nils-herrmann/clean_scirepeval huggingface dataset:
Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry

This paper tackles the problem of relative pose estimation between two monocular camera images in textureless scenes. Due to a lack of point matches, point-based approaches such as the 5-point algorithm often fail when used in these scenarios. Therefore we investigate relative pose estimation from line observations. We propose a new approach in which the relative pose estimation from lines is extended by a 3D line direction estimation step. The estimated line directions serve to improve the robustness and the efficiency of all processing phases: they enable us to guide the matching of line features and allow an efficient calculation of the relative pose. First, we describe in detail the novel 3D line direction estimation from a single image by clustering of parallel lines in the world. Secondly, we propose an innovative guided matching in which only clusters of lines with corresponding 3D line directions are considered. Thirdly, we introduce the new relative pose estimation based on 3D line directions. Finally, we combine all steps to a visual odometry system. We evaluate the different steps on synthetic and real sequences and demonstrate that in the targeted scenarios we outperform the state-of-the-art in both accuracy and computation time.
A Structural Model of Network Dynamics : Tie Formation , Product Adoption , and Content Generation

We develop a structural model for the co-evolution of individuals’ friendship tie formations and their concurrent online activities (product adoptions and production of user-generated content) within a social network. Explicitly modeling the endogenous formation of the network and accounting for the interdependence between decisions in these two areas (friendship formations and concurrent online activities) provides a clean identification of peer effects and of important drivers of individuals’ friendship decisions. We estimate our model using a novel data set capturing the continuous development of a network and users’ entire action histories within the network. Our results reveal that, compared to a potential friend’s product adoptions and content generation activities, the total number of friends and the number of common friends this potential friend has with the focal individual are the most important drivers of friendship formation. Further, while having more friends does not make a person more active, having more active friends does increase a user’s activity levels in terms of both product adoptions and content generation through peer effects. Via counterfactuals we assess the effectiveness of various seeding and stimulation strategies in increasing website traffic while taking the endogenous network formation into account. We find that seeding to users with the most friends is not always the best strategy to increase users’ activity levels on the website.
Cyber-Security Incidents: A Review Cases in Cyber-Physical Systems

Cyber-Physical Systems refer to systems that have an interaction between computers, communication channels and physical devices to solve a real-world problem. Towards industry 4.0 revolution, Cyber-Physical Systems currently become one of the main targets of hackers and any damage to them lead to high losses to a nation. According to valid resources, several cases reported involved security breaches on Cyber-Physical Systems. Understanding fundamental and theoretical concept of security in the digital world was discussed worldwide. Yet, security cases in regard to the cyber-physical system are still remaining less explored. In addition, limited tools were introduced to overcome security problems in Cyber-Physical System. To improve understanding and introduce a lot more security solutions for the cyber-physical system, the study on this matter is highly on demand. In this paper, we investigate the current threats on Cyber-Physical Systems and propose a classification and matrix for these threats, and conduct a simple statistical analysis of the collected data using a quantitative approach. We confirmed four components i.e., (the type of attack, impact, intention and incident categories) main contributor to threat taxonomy of Cyber-Physical Systems. Cyber-Physical Systems; threats; incidents; security; cybersecurity; taxonomies; matrix; threats analysis
An Introduction to Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix (DSM) Method

The design and development of complex engineering products require the efforts and collaboration of hundreds of participants from diverse backgrounds resulting in complex relationships among both people and tasks. Many of the traditional project management tools (PERT, Gantt and CPM methods) do not address problems stemming from this complexity. While these tools allow the modeling of sequential and parallel processes, they fail to address interdependency (feedback and iteration), which is common in complex product development (PD) projects. To address this issue, a matrix-based tool called the Design Structure Matrix (DSM) has evolved. This method differs from traditional project-management tools because it focuses on representing information flows rather than work flows. The DSM method is an information exchange model that allows the representation of complex task (or team) relationships in order to determine a sensible sequence (or grouping) for the tasks (or teams) being modeled. This article will cover how the basic method works and how you can use the DSM to improve the planning, execution, and management of complex PD projects using different algorithms (i.e., partitioning, tearing, banding, clustering, simulation, and eigenvalue analysis).  matrices and projects Consider a system (or project) that is composed of two elements /sub-systems (or activities/phases): element "A" and element "B". A graph may be developed to represent this system pictorially. The graph is constructed by allowing a vertex/node on the graph to represent a system element and an edge joining two nodes to represent the relationship between two system elements. The directionality of influence from one element to another is captured by an arrow instead of a simple link. The resultant graph is called a directed graph or simply a digraph. There are three basic building blocks for describing the relationship amongst system elements: parallel (or concurrent), sequential (or dependent) and coupled (or interdependent) (fig. 1) Fig.1 Three Configurations that Characterize a System Relationship Parallel Sequential Coupled Graph Representation A B A
Vehicle trajectory and lane change prediction using ANN and SVM classifiers

