nils-herrmann/clean_scirepeval huggingface dataset:
Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools

various concentrated work on detection of defects on printed circuit boards (PCBs) have been done, but it is also crucial to classify these defects in order to analyze and identify the root causes of the defects. This project is aimed in detecting and classifying the defects on bare single layer PCBs by introducing a hybrid algorithm by combining the research done by Heriansyah et al  and Khalid . This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm  and simple the image processing theories . Based on initial studies, some PCB defects can only exist in certain groups. Thus, it is obvious that the image processing algorithm could be improved by applying a segmentation exercise. This project uses template and test images of single layer, bare, grayscale computer generated PCBs. The research improves Khalid  work by increasing the number of defect categories from 5 to 7, with each category classifying a minimum of 1 to a maximum 4 different types of defects and a total of 13 out of 14 defects were classified.
CIO Roles and Responsibilities: Twenty-Five Years of Evolution and Change

The IS research community has investigated the evolving and changing role of the Chief Information Officer (CIO) for more than twenty-five years. This research sought to better understand the recent changes of the CIO role. Our research goals were threefold: 1) To identify whether the CIO’s job has changed from the characteristics suggested by previous studies; 2) to identify a profile of the attributes of CIOs, and 3) to understand what these developments suggest for the education and professional development of CIOs. We found that much of CIO role has evolved to the executive-level management and is centered on working with other business executives inside and outside of the firm to change the firm’s strategy and processes. CIOs are now seen as multi-dimensional C-level executives who need to be experienced with many functions within the organization and possess a diverse set of skills needed to influence the organization.
Question Answering and Question Generation as Dual Tasks

We study the problem of joint question answering (QA) and question generation (QG) in this paper. Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. On one side, the QA model judges whether the generated question of a QG model is relevant to the answer. On the other side, the QG model provides the probability of generating a question given the answer, which is a useful evidence that in turn facilitates QA. In this paper we regard QA and QG as dual tasks. We propose a training framework that trains the models of QA and QG simultaneously, and explicitly leverages their probabilistic correlation to guide the training process of both models. We implement a QG model based on sequence-to-sequence learning, and a QA model based on recurrent neural network. As all the components of the QA and QG models are differentiable, all the parameters involved in these two models could be conventionally learned with back propagation. We conduct experiments on three datasets. Empirical results show that our training framework improves both QA and QG tasks. The improved QA model performs comparably with strong baseline approaches on all three datasets.
Fuzzy Number Intuitionistic Fuzzy Arithmetic Aggregation Operators

A fuzzy number intuitionistic fuzzy set (FNIFS) is a generalization of intuitionistic fuzzy set. The fundamental characteristic of FNIFS is that the values of its membership function and non-membership function are trigonometric fuzzy numbers rather than exact numbers. In this paper, we define some operational laws of fuzzy number intuitionistic fuzzy numbers, and, based on these operational laws, develop some new arithmetic aggregation operators, such as the fuzzy number intuitionistic fuzzy weighted averaging (FIFWA) operator, the fuzzy number intuitionistic fuzzy ordered weighted averaging (FIFOWA) operator and the fuzzy number intuitionistic fuzzy hybrid aggregation (FIFHA) operator for aggregating fuzzy number intuitionistic fuzzy information. Furthermore, we give an application of the FIFHA operator to multiple attribute decision making based on fuzzy number intuitionistic fuzzy information. Finally, an illustrative example is given to verify the developed approach.
Kinematics and Dynamics Modelling of a Mecanum Wheeled Mobile Platform

Omni-directional mobile platforms have the ability to move instantaneously in any direction from any configuration. As such, it is important to have a mathematical model of the platform, especially if the platform is to be used as an autonomous vehicle. Autonomous behaviour requires that the mobile robot choose the optimum vehicle motion in different situations for object/collision avoidance and task achievement. This paper develops and verifies a mathematical model of a mobile robot platform that implements mecanum wheels to achieve omni-directionality. The mathematical model will be used to achieve optimum autonomous control of the developed mobile robot as an office service robot. Omni-directional mobile platforms have improved performance in congested environments and narrow aisles, such as those found in factory workshops, offices, warehouses, hospitals, etc.
An artificial neural network (p, d, q) model for timeseries forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can speed up optimization. The effect of depth on optimization is decoupled from expressiveness by focusing on settings where additional layers amount to overparameterization – linear neural networks, a wellstudied model. Theoretical analysis, as well as experiments, show that here depth acts as a preconditioner which may accelerate convergence. Even on simple convex problems such as linear regression with `p loss, p > 2, gradient descent can benefit from transitioning to a non-convex overparameterized objective, more than it would from some common acceleration schemes. We also prove that it is mathematically impossible to obtain the acceleration effect of overparametrization via gradients of any regularizer.
Intelligent Phishing Website Detection and Prevention System by Using Link Guard Algorithm

