nils-herrmann/clean_scirepeval huggingface dataset:
Transitioning from federated avionics architectures to Integrated Modular Avionics

This paper identifies considerations for transitioning from a federated avionics architecture to an integrated modular avionics (IMA) architecture. Federated avionics architectures make use of distributed avionics functions that are packaged as self-contained units (LRUs and LRMs). IMA architectures employ a high-integrity, partitioned environment that hosts multiple avionics functions of different criticalities on a shared computing platform. This provides for weight and power savings since computing resources can be used more efficiently. This paper establishes the benefits of transitioning to IMA. To aid in the planning process, the paper also identifies factors to consider before transitioning to IMA. The approach to resource management (computing, communication, and I/O) is identified as the fundamental architectural difference between federated and IMA systems. The paper describes how this difference changes the development process and benefits the systems integrator. This paper also addresses misconceptions about the resource management mechanisms that can occur during a transition to IMA and concludes that resources are not inherently constrained by IMA architectures. Guidance is provided for transitioning to both "open" and "closed" IMA architectures. Open IMA architectures utilize open interface standards that are available in the public domain. Closed IMA architectures utilize proprietary interfaces that can be customized. The analysis of these avionics architectures is based upon the authors' experience in developing platform computing systems at GE Aviation. GE Aviation has developed open system IMA architectures for commercial aircraft (Boeing 787 Dreamliner), as well as military aircraft (Boeing C-130 combat aircraft, and Boeing KC-767 Tanker).
Visualization of the Static Aspects of Software: A Survey

Software is usually complex and always intangible. In practice, the development and maintenance processes are time-consuming activities mainly because software complexity is difficult to manage. Graphical visualization of software has the potential to result in a better and faster understanding of its design and functionality, thus saving time and providing valuable information to improve its quality. However, visualizing software is not an easy task because of the huge amount of information comprised in the software. Furthermore, the information content increases significantly once the time dimension to visualize the evolution of the software is taken into account. Human perception of information and cognitive factors must thus be taken into account to improve the understandability of the visualization. In this paper, we survey visualization techniques, both 2D- and 3D-based, representing the static aspects of the software and its evolution. We categorize these techniques according to the issues they focus on, in order to help compare them and identify the most relevant techniques and tools for a given problem.
Automatic Extraction of Commonsense LocatedNear Knowledge

1234 < 56, 78, 79 > LocatedNear Relation: A kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. For example, <table, LocatedNear, chair>. v LocatedNear facts are helpful prior knowledge for object detection in complex image scenes. v It can potentially benefit general reasoning in reading comprehension, question answering, and other AI tasks. v Existing knowledge bases have very few facts for this relation (ConceptNet has only 49 triplets).
Continual Learning Through Synaptic Intelligence

While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency.
Human-Assisted Graph Search: It's Okay to Ask Questions

We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.
Surfel-Based Next Best View Planning

Next best view (NBV) planning is a central task for automated three-dimensional (3-D) reconstruction in robotics. The most expensive phase of NBV computation is the view simulation step, where the information gain of a large number of candidate sensor poses are estimated. Usually, information gain is related to the visibility of unknown space from the simulated viewpoint. A well-established technique is to adopt a volumetric representation of the environment and to compute the NBV from ray casting by maximizing the number of unknown visible voxels. This letter explores a novel approach for NBV planning based on surfel representation of the environment. Surfels are oriented surface elements, such as circular disks, without explicit connectivity. A new kind of surfel is introduced to represent the frontier between empty and unknown space. Surfels are extracted during 3-D reconstruction, with minimal overhead, from a KinectFusion volumetric representation. Surfel rendering is used to generate images from each simulated sensor pose. Experiments in a real robot setup are reported. The proposed approach achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality. Moreover, surfel-based NBV planning can be applied in larger environments as a volumetric representation is limited by GPU memory.
Tagging English Text with a Probabilistic Model

In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used a simple triclass Markov model and are looking for the best way to estimate the parameters of this model, depending on the kind and amount of training data provided. Two approaches in particular are compared and combined: to use text that has been tagged by hand and compute relative frequency counts, to use text without tags and train the model as a hidden Markov process, according to a Maximum Likelihood principle. Experiments show that the best training is obtained by using as much tagged text as possible. They also show that Maximum Likelihood training, the procedure which is routinely used to estimate Hidden Markov Models parameters from training data, will not necessarily improve the tagging accuracy. In fact, it will generally degrade this accuracy, except when only a limited amount of hand tagged text is available.
Local potential connectivity in cat primary visual cortex.

Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron. In this paper, we attempt to reconstruct the potential connectivity in local cortical circuits of the cat primary visual cortex (V1). Based on multiple single-neuron reconstructions of axonal and dendritic arbors in 3 dimensions, we evaluate the expected number of potential synapses and the probability of potential connectivity among excitatory (pyramidal and spiny stellate) neurons and inhibitory basket cells. The results provide a quantitative description of structural organization of local cortical circuits. For excitatory neurons from different cortical layers, we compute local domains, which contain their potentially pre- and postsynaptic excitatory partners. These domains have columnar shapes with laminar specific radii and are roughly of the size of the ocular dominance column. Therefore, connections between most excitatory neurons in the ocular dominance column can be implemented by local synaptogenesis. Structural connectivity involving inhibitory basket cells is generally weaker than excitatory connectivity. Here, only nearby neurons are capable of establishing more than one potential synapse, implying that within the ocular dominance column these connections have more limited potential for circuit remodeling.
Principal Component Analysis by  $L_{p}$ -Norm Maximization

This paper proposes several principal component analysis (PCA) methods based on Lp-norm optimization techniques. In doing so, the objective function is defined using the Lp-norm with an arbitrary p value, and the gradient of the objective function is computed on the basis of the fact that the number of training samples is finite. In the first part, an easier problem of extracting only one feature is dealt with. In this case, principal components are searched for either by a gradient ascent method or by a Lagrangian multiplier method. When more than one feature is needed, features can be extracted one by one greedily, based on the proposed method. Second, a more difficult problem is tackled that simultaneously extracts more than one feature. The proposed methods are shown to find a local optimal solution. In addition, they are easy to implement without significantly increasing computational complexity. Finally, the proposed methods are applied to several datasets with different values of p and their performances are compared with those of conventional PCA methods.
Mitigating Inter-network Interference in LoRa Networks

Long Range (LoRa) is a popular technology used to construct Low-Power Wide-Area Networks (LPWAN). Given the popularity of LoRa it is likely that multiple independent LoRa networks are deployed in close proximity. In this situation, neighbouring networks interfere and methods have to be found to combat this interference. In this paper we investigate the use of directional antennae and the use of multiple base stations as methods of dealing with inter-network interference. Directional antennae increase signal strength at receivers without increasing transmission energy cost. Thus, the probability of successfully decoding the message in an interference situation is improved. Multiple base stations can alternatively be used to improve the probability of receiving a message in a noisy environment. We compare the effectiveness of these two approaches via simulation. Our findings show that both methods are able to improve LoRa network performance in interference settings. However, the results show that the use of multiple base stations clearly outperforms the use of directional antennae. For example, in a setting where data is collected from 600 nodes which are interfered by four networks with 600 nodes each, using three base stations improves the Data Extraction Rate (DER) from 0.24 to 0.56 while the use of directional antennae provides an increase to only 0.32.
Selective Decoding for Cross-lingual Open Information Extraction

Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.
An event-based conceptual model for context-aware movement analysis

Current tracking technologies enable collection of data describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods, etc. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.
Routing Attacks and Countermeasures in the RPL-Based Internet of Things

The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.
Torque mode control of a Bowden cable-driven assistive system

