nils-herrmann/clean_scirepeval huggingface dataset:
The coming commoditization of processes.

Despite the much-ballyhooed increase in outsourcing, most companies are in do-it-yourself mode for the bulk of their processes, in large part because there's no way to compare outside organizations' capabilities with those of internal functions. Given the lack of comparability, it's almost surprising that anyone outsources today. But it's not surprising that cost is by far companies' primary criterion for evaluating outsourcers or that many companies are dissatisfied with their outsourcing relationships. A new world is coming, says the author, and it will lead to dramatic changes in the shape and structure of corporations. A broad set of process standards will soon make it easy to determine whether a business capability can be improved by outsourcing it. Such standards will also help businesses compare service providers and evaluate the costs versus the benefits of outsourcing. Eventually these costs and benefits will be so visible to buyers that outsourced processes will become a commodity, and prices will drop significantly. The low costs and low risk of outsourcing will accelerate the flow of jobs offshore, force companies to reassess their strategies, and change the basis of competition. The speed with which some businesses have already adopted process standards suggests that many previously unscrutinized areas are ripe for change. In the field of technology, for instance, the Carnegie Mellon Software Engineering Institute has developed a global standard for software development processes, called the Capability Maturity Model (CMM). For companies that don't have process standards in place, it makes sense for them to create standards by working with customers, competitors, software providers, businesses that processes may be outsourced to, and objective researchers and standard-setters. Setting standards is likely to lead to the improvement of both internal and outsourced processes.
Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion

The event-related potential (ERP) is one of the most popular measures in human cognitive neuroscience. During the last few years there has been a debate about the neural fundamentals of ERPs. Two models have been proposed: The evoked model states that additive evoked responses which are completely independent of ongoing background electroencephalogram generate the ERP. On the other hand the phase reset model suggests a resetting of ongoing brain oscillations to be the neural generator of ERPs. Here, evidence for either of the two models is presented and validated, and their possible impact on cognitive neuroscience is discussed. In addition, future prospects on this field of research are presented.
Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review

Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the underlying RNN, typically in the form of finite state machines, that mimic the network to a satisfactory degree while having the advantage of being more transparent. RE from RNNs can be argued to allow a deeper and more profound form of analysis of RNNs than other, more or less ad hoc methods. RE may give us understanding of RNNs in the intermediate levels between quite abstract theoretical knowledge of RNNs as a class of computing devices and quantitative performance evaluations of RNN instantiations. The development of techniques for extraction of rules from RNNs has been an active field since the early 1990s. This article reviews the progress of this development and analyzes it in detail. In order to structure the survey and evaluate the techniques, a taxonomy specifically designed for this purpose has been developed. Moreover, important open research issues are identified that, if addressed properly, possibly can give the field a significant push forward.
Cats and dogs

We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.
Casual 3D photography

We present an algorithm that enables casual 3D photography. Given a set of input photos captured with a hand-held cell phone or DSLR camera, our algorithm reconstructs a 3D photo, a central panoramic, textured, normal mapped, multi-layered geometric mesh representation. 3D photos can be stored compactly and are optimized for being rendered from viewpoints that are near the capture viewpoints. They can be rendered using a standard rasterization pipeline to produce perspective views with motion parallax. When viewed in VR, 3D photos provide geometrically consistent views for both eyes. Our geometric representation also allows interacting with the scene using 3D geometry-aware effects, such as adding new objects to the scene and artistic lighting effects.
 Our 3D photo reconstruction algorithm starts with a standard structure from motion and multi-view stereo reconstruction of the scene. The dense stereo reconstruction is made robust to the imperfect capture conditions using a novel near envelope cost volume prior that discards erroneous near depth hypotheses. We propose a novel parallax-tolerant stitching algorithm that warps the depth maps into the central panorama and stitches two color-and-depth panoramas for the front and back scene surfaces. The two panoramas are fused into a single non-redundant, well-connected geometric mesh. We provide videos demonstrating users interactively viewing and manipulating our 3D photos.
Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems

Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.
GraphDB : Modeling and Querying Graphs in Databases

