nils-herrmann/clean_scirepeval huggingface dataset:
Deep Model-Based 6D Pose Refinement in RGB

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.
Algorithm for efficient seating plan for centralized exam system

Exam seat allocation is one of the major concerns in quality education. With the increasing number of students, subjects, departments and rooms, exam seat management becomes complex. Maintaining a decent exam environment with the proper seating arrangement is one of the difficult jobs for authority. This research offers solution for exam seating arrangement problems that can be achieved through the sequential execution of three proposed algorithms. This research offers a solution for preventing some exam hall cheating by arranging seats for large students and it also finds out the best combination of rooms to be assigned for the exam to organize perfect seating based on the room orientation and size, number of students, differentiation of subjects. To do this research, we have collected data and methods from a university those are being used for their exam seating arrangement. By using university exam information we test our algorithms. It provides better seating plan then the manual system used by the university.
Ford Campus vision and lidar data set

This paper describes a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research campus and downtown Dearborn, Michigan during NovemberDecember 2009. The vehicle path trajectory in these data sets contain several large and small-scale loop closures, which should be useful for testing various state of the art computer vision and simultaneous localization and mapping (SLAM) algorithms.  The modified Ford F-250 pickup truck.
Do personal stories make patient decision aids more effective? A critical review of theory and evidence

Patient decision aids support people to make informed decisions between healthcare options. Personal stories provide illustrative examples of others' experiences and are seen as a useful way to communicate information about health and illness. Evidence indicates that providing information within personal stories affects the judgments and values people have, and the choices they make, differentially from facts presented in non-narrative prose. It is unclear if including narrative communications within patient decision aids enhances their effectiveness to support people to make informed decisions.



A survey of primary empirical research employing a systematic review method investigated the effect of patient decision aids with or without a personal story on people's healthcare judgements and decisions. Searches were carried out between 2005-2012 of electronic databases (Medline, PsycINFO), and reference lists of identified articles, review articles, and key authors. A narrative analysis described and synthesised findings.



Of 734 citations identified, 11 were included describing 13 studies. All studies found participants' judgments and/or decisions differed depending on whether or not their decision aid included a patient story. Knowledge was equally facilitated when the decision aids with and without stories had similar information content. Story-enhanced aids may help people recall information over time and/or their motivation to engage with health information. Personal stories affected both "system 1" (e.g., less counterfactual reasoning, more emotional reactions and perceptions) and "system 2" (e.g., more perceived deliberative decision making, more stable evaluations over time) decision-making strategies. Findings exploring associations with narrative communications, decision quality measures, and different levels of literacy and numeracy were mixed. The pattern of findings was similar for both experimental and real-world studies.



There is insufficient evidence that adding personal stories to decision aids increases their effectiveness to support people's informed decision making. More rigorous research is required to elicit evidence about the type of personal story that a) encourages people to make more reasoned decisions, b) discourages people from making choices based on another's values, and c) motivates people equally to engage with healthcare resources.
Vagueness and grammar: the semantics of relative and absolute gradable adjectives

This paper investigates the way that lexical semantic properties of linguistic expressions influence vagueness, focusing on the interpretation of the positive (unmarked) form of gradable adjectives. I begin by developing a semantic analysis of the positive form of ‘relative’ gradable adjectives, expanding on previous proposals by further motivating a semantic basis for vagueness and by precisely identifying and characterizing the division of labor between the compositional and contextual aspects of its interpretation. I then introduce a challenge to the analysis from the class of ‘absolute’ gradable adjectives: adjectives that are demonstrably gradable, but which have positive forms that relate objects to maximal or minimal degrees, and do not give rise to vagueness. I argue that the truth conditional difference between relative and absolute adjectives in the positive form stems from the interaction of lexical semantic properties of gradable adjectives—the structure of the scales they use—and a general constraint on interpretive economy that requires truth conditions to be computed on * This paper grew out of joint work with Louise McNally, and owes a great debt to this collaboration. I am also grateful to Chris Barker, Gennaro Chierchia, Delia Graff, Irene Heim, Ed Keenan, Jeffrey King, Sally McConnell-Ginet, Jason Stanley, and audiences at MIT, Princeton, Northwestern, the University of British Columbia, the University of Chicago, the University of Massachusetts, Amherst, the University of Michigan, the University of Milan-Bicocca, the University of Pennsylvania, the University of Stuttgart, Yale University and the Zentrum für allgemeine Sprachwissenschaft, Berlin, for very helpful comments and feedback on earlier versions of this work. I would like to express particular gratitude to John MacFarlane and Michael Glanzburg for their thoughtful and insightful reviews of the manuscript, to Mark Richard for two challenging and extremely helpful commentaries on earlier versions of the paper, and to the Associate Editor Kent Bach for probing suggestions, questions, and comments both before and during the L&P review process. Any remaining shortcomings, incoherence, or vagueness are (definitely) my responsibility. This paper is based upon work supported by the National Science Foundation under Grant No. 0094263 and 0618917 and by the American Council of Learned Societies under a Charles Ryskamp Research Fellowship. C. Kennedy (&) Department of Linguistics, University of Chicago, 1010 East 59th St, Chicago, IL 60637, USA e-mail: ck@uchicago.edu 123 Linguist Philos DOI 10.1007/s10988-006-9008-0
Explaining the Poor Performance of Consumption-Based Asset Pricing Models