A cable-driven actuating system is proposed in this paper. The proposed system is attractive for rehabilitation systems because the weight of the actuator is not imposed on the human body. Since the end-effector and the actuators are connected by Bowden cables, the humans are allowed to freely move in a certain range while being assisted. However, it is a challenge to account for the variable friction of the Bowden cable and the inertia and friction of the actuator in the design of control algorithms. In this paper, a hierarchical control strategy is adopted to control the proposed cable-driven actuating system. To determine the reference trajectory of the motor in real-time, a sensor fusion method based on a kinematic Kalman filter with MEMS accelerometers is proposed. By the proposed control methods, the cable-driven actuating system realizes a precise force-mode actuation. The system performance is evaluated by experiments.
Learning to generate chairs with convolutional neural networks

We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.
Bayesian face recognition

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases.  This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Multi-style paper pop-up designs from 3D models

Paper pop-ups are interesting three-dimensional books that fascinate people of all ages. The design and construction of these pop-up books however are done manually and require a lot of time and effort. This has led to computer-assisted or automated tools for designing paper pop-ups. This paper proposes an approach for automatically converting a 3D model into a multi-style paper pop-up. Previous automated approaches have only focused on single-style pop-ups, where each is made of a single type of pop-up mechanisms. In our work, we combine multiple styles in a pop-up, which is more representative of actual artist’s creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop-up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop-up. We define geometric conditions that ensure the validity of the combined pop-up structures. In addition, our method also employs an image-based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop-ups from the designs generated by our system.
Continuous Electrowetting of Non-toxic Liquid Metal for RF Applications

Continuous electrowetting (CEW) is demonstrated to be an effective actuation mechanism for reconfigurable radio frequency (RF) devices that use non-toxic liquid-metal tuning elements. Previous research has shown CEW is an efficient means of electrically inducing motion in a liquid-metal slug, but precise control of the slug's position within fluidic channels has not been demonstrated. Here, the precise positioning of liquid-metal slugs is achieved using CEW actuation in conjunction with channels designed to minimize the liquid-metal surface energy at discrete locations. This approach leverages the high surface tension of liquid metal to control its resting position with submillimeter accuracy. The CEW actuation and fluidic channel design were optimized to create reconfigurable RF devices. In addition, solutions for the reliable actuation of a gallium-based, non-toxic liquid-metal alloy (Galinstan) are presented that mitigate the tendency of the alloy to form a surface oxide layer capable of wetting to the channel walls, inhibiting motion. A reconfigurable slot antenna utilizing these techniques to achieve a 15.2% tunable frequency bandwidth is demonstrated.
Energy detection spectrum sensing on DPSK modulation transceiver using GNU radio

Cognitive radio (CR) can facilitate the issue of range shortage by permitting secondary users to coincide with incumbent clients in authorized range groups, while it causes no interference to occupant interchanges. Spectrum sensing is the essential in enabling innovation of CR to detect the presence of primary users (PUs) or authorized user signals and adventure the spectrum holes. Henceforth, this paper addresses the testing of spectrum sensing method for Differential Phase Shift Keying (DPSK) transceiver by utilizing an open source programming, GNU Radio. Moreover, the effect of various reproduction channels, for example, Dynamic channel demonstrate with Rayleigh fading, dynamic channel display with Rician fading, frequency selective faded channel show with Rayleigh and frequency selective faded channel show with Rician fading are exhibited.
Early forest fire detection by vision-enabled wireless sensor networks 1

Wireless sensor networks constitute a powerful technology especially suitable for 10 environmental monitoring. With regard to wildfires, in particular, they enable low-cost fine11 grained surveillance of hazardous locations like wildland urban interfaces. This paper presents 12 the work developed during the last four years targeting a vision-enabled wireless sensor network 13 node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from 14 devising a robust vision algorithm for smoke detection to the design and physical 15 implementation of a power-efficient smart imager tailored to the characteristics of such an 16 algorithm. By integrating this smart imager with a commercial wireless platform, we endowed 17 the resulting system with vision capabilities and radio communication. Numerous tests were 18 arranged in different natural scenarios in order progressively to tune all the parameters involved 19 in the autonomous operation of this prototype node. The latest test carried out, involving the 20 prescribed burning of a 95m×20m shrub plot, has confirmed the high degree of reliability of our 21 approach in terms of both successful early detection and very low false alarm rate. 22