Phishing is a new type of network attack where the attacker creates a replica of an existing Web page to fool users (e.g., by using specially designed e-mails or instant messages) into submitting personal, financial, or password data to what they think is their service provides’ Web site. In this project, we proposed a new end-host based anti-phishing algorithm, which we call Link Guard, by utilizing the generic characteristics of the hyperlinks in phishing attacks. These characteristics are derived by analyzing the phishing data archive provided by the Anti-Phishing Working Group (APWG). Because it is based on the generic characteristics of phishing attacks, Link Guard can detect not only known but also unknown phishing attacks. We have implemented LinkGuard in Windows XP. Our experiments verified that LinkGuard is effective to detect and prevent both known and unknown phishing attacks with minimal false negatives. LinkGuard successfully detects 195 out of the 203 phishing attacks. Our experiments also showed that LinkGuard is light weighted and can detect and prevent phishing attacks in real time.
Automation of the Arm-Aided Climbing Maneuver for Tracked Mobile Manipulators

Surmounting terrain elevations, such as terraces, is useful to increase the reach of mobile robots operating in disaster areas, construction sites, and natural environments. This paper proposes an autonomous climbing maneuver for tracked mobile manipulators with the help of the onboard arm. The solution includes a fast 3-D scan processing method to estimate a simple set of geometric features for the ascent: three lines that correspond to the low and high edges, and the maximum inclination axis. Furthermore, terraces are classified depending on whether they are reachable through a slope or an abrupt step. In the proposed maneuver, the arm is employed both for shifting the center of gravity of the robot and as an extra limb that can be pushed against the ground. Feedback during climbing can be obtained through an inertial measurement unit, joint absolute encoders, and pressure sensors. Experimental results are presented for terraces of both kinds on rough terrain with the hydraulic mobile manipulator Alacrane.
Refining Event Extraction through Cross-Document Inference

We apply the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995) to information extraction (IE), and extend the scope of “discourse” from one single document to a cluster of topically-related documents. We employ a similar approach to propagate consistent event arguments across sentences and documents. Combining global evidence from related documents with local decisions, we design a simple scheme to conduct cross-document inference for improving the ACE event extraction task. Without using any additional labeled data this new approach obtained 7.6% higher F-Measure in trigger labeling and 6% higher F-Measure in argument labeling over a state-of-the-art IE system which extracts events independently for each sentence.
Manifold-based multi-objective policy search with sample reuse

Many real-world applications are characterized by multiple conflicting objectives. In such problems optimality is replaced by Pareto optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an important challenge. Building on recent advances in reinforcement learning and multi-objective policy search, we present two novel manifold-based algorithms to solve multi-objective Markov decision processes. These algorithms combine episodic exploration strategies and importance sampling to efficiently learn a manifold in the policy parameter space such that its image in the objective space accurately approximates the Pareto frontier. We show that episode-based approaches and importance sampling can lead to significantly better results in the context of multi-objective reinforcement learning. Evaluated on three multi-objective problems, our algorithms outperform state-of-the-art methods both in terms of quality of the learned Pareto frontier and sample efficiency.
Detection of New Malicious Code Using N-grams Signatures

Signature-based malicious code detection is the standard technique in all commercial anti-virus software. This method can detect a virus only after the virus has appeared and caused damage. Signature-based detection performs poorly whe n attempting to identify new viruses. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, n-grams analysis, we explo re the idea of automatically detecting new malicious code. We employ n-grams analysis to automatically generate signatures from malicious and benign software collections. The n-gramsbased signatures are capable of classifying unseen benign and malicious code. The datasets used are large compared to earlier applications of n-grams analysis.
A Secure Biometrics-based Authentication Scheme for Telecare Medicine Information Systems

The telecare medicine information system (TMIS) allows patients and doctors to access medical services or medical information at remote sites. Therefore, it could bring us very big convenient. To safeguard patients’ privacy, authentication schemes for the TMIS attracted wide attention. Recently, Tan proposed an efficient biometrics-based authentication scheme for the TMIS and claimed their scheme could withstand various attacks. However, in this paper, we point out that Tan’s scheme is vulnerable to the Denial-of-Service attack. To enhance security, we also propose an improved scheme based on Tan’s work. Security and performance analysis shows our scheme not only could overcome weakness in Tan’s scheme but also has better performance.
Modulation and Coding for Linear Gaussian Channels

Shannon’s determination of the capacity of the linear Gaussian channel has posed a magnificent challenge to succeeding generations of researchers. This paper surveys how this challenge has been met during the past half century. Orthogonal minimumbandwidth modulation techniques and channel capacity are discussed. Binary coding techniques for low-signal-to-noise ratio (SNR) channels and nonbinary coding techniques for high-SNR channels are reviewed. Recent developments, which now allow capacity to be approached on any linear Gaussian channel, are surveyed. These new capacity-approaching techniques include turbo coding and decoding, multilevel coding, and combined coding/precoding for intersymbol-interference channels.
Strategy for Navigation Inside Pipelines with Differential-Drive Inpipe Robot