Weproposeadatamodelandqueaylanguagethat integrates an explicit modeling and querying of graphssmoothlyintoastandsrddambaseenvimnment For standard applications, some hey featuresofobjectuientedmodeling~offesedsuchas object classes organized into a hierarchy, object identity, and attributes referencing objects. Queryingcanbedoneinafamiliarstylewitha &rive statement that can be used like a select . ..from . . . wkre.Gntheotherhand,themodel allows for an explicit mpresentation of graphs by partitioning object classes into simple classes, linkclasses,andpathclasseswhoseobjectscan be viewed as nodes, edges, and explicitly stored paths of a graph (which is the whole dambase ins-). For querying graphs, the derive statement has an extended meaning in that it allows onetoiefertosubgraphsofthedambasegraph.A powerful rewrite operation is offered for the manipulation of heterogeneous sequences of objects which often occur as a result of accessing thedambasegraph.Additionallytherearespecial graphoperationslikedekmnn@a&ortestpath or a subgmph and the model is extensible by such operations. Besides being attractive for standard applications, the model permits a natural representation and sophisticated querying of nelworks, in parhk of spatially embedded networks like highways, public transpart, etc. This work was suppoxted by the ESPRIT Basic Resepch Project 6881 AMUSING Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 20th VLDB Conference Santiago, Chile, 1994
Creating adaptive Web sites through usage-based clustering of URLs

Web personalization has quickly moved from an added value feature to a necessity, particularly for large information services and sites that generate revenue by selling products. Web personalization can be viewed as using user preferences profiles to dynamically serve customized content to particular users. User preferences may be obtained explicitly, or by passive observation of users over time as they interact with the system. Principal elements of Web personalization include modeling of Web objects (pages, etc.) and subjects (users), matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. Existing approaches used by many Web-based companies, as well as approaches based on collaborative filtering (e.g., GroupLens [HKBR99] and Firefly [SM95]), rely heavily on human input for determining the personalization actions. This type of input is often a subjective description of the users by the users themselves, and thus prone to biases. Furthermore, the profile is static, and its performance degrades over time as the profile ages. Recently, a number of approaches have been developed dealing with specific aspects of Web usage mining for the purpose of automatically discovering user profiles. For example, Perkowitz and Etzioni [PE98] proposed the idea of optimizing the structure of Web sites based co-occurrence patterns of pages within usage data for the site. Schechter et al [SKS98] have developed techniques for using path profiles of users to predict future HTTP requests, which can be used for network and proxy caching. Spiliopoulou et al [SF99], Cooley et al [CMS99], and Buchner and Mulvenna [BM99] have applied data mining techniques to extract usage patterns from Web logs, for the purpose of deriving marketing intelligence. Shahabi et al [SZA97], Yan et al [YJGD96], and Nasraoui et al [NFJK99] have proposed clustering of user sessions to predict future user behavior. In this paper we describe an approach to usage-based Web personalization taking into account both the offline tasks related to the mining of usage data, and the online process of automatic Web page customization based on the mined knowledge. Specifically, we propose an effective technique for capturing common user profiles based on association-rule discovery and usage-based clustering. We also propose techniques for combining this knowledge with the current status of an ongoing Web activity to perform realtime personalization. Finally, we provide an experimental evaluation of the proposed techniques using real Web usage data.
Unsupervised Monocular Depth Estimation with Left-Right Consistency

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Ex-ploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.
Determining the Origin of Downloaded Files Using Metadata Associations

Determining the “origin of a file” in a file system is often required during digital investigations. While the problem of “origin of a file” appears intractable in isolation, it often becomes simpler if one considers the environmental context, viz., the presence of browser history, cache logs, cookies and so on. Metadata can help bridge this contextual gap. Majority of the current tools, with their search-and-query interface, while enabling extraction of metadata stops short of leading the investigator to the “associations” that metadata potentially point to, thereby enabling an approach to solving the “origin of a file” problem. In this paper, we develop a method to identify the origin of files downloaded from the Internet using metadata based associations. Metadata based associations are derived though metadata value matches on the digital artifacts and the artifacts thus associated, are grouped together automatically. These associations can reveal certain higher-order relationships across different sources such as file systems and log files. We define four relationships between files on file systems and log records in log files which we use to determine the origin of a particular file. The files in question are tracked from the user file system under examination to the different browser logs generated during a user’s online activity to their points of origin in the Internet.
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets

Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.
What do Constraint Programming Users Want to See? Exploring the Role of Visualisation in Profiling of Models and Search

