nils-herrmann/clean_scirepeval huggingface dataset:
New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.
Estimating misclassification error: a closer look at cross-validation based methods

To estimate a classifier’s error in predicting future observations, bootstrap methods have been proposed as reduced-variation alternatives to traditional cross-validation (CV) methods based on sampling without replacement. Monte Carlo (MC) simulation studies aimed at estimating the true misclassification error conditional on the training set are commonly used to compare CV methods. We conducted an MC simulation study to compare a new method of bootstrap CV (BCV) to k-fold CV for estimating clasification error. For the low-dimensional conditions simulated, the modest positive bias of k-fold CV contrasted sharply with the substantial negative bias of the new BCV method. This behavior was corroborated using a real-world dataset of prognostic gene-expression profiles in breast cancer patients. Our simulation results demonstrate some extreme characteristics of variance and bias that can occur due to a fault in the design of CV exercises aimed at estimating the true conditional error of a classifier, and that appear not to have been fully appreciated in previous studies. Although CV is a sound practice for estimating a classifier’s generalization error, using CV to estimate the fixed misclassification error of a trained classifier conditional on the training set is problematic. While MC simulation of this estimation exercise can correctly represent the average bias of a classifier, it will overstate the between-run variance of the bias. We recommend k-fold CV over the new BCV method for estimating a classifier’s generalization error. The extreme negative bias of BCV is too high a price to pay for its reduced variance.
Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity

The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify the general modeling principles minimum description length (MDL) and minimum message length (MML), abstracted as the ideal MDL principle and defined from Bayes’s rule by means of Kolmogorov complexity. The basic condition under which the ideal principle should be applied is encapsulated as the fundamental inequality, which in broad terms states that the principle is valid when the data are random, relative to every contemplated hypothesis and also these hypotheses are random relative to the (universal) prior. The ideal principle states that the prior probability associated with the hypothesis should be given by the algorithmic universal probability, and the sum of the log universal probability of the model plus the log of the probability of the data given the model should be minimized. If we restrict the model class to finite sets then application of the ideal principle turns into Kolmogorov’s minimal sufficient statistic. In general, we show that data compression is almost always the best strategy, both in model selection and prediction.
The New Routing Algorithm for the ARPANET

Absrracr-The new ARPANET routing algorithm is an improvement over the old procedure in that it uses fewer network resources, operates on more realistic estimates of network conditions, reacts faster to important network changes, and does not suffer from long-term loops r oscillations. In the new procedure, each node in the network maintains a database describing the complete network topology and the delays on all lines, and uses the database describing the network to generate a tree representing the minimum delay paths from a given root node to every other network node. Because  the  traffic in the  network  can  be  quite  variable,  each  node periodically measures the delays along its outgoing lines and forwards this information to all other nodes. The delay information propagates quickly through the network so that all nodes can update their databases and continue to route traffic in a consistent and efficient manner. An extensive series of tests were conducted on the ARPANET, showing that line overhead and CPU overhead are 60th less than two percent, most nodes  learn of  an update  within 100 ms, and  the  algorithm  detects congestion and routes packets around congested areas.
Layout-sensitive language extensibility with SugarHaskell

Programmers need convenient syntax to write elegant and concise programs. Consequently, the Haskell standard provides syntactic sugar for some scenarios (e.g., do notation for monadic code), authors of Haskell compilers provide syntactic sugar for more scenarios (e.g., arrow notation in GHC), and some Haskell programmers implement preprocessors for their individual needs (e.g., idiom brackets in SHE). But manually written preprocessors cannot scale: They are expensive, error-prone, and not composable. Most researchers and programmers therefore refrain from using the syntactic notations they need in actual Haskell programs, but only use them in documentation or papers. We present a syntactically extensible version of Haskell, SugarHaskell, that empowers ordinary programmers to implement and use custom syntactic sugar.
 Building on our previous work on syntactic extensibility for Java, SugarHaskell integrates syntactic extensions as sugar libraries into Haskell's module system. Syntax extensions in SugarHaskell can declare arbitrary context-free and layout-sensitive syntax. SugarHaskell modules are compiled into Haskell modules and further processed by a Haskell compiler. We provide an Eclipse-based IDE for SugarHaskell that is extensible, too, and automatically provides syntax coloring for all syntax extensions imported into a module.
 We have validated SugarHaskell with several case studies, including arrow notation (as implemented in GHC) and EBNF as a concise syntax for the declaration of algebraic data types with associated concrete syntax. EBNF declarations also show how to extend the extension mechanism itself: They introduce syntactic sugar for using the declared concrete syntax in other SugarHaskell modules.
Socially compliant mobile robot navigation via inverse reinforcement learning

Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot’s current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which makes it also applicable to fields such as behavioral science or computer graphics.
The reliability and validity of the Face, Legs, Activity, Cry, Consolability observational tool as a measure of pain in children with cognitive impairment.

