nils-herrmann/clean_scirepeval huggingface dataset:
Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.
Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality

The nearest neighbor problem is the follolving: Given a set of n points P = (PI, . . . ,p,} in some metric space X, preprocess P so as to efficiently answer queries which require finding bhe point in P closest to a query point q E X. We focus on the particularly interesting case of the d-dimensional Euclidean space where X = Wd under some Zp norm. Despite decades of effort, t,he current solutions are far from saabisfactory; in fact, for large d, in theory or in practice, they provide litt,le improvement over the brute-force algorithm which compares the query point to each data point. Of late, t,here has been some interest in the approximate newest neighbors problem, which is: Find a point p E P that is an c-approximate nearest neighbor of the query q in t,hat for all p’ E P, d(p, q) < (1 + e)d(p’, q). We present two algorithmic results for the approximate version t,hat significantly improve the known bounds: (a) preprocessing cost polynomial in n and d, and a truly sublinear query t.ime (for 6 > 1); and, (b) query time polynomial in log-n and d, and only a mildly exponential preprocessing cost* O(n) x 0(1/~)~. Furt.her, applying a classical geometric lemma on random projections (for which we give a simpler proof), we obtain t.he first known algorithm with polynomial preprocessing and query t.ime polynomial in d and log n. Unfortunately, for small E, the latter is a purely theoretical result since bhe e?rponent depends on l/e. Experimental resuits indicate that our tit algori&m offers orders of magnitude improvement on running times over real data sets. Its key ingredient is the notion of locality-sensitive hashing which may be of independent interest; here, we give applications to information ret,rieval, pattern recognition, dynamic closest-pairs, and fast clustering algorithms. Supported by a Stanford Graduate Fellowship and NSF
Artificial-Life Ecosystems - What are they and what could they become?

This paper summarises the history of the terms ecology and ecosystem, before examining their application in the early and recent literature of A-Life agent-based software simulation. It investigates trends in A-Life that have led to a predominance of simulations incorporating artificial evolution acting on generic agents, but lacking a level of detail that would allow the emergence of phenomena relating to the transfer and transformation of energy and matter between the virtual abiotic environment and biota. Implications of these characteristics for the relevance of A-Life’s virtual ecosystem models to Ecology are discussed. We argue a position that the inclusion of low-level representations of energetics, matter and evolution, in concert with pattern-oriented modelling techniques from Ecology for model validation, will improve the relevance of A-Life models to Ecology. We also suggest two methods that may allows us to meet this goal: artificial evolution can be employed as a mechanism for automating pattern-oriented ecological modelling from the level of individual species up to that of the ecosystem, or it may be employed to explore general principles of ecosystem behaviour over evolutionary time periods.
Upper Body Pose Estimation with Temporal Sequential Forests

Our objective is to efficiently and accurately estimate human upper body pose in gesture videos. To this end, we build on the recent successful applications of random forests (RF) classifiers and regressors, and develop a pose estimation model with the following novelties: (i) the joints are estimated sequentially, taking account of the human kinematic chain. This means that we don’t have to make the simplifying assumption of most previous RF methods – that the joints are estimated independently; (ii) by combining both classifiers (as a mixture of experts) and regressors, we show that the learning problem is tractable and that more context can be taken into account; and (iii) dense optical flow is used to align multiple expert joint position proposals from nearby frames, and thereby improve the robustness of the estimates. The resulting method is computationally efficient and can overcome a number of the errors (e.g. confusing left/right hands) made by RF pose estimators that infer their locations independently. We show that we improve over the state of the art on upper body pose estimation for two public datasets: the BBC TV Signing dataset and the ChaLearn Gesture Recognition dataset.
Water-walking devices