Millions of traffic accidents take place every year on roads around the world. Some advanced assistance systems have been released in commercial vehicles in the past few years, contributing to the transition towards semiautonomous vehicles. Some of the best known are the adaptive cruise control and the lane keeping systems. These systems keep a desired distance with respect to the preceding vehicle or a fixed speed on the center of the lane. It is very useful for these systems to know what the surrounding vehicles trajectories will be or if they will perform a lane change manoeuvre. This paper evaluates two kinds of artificial neural networks over two different datasets to predict its trajectories. A Support Vector Machine classifier is used to classify the action that will be carried out. The proposed trajectory prediction systems are 30% better than the vehicle motion model in a time horizon of 4 seconds and are able to predict a lane change action 3 seconds before it happens.
Top-down and bottom-up control of visual selection.

The present paper argues for the notion that when attention is spread across the visual field in the first sweep of information through the brain visual selection is completely stimulus-driven. Only later in time, through recurrent feedback processing, volitional control based on expectancy and goal set will bias visual selection in a top-down manner. Here we review behavioral evidence as well as evidence from ERP, fMRI, TMS and single cell recording consistent with stimulus-driven selection. Alternative viewpoints that assume a large role for top-down processing are discussed. It is argued that in most cases evidence supporting top-down control on visual selection in fact demonstrates top-down control on processes occurring later in time, following initial selection. We conclude that top-down knowledge regarding non-spatial features of the objects cannot alter the initial selection priority. Only by adjusting the size of the attentional window, the initial sweep of information through the brain may be altered in a top-down way.
Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer’s disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.
Image processing techniques for insect shape detection in field crops

In agriculture, crop pest detection is considered as one of the challenging tasks for the farmers. An automatic insect detection system using machine vision and image analysis provides better identification of crop insects on early stage with reduced time and greater accuracy which helps farmers to increase the crop yield. In the present work, digital image processing techniques were applied for crop insects images to perform preprocessing, segmentation and feature extraction to detect the shape of insects in the sugarcane crop. Sobel edge detection is applied to segment the insect image against background. In feature extraction, shape of the insect can be recognized by nine geometric shape features. This insect shape identification method performs well and achieves high accuracy for sugarcane crop insects with round (circle), oval, triangle and rectangle shapes. The present work was implemented in MATLAB 2015b using Image Processing Toolbox.
A Comparison of Sequence-to-Sequence Models for Speech Recognition

In this work, we conduct a detailed evaluation of various allneural, end-to-end trained, sequence-to-sequence models applied to the task of speech recognition. Notably, each of these systems directly predicts graphemes in the written domain, without using an external pronunciation lexicon, or a separate language model. We examine several sequence-to-sequence models including connectionist temporal classification (CTC), the recurrent neural network (RNN) transducer, an attentionbased model, and a model which augments the RNN transducer with an attention mechanism. We find that the sequence-to-sequence models are competitive with traditional state-of-the-art approaches on dictation test sets, although the baseline, which uses a separate pronunciation and language model, outperforms these models on voice-search test sets.
Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped

Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, deep reinforcement learning has shown promise at automatically learning controllers for complex systems in simulation. This has been followed by a push towards learning controllers that can be transferred between simulation and hardware, primarily with the use of domain randomization. However, domain randomization can make the problem of finding stable controllers even more challenging, especially for underactuated bipedal robots. In this work, we explore whether policies learned in simulation can be transferred to hardware with the use of high-fidelity simulators and structured controllers. We learn a neural network policy which is a part of a more structured controller. While the neural network is learned in simulation, the rest of the controller stays fixed, and can be tuned by the expert as needed. We show that using this approach can greatly speed up the rate of learning in simulation, as well as enable transfer of policies between simulation and hardware. We present our results on an ATRIAS robot and explore the effect of action spaces and cost functions on the rate of transfer between simulation and hardware. Our results show that structured policies can indeed be learned in simulation and implemented on hardware successfully. This has several advantages, as the structure preserves the intuitive nature of the policy, and the neural network improves the performance of the hand-designed policy. In this way, we propose a way of using neural networks to improve expert designed controllers, while maintaining ease of understanding.
Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study.

We sought to modify existing sex-specific health risk appraisal functions (profile functions) for the prediction of first stroke that better assess the effects of the use of antihypertensive medication.