Pain assessment remains difficult in children with cognitive impairment (CI). In this study, we evaluated the validity and reliability of the Face, Legs, Activity, Cry, Consolability (FLACC) tool for assessing pain in children with CI. Each child's developmental level and ability to self-report pain were evaluated. The child's nurse observed and scored pain with the FLACC tool before and after analgesic administration. Simultaneously, parents scored pain with a visual analog scale, and scores were obtained from children who were able to self-report pain. Observations were videotaped and later viewed by nurses blinded to analgesics and pain scores. One-hundred-forty observations were recorded from 79 children. FLACC scores correlated with parent scores (P < 0.001) and decreased after analgesics (P = 0.001), suggesting good validity. Correlations of total scores (r = 0.5-0.8; P < 0.001) and of each category (r = 0.3-0.8; P < 0.001), as well as measures of exact agreement (kappa = 0.2-0.65), suggest good reliability. Test-retest reliability was supported by excellent correlations (r = 0.8-0.883; P < 0.001) and categorical agreement (r = 0.617-0.935; kappa = 0.400-0.881; P < 0.001). These data suggest that the FLACC tool may be useful as an objective measure of postoperative pain in children with CI.



The FLACC pain assessment tool may facilitate reliable and valid observational pain assessment in children with cognitive impairment who cannot self-report their pain. Objective pain assessment is important to facilitate effective postoperative pain management in these vulnerable children.
DEVELOPING AN ALGORITHM FOR DEFECT DETECTION OF DENIM FABRIC : GABOR FILTER METHOD

In this study, an algorithm is developed by using Gabor filtering and double thresholding methods for fabric defect detection automatically. The defective area of the fabric image is accentuated, and the defect-free texture is attenuated as result of convolution operation with the Gabor filter. The image is then converted into binary form for by using double thresholding method. The noises are removed and the defective area is determined clearly by using dilation and erosion morphological operations. The boundary of the defect is labeled. A fabric defect image database consists of five defect types; warp lacking, weft lacking, hole, soiled yarn and knot is formed. The database includes 30 different images for each type of defect and defect-free fabric samples. Thus, the algorithm is applied over 180 fabric images. All defective areas are detected with high success rates. The performance of the algorithm is given statistically.
Fears of compassion: development of three self-report measures.

There is increasing evidence that helping people develop compassion for themselves and others has powerful impacts on negative affect and promotes positive affect. However, clinical observations suggest that some individuals, particularly those high in self-criticism, can find self-compassion and receiving compassion difficult and can be fearful of it. This study therefore developed measures of fear of: compassion for others, compassion from others, and compassion for self. We also explored the relationship of these fears with established compassion for self and compassion for others measures, self-criticism, attachment styles, and depression, anxiety, and stress.



Students (N= 222) and therapists (N= 53) completed measures of fears of compassion, self-compassion, compassion for others, self-criticism, adult attachment, and psychopathology.



Fear of compassion for self was linked to fear of compassion from others, and both were associated with self-coldness, self-criticism, insecure attachment, and depression, anxiety, and stress. In a multiple regression, self-criticism was the only significant predictor of depression.



This study suggests the importance of exploring how and why some people may actively resist engaging in compassionate experiences or behaviours and be fearful of affiliative emotions in general. This has important implications for therapeutic interventions and the therapeutic relationship because affiliative emotions are major regulators of threat-based emotions.
Application of Representation Theory to Dual-Mode Microwave Bandpass Filters

This paper discusses the physics behind the operation of dual-mode bandpass filters from a field theoretical point of view. It is argued that the two degenerate modes of the empty dual-mode cavity, commonly taken as the vertical and horizontal polarizations, become nonphysical when coupling and tuning elements are inserted. Instead, the original degenerate modes are rotated, or modified in a complex way, to generate two new modes whose characteristics depend on the coupling and tuning elements. It is shown that the tuning elements, as placed in existing dual-mode filter designs, act as both tuning and coupling elements. A working dual-mode filter can be designed with only ldquotuningrdquo elements present. A physical representation of dual-mode filters in terms of the eigenresonances of the dual-mode cavities, with the tuning and coupling elements present, is introduced. Two fourth-order dual-mode rectangular cavity filters with the same response in the passband and its vicinity are also presented to demonstrate the similar role played by ldquotuningrdquo and coupling elements in dual-mode cavities. The first filter uses only ldquotuningrdquo elements, while the second is based only on ldquocouplingrdquo elements.
Design considerations for high-voltage high-power full-bridge zero-voltage-switched PWM converter

