nils-herrmann/clean_scirepeval huggingface dataset:
Synthesis of Complex Image Appearance from Limited Exemplars

Editing materials in photos opens up numerous opportunities like turning an unappealing dirt ground into luscious grass and creating a comfortable wool sweater in place of a cheap t-shirt. However, such edits are challenging. Approaches such as 3D rendering and BTF rendering can represent virtually everything, but they are also data intensive and computationally expensive, which makes user interaction difficult. Leaner methods such as texture synthesis are more easily controllable by artists, but also more limited in the range of materials that they handle, for example, grass and wool are typically problematic because of their non-Lambertian reflectance and numerous self-occlusions. We propose a new approach for editing of complex materials in photographs. We extend the texture-by-numbers approach with ideas from texture interpolation. The inputs to our method are coarse user annotation maps that specify the desired output, such as the local scale of the material and the illumination direction. Our algorithm then synthesizes the output from a discrete set of annotated exemplars. A key component of our method is that it can cope with missing data, interpolating information from the available exemplars when needed. This enables production of satisfying results involving materials with complex appearance variations such as foliage, carpet, and fabric from only one or a couple of exemplar photographs.
Speedy transactions in multicore in-memory databases

Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.
UvA-DARE ( Digital Academic Repository ) Recognizing Textual Entailment Using Lexical Similarity

We describe our participation in the PASCAL-2005 Recognizing Textual Entailment Challenge. Our method is based on calculating “directed” sentence similarity: checking the directed “semantic” word overlap between the text and the hypothesis. We use frequency-based term weighting in combination with two different lexical similarity measures. Our best run shows 0.55 accuracy on the test data, although the difference between our two runs is not significant. We found remarkably different optimal threshold values for the development and test data. We argue that, in addition to accuracy, precision and recall are valuable measures to consider for textual entailment.
Serious Games – An Overview

This report discusses some issues concerning serious games, that is, (digital) games used for purposes other than mere entertainment. The starting point is the serious games concept itself, and what the actually means. Further, serious games allow learners to experience situations that are impossible in the real world for reasons of safety, cost, time, etc., but they are also claimed to have positive impacts on the players’ development of a number of different skills. Subsequently, some possible positive (and negative) impacts of serious games are discussed. Further, some of the markets such games are used in are considered here, including, military games, government games, educational games, corporate games, and healthcare games. This report also identifies some (mainly academic) actors in the North American and the European serious games market. This report is part of the DISTRICT (Developing Industrial Strategies Through Innovative Cluster and Technologies) project: Serious Games Cluster and Business Network (SER3VG), which is part of the Interreg IIIC Programme.
Design criteria for the RF section of UHF and microwave passive RFID transponders

A set of design criteria for the radio-frequency (RF) section of long-range passive RF identification (RFID) transponders operating in the 2.45-GHz or 868-MHz industrial, scientific, and medical (ISM) frequency ranges is derived in this paper, focusing in particular on the voltage multiplier, the power-matching network, and the backscatter modulation. The paper discusses the design tradeoffs between the error probability at the reader receiver and the converted RF-dc power at the transponder, determining the regions of the design space that allow optimization of the operating range and the data rate of the RFID system.
A Fast and Accurate Dependency Parser using Neural Networks

Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.
Modeling astrocyte-neuron interactions in a tripartite synapse

Glial cells (microglia, oligodendrocytes, and especially astrocytes) play a critical role in the central nervous system by affecting in various ways the neuronal single cell level interactions as well as connectivity and communication at the network level, both in the developing and mature brain. Numerous studies (see, e.g., [1-3]) indicate an important modulatory role of astrocytes in brain homeostasis but most specifically in neuronal metabolism, plasticity, and survival. Astrocytes are also known to play an important role in many neurological disorders and neurodegenerative diseases. It is therefore important in the light of recent evidence to assess how the astrocytes interact with neurons, both in situ and in silico. The integration of biological knowledge into computational models is becoming increasingly important to help understand the role of astrocytes both in health and disease. We have previously addressed the role of transmitters and amyloid-beta peptide on calcium signals in rat cortical astrocytes . In this work, we extend the work by using a modified version of the previously developed model  for astrocyte-neuron interactions in a tripartite synapse to explore the effects of various preand postsynaptic as well as extrasynaptic mechanisms on neuronal activity. We consider extending the model to include various additional mechanisms, such as the role of IP3 receptor function, recycling of neurotransmitters, K+ buffering by the Na+/K+ pump, and retrograde signaling by endocannabinoids. The improved tripartite synapse model for astrocyte-neuron interactions will provide an essential modeling tool for facilitating studies of local network dynamics in the brain. The model may also serve as an important step toward understanding mechanisms behind induction and maintenance of plastic changes in the brain.
COTS-based software development: Processes and open issues

