nils-herrmann/clean_scirepeval huggingface dataset:
Efficient methods for physically-based rendering of participating media

This thesis proposes several novel methods for realistic synthesis of images containing participating media. This is a challenging problem, due to the multitude and complexity of ways how light interacts with participating media, but also an important one, since such media are ubiquitous in our environment and therefore are one of the main constituents of its appearance. The main paradigm we follow is designing efficient methods that provide their user with an interactive feedback, but are still physically plausible. The presented contributions have varying degrees of specialisation and, in a loose connection to that, their resulting efficiency. First, the screen-space scattering algorithm simulates scattering in homogeneous media, such as fog and water, as a fast image filtering process. Next, the amortised photon mapping method focuses on rendering clouds as arguably one of the most difficult media due to their high scattering anisotropy. Here, interactivity is achieved through adapting to certain conditions specific to clouds. A generalisation of this approach is principal-ordinates propagation, which tackles a much wider class of heterogeneous media. The resulting method can handle almost arbitrary optical properties in such media, thanks to a custom finite-element propagation scheme. Finally, spectral ray differentials aim at an efficient reconstruction of chromatic dispersion phenomena, which occur in transparent media such as water, glass and gemstones. This method is based on analytical ray differentiation and as such can be incorporated to any ray-based rendering framework, increasing the efficiency of reproducing dispersion by about an order of magnitude. All four proposed methods achieve efficiency primarily by utilising high-level mathematical abstractions, building on the understanding of the underlying physical principles that guide light transport. The methods have also been designed around simple data structures, allowing high execution parallelism and removing the need to rely on any sort of preprocessing. Thanks to these properties, the presented work is not only suitable for interactively computing light transport in participating media, but also allows dynamic changes to the simulated environment, all while maintaining high levels of visual realism.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github. com/hexiangnan/attentional factorization machine
Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model

Two-wheeled self-balancing robot is a mechanically unstable, nonholonomic constraint robot. This paper has established a dynamics and kinematics for nonholonomic mobile robot. In order to control more effective for self-balancing robot's balance model, has designed fuzzy controller based on the T-S fuzzy model with the parallel distribution compensator (PDC) the structure. It realizes the robot balance and the movement control. Finally, validity and feasibility of the system modeling and the designed controller were confirmed through the actual test results and the simulation results contrast analysis.
Intrinsic firing patterns of diverse neocortical neurons

Neurons of the neocortex differ dramatically in the patterns of action potentials they generate in response to current steps. Regular-spiking cells adapt strongly during maintained stimuli, whereas fast-spiking cells can sustain very high firing frequencies with little or no adaptation. Intrinsically bursting cells generate clusters of spikes (bursts), either singly or repetitively. These physiological distinctions have morphological correlates. RS and IB cells can be either pyramidal neurons or spiny stellate cells, and thus constitute the excitatory cells of the cortex. FS cells are smooth or sparsely spiny non-pyramidal cells, and are likely to be GABAergic inhibitory interneurons. The different firing properties of neurons in neocortex contribute significantly to its network behavior.
Investigating Crime-to-Twitter Relationships in Urban Environments - Facilitating a Virtual Neighborhood Watch

Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes.
Artificial Neural Networks in the Detection of Known and Unknown DDoS Attacks: Proof-of-Concept

A Distributed Denial of Service attack (DDoS) is designed to overload a target device and its networks with packets to damage its resources or services. This paper proposes an Artificial Neural Network (ANN) detection engine to flag known and unknown attacks from genuine traffic. Based on experiments and data analysis, specific patterns are selected to separate genuine from DDoS packets, thus allowing normal traffic to reach its destination. The mitigation process is triggered when the detection system identifies attacks based on the known characteristic features (patterns) that were fed to the ANN during the training process. Such characteristic patterns separate attacks from normal traffic. We have evaluated our solution against related work based on accuracy, sensitivity, specificity and precision.
A Process Mining Driven Framework for Clinical Guideline Improvement in Critical Care

This paper presents a framework for process mining in critical care. The framework uses the CRISP-DM model, extended to incorporate temporal and multidimensional aspects (CRISP-TDM), combined with the Patient Journey Modeling Architecture (PaJMa), to provide a structured approach to knowledge discovery of new condition onset pathophysiologies in physiological data streams. The approach is based on temporal abstraction and mining of physiological data streams to develop process flow mappings that can be used to update patient journeys; instantiated in critical care within clinical practice guidelines. We demonstrate the framework within the neonatal intensive care setting, where we are performing clinical research in relation to pathophysiology within physiological streams of patients diagnosed with late onset neonatal sepsis. We present an instantiation of the framework for late onset neonatal sepsis, using CRISP-TDM for the process mining model and PaJMa for the knowledge representation.
Managing Risk in Software Process Improvement: An Action Research Approach

