nils-herrmann/clean_scirepeval huggingface dataset:
Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Hybridization in optimization methods plays a very vital role to make it effective and efficient. Different optimization methods have different search tendency and it is always required to experiment the effect of hybridizing different search tendency of the optimization algorithm with each other. This paper presents the effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO) in two different ways. So, four different variants of hybrid BBO, viz. two variants of hybrid BBO with AIA and two with ACO, are developed and experimented in this paper. All the considered optimization techniques have altogether a different search tendency. The iogeography Based Optimization nt Colony Optimization rtificial Immune Algorithm proposed hybrid method is tested on many benchmark problems and real life problems. Friedman test and Holm–Sidak test are performed to have the statistical validity of the results. Results show that proposed hybridization of BBO with ACO and AIA is effective over a wide range of problems. Moreover, the proposed hybridization is also effective over other proposed hybridization of BBO and different variants of BBO available in the literature.
Kinematics and Jacobian analysis of a 6UPS Stewart-Gough platform

In this paper a complete kinematics analysis of a 6UPS Stewart-Gough platform is performed. A new methodology to deal with the forward kinematics problem based on a multibody formulation and a numerical method is presented, resulting in a highly efficient formulation approach. The idea of a multibody formulation is basically to built for each joint that links the body of the robot, a set of equations that define the constraints on their movement so that the joint variables are included in these equations. Once the constraint function is defined, we may use a numerical method to find the kinematic solution. In order to avoid the problem reported with the orientation (Euler's Angles) of the moving platform, a generalized coordinates vector using quaternions is applied. Then, the Jacobian analysis using reciprocal screw systems is described and the workspace is determined. Finally, a design of a simulator using MATLAB as programming tool is presented.
Identification of radar signals using neural network classifier with low-discrepancy optimisation

A hybrid low-discrepancy sequences optimisation approach is investigated and used for training neural network classifiers for recognition of radar signal emitters. Two sample case studies are developed in order to demonstrate and evaluate the presented approach. In the first one, generic intercepted radar signals are classified in two broad classes - with civil or military application, based on patterns in their pulse trains, whereas in the second one the classifier is trained to distinguish between several more specific radar functions. Very competitive results of about 84% accuracy are achieved on the testing data sets.
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a firststage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence’s first-stage representation to attend words appeared in itself, which is called ”Inner-Attention” in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of ”Inner-Attention” mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.
A New ADS-B Authentication Framework Based on Efficient Hierarchical Identity-Based Signature with Batch Verification

Automatic dependent surveillance-broadcast (ADS-B) has become a crucial part of next generation air traffic surveillance technology and will be mandatorily deployed for most of the airspaces worldwide by 2020. Each aircraft equipped with an ADS-B device keeps broadcasting plaintext messages to other aircraft and the ground station controllers once or twice per second. The lack of security measures in ADS-B systems makes it susceptible to different attacks. Among the various security issues, we investigate the integrity and authenticity of ADS-B messages. We propose a new framework for providing ADS-B with authentication based on three-level hierarchical identity-based signature (HIBS) with batch verification. Previous signature-based ADS-B authentication protocols focused on how to generate signatures efficiently, while our schemes can also significantly reduce the verification cost, which is critical to ADS-B systems, since at any time an ADS-B receiver may receive lots of signatures. We design two concrete schemes. The basic scheme supports partial batch verification and the extended scheme provides full batch verification. We give a formal security proof for the extended scheme. Experiment results show that our schemes with batch verification are tremendously more efficient in batch verifying <inline-formula><tex-math notation="LaTeX">$n$ </tex-math><alternatives><inline-graphic xlink:href="yang-ieq1-2459709.gif"/></alternatives></inline-formula> signatures than verifying <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq2-2459709.gif"/></alternatives></inline-formula> signatures independently. For example, the running time of verifying 100 signatures is 502 and 484 ms for the basic scheme and the extended scheme respectively, while the time is 2500 ms if verifying the signatures independently.
Variational Network Quantization

In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).
Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk

We determine optimal trading strategies for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. We take the market impact cost per share to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square-root law that has been proposed based on market microstructure theory. In analogy to the linear model, we define a “characteristic time” for optimal trading, which now depends on the initial portfolio size and decreases as execution proceeds. We also consider a model in which uncertainty of the realized price is increased by demanding rapid execution; we show that optimal trajectories are described by a “critical portfolio size” above which this effect is dominant and below which it may be neglected.
Evolving multi-objective strategies for task allocation of scientific workflows on public clouds

