂections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artiﬁcial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by ﬂuctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patterns, etc., those noises and patterns should be removed from captured images to recover original texture. To solve these problems, we propose three approaches; a depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method and CNN-based texture recovery method. A depth-dependent calibration I sour analysis to ﬁnd the acceptable depth range for approximation by center projection to ﬁnd the certain target depth for calibration. In terms of CNN stereo, unlike common CNN based stereo methods which do not consider strong disturbances like refraction or bubbles, we designed a novel CNN architecture for stereo matching using multi-scale information, which is intended to be robust against such disturbances. Finally, we propose a multi-scale method for bubble and a projected-pattern removal method using CNNs to recover original textures. Experimental results are shown to prove the effectiveness of our method compared with the state of the art techniques. Furthermore, reconstruction of a live swimming ﬁsh is demonstrated to conﬁrm the feasibility of our techniques.
Uninterruptible power supply systems provide protection

Nowadays, uninterruptible power supply (UPS) systems are in use throughout the world, helping to supply a wide variety of critical loads, in situations of power outage or anomalies of the mains. This article describes the most common line problems and the relationship between these and the different existing kinds of UPS, showing their operation modes as well as the existent energy storage systems. It also addresses an overview of the control schemes applied to different distributed UPS configurations. Finally, it points out the applicability of such systems in distributed generation, microgrids, and renewable energy systems.
The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.

The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).
Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging fMRI brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.
High-voltage pulse waveform modulator based on solid-state Marx generator

Many research fields need special high-voltage pulses in pulsed power applications, which are different from typical rectangular pulses. In this paper, a high-voltage waveform modulator of two methods using a solid-state Marx generator has been developed and tested. Instead of manufacturing a generator with a specialized structure, the modulator can control a general Marx generator by low-voltage programmable logic devices to produce an irregular but controllable high-voltage triangular pulse with pulse length of ~4.5 μs and peak voltage of ~123 kV.
Multi-source information fusion for drowsy driving detection based on wireless sensor networks

Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiological signals such as eye activity measures, the inclination of the driver's head, sagging posture, heart beat rate, skin electric potential, and electroencephalographic (EEG) activities, as well as response characteristics, decline in gripping force on the steering wheel and lane keeping characteristics. By developing a hierarchical model that is able to collect the sensing data, analyze the driving behavior and the reactions to the driver, it can provide a safe and a comfortable driving environment. Combining different indications of drowsiness and processing the contextual information to predict whether a driver is drowsy, the system not only issues a warning for the driver, but also provides the drowsy driving information to transportation control center and other vehicles if necessary.
Model based control of fiber reinforced elastofluidic enclosures

Fiber-Reinforced Elastofluidic Enclosures (FREEs), are a subset of pneumatic soft robots with an asymmetric continuously deformable skin that are able to generate a wide range of deformations and forces, including rotation and screw motions. Though these soft robots are able to generate a variety of motions, simultaneously controlling their end effector rotation and position has remained challenging due to the lack of a simple model. This paper presents a model that establishes a relationship between the pressure, torque due to axial loading, and axial rotation to enable a model-driven open-loop control for FREEs. The modeling technique relies on describing force equilibrium between the fiber, fluid, and an elastomer model which is computed via system identification. The model is experimentally tested as these variables are changed, illustrating that it provides good agreement with the real system. To further illustrate the potential of the model, a precision open-loop control experiment of opening a rotational combination lock is presented2.
A Study of Big Data in Cloud Environment with their Related Challenges

Big Data , it is a word which is used for the huge amount of data sets which have the big, extra various and difficu lt structure facing some difficult ies in storing the data then analyzing and visualizing the data for the final outcome  . Big data analytics is the procedure of probing huge amounts of data. Here we use cloud computing technology so firstly acquaint the general environment of big data and some associated technology like Hadoop. This powerful technology is used to execute the huge s cale and complicated data computing. It eradicates the requirements of hardware computing which is costly dedicated space and software’s . It is a resolution for analyzing Big Data by deploying Big Data softwares like Hadoop on cloud background which gives users an innovative pattern of service called as Analytics as a Service [7]. Hadoop uses the map-reduce paradigm .We also talk about various advantages, challenges, and issues associated with Big Data.
Teacher Retention and Attrition : A Review of the Literature

The teaching profession and the essential roles teachers play in building the human capital base of any nation cannot be underestimated. These essential roles are central to basic education, most especially in developing nations. There may however seem to be close substitutes for the roles of education managers in the areas of education delivering apparatus with programmed instructions. Apparently, in emerging economies such as Nigeria, teachers no doubt are still the key managers of knowledge. The main engine of economic growth in any country is through a well-trained and knowledge driven education workforce. Due to the persistent labour turnover in schools and high job dissatisfaction among teachers in Nigeria and other parts of the world, this study seeks to find out those factors that encourage teachers’ attrition. The study adopts an extensive review of literature such as conference papers, journal articles, internet sources, books, newspapers to find out the factors that determine teachers’ attrition and retention in schools particularly in Nigeria. The study concludes that for teachers to be retained in schools, certain management issues as they affect teacher education, must be thoroughly evaluated. The study further recommends that educational managers should be made to go through staff training and development programmes and curricula enhancement. This is necessary and important for teachers to be well equipped for the task ahead, provide adequate remuneration, welfare packages, proper work environment amongst others as may be obtainable in other professions.
Fast Hardware Architectures for Supersingular Isogeny Diffie-Hellman Key Exchange on FPGA