The work described in this paper is an investigation of COTS-based software development within a particular NASA environment, with an emphasis on the processes used. Fifteen projects using a COTS-based approach were studied and their actual process was documented. This process is evaluated to identify essential differences in comparison to traditional software development. The main differences, and the activities for which projects require more guidance, are requirements definition and COTS selection, high level design, integration and testing. Starting from these empirical observations, a new process and set of guidelines for COTS-based development are developed and briefly presented.
Facial expression recognition in peripheral versus central vision: role of the eyes and the mouth.

This study investigated facial expression recognition in peripheral relative to central vision, and the factors accounting for the recognition advantage of some expressions in the visual periphery. Whole faces or only the eyes or the mouth regions were presented for 150 ms, either at fixation or extrafoveally (2.5° or 6°), followed by a backward mask and a probe word. Results indicated that (a) all the basic expressions were recognized above chance level, although performance in peripheral vision was less impaired for happy than for non-happy expressions, (b) the happy face advantage remained when only the mouth region was presented, and (c) the smiling mouth was the most visually salient and most distinctive facial feature of all expressions. This suggests that the saliency and the diagnostic value of the smile account for the advantage in happy face recognition in peripheral vision. Because of saliency, the smiling mouth accrues sensory gain and becomes resistant to visual degradation due to stimulus eccentricity, thus remaining accessible extrafoveally. Because of diagnostic value, the smile provides a distinctive single cue of facial happiness, thus bypassing integration of face parts and reducing susceptibility to breakdown of configural processing in peripheral vision.
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing centralized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function modules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance.
Comics on the Brain: Structure and Meaning in Sequential Image Comprehension

Just as syntax differentiates coherent sentences from scrambled word strings, sequential images must also use a cognitive system to distinguish coherent narratives from random strings of images. We conducted experiments analogous to two classic psycholinguistic studies to examine structure and semantics in sequential images. We compared Normal comic strips with both structure and meaning to sequences with Semantics Only, Structure Only, or randomly Scrambled panels. Experiment 1 used a target-monitoring paradigm, and found that RTs were slowest to target panels in Scrambled sequences, intermediate in Structural Only and Semantic Only sequences, and fastest in Normal sequences. Experiment 2 measured ERPs to the same strips. The largest N400 appeared in Scrambled and Structural Only sequences, intermediate in Semantic Only sequences and smallest in Normal sequences. Together, these findings suggest that sequential image comprehension is guided by an interaction between a structure and meaning, broadly analogous to syntax and semantics in language.
The AT&t-DARPA communicator mixed-initiative spoken dialog system

The design and implementation of the AT&T Communicator mixed-initiative spoken dialog system is described. The Communicator project, sponsored by DARPA and launched in 1999, is a multi-year multi-site project on advanced spoken dialog systems research. The main focus of this paper is on the issues related to the design of mixed-initiative systems. In addition to describing our architecture and implementation of the complex travel task, the paper reports some preliminary evaluation results.
Privacy-Preserving Image Denoising From External Cloud Databases

Along with the rapid advancement of digital image processing technology, image denoising remains a fundamental task, which aims to recover the original image from its noisy observation. With the explosive growth of images on the Internet, one recent trend is to seek high quality similar patches at cloud image databases and harness rich redundancy therein for promising denoising performance. Despite the well-understood benefits, such a cloud-based denoising paradigm would undesirably raise security and privacy issues, especially for privacy-sensitive image data sets. In this paper, we initiate the first endeavor toward privacy-preserving image denoising from external cloud databases. Our design enables the cloud hosting encrypted databases to provide secure query-based image denoising services. Considering that image denoising intrinsically demands high quality similar image patches, our design builds upon recent advancements on secure similarity search, Yao’s garbled circuits, and image denoising operations, where each is used at a different phase of the design for the best performance. We formally analyze the security strengths. Extensive experiments over real-world data sets demonstrate that our design achieves the denoising quality close to the optimal performance in plaintext.
A compact soft actuator unit for small scale human friendly robots

This paper presents the development of a new compact soft actuation unit intended to be used in multi degree of freedom and small scale robotic systems such as the child humanoid robot “iCub” . Compared to the other existing series elastic linear or rotary implementations the proposed design shows high integration density and wider passive deflection. The miniaturization of the newly developed high performance unit was achieved with a use of a new rotary spring module based on a novel arrangement of linear springs.
An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma

Glaucoma is a serious ocular disease and leads blindness if it couldn¡¦t be detected and treated in proper way. The diagnostic criteria for glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defect. The observation of optic nerve head, cup to disc ratio and neural rim configuration are important for early detecting glaucoma in clinical practice. However, the broad range of cup to disc ratio is difficult to identify early changes of optic nerve head, and different ethnic groups possess various features in optic nerve head structures. Hence, it is still important to develop various detection techniques to assist clinicians to diagnose glaucoma at early stages. In this study, we developed an automatic detection system which contains two major phases: the first phase performs a series modules of digital fundus retinal image analysis including vessel detection, vessel in painting, cup to disc ratio calculation, and neuro-retinal rim for ISNT rule, the second phase determines the abnormal status of retinal blood vessels from different aspect of view. In this study, the novel idea of integrating image in painting and active contour model techniques successfully assisted the correct identification of cup and disk regions. Several clinical fundus retinal images containing normal and glaucoma images were applied to the proposed system for demonstration.
Enabling ECN in Multi-Service Multi-Queue Data Centers

Recent proposals have leveraged Explicit Congestion Notification (ECN) to achieve high throughput low latency data center network (DCN) transport. However, most of them implicitly assume each switch port has one queue, making the ECN schemes they designed inapplicable to production DCNs where multiple service queues per port are employed to isolate different traffic classes through weighted fair sharing. In this paper, we reveal this problem by leveraging extensive testbed experiments to explore the intrinsic tradeoffs between throughput, latency, and weighted fair sharing in multi-queue scenarios. Using the guideline learned from the exploration, we design MQ-ECN, a simple yet effective solution to enable ECN for multi-service multiqueue production DCNs. Through a series of testbed experiments and large-scale simulations, we show that MQ-ECN breaks the tradeoffs by delivering both high throughput and low latency simultaneously, while still preserving weighted fair sharing.
Mini-buckets: A general scheme for bounded inference

This article presents a class of approximation algorithms that extend the idea of bounded-complexity inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algorithms. This yields a parameterized scheme, called mini-buckets, that offers adjustable trade-off between accuracy and efficiency. The mini-bucket approach to optimization problems, such as finding the most probable explanation (MPE) in Bayesian networks, generates both an approximate solution and bounds on the solution quality. We present empirical results demonstrating successful performance of the proposed approximation scheme for the MPE task, both on randomly generated problems and on realistic domains such as medical diagnosis and probabilistic decoding.
Screening for plant viruses by next generation sequencing using a modified double strand RNA extraction protocol with an internal amplification control.

The majority of plant viruses contain RNA genomes. Detection of viral RNA genomes in infected plant material by next generation sequencing (NGS) is possible through the extraction and sequencing of total RNA, total RNA devoid of ribosomal RNA, small RNA interference (RNAi) molecules, or double stranded RNA (dsRNA). Plants do not typically produce high molecular weight dsRNA, therefore the presence of dsRNA makes it an attractive target for plant virus diagnostics. The sensitivity of NGS as a diagnostic method demands an effective dsRNA protocol that is both representative of the sample and minimizes sample cross contamination. We have developed a modified dsRNA extraction protocol that is more efficient compared to traditional protocols, requiring reduced amounts of starting material, that is less prone to sample cross contamination. This was accomplished by using bead based homogenization of plant material in closed, disposable 50ml tubes. To assess the quality of extraction, we also developed an internal control by designing a real-time (quantitative) PCR (qPCR) assay that targets endornaviruses present in Phaseolus vulgaris cultivar Black Turtle Soup (BTS).
Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation

Region-growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objective function is proposed for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.
Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. Using mind maps takes advantage of human creativity, while fuzzy cognitive maps can store and retrieve information expressed in natural language. Applying the concepts of cognitive computing makes it possible to gather and extract relevant information from a data pool. Therefore, this approach is intended to provide a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.
Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.
The Infinite Partially Observable Markov Decision Process

The Partially Observable Markov Decision Process (POMDP) framework has proven useful in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Unfortunately, most POMDPs are complex structures with a large number of parameters. In many real-world problems, both the structure and the parameters are difficult to specify from domain knowledge alone. Recent work in Bayesian reinforcement learning has made headway in learning POMDP models; however, this work has largely focused on learning the parameters of the POMDP model. We define an infinite POMDP (iPOMDP) model that does not require knowledge of the size of the state space; instead, it assumes that the number of visited states will grow as the agent explores its world and only models visited states explicitly. We demonstrate the iPOMDP on several standard problems.
Probabilistic Frame Induction

In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.