A steady-state analysis is presented with complete characterization of the converter operation. A small-signal model of the converter is established. The design procedures based on the analysis are presented and the various losses in the circuit assessed. Critical design considerations for a high-power, high-voltage application are analyzed. The results of the analysis are verified using a high-voltage. 2 kW prototype.<<ETX>>
Very Compact FPGA Implementation of the AES Algorithm

In this paper a compact FPGA architecture for the AES algorithm with 128-bit key targeted for low-cost embedded applications is presented. Encryption, decryption and key schedule are all implemented using small resources of only 222 Slices and 3 Block RAMs. This implementation easily fits in a low-cost Xilinx Spartan II XC2S30 FPGA. This implementation can encrypt and decrypt data streams of 150 Mbps, which satisfies the needs of most embedded applications, including wireless communication. Specific features of Spartan II FPGAs enabling compact logic implementation are explored, and a new way of implementing MixColumns and InvMixColumns transformations using shared logic resources is presented.
Image compression with auto-encoder algorithm using deep neural network (DNN)

An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

Heterogeneous information network (HIN) has drawn significant research attention recently, due to its power of modeling multi-typed multi-relational data and facilitating various downstream applications. In this decade, many algorithms have been developed for HIN modeling, including traditional similarity measures and recent embedding techniques. Most algorithms on HIN leverage meta-graphs or meta-paths (special cases of meta-graphs) to capture various semantics. Given any arbitrary set of meta-graphs, existing algorithms either consider them as equally important or study their different importance through supervised learning. Their performance largely relies on prior knowledge and labeled data. While unsupervised embedding has shown to be a fundamental solution for various homogeneous network mining tasks, for HIN, it is a much harder problem due to such a presence of various meta-graphs. In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with l2,1-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.
DeepDefense: Training Deep Neural Networks with Improved Robustness

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in securitycritical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named “deep defense”. Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results will be made publicly available.
Computational thinking

omputational thinking builds on the power and limits of computing processes, whether they are executed by a human or by a machine. Computational methods and models give us the courage to solve problems and design systems that no one of us would be capable of tackling alone. Computational thinking confronts the riddle of machine intelligence: What can humans do better than computers? And What can computers do better than humans? Most fundamentally it addresses the question: What is computable? Today, we know only parts of the answers to such questions.
Understanding Hand-Object Manipulation with Grasp Types and Object Attributes

Our goal is to automate the understanding of natural hand-object manipulation by developing computer visionbased techniques. Our hypothesis is that it is necessary to model the grasp types of hands and the attributes of manipulated objects in order to accurately recognize manipulation actions. Specifically, we focus on recognizing hand grasp types, object attributes and actions from a single image within an unified model. First, we explore the contextual relationship between grasp types and object attributes, and show how that context can be used to boost the recognition of both grasp types and object attributes. Second, we propose to model actions with grasp types and object attributes based on the hypothesis that grasp types and object attributes contain complementary information for characterizing different actions. Our proposed action model outperforms traditional appearance-based models which are not designed to take into account semantic constraints such as grasp types or object attributes. Experiment results on public egocentric activities datasets strongly support our hypothesis.
A Verification Platform for SDN-Enabled Applications

Recent work on integration of SDNs with application-layer systems like Hadoop has created a class of system, SDN-Enabled Applications, which implement application-specific functionality on the network layer by exposing network monitoring and control semantics to application developers. This requires domain-specific knowledge to correctly reason about network behavior and properties, as the SDN is now tightly coupled to the larger system. Existing tools for SDN verification and analysis are insufficiently expressive to capture this composition of network and domain models. Unfortunately, it is exactly this kind of automated reasoning and verification that is necessary to develop robust SDN-enabled applications for real-world systems. In this paper, we present ongoing work on Verificare, a verification platform being built to enable formal verification of SDNs as components of a larger domain-specific system. SLA, safety, and security requirements can selected from a variety of formal libraries and automatically verified using a variety of off-the-shelf tools. This approach not only extends the flexibility of existing SDN verification systems, but can actually provide more fine-grained analysis of possible network states due to extra information supplied by the domain model.