nils-herrmann/clean_scirepeval huggingface dataset:
Understanding face recognition.

The aim of this paper is to develop a theoretical model and a set of terms for understanding and discussing how we recognize familiar faces, and the relationship between recognition and other aspects of face processing. It is suggested that there are seven distinct types of information that we derive from seen faces; these are labelled pictorial, structural, visually derived semantic, identity-specific semantic, name, expression and facial speech codes. A functional model is proposed in which structural encoding processes provide descriptions suitable for the analysis of facial speech, for analysis of expression and for face recognition units. Recognition of familiar faces involves a match between the products of structural encoding and previously stored structural codes describing the appearance of familiar faces, held in face recognition units. Identity-specific semantic codes are then accessed from person identity nodes, and subsequently name codes are retrieved. It is also proposed that the cognitive system plays an active role in deciding whether or not the initial match is sufficiently close to indicate true recognition or merely a 'resemblance'; several factors are seen as influencing such decisions. This functional model is used to draw together data from diverse sources including laboratory experiments, studies of everyday errors, and studies of patients with different types of cerebral injury. It is also used to clarify similarities and differences between processes for object, word and face recognition.
The Tire as an Intelligent Sensor

Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensors inside the tire that form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.
Luhn Revisited: Significant Words Language Models

Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the initial query retrieves only little relevant information when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Luhn [23], we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model.
 Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models in- sensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.
Novel Broadband Circularly Polarized Cavity-Backed Aperture Antenna With Traveling Wave Excitation

A novel broadband circularly polarized aperture antenna is presented that uses traveling-wave excitation as the design concept. The antenna configuration consists of a circular radiating aperture, a backed cavity, and an equiangular tapered strip outer-fed by a microstrip transmission line. Operating with a traveling wave excitation, the proposed antenna contains inherent broadband characteristics in terms of the impedance, axial ratio, and gain performances. The presented antenna is comprehensively investigated, including the working principles, design consideration, and parametric studies. In addition, the research interests are extended to a 2 × 2 antenna array. Promising results from the experimental 2 × 2 array are achieved, including the 10-dB return loss bandwidth of 70%, the 3-dB axial ratio bandwidth of 50%, and the half-power (3-dB) gain bandwidth of 40% with a maximum gain about 11 dBi. The measured and simulated results are well complied with each other.
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.
ACE: abstracting, characterizing and exploiting peaks and valleys in datacenter power consumption

Peak power management of datacenters has tremendous cost implications. While numerous mechanisms have been proposed to cap power consumption, real datacenter power consumption data is scarce. To address this gap, we collect power demands at multiple spatial and fine-grained temporal resolutions from the load of geo-distributed datacenters of Microsoft over 6 months. We conduct aggregate analysis of this data, to study its statistical properties. With workload characterization a key ingredient for systems design and evaluation, we note the importance of better abstractions for capturing power demands, in the form of peaks and valleys. We identify and characterize attributes for peaks and valleys, and important correlations across these attributes that can influence the choice and effectiveness of different power capping techniques. With the wide scope of exploitability of such characteristics for power provisioning and optimizations, we illustrate its benefits with two specific case studies.
An Experimental Comparison of Pregel-like Graph Processing Systems

The introduction of Google’s Pregel generated much interest in the field of large-scale graph data processing, inspiring the development of Pregel-like systems such as Apache