We report recent efforts in the design and construction of water-walking machines inspired by insects and spiders. The fundamental physical constraints on the size, proportion and dynamics of natural water-walkers are enumerated and used as design criteria for analogous mechanical devices. We report devices capable of rowing along the surface, leaping off the surface and climbing menisci by deforming the free surface. The most critical design constraint is that the devices be lightweight and non-wetting. Microscale manufacturing techniques and new man-made materials such as hydrophobic coatings and thermally actuated wires are implemented. Using highspeed cinematography and flow visualization, we compare the functionality and dynamics of our devices with those of their natural counterparts.
An evaluation of dynamic adaptive streaming over HTTP in vehicular environments

MPEGs' Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is an emerging standard designed for media delivery over the top of existing infrastructures and able to handle varying bandwidth conditions during a streaming session. This requirement is very important, specifically within mobile environments and, thus, DASH could potentially become a major driver for mobile multimedia streaming. Hence, this paper provides a detailed evaluation of our implementation of MPEG DASH compared to the most popular propriety systems, i.e., Microsoft Smooth Steaming, Adobe HTTP Dynamic Streaming, and Apple HTTP Live Streaming. In particular, these systems will be evaluated under restricted conditions which are due to vehicular mobility. In anticipation of the results, our prototype implementation of MPEG-DASH can very well compete with state-of-the-art solutions and, thus, can be regarded as a mature standard ready for industry adaption.
When the CRC and TCP checksum disagree

Traces of Internet packets from the past two years show that between 1 packet in 1,100 and 1 packet in 32,000 fails the TCP checksum, even on links where link-level CRCs should catch all but 1 in 4 billion errors. For certain situations, the rate of checksum failures can be even higher: in one hour-long test we observed a checksum failure of 1 packet in 400. We investigate why so many errors are observed, when link-level CRCs should catch nearly all of them.
We have collected nearly 500,000 packets which failed the TCP or UDP or IP checksum. This dataset shows the Internet has a wide variety of error sources which can not be detected by link-level checks. We describe analysis tools that have identified nearly 100 different error patterns. Categorizing packet errors, we can infer likely causes which explain roughly half the observed errors. The causes span the entire spectrum of a network stack, from memory errors to bugs in TCP.
After an analysis we conclude that the checksum will fail to detect errors for roughly 1 in 16 million to 10 billion packets. From our analysis of the cause of errors, we propose simple changes to several protocols which will decrease the rate of undetected error. Even so, the highly non-random distribution of errors strongly suggests some applications should employ application-level checksums or equivalents.
CEC : Research in visualization techniques for field construction

Field construction can be planned, monitored, and controlled at two distinct levels: 1) the activity or schedule level; and 2) the operation or process level. Graphical 3D visualization can serve as an effective communication method at both levels. Several research efforts in visualizing construction are rooted in scheduling. They typically involve linking activity-based construction schedules and 3D CAD models of facilities to describe discretely-evolving construction “product” visualizations (often referred to as 4D CAD). The focus is on communicating what component(s) are built where and when, with the intention of studying the optimal activity sequence, spatial, and temporal interferences, etc. The construction processes or operations actually involved in building the components are usually implied. A second approach in visualizing construction is rooted in discrete-event simulation that, in addition to visualizing evolving construction products, also concerns the visualization of the operations and processes that are performed in building them. In addition to what is built where and when, the approach communicates who builds it and how by depicting the interaction between involved machines, resources, and materials. This paper introduces the two approaches, and describes the differences in concept, form, and content between activity level and operations level construction visualization. An example of a structural steel framing operation is presented to elucidate the comparison. This work was originally published in the proceedings of the 2002 IEEE Winter Simulation Conference (Kamat and Martinez 2002). This paper expands on the original work, by describing recent advances in both activity and operations level construction visualization.
Overview of BioNLP'09 Shared Task on Event Extraction

The paper presents the design and implementation of the BioNLP’09 Shared Task, and reports the final results with analysis. The shared task consists of three sub-tasks, each of which addresses bio-molecular event extraction at a different level of specificity. The data was developed based on the GENIA event corpus. The shared task was run over 12 weeks, drawing initial interest from 42 teams. Of these teams, 24 submitted final results. The evaluation results are encouraging, indicating that state-of-the-art performance is approaching a practically applicable level and revealing some remaining challenges.
Detecting Events and Key Actors in Multi-person Videos

Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features at each time-instant. The attended features are then processed using another RNN for event detection/ classification. Since most video datasets with multiple people are restricted to a small number of videos, we also collected a new basketball dataset comprising 257 basketball games with 14K event annotations corresponding to 11 event classes. Our model outperforms state-of-the-art methods for both event classification and detection on this new dataset. Additionally, we show that the attention mechanism is able to consistently localize the relevant players.
Challenge AI Mind: A Crowd System for Proactive AI Testing

Arti cial Intelligence (AI) has burrowed into our lives in various aspects; however, without appropriate testing, deployed AI systems are often being criticized to fail in critical and embarrassing cases. Existing testing approaches mainly depend on xed and pre-de ned datasets, providing a limited testing coverage. In this paper, we propose the concept of proactive testing to dynamically generate testing data and evaluate the performance of AI systems. We further introduce Challenge.AI, a new crowd system that features the integration of crowdsourcing and machine learning techniques in the process of error generation, error validation, error categorization, and error analysis. We present experiences and insights into a participatory design with AI developers. The evaluation shows that the crowd work ow is more e ective with the help of machine learning techniques. AI developers found that our system can help them discover unknown errors made by the AI models, and engage in the process of proactive testing.
Marriage, honesty, and stability

Many centralized two-sided markets form a matching between participants by running a stable marriage algorithm. It is a well-known fact that no matching mechanism based on a stable marriage algorithm can guarantee truthfulness as a dominant strategy for participants. However, as we will show in this paper, in a probabilistic setting where the preference lists of one side of the market are composed of only a constant (independent of the the size of the market) number of entries, each drawn from an arbitrary distribution, the number of participants that have more than one stable partner is vanishingly small. This proves (and generalizes) a conjecture of Roth and Peranson [23]. As a corollary of this result, we show that, with high probability, the truthful strategy is the best response for a given player when the other players are truthful. We also analyze equilibria of the deferred acceptance stable marriage game. We show that the game with complete information has an equilibrium in which a (1 - o(1)) fraction of the strategies are truthful in expectation. In the more realistic setting of a game of incomplete information, we will show that the set of truthful strategies form a (1 + o(1))-approximate Bayesian-Nash equilibrium. Our results have implications in many practical settings and were inspired by the work of Roth and Peranson [23] on the National Residency Matching Program.
Fastest Mixing Markov Chain on a Graph

We consider a symmetric random walk on a connected graph, where each edge is labeled with the probability of transition between the two adjacent vertices. The associated Markov chain has a uniform equilibrium distribution; the rate of convergence to this distribution, i.e., the mixing rate of the Markov chain, is determined by the second largest (in magnitude) eigenvalue of the transition matrix. In this paper we address the problem of assigning probabilities to the edges of the graph in such a way as to minimize the second largest magnitude eigenvalue, i.e., the problem of finding the fastest mixing Markov chain on the graph. We show that this problem can be formulated as a convex optimization problem, which can in turn be expressed as a semidefinite program (SDP). This allows us to easily compute the (globally) fastest mixing Markov chain for any graph with a modest number of edges (say, 1000) using standard numerical methods for SDPs. Larger problems can be solved by exploiting various types of symmetry and structure in the problem, and far larger problems (say 100000 edges) can be solved using a subgradient method we describe. We compare the fastest mixing Markov chain to those obtained using two commonly used heuristics: the maximum-degree method, and the Metropolis-Hastings algorithm. For many of the examples considered, the fastest mixing Markov chain is substantially faster than those obtained using these heuristic methods. We derive the Lagrange dual of the fastest mixing Markov chain problem, which gives a sophisticated method for obtaining (arbitrarily good) bounds on the optimal mixing rate, as well the optimality conditions. Finally, we describe various extensions of the method, including a solution of the problem of finding the fastest mixing reversible Markov chain, on a fixed graph, with a given equilibrium distribution.
Local Rademacher Complexity Bounds based on Covering Numbers