With the increase in deployment of scientific application on public and private clouds, the allocation of workflow tasks to specific cloud instances to reduce runtime and cost has emerged as an important challenge. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters and has been proved to be NP-complete. This paper presents an optimization framework for task allocation on public clouds. We present a solution that considers important parameters such as workflow runtime, communication overhead, and overall execution cost. Our multi-objective optimization framework builds on a simple and extensible cost model and uses a heuristic to determine the optimal number of cloud instances to be used. Using the Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3) as an example, we show how our optimization heuristics lead to significantly better strategies than other state-of-the-art approaches. Specifically, our single-objective optimization is slightly better than a simple heuristic and a particle swarm optimization approach for small workflows, and achieves significant improvements for larger workflows. In a similar manner, our multi-objective optimization obtains similar results to our single-objective optimization for small-size workflows, and achieves up to 80% improvement for large-size workflows.
High-gain 60-GHz patch antenna array using GaN MMIC technology

In this paper, a high-gain 2×2 antenna array at 60 GHz using GaN MMIC process technology is presented. For gain enhancement, a modified patch antenna element with two parasitic strips on each non-radiation edge and a parasitic patch on one of the radiation edges is proposed. This antenna element achieves a gain of 3.42 dBi at 60 GHz, about 2 dBi higher than a conventional patch antenna element using the same process technology. The 2×2 antenna array consisting of four modified patch antenna element exhibits a 10-dB impedance bandwidth of 10.1% and a peak gain of 5.7 dBi at 60 GHz.
Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet

This study examines the utility of three prospective models for understanding the continued IT usage behavior. The three models include: Expectation-Confirmation Model in IT Domain (ECM-IT), Technology Acceptance Model (TAM), and a hybrid model integrating TAM and ECM-IT (extended ECM-IT). Based on a survey of 1826 mobile Internet users, the LISREL analysis shows that all three models meet the various goodness-of-fit criteria. When compared using special indices for differentiating among alternative good models, TAM has the best fit to the data followed by ECM-IT, and the extended ECM-IT. In terms of variance explained for intention to continue IT usage, the extended ECM-IT has the highest R (67%) followed by TAM (63%), and ECM-IT (50%). We conclude that TAM is the most parsimonious and generic model that can be used to study both initial and continued IT adoption; the extended ECM-IT explains continued IT usage behavior as well as TAM; and both the ECM-IT and extended ECM-IT models provide additional information to increase our understanding of continued IT usage.
Structure Based User Identification across Social Networks

Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.
Decentralization of health systems in low and middle income countries: a systematic review

A substantial number of low and middle income countries (LMICs) have implemented health sector reforms in the last 40 years, and the majority of them have included some degree of decentralization of the health system as part of the wider reform. This review will provide an updated and comprehensive assessment of the effects of decentralization in LMICs. We conducted a systematic review of published and grey literature till May 2015, following standard methods. 54 qualitative, quantitative and mixed methods studies conducted in 26 countries were included in the review. We found positive and negative effects of decentralization of health systems in LMICs. Whereas decentralization of governance, financing and service delivery, have been shown to have positive effects on the system; decentralization of resource management has been challenging in several settings. Overall, lessons learned from LMICs suggest that factors such as adequate mix of technical skills at the local level to perform decentralized tasks, effective decentralization of decision-making to the periphery, and political leadership are key factors for a successful decentralization.
Aphasia after stroke: type, severity and prognosis. The Copenhagen aphasia study.

To determine the types, severity and evolution of aphasia in unselected, acute stroke patients and evaluate potential predictors for language outcome 1 year after stroke.



270 acute stroke patients with aphasia (203 with first-ever strokes) were included consecutively and prospectively from three hospitals in Copenhagen, Denmark, and assessed with the Western Aphasia Battery. The assessment was repeated 1 year after stroke.