The poor performance of consumption-based asset pricing models relative to traditional portfoliobased asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models. John Y. Campbell John H. Cochrane Department of Economics Graduate School of Business Harvard University University of Chicago Littauer Center 213 1101 E. 58th.
Going deeper with convolutions

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
CellSDN : Software-Defined Cellular Networks

Existing cellular networks suffer from inflexible and expen sive equipment, complex control-plane protocols, and vend orspecific configuration interfaces. In this paper, we argue th at software defined networking (SDN) can simplify the design and management of cellular data networks, while enabling new services. However, supporting many subscribers, frequent mobility, fine-grained measurement and control, and real-time adaptation introduces new scalability challeng es that future SDN architectures should address. As a first step , we present a software-defined cellular network architectur that (i) allows controller applications to express policie s based on the attributes of subscribers, rather than network addre sses and locations, (ii) enables real-time, fine-grained contro l via a local agent on each switch, and (iii) extends switches to support features like deep packet inspection and header com pression to meet the needs of cellular data services, (iv) su pports flexible “slicing” of network resources based on the at tributes of subscribers, rather than the packet header field s, and flexible “slicing” of base stations and radio resources b y having the controller to handle radio resource management, admission control and mobility in each slice.
Scap: stream-oriented network traffic capture and analysis for high-speed networks

Many network monitoring applications must analyze traffic beyond the network layer to allow for connection-oriented analysis, and achieve resilience to evasion attempts based on TCP segmentation. However, existing network traffic capture frameworks provide applications with just raw packets, and leave complex operations like flow tracking and TCP stream reassembly to application developers. This gap leads to increased application complexity, longer development time, and most importantly, reduced performance due to excessive data copies between the packet capture subsystem and the stream processing module. This paper presents the Stream capture library (Scap), a network monitoring framework built from the ground up for stream-oriented traffic processing. Based on a kernel module that directly handles flow tracking and TCP stream reassembly, Scap delivers to user-level applications flow-level statistics and reassembled streams by minimizing data movement operations and discarding uninteresting traffic at early stages, while it inherently supports parallel processing on multi-core architectures, and uses advanced capabilities of modern network cards. Our experimental evaluation shows that Scap can capture all streams for traffic rates two times higher than other stream reassembly libraries, and can process more than five times higher traffic loads when eight cores are used for parallel stream processing in a pattern matching application.
Constraint-based Round Robin Tournament Planning

Sport tournament planning becomes a complex task in the presence of heterogeneous requirements from teams, media, fans and other parties. Existing approaches to sport tournament planning often rely on precomputed tournament schemes which may be too rigid to cater for these requirements. Existing work on sport tournaments suggests a separation of the planning process into three phases. In this work, it is shown that all three phases can be solved using nite-domain constraint programming. The design of Friar Tuck, a generic constraint-based round robin planning tool, is outlined. New numerical results on round robin tournaments obtained with Friar Tuck underline the potential of constraints over nite domains in this area.
Dynamic visual attention: searching for coding length increments