Many software organizations engage in software process improvement (SPI) initiatives to increase their capability to develop quality solutions at a competitive level. Such efforts, however, are complex and very demanding. A variety of risks makes it difficult to develop and implement new processes. We studied SPI in its organizational context through collaborative practice research (CPR), a particular form of action research. The CPR program involved close collaboration between practitioners and researchers over a three-year period to understand and improve SPI initiatives in four Danish software organizations. The problem of understanding and managing risks in SPI teams emerged in one of the participating organizations and led to this research. We draw upon insights from the literature on SPI and software risk management as well as practical lessons learned from managing SPI risks in the participating software organizations. Our research offers two contributions. First, we contribute to knowledge on SPI by proposing an approach to understand and manage risks in SPI teams. This risk management approach consists of a framework for understanding risk areas and risk resolution strategies within SPI and a related Iversen et al./Managing Risk in SPI 396 MIS  process for managing SPI risks. Second, we contribute to knowledge on risk management within the information systems and software engineering disciplines. We propose an approach to tailor risk management to specific contexts. This approach consists of a framework for understanding and choosing between different forms of risk management and a process to tailor risk management to specific contexts.
Design, Modeling, and Validation of a Soft Magnetic 3-D Force Sensor

Recent advances in robotics promise a future where robots co-exist and collaborate with humans in unstructured environments, which will require frequent physical interactions where accurate tactile information will be crucial for performance and safety. This article describes the design, fabrication, modeling, and experimental validation of a soft-bodied tactile sensor that accurately measures the complete 3-D force vector for both normal and shear loading conditions. Our research considers the detection of changes in the magnetic field vector due to the motion of a miniature magnet in a soft substrate to measure normal and shear forces with high accuracy and bandwidth. The proposed sensor is a pyramid-shaped tactile unit with a tri-axis Hall element and a magnet embedded in a silicone rubber substrate. The non-linear mapping between the 3-D force vector and the Hall effect voltages is characterized by training a neural network. We validate the proposed soft force sensor over static and dynamic loading experiments and obtain a mean absolute error below 11.7 mN or 2.2% of the force range. These results were obtained for a soft force sensor prototype and loading conditions not included in the training process, indicating strong generalization of the model. To demonstrate its utility, the proposed sensor is used in a force-controlled pick-and-place experiment as a proof-of-concept case study.
Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles

An application of insect visuomotor behavior to automatic control of landing is explored. Insects, being perhaps more reliant on image motion cues than mammals or higher vertebrates, are proving to be an excellent organism in which to investigate how information on optic flow is exploited to guide locomotion and navigation. We have observed how bees perform grazing landings on a flat surface and have deduced the algorithmic basis for the behavior. A smooth landing is achieved by a surprisingly simple and elegant strategy: image velocity is held constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. The feasibility of this landing strategy was tested by implementation in a robotic gantry. We also outline our current efforts at exploring the applicability of this and related techniques to the guidance of uninhabited airborne vehicles (UAVs). Aspects of the algorithm were tested on a small UAV using real imagery to control
A hybrid machine learning approach to automatic plant phenotyping for smart agriculture

Recently, a new ICT approach to agriculture called “Smart Agriculture” has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.
Partitioning and Segment Organization Strategies for Real-Time Selective Search on Document Streams

The basic idea behind selective search is to partition a collection into topical clusters, and for each query, consider only a subset of the clusters that are likely to contain relevant documents. Previous work on web collections has shown that it is possible to retain high-quality results while considering only a small fraction of the collection. These studies, however, assume static collections where it is feasible to run batch clustering algorithms for partitioning. In this work, we consider the novel formulation of selective search on document streams (specifically, tweets), where partitioning must be performed incrementally. In our approach, documents are partitioned into temporal segments and selective search is performed within each segment: these segments can either be clustered using batch or online algorithms, and at different temporal granularities. For efficiency, we take advantage of word embeddings to reduce the dimensionality of the document vectors. Experiments with test collections from the TREC Microblog Tracks show that we are able to achieve precision indistinguishable from exhaustive search while considering only around 5% of the collection. Interestingly, we observe no significant effectiveness differences between batch vs. online clustering and between hourly vs. daily temporal segments, despite them being very different index organizations. This suggests that architectural choices should be primarily guided by efficiency considerations.
A 21-Gb/s 87-mW Transceiver With FFE/DFE/Analog Equalizer in 65-nm CMOS Technology

