nils-herrmann/clean_scirepeval huggingface dataset:
Construction and Refinement of Panoramic Mosaics with Global and Local Alignment

This paper presents techniques for constructing full view panoramic mosaics from sequences of images. Our representation associates a rotation matrix (and optionally a focal length) with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimal image mosaic (in the least-squares sense). To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we develop a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.
On the feasibility of hybrid Battery/Ultracapacitor Energy Storage Systems for next generation shipboard power systems

This paper explores a Battery/Ultracapacitor Energy Storage System (BUCESS) for navy application. A new configuration of the battery and ultracapacitor combined system is introduced for propulsion system and pulse power loads. A dual active bridge (DAB) topology is selected to control the bidirectional power flow through phase shifting for charging and discharging batteries and ultracapacitors. High-frequency switching devices are selected to achieve dc-dc conversion at high voltage and high power levels. A 500V–1kV BUCESS is designed and analyzed to investigate 100kW transmission for batteries and 1MW for ultracapacitors both in charging and discharging modes.
A Highly Linear Two-Stage Amplifier Integrated Circuit Using InGaP/GaAs HBT

This paper presents a linearized two-stage amplifier integrated circuit (IC), having an output power level of about 1 Watt, used for general purpose applications. A predistortion method, based on alignment of the nonlinear characteristics between the first- and the second-stage amplifiers, has been proposed and analyzed in order to enhance the linearization aspects. The resistors in the active bias circuits of both the stages are optimized to achieve the best cancellation conditions for the third-order intermodulation components. The two-stage amplifier IC, which is based on an InGaP/GaAs hetero-junction bipolar transistor (HBT) technology, has been designed and implemented for the 900 MHz band. An output 1 dB compression point (P1dB) of 29.6 dBm, a maximum third-order output intercept point (OIP3) of as high as 48.7 dBm at a two-tone average output power of 21 dBm have been obtained while having a quiescent current of 375 mA and a single bias supply of 5.5 V. The implemented amplifier is able to maintain its IMD3 performance below -60 dBc up to an output power of 21 dBm.
Factors that Significantly Impact the Implementation of an Agile Software Development Methodology

The Internet economy has altered the current rules of software engineering. Traditional development methodologies have proven too cumbersome to meet the rapidly changing requirements and short product cycles demanded by business today. To meet these rapidly changing requirements, software developers have developed agile software development methodologies (SDMs) utilizing iterative development, prototyping, templates, and minimal documentation requirements. This research project investigated agile SDM implementation using an online survey sent to software development practitioners worldwide. This survey data was used to identify factors related to agile SDM implementation. The factors that significantly impacted agile methodology implementations included training, management involvement, access to external resources, and corporation size. Other factors such as using models, having an implementation plan, collocating the development team, and developing software for Internet or intranet use did not significantly impact the implementation of an agile software development methodology. A number of the factors that impact the implementation of an agile development methodology are completely under the control of management. Organizations that are considering implement ting an agile methodology are able to manipulate some of these factors to increase the opportunities for success of their methodology.
Open source robotic 3D mapping framework with ROS — Robot Operating System, PCL — Point Cloud Library and Cloud Compare

We propose an open source robotic 3D mapping framework based on Robot Operating System, Point Cloud Library and Cloud Compare software extended by functionality of importing and exporting datasets. The added value is an integrated solution for robotic 3D mapping and new publicly available datasets (accurate 3D maps with geodetic precision) for evaluation purpose Datasets were gathered by mobile robot in stop scan fashion. Presented results are a variety of tools for working with such datasets, for task such as: preprocessing (filtering, down sampling), data registration (ICP, NDT), graph optimization (ELCH, LUM), tools for validation (comparison of 3D maps and trajectories), performance evaluation (plots of various outputs of algorithms). The tools form a complete pipeline for 3D data processing. We use this framework as a reference methodology in recent work on SLAM algorithms.
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring

In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method ‘‘Random Subspace” outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg–Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit.
A High-Performance FPGA-Based Implementation of the LZSS Compression Algorithm

The increasing growth of embedded networking applications has created a demand for high-performance logging systems capable of storing huge amounts of high-bandwidth, typically redundant data. An efficient way of maximizing the logger performance is doing a real-time compression of the logged stream. In this paper we present a flexible high-performance implementation of the LZSS compression algorithm capable of processing up to 50 MB/s on a Virtex-5 FPGA chip. We exploit the independently addressable dual-port block RAMs inside the FPGA chip to achieve an average performance of 2 clock cycles per byte. To make the compressed stream compatible with the ZLib library  we encode the LZSS algorithm output using a fixed Huffman table defined by the Deflate specification . We also demonstrate how changing the amount of memory allocated to various internal tables impacts the performance and compression ratio. Finally, we provide a cycle-accurate estimation tool that allows finding a trade-off between FPGA resource utilization, compression ratio and performance for a specific data sample.
Detecting opinion spams and fake news using text classification