A visual attention system should respond placidly when comm n stimuli are presented, while at the same time keep alert to anomalous visual inputs. In this paper, a dynamic visual attention model based on the rarity of featu r s is proposed. We introduce the Incremental Coding Length (ICL) to measure th e perspective entropy gain of each feature. The objective of our model is to ma xi ize the entropy of the sampled visual features. In order to optimize energy c onsumption, the limit amount of energy of the system is re-distributed among st features according to their Incremental Coding Length. By selecting featur es with large coding length increments, the computational system can achieve at tention selectivity in both static and dynamic scenes. We demonstrate that the prop osed model achieves superior accuracy in comparison to mainstream approaches i n stat c saliency map generation. Moreover, we also show that our model captures s everal less-reported dynamic visual search behaviors, such as attentional swing and inhibition of return.
Value-based requirements engineering: exploring innovative e-commerce ideas

Innovative e-commerce ideas are characterised by commercial products yet unknown to the market, enabled by information technology such as the Internet and technologies on top of it. How to develop such products is hardly known. We propose an interdisciplinary approach, e 3 -value, to explore an innovative e-commerce idea with the aim of understanding such an idea thoroughly and evaluating it for potential profitability. Our methodology exploits a requirements engineering way of working, but employs concepts and terminology from business science, marketing and axiology. It shows how to model business requirements and improve business–IT alignment, in sophisticated multi-actor value constellations that are common in electronic commerce. In addition to the e 3 -value approach methodology, we also present the action research-based development of our methodology, by using one of the longitudinal projects we carried out in the field of online news article provisioning.
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resourcescarce and do not have such parallel data due to low volume of queries and nonexistence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resourcescarce languages. We propose a sequenceto-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.
Rational Protocol Design: Cryptography against Incentive-Driven Adversaries

Existing work on "rational cryptographic protocols" treats each party (or coalition of parties) running the protocol as a selfish agent trying to maximize its utility. In this work we propose a fundamentally different approach that is better suited to modeling a protocol under attack from an external entity. Specifically, we consider a two-party game between an protocol designer and an external attacker. The goal of the attacker is to break security properties such as correctness or privacy, possibly by corrupting protocol participants; the goal of the protocol designer is to prevent the attacker from succeeding. We lay the theoretical groundwork for a study of cryptographic protocol design in this setting by providing a methodology for defining the problem within the traditional simulation paradigm. Our framework provides ways of reasoning about important cryptographic concepts (e.g., adaptive corruptions or attacks on communication resources) not handled by previous game-theoretic treatments of cryptography. We also prove composition theorems that-for the first time-provide a sound way to design rational protocols assuming "ideal communication resources" (such as broadcast or authenticated channels) and then instantiate these resources using standard cryptographic tools. Finally, we investigate the problem of secure function evaluation in our framework, where the attacker has to pay for each party it corrupts. Our results demonstrate how knowledge of the attacker's incentives can be used to circumvent known impossibility results in this setting.
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements

The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two fewshot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
On speaker adaptation of long short-term memory recurrent neural networks

Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture specializing in modeling long-range temporal dynamics. On acoustic modeling tasks, LSTM-RNNs have shown better performance than DNNs and conventional RNNs. In this paper, we conduct an extensive study on speaker adaptation of LSTM-RNNs. Speaker adaptation helps to reduce the mismatch between acoustic models and testing speakers. We have two main goals for this study. First, on a benchmark dataset, the existing DNN adaptation techniques are evaluated on the adaptation of LSTM-RNNs. We observe that LSTMRNNs can be effectively adapted by using speaker-adaptive (SA) front-end, or by inserting speaker-dependent (SD) layers. Second, we propose two adaptation approaches that implement the SD-layer-insertion idea specifically for LSTM-RNNs. Using these approaches, speaker adaptation improves word error rates by 3-4% relative over a strong LSTM-RNN baseline. This improvement is enlarged to 6-7% if we exploit SA features for further adaptation.
Cloud Log Forensics Metadata Analysis

The increase in the quantity and questionable quality of the forensic information retrieved from the current virtualized data cloud system architectures has made it extremely difficult for law enforcement to resolve criminal activities within these logical domains. This paper poses the question of what kind of information is desired from virtual machine (VM) hosted operating systems (OS) investigated by a cloud forensic examiner. The authors gives an overview of the information that exists on current VM OS by looking at it's kernel hypervisor logs and discusses the shortcomings. An examination of the role that the VM kernel hypervisor logs provide as OS metadata in cloud investigations is also presented.