This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions. We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.
A path decomposition approach for computing blocking probabilities in wavelength-routing networks

We study a class of circuit-switched wavelength-routing networks with fixed or alternate routing and with random wavelength allocation. We present an iterative path decomposition algorithm to evaluate accurately and efficiently the blocking performance of such networks with and without wavelength converters. Our iterative algorithm analyzes the original network by decomposing it into single-path subsystems. These subsystems are analyzed in isolation, and the individual results are appropriately combined to obtain a solution for the overall network. To analyze individual subsystems, we first construct an exact Markov process that captures the behavior of a path in terms of wavelength use. We also obtain an approximate Markov process which has a closed-form solution that can be computed efficiently for short paths. We then develop an iterative algorithm to analyze approximately arbitrarily long paths. The path decomposition approach naturally captures the correlation of both link loads and link blocking events. Our algorithm represents a simple and computationally efficient solution to the difficult problem of computing call-blocking probabilities in wavelength-routing networks. We also demonstrate how our analytical techniques can be applied to gain insight into the problem of converter placement in wavelength-routing networks.
Hey! There is someone at your door. A hearing robot using visual communication signals of hearing dogs to communicate intent

This paper presents a study of the readability of dog-inspired visual communication signals in a human-robot interaction scenario. This study was motivated by specially trained hearing dogs which provide assistance to their deaf owners by using visual communication signals to lead them to the sound source. For our human-robot interaction scenario, a robot was used in place of a hearing dog to lead participants to two different sound sources. The robot was preprogrammed with dog-inspired behaviors, controlled by a wizard who directly implemented the dog behavioral strategy on the robot during the trial. By using dog-inspired visual communication signals as a means of communication, the robot was able to lead participants to the sound sources (the microwave door, the front door). Findings indicate that untrained participants could correctly interpret the robot's intentions. Head movements and gaze directions were important for communicating the robot's intention using visual communication signals.
Circularly Polarized U-Slot Antenna

Circularly polarized single-layer U-slot microstrip patch antenna has been proposed. The suggested asymmetrical U-slot can generate the two orthogonal modes for circular polarization without chamfering any corner of the probe-fed square patch microstrip antenna. A parametric study has been carried out to investigate the effects caused by different arm lengths of the U-slot. The thickness of the foam substrate is about 8.5% of the wavelength at the operating frequency. The 3 dB axial ratio bandwidth of the antenna is 4%. Both experimental and theoretical results of the antenna have been presented and discussed. Circular polarization, printed antennas, U-slot.
Hierarchical Attention Networks for Document Classification

We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences.
A SIMULATION STUDY OF LOGISTICS AND MANUFACTURING ACTIVITIES IN AN AUTOMOBILE SUPPLY CHAIN

This article studies an application of discrete-event simulation (DES) combined with multi-criteria decision analysis (MCDA) to study manufacturing procedure integrated with logistics activities in an automotive supply chain. Automotive Industries Development Pressed Components Company (AIDCO PRESS) as an important supplier is linked to IRANKHODRO CO, a distinguished automobile company at Iran, by a web based inventory management system implementing Kanban philosophy. AIDCO PRESS’s inefficiencies in responsiveness to the received Kanabns from IRANKHODRO CO endanger the automotive supply chain’s agility, and also threaten its future role as a reliable supplier. DES modeling approach is used to analyze such a complex system in order to determine the causes of lateness in receiving the requested parts by IRANKHODRO CO. Consulting with the executive and plant managers, improvement scenarios are defined in order to obviate the inefficiencies’ origins, and then MCDA methodology considering responsiveness, cost and utility criterions is pursued to find the best scenario. Collectivity of the studied problem together with the reality nature of the case distinguishes this paper from the decision analysis studies in the automotive supply chain literature.