*Correspondence Traore, Issa, ECE Department, University of Victoria, Victoria, BC, Canada.  In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n-gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state-of-the-art methods.
Combining Over-Sampling and Under-Sampling Techniques for Imbalance Dataset

An important problem in medical data analysis is imbalance dataset. This problem is a cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails in diagnostic of patient who have disease that means he cannot treat patient in timely. However, the problem can be easily solved by adding or removing the data to closely balance for performance of diagnostic in medically. This paper proposed a solution to adjust imbalance dataset by combining Neighbor Cleaning Rule (NCL) and Synthetic Minority Over-Sampling Technique (SMOTE) techniques. The process of work is using NCL technique for removing sample data that are outliers in majority class and SMOTE technique is used for increasing sample data in minority class to closely balance dataset. After that, the balanced medical dataset is classified by Naive Bayes, SMO and KNN algorithm. The experimental results show that the recall rate can be improved from the models that were created from balanced dataset.
Zero-Bending Piezoelectric Micromachined Ultrasonic Transducer (pMUT) With Enhanced Transmitting Performance

A piezoelectric micromachined ultrasonic transducer (pMUT) has enabled numerous exciting ultrasonic applications. However, residual stress and initial buckling may worsen the transmitting sensitivity of a pMUT, and also limit its application and commercialization. In this paper, we report a new innovative pMUT with a perfectly flat membrane, i.e., zero-bending membrane. Leveraging on the stress-free AlN thin film, framelike top electrode layout, and integrated vacuum cavity, the initial deflection of suspended membrane is significantly suppressed to only 0.005%. The transmitting sensitivity of the zero-bending pMUT is measured as 123 nm/V at a resonant frequency of 2.21 MHz, which is 450% higher than that of the reference pMUT with slightly non-zero initial deflection. Compared with the simulation results, the measured data of zero-bending pMUT achieve 94.5% of its ideal transmitting sensitivity. It is solid evidence that our approach is an effective and reliable way to overcome the residual stress and the initial buckling issue.
A Logic of Authentication

Authentication protocols are the basis of security in many distributed systems, and it is therefore essential to ensure that these protocols function correctly. Unfortunately, their design has been extremely error prone. Most of the protocols found in the literature contain redundancies or security flaws. A simple logic has allowed us to describe the beliefs of trustworthy parties involved in authentication protocols and the evolution of these beliefs as a consequence of communication. We have been able to explain a variety of authentication protocols formally, to discover subtleties and errors in them, and to suggest improvements. In this paper we present the logic and then give the results of our analysis of four published protocols, chosen either because of their practical importance or because they serve to illustrate our method.
The ROUGE-AR : A Proposed Extension to the ROUGE Evaluation Metric for Abstractive Text Summarization

Abstractive text summarization refers to summary generation that is based on semantic understanding, and is thus not strictly limited to the words found in the source. Despite its success in deep learning, however, the task of text summarization has no reliably effective metric for evaluating performance. In this paper, we describe the standard evaluative measure for abstractive text summarization, the ROUGE metric. We then propose our extension to the standard ROUGE measure, the ROUGE-AR. Drawing from methodologies pertaining to latent semantic analysis (LSA) and part-of-speech tagging, the ROUGE-AR metric reweights the final ROUGE output by incorporating both anaphor resolution and other intrinsic methods that are largely absent from non-human text summary evaluation.
Design of air quality meter and pollution detector

This work describes the design and implementation of a Air Quality Meter and Air Pollution Detector. The technology adopted is a practical implementation of the concept of Internet of Things. This project is an exploration of the possibilities of use of this technology in a rapidly changing world where environmental health is becoming a serious threat. Using the Bluetooth communication technology and microcontroller board of Arduino, the work is implemented. A few sensors are also used such as temperature and humidity sensors and a few gas sensors to monitor changes.
Implant Removal after Percutaneous Short Segment Fixation for Thoracolumbar Burst Fracture : Does It Preserve Motion?

The purpose of this study was to evaluate the efficacy of implant removal of percutaneous short segment fixation after vertebral fracture consolidation in terms of motion preservation.



Between May 2007 and January 2011, 44 patients underwent percutaneous short segment screw fixation due to a thoracolumbar burst fracture. Sixteen of these patients, who underwent implant removal 12 months after screw fixation, were enrolled in this study. Motor power was intact in all patients, despite significant vertebral height loss and canal compromise. The patients were divided into two groups by degree of osteoporosis : Group A (n=8), the non-osteoporotic group, and Group B (n=8), the osteoporotic group. Imaging and clinical findings including vertebral height loss, kyphotic angle, range of motion (ROM), and complications were analyzed.



Significant pain relief was achieved in both groups at final follow-up versus preoperative values. In terms of vertebral height loss, both groups showed significant improvement at 12 months after screw fixation and restored vertebral height was maintained to final follow-up in spite of some correction loss. ROM (measured using Cobb's method) in flexion and extension in Group A was 10.5° (19.5/9.0°) at last follow-up, and in Group B was 10.2° (18.8/8.6°) at last follow-up. Both groups showed marked improvement in ROM as compared with the screw fixation state, which was considered motionless.