The frequencies of the different types of aphasia in acute first-ever stroke were: global 32%, Broca's 12%, isolation 2%, transcortical motor 2%, Wernicke's 16%, transcortical sensory 7%, conduction 5% and anomic 25%. These figures are not substantially different from what has been found in previous studies of more or less selected populations. The type of aphasia always changed to a less severe form during the first year. Nonfluent aphasia could evolve into fluent aphasia (e.g., global to Wernicke's and Broca's to anomic), whereas a fluent aphasia never evolved into a nonfluent aphasia. One year after stroke, the following frequencies were found: global 7%, Broca's 13%, isolation 0%, transcortical motor 1%, Wernicke's 5%, transcortical sensory 0%, conduction 6% and anomic 29%. The distribution of aphasia types in acute and chronic aphasia is, thus, quite different. The outcome for language function was predicted by initial severity of the aphasia and by the initial stroke severity (assessed by the Scandinavian Stroke Scale), but not by age, sex or type of aphasia. Thus, a scoring of general stroke severity helps to improve the accuracy of the prognosis for the language function. One year after stroke, fluent aphasics were older than nonfluent aphasics, whereas such a difference was not found in the acute phase.
A Software Library for Conducting Large Scale Experiments on Learning to Rank Algorithms

is paper presents an ecient application for driving large scale experiments on Learning to Rank (LtR) algorithms. We designed a soware library that exploits caching mechanisms and ecient data structures to make the execution of massime experiments on LtR algorithms as fast as possible in order to try as many combinations of components as possible. is presented soware has been tested on dierent algorithms as well as on dierent implementations of the same algorithm in dierent libraries. is soware is highly congurable and extensible in order to enable the seamless addition of new features, algorithms, and libraries.
Non-Uniform Metasurface Luneburg Lens Antenna Design

A metasurfing concept is demonstrated and applied in the design of Luneburg lens antennas. Using an array of size-varying circular patches on a dielectric substrate inside a parallel-plate waveguide (PPW) structure variable surface impedance is obtained, which realizes an equivalent refraction index as that of a Luneburg lens. The obtained lens has good bandwidth characteristics and significant fabrication advantages with respect to conventional dielectric lenses. Based on this PPW lens, an H-plane antenna has been designed and simulated.
Classification and Regression using SAS

K-Nearest Neighbor (KNN) classification and regression are two widely used analytic methods in predictive modeling and data mining fields. They provide a way to model highly nonlinear decision boundaries, and to fulfill many other analytical tasks such as missing value imputation, local smoothing, etc. In this paper, we discuss ways in SAS R © to conduct KNN classification and KNN Regression. Specifically, PROC DISCRIM is used to build multi-class KNN classification and PROC KRIGE2D is used for KNN regression tasks. Technical details such as tuning parameter selection, etc are discussed. We also discuss tips and tricks in using these two procedures for KNN classification and regression. Examples are presented to demonstrate full process flow in applying KNN classification and regression in real world business projects.
Linearizing Dual-Slope Digital Converter Suitable for a Thermistor

To measure temperature using a thermistor as the sensing element, linearization to compensate for the inverse exponential nature of the resistance-temperature characteristic of the thermistor is required. A linearizing dual-slope digital converter (LDSDC) that accepts a thermistor sensor as input and provides a digital output that is directly proportional to the temperature being sensed is presented here. A logarithmic amplifier at the input of the LDSDC compensates for the exponential characteristics. The conversion logic of the underlying dual-slope converter is suitably modified to implement the required inversion and offset correction and thus obtain linearization over a wide range of input temperature. The efficacy of the proposed LDSDC is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested. Analysis of the proffered method to identify possible sources of errors is also presented.
AN ALGORITHM TO GROUP DEFECTS ON PRINTED CIRCUIT BOARD FOR AUTOMATED VISUAL INSPECTION

Due to disadvantages in manual inspection, an automated visual inspection system is needed to eliminate subjective aspects and provides fast and quantitative assessment of printed circuit board (PCB). Up to the present, there has been a lot of work and research concentrated on PCB defect detection. PCB defects detection is necessary for verification of the characteristics of PCB to make sure it is in conformity with the design specifications. However, besides the need to detect the defects, it is also essential to classify these defects so that the source of these defects can be identified. Unfortunately, this area has been neglected and not been given enough attention. Hence, this study proposes an algorithm to group the defects found on bare PCB. Using a synthetically generated PCB image, the algorithm is able to group 14 commonly known PCB defects into five groups. The proposed algorithm includes several image processing operations such as image subtraction, image adding, logical XOR and NOT, and flood fill operator.
Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph

Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.
Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications

Nowadays, there is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystems for people. In such a transformation, millions of homes are being equipped with smart devices (e.g., smart meters, sensors, and so on), which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using a shower/bath. This paper addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this paper uses the U.K. Domestic Appliance Level Electricity data set—time series data of power consumption collected from 2012 to 2015 with the time resolution of 6 s for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 h, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in detail in this paper along with the accuracy of short- and long-term predictions.