In this paper, a new approach for moving an inpipe robot inside underground urban gas pipelines is proposed. Since the urban gas supply system is composed of complicated configurations of pipelines, the inpipe inspection requires a robot with outstanding mobility and corresponding control algorithms to apply for. In advance, this paper introduces a new miniature miniature inpipe robot, called MRINSPECT (Multifunctional Robotic crawler for INpipe inSPECTion) IV, which has been developed for the inspection of urban gas pipelines with a nominal 4-inch inside diameter. Its mechanism for steering with differential–drive wheels arranged three-dimensionally makes itself easily adjust to most pipeline configurations and provides excellent mobility in navigation. Also, analysis for pipelines with fittings are given in detail and geometries of the fittings are mathematically described. It is prerequisite to estimate moving pattern of the robot while passing through the fittings and based on the analysis, a method modulating speed of each drive wheel is proposed. Though modulation of speed is very important during proceeding thought the fittings, it is not easy to control the speeds because each wheel of the robot has contact with the walls having different curvatures. A new and simple way of controlling the speed is developed based on the analysis of the geometrical features of the fittings. This algorithm has the advantage to be applicable without using complicated sensor information. To confirm the effectiveness of the proposed method experiments are performed and additional considerations for the design of an inpipe robot are discussed.
Real Time Vehicle Detection and Counting Method for Unsupervised Traffic Video on Highways

Automatic detecting and counting vehicles in unsupervised video on highways is a very challenging problem in computer vision with important practical applications such as to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. Manually reviewing the large amount of data they generate is often impractical. The background subtraction and image segmentation based on morphological transformation for tracking and counting vehicles on highways is proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm segments the image by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments.
Modeling and benchmarking energy efficiency of Variable Stiffness Actuators on the example of the DLR FSJ

Robots with Variable Stiffness Actuators (VSA) are intrinsically flexible in the joints. The built-in mechanical spring has the advantage of a higher peak performance, in some extend increased safety for humans interacting physically with the robot, and promises a more energy efficient robot for certain trajectories. This paper shows the modeling process of a VSA including energy losses on the example of the DLR Floating Spring Joint (FSJ). The model includes the full actuator dynamics with losses in electromechanical transformation of the motors. Furthermore, it models bearing and gear friction with stiction, Coulomb friction, viscous friction, and load dependent effects. With the obtained model the energy losses of benchmark trajectories are investigated and compared with a comparable stiff joint actuator.
Taxonomy-Regularized Semantic Deep Convolutional Neural Networks

We propose a novel convolutional network architecture that abstracts and differentiates the categories based on a given class hierarchy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the minand difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classification performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains significant improvements over the base convolutional networks.
Evidence based physical activity for school-age youth.

To review the effects of physical activity on health and behavior outcomes and develop evidence-based recommendations for physical activity in youth.



A systematic literature review identified 850 articles; additional papers were identified by the expert panelists. Articles in the identified outcome areas were reviewed, evaluated and summarized by an expert panelist. The strength of the evidence, conclusions, key issues, and gaps in the evidence were abstracted in a standardized format and presented and discussed by panelists and organizational representatives.



Most intervention studies used supervised programs of moderate to vigorous physical activity of 30 to 45 minutes duration 3 to 5 days per week. The panel believed that a greater amount of physical activity would be necessary to achieve similar beneficial effects on health and behavioral outcomes in ordinary daily circumstances (typically intermittent and unsupervised activity).



School-age youth should participate daily in 60 minutes or more of moderate to vigorous physical activity that is developmentally appropriate, enjoyable, and involves a variety of activities.
Comparison of radial vibration forces in 10-pole/12-slot fractional slot surface-mounted and interior PM brushless AC machines

This paper compares the radial vibration forces in 10-pole/12-slot fractional-slot SPM and IPM machines which are designed to produce the same output torque, and employ an identical stator but different SPM, V-shape and arc-shape IPM rotor topologies. The airgap field and radial vibration force density distribution as a function of angular position and corresponding space harmonics (vibration modes) are analysed using the finite element method together with frozen permeability technique. It is shown that not only the lowest harmonic of radial force in IPM machine is much higher, but also the (2p)th harmonic of radial force in IPM machine is also higher than that in SPM machine.
Automated valet parking as part of an integrated travel assistance

The integration of automated valet parking (automated search of a parking space and execution of the parking maneuver) in a comprehensive travel assistance approach promises great benefits for a traveler. In particular, it aims at increasing comfort, optimizing travel time and improving energy efficiency for changing means of transport (where one of them is a car) within a whole multimodal travel chain. In this paper an automated valet parking system as part of a travel assistant is presented. Besides giving an overview of the overall system, the main components (namely the environment perception and automation modules of the fully automated vehicle, a mobile phone application as human machine interface and a parking space occupancy detection camera as part of the parking area infrastructure) are described. The system was successfully tested on three different parking areas, where one of these areas was located at Braunschweig main station.