Removal of percutaneous implants after vertebral fracture consolidation can be an effective treatment to preserve motion regardless of osteoporosis for thoracolumbar burst fractures.
Software Architecture Quality Measurement Stability and

Over the past years software architecture has become an important sub-field of software engineering. There has been substantial advancement in developing new technical approaches to start handling architectural design as an engineering discipline. Measurement is an essential part of any engineering discipline. Quantifying the quality attributes of the software architecture will reveal good insights about the architecture. It will also help architects and practioners to choose the best fit of alternative architectures that meets their needs. This work paves the way for researchers to start investigating ways to measure software architecture quality attributes. Measurement of these qualities is essential for this sub-field of software engineering. This work explores Stability and Understandability of software architecture, several metrics that affect them, and literature review of these qualities. Software Engineering; Software Architecture; Quality Attributes; Stability; Understandability
Bluetooth Based Home Automation and Security System Using ARM 9

-Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.
Deep learning methods and applications - Classification of traffic signs and detection of Alzheimer's disease from images

In this thesis, the deep learning method convolutional neural networks (CNNs) has been used in an attempt to solve two classification problems, namely traffic sign recognition and Alzheimer’s disease detection. The two datasets used are from the German Traffic Sign Recognition Benchmark (GTSRB) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The final test results on the traffic sign dataset generated a classification accuracy of 98.81 %, almost as high as human performance on the same dataset, 98.84 %. Different parameter settings of the selected CNN structure have also been tested in order to see their impact on the classification accuracy. Trying to distinguish between MRI images of healthy brains and brains afflicted with Alzheimer’s disease gained only about 65 % classification accuracy. These results show that the convolutional neural network approach is very promising for classifying traffic signs, but more work needs to be done when working with the more complex problem of detecting Alzheimer’s disease.
Evolving computational intelligence systems

A new paradigm of the evolving computational intelligence systems (ECIS) is introduced in a generic framework of the knowledge and data integration (KDI). This generalization of the recent advances in the development of evolving fuzzy and neuro-fuzzy models and the more analytical angle of consideration through the prism of knowledge evolution as opposed to the usually used datacentred approach marks the novelty of the present paper. ECIS constitutes a suitable paradigm for adaptive modeling of continuous dynamic processes and tracing the evolution of knowledge. The elements of evolution, such as inheritance and structure development are related to the knowledge and data pattern dynamics and are considered in the context of an individual system/model. Another novelty of this paper consists of the comparison at a conceptual level between the concept of models and knowledge captured by these models evolution and the well known paradigm of evolutionary computation. Although ECIS differs from the concept of evolutionary (genetic) computing, both paradigms heavily borrow from the same source – nature and human evolution. As the origin of knowledge, humans are the best model of an evolving intelligent system. Instead of considering the evolution of population of spices or genes as the evolutionary computation algorithms does the ECIS concentrate on the evolution of a single intelligent system. The aim is to develop the intelligence/knowledge of this system through an evolution using inheritance and modification, upgrade and reduction. This approach is also suitable for the integration of new data and existing models into new models that can be incrementally adapted to future incoming data. This powerful new concept has been recently introduced by the authors in a series of parallel works and is still under intensive development. It forms the conceptual basis for the development of the truly intelligent systems. Another specific of this paper includes bringing together the two working examples of ECIS, namely ECOS and EFS. The ideas are supported by illustrative examples (a synthetic non-linear function for the ECOS case and a benchmark problem of house price modelling from UCI repository for the case of EFS).
Early Prediction of Students' Grade Point Averages at Graduation: A Data Mining Approach.

There has recently been interest in educational databases containing a variety of valuable but sometimes hidden data that can be used to help less successful students to improve their academic performance. The extraction of hidden information from these databases often implements aspects of the educational data mining (EDM) theory, which aims to study available data in order to shed light on more valuable, hidden information. Data clustering, classification, and regression methods such as K-means clustering, neural networks (NN), extreme learning machine (ELM), and support vector machines (SVM) can be used for to predict aspects of the educational data. EDM outputs can ultimately identify which students will need additional help to improve their grade point averages (GPAs) at graduation.  This study aims to implement several prediction techniques in data mining to assist educational institutions with predicting their students’ GPAs at graduation. If students are predicted to have low GPAs at graduation, then extra efforts can be made to improve their academic performance and, in turn, GPAs.  NN, SVM, and ELM algorithms are applied to data of computer education and instructional technology students to predict their GPAs at graduation.  A comparative analysis of the results indicates that the SVM technique yielded more accurate predictions at a rate of 97.98%. By contrast, the ELM method yielded the second most accurate prediction rate (94.92%) evaluated based on the criterion of correlation coefficient. NN reported the least accurate prediction rate (93.76%).  The use of data mining methodologies has recently expanded for a variety of educational purposes. The *Dr. Fırat University, Turkey,
A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.