Health risk appraisal functions were previously developed from the Framingham Study cohort. These functions were Cox proportional hazards regression models relating age, systolic blood pressure, diabetes mellitus, cigarette smoking, prior cardiovascular disease, atrial fibrillation, left ventricular hypertrophy by electrocardiogram, and the use of antihypertensive medication to the occurrence of stroke. Closer examination of the data indicated that antihypertensive therapy effect is present only for systolic blood pressures between 110 and 200 mm Hg. Adjustments to the regressions to better fit the observed data were developed and tested for statistical significance and goodness-of-fit of the model residuals.



Modified functions more consistent with the data were developed, and, from these, tables to evaluate 10-year risk of first stroke were computed.



The stroke profile can be used for evaluation of the risk of stroke and suggestion of risk factor modification to reduce risk. The effect of antihypertensive therapy in the evaluation of stroke risk can now be better evaluated.
Temporal supervised learning for inferring a dialog policy from example conversations

This paper tackles the problem of learning a dialog policy from example dialogs - for example, from Wizard-of-Oz style dialogs, where an expert (person) plays the role of the system. Learning in this setting is challenging because dialog is a temporal process in which actions affect the future course of the conversation - i.e., dialog requires planning. Past work solved this problem with either conventional supervised learning or reinforcement learning. Reinforcement learning provides a principled approach to planning, but requires more resources than a fixed corpus of examples, such as a dialog simulator or a reward function. Conventional supervised learning, by contrast, operates directly from example dialogs but does not take proper account of planning. We introduce a new algorithm called Temporal Supervised Learning which learns directly from example dialogs, while also taking proper account of planning. The key idea is to choose the next dialog action to maximize the expected discounted accuracy until the end of the dialog. On a dialog testbed in the calendar domain, in simulation, we show that a dialog manager trained with temporal supervised learning substantially outperforms a baseline trained using conventional supervised learning.
FOGA : A Fuzzy Ontology Generation Framework for Scholarly Semantic Web

This paper proposes the FOGA (Fuzzy Ontology Generation frAmework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: Fuzzy Formal Concept Analysis, Fuzzy Conceptual Clustering and Fuzzy Ontology Generation. First, Fuzzy Formal Concept Analysis incorporates fuzzy logic into Formal Concept Analysis (FCA) to form a fuzzy concept lattice. Fuzzy Conceptual Clustering then constructs the concept hierarchy from the fuzzy concept lattice. Finally, Fuzzy Ontology Generation generates the fuzzy ontology from the concept hierarchy. In this paper, we will also discuss the application of the FOGA framework to generate a scholarly ontology for the Scholarly Semantic Web from a citation database. The performance of the proposed FOGA framework is given based on the scholarly ontology generated.
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user’s preferences and targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.
Designing Neural Network Architectures using Reinforcement Learning

At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using Qlearning with an -greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks.
Double-Tiered Switched-Capacitor Battery Charge Equalization Technique

The automobile industry is progressing toward hybrid, plug-in hybrid, and fully electric vehicles in their future car models. The energy storage unit is one of the most important blocks in the power train of future electric-drive vehicles. Batteries and/or ultracapacitors are the most prominent storage systems utilized so far. Hence, their reliability during the lifetime of the vehicle is of great importance. Charge equalization of series-connected batteries or ultracapacitors is essential due to the capacity imbalances stemming from manufacturing, ensuing driving environment, and operational usage. Double-tiered capacitive charge shuttling technique is introduced and applied to a battery system in order to balance the battery-cell voltages. Parameters in the system are varied, and their effects on the performance of the system are determined. Results are compared to a single-tiered approach. MATLAB simulation shows a substantial improvement in charge transport using the new topology. Experimental results verifying simulation are presented.
Hooked on smartphones: an exploratory study on smartphone overuse among college students

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.
Identifying critical issues in enterprise resource planning (ERP) implementation

Much has been written on implementation of enterprise resource planning (ERP) systems in organizations of various sizes. The literature is replete with many cases studies of both successful and unsuccessful ERP implementations. However, there have been very few empirical studies that attempt to delineate the critical issues that drive successful implementation of ERP systems. Although the failure rates of ERP implementations have been publicized widely, this has not distracted companies from investing large sums of money on ERP systems. This study reports the results of an empirical research on the critical issues affecting successful ERP implementation. Through the study, eight factors were identified that attempts to explain 86% of the variances that impact ERP implementation. There was a strong correlation between successfully implementing ERP and six out of the eight factors identified.