Constraint programming allows difficult combinatorial problems to be modelled declaratively and solved automatically. Advances in solver technologies over recent years have allowed the successful use of constraint programming in many application areas. However, when a particular solver's search for a solution takes too long, the complexity of the constraint program execution hinders the programmer's ability to profile that search and understand how it relates to their model. Therefore, effective tools to support such profiling and allow users of constraint programming technologies to refine their model or experiment with different search parameters are essential. This paper details the first user-centred design process for visual profiling tools in this domain. We report on: our insights and opportunities identified through an on-line questionnaire and a creativity workshop with domain experts carried out to elicit requirements for analytical and visual profiling techniques; our designs and functional prototypes realising such techniques; and case studies demonstrating how these techniques shed light on the behaviour of the solvers in practice.
Surgical anatomy of the midcheek: facial layers, spaces, and the midcheek segments.

This article describes the surgical anatomy of the midcheek. The article's key points: (1) Structurally, the midcheek is formed by the convergence of three components: the lid-cheek segment, the malar segment, and the nasolabial segment. (2) The midcheek skeleton provides the attachment for the muscles and ligaments of both the lower lid and the upper lip. (3) The midcheek contains proportionally more spaces and fewer ligaments than other parts of the face. (4) Each midcheek segment overlies a specific facial space. (5) The lid-cheek contour transition does not have a fixed relationship to the inferior orbital rim. (6) The youthful lid-cheek transition is above the orbital rim and the aged is below the rim. (7) Facial nerve branches course in predictable locations.
Fast frequency acquisition phase-frequency detectors for GSa/s phase-locked loops

This paper describes two techniques for designing phase-frequency detectors (PFDs) with higher operating frequencies (periods of less than 8x the delay of a fan-out-4 inverter (FO-4)) and faster frequency acquisition. Prototypes designed in  CMOS process exhibit operating frequencies of 1.25 GHz ( = 1/(8 ċ FO-4) ) and 1.5 GHz ( = 1/(6.7 ċ FO-4) ) for two techniques respectively whereas a conventional PFD operates < 1 GHz ( = 1/(10 ċ FO-4) ). The two proposed PFDs achieve a capture range of 1.7x and 1.2x the conventional design.
Tubular linear permanent magnet motors: an overall comparison

This paper deals with tubular linear permanent magnet (TL-PM) motors, including different topologies, i.e. interior and surface-mounted PMs, slotted and slotless motors. After some preliminary analytical considerations, the performance of the TL-PM motor are highlighted, including specific force, maximum acceleration and cost. Not only the advantages are emphasised, but also the drawbacks such as detent forces and attractive radial forces due to eccentricity. Different models have been adopted in the various analyses, including 2D and 3D finite element analysis as necessary. In addition, a comparison between predicted and measured performance on a motor prototype are reported.
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a riskadjusted Markov Decision Process for a discrete-time version of the classical Black-ScholesMerton (BSM) model, where the option price is an optimal Q-function, while the optimal hedge is a second argument of this optimal Q-function, so that both the price and hedge are parts of the same formula. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in the Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model is able to go model-free and learn to price and hedge an option directly from data generated from a dynamic replicating portfolio which is rebalanced at discrete times. If the world is according to BSM, our risk-averse Q-Learner converges, given enough training data, to the true BSM price and hedge ratio of the option in the continuous time limit ∆t → 0, even if hedges applied at the stage of data generation are completely random (i.e. it can learn the BSM model itself, too!), because QLearning is an off-policy algorithm. If the world is different from a BSM world, the Q-Learner will find it out as well, because Q-Learning is a model-free algorithm. For finite time steps ∆t, the Q-Learner is able to efficiently calculate both the optimal hedge and optimal price for the option directly from trading data, and without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for optimal pricing and hedging of options, once we depart from the academic continuous-time limit ∆t → 0, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based Reinforcement Learning. Further, due to simplicity and tractability of our model which only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data), and its close relation to the original BSM model, we suggest that our model could be used for benchmarking of different RL algorithms for financial trading applications. I would like to thank my students for their interest in this work and stimulating discussions that challenged me to look for simple explanations of complex topics. I thank Tom N.L. for an initial implementation of a timediscretized BSM model. This work is dedicated to my wife Lola on the occasion of her birthday and receiving a doctoral degree.