A 21-Gb/s backplane transceiver has been presented. The transmitter incorporates half-rate topology with purely digital blocks to substantially reduce power consumption. The receiver employs analog and decision-feedback equalizers in a full-rate structure to avoid complicated structure. The one-tap decision-feedback equalizer merges the summer and the slicer into the flipflop, shortening the feedback path and speeding up the operation considerably. Fabricated in 65-nm CMOS, the transceiver (excluding clock generating PLL and CDR circuits) delivers 21-Gb/s data (231- 1 PRBS) over 40-cm FR4 channel while consuming 87 mW from a 1.2-V supply.
Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores

Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (offline data) and the drawing made by the e-pen (online data) to improve classification. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classification over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.
Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.

Using simulated falls performed under supervised conditions and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated using tri-axial accelerometer sensors, mounted on the trunk and thigh. Data analysis was performed using MATLAB to determine the peak accelerations recorded during eight different types of falls. These included; forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed. Falls detection algorithms were devised using thresholding techniques. Falls could be distinguished from ADL for a total data set from 480 movements. This was accomplished using a single threshold determined by the fall-event data-set, applied to the resultant-magnitude acceleration signal from a tri-axial accelerometer located at the trunk.
Online public communications by police & fire services during the 2012 Hurricane Sandy

Social media and other online communication tools are a subject of great interest in mass emergency response. Members of the public are turning to these solutions to seek and offer emergency information. Emergency responders are working to determine what social media policies should be in terms of their "public information" functions. We report on the online communications from all the coastal fire and police departments within a 100 mile radius of Hurricane Sandy's US landfall. Across four types of online communication media, we collected data from 840 fire and police departments. Findings indicate that few departments used these online channels in their Sandy response efforts, and that communications differed between fire and police departments and across media type. However, among the highly engaged departments, there is evidence that they bend and adapt policies about what constitutes appropriate public communication in the face of emergency demands; therefore, we propose that flexibility is important in considering future emergency online communication policy. We conclude with design recommendations for making online communication media more "listenable" for both emergency managers and members of the public.
A Visually Interpretable Iris Recognition System with Crypt Features

by Feng Shen One important barrier to the use of current iris recognition techniques in law enforcement areas such as forensics is that the iris features used by these techniques are not interpretable to human eyes. This dissertation proposes a novel iris recognition technique that determines a human’s identity based on the crypt, a visible feature on the iris. The major motivation is to aid the law enforcement applications that require human judgment in the process. The crypt is a visible feature that is relatively stable and easy for human eyes to rely on for iris recognition tasks. To verify the human-in-the-loop system design, we conducted two human-subject experiments to evaluate human perception of crypt features. The results support the applicability of using crypt features for semi-automatic iris recognition. An automatic feature detector and two crypt matchers are implemented to reduce human workload for large datasets and assist humans to make informative decisions. Our feature detector deploys a multi-scale pyramid framework in order to detect crypts of various sizes. An optimization step is then performed to improve boundary accuracies. The first matcher uses a two-stage design that approximates the overall speed of of a simple global matcher while still being able Feng Shen to locate common crypts between two irises. The second matcher uses local binary compatibility to evaluate crypt similarities, which is more robust to shape variations and partial detection errors. The performance of the crypt detector and matchers is evaluated in both identification and verification scenarios. The testing dataset is composed of 3505 images from 701 irises. It is the largest dataset that has been used to evaluate visible feature based iris recognition techniques. A final contribution of this work is the preliminary study of a semi-automatic identity verification system. Image pairs with automatically annotated crypt features and local similarity scores are presented to humans to classify each iris pair as match or non-match. The results suggest that the semi-automatic identity verification framework is feasible when the conclusion is retrieved through a voting strategy among several humans. We expect the accuracy to be considerably higher for experienced forensic examiners. To my parents, Yujun Shen and Lianqian Zhang.
Cytoscape.js: a graph theory library for visualisation and analysis

Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js.



Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org.