In this paper, we present a constant-time hardware implementation that achieves new speed records for the supersingular isogeny Di e-Hellman (SIDH), even when compared to highly optimized Haswell computer architectures. We employ inversion-free projective isogeny formulas presented by Costello et al. at CRYPTO 2016 on an FPGA. Modern FPGA's can take advantage of heavily parallelized arithmetic in Fp2 , which lies at the foundation of supersingular isogeny arithmetic. Further, by utilizing many arithmetic units, we parallelize isogeny evaluations to accelerate the computations of large-degree isogenies by approximately 57%. On a constant-time implementation of 124-bit quantum security SIDH on a Virtex-7, we generate ephemeral public keys in 10.6 and 11.6 ms and generate the shared secret key in 9.5 and 10.8 ms for Alice and Bob, respectively. This improves upon the previous best time in the literature for 768-bit implementations by a factor of 1.48. Our 83-bit quantum security implementation improves upon the only other implementation in the literature by a speedup of 1.74 featuring fewer resources and constant-time.
Transformer-less bidirectional PWM converter integrating cell voltage equalizer using voltage multiplier for series connected energy storage cells

Traditional energy storage systems consisting of lithium-ion battery cells or electric double-layer capacitors (EDLCs) connected in series require a cell voltage equalizer in addition to a bidirectional PWM converter. The system is prone to complexity as two converters (i.e., the equalizer and bidirectional converter) are separately necessary. To simplify the systems, this paper proposes the transformer-less bidirectional PWM converter integrating a cell voltage equalizer using a voltage multiplier. An inductor in a conventional bidirectional PWM converter is replaced with series-connected inductors in the proposed converter in order to produce square wave voltage with an arbitrary amplitude, by which the voltage multiplier is driven. The voltage multiplier in the proposed integrated converter is essentially a switchless circuit, and therefore, the bidirectional converter and voltage equalizer can be integrated with no additional switch, reducing both the system and circuit complexities. The charge-discharge cycling test for nice EDLCs connected in series was performed using the proposed integrated converter from a voltage-imbalanced condition. The voltage imbalance was gradually eliminated while charge-discharge cycling, demonstrating the integrated functions.
Key organizational factors in data warehouse architecture selection

a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates what is variously called the hub and spoke architecture (i.e., a centralized data warehouse with dependent data marts), Corporate Information Factory, DW 2.0 (i.e., Inmon's current term), or enterprise data warehouse architecture . Another architecture alternative is the data mart bus architecture with linked dimensional data marts (i.e., bus architecture), advocated by Ralph Kimball, the other preeminent figure in data warehousing [52]. 1 These and other architectures (e.g., independent data marts and federated) have fundamental differences and strong advocates. The selection of an appropriate architecture is an important decision. A study by the Meta Group found that architecture selection is one of the key factors influencing data warehousing success [54]. A Gartner report identified the architecture selection decision as one of the five problem …
Application of customer satisfaction study to derive customer knowledge

Customer knowledge has become one of the most important economic assets to a firm. Knowing what customers value most in a company’s products or services helps companies optimize resource utilization in areas for continuous improvement based on their needs and wants. This study extends previous research in customer satisfaction by showing how a customer satisfaction study can help answer questions, such as why customers are satisfied or dissatisfied, whether the company’s complaint handling is effective and how to improve customers’ satisfaction. We surveyed 532 customers served by a commercial banking organization in Taiwan. In addition, researchers demonstrated that there are differences in satisfaction levels among different genders. To explore further the reasons for these differences, we separated our sample to compare the specific causal links between the two gender samples and we draw broader inference from the results. Partial Least Squares (PLS) was used to test the theoretical model. PLS not only bridges theoretical and empirical knowledge but also facilitates the construction of a strategic management map and provides latent variable scores that could be utilized to report CSI scores. Our findings provide support for the application of CSI study to derive customer knowledge, which also reveals some interesting suggestions for future research.
A whole brain fMRI atlas generated via spatially constrained spectral clustering.

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.
Solar powered unmanned aerial vehicle for continuous flight: Conceptual overview and optimization

An aircraft that is capable of continuous flight offers a new level of autonomous capacity for unmanned aerial vehicles. We present an overview of the components and concepts of a small scale unmanned aircraft that is capable of sustaining powered flight without a theoretical time limit. We then propose metrics that quantify the robustness of continuous flight achieved and optimization criteria to maximize these metrics. Finally, the criteria are applied to a fabricated and flight tested small scale high efficiency aircraft prototype to determine the optimal battery and photovoltaic array mass for robust continuous flight.
Swara Histogram Based Structural Analysis And Identification Of Indian Classical Ragas

This work is an attempt towards robust automated analysis of Indian classical ragas through machine learning and signal processing tools and techniques. Indian classical music has a definite heirarchical structure where macro level concepts like thaats and raga are defined in terms of micro entities like swaras and shrutis. Swaras or notes in Indian music are defined only in terms of their relation to one another (akin to the movable do-re-mi-fa system), and an inference must be made from patterns of sounds, rather than their absolute frequency structure. We have developed methods to perform scale-independent raga identification using a random forest classifier on swara histograms and achieved state-of-the-art results for the same. The approach is robust as it directly works on partly noisy raga recordings from Youtube videos without knowledge of the scale used, whereas previous work in this direction often use audios generated in a controlled environment with the desired scale. The current work demonstrates the approach for 8 ragas namely Darbari, Khamaj, Malhar, Sohini, Bahar, Basant, Bhairavi and Yaman and we have achieved an average identification accuracy of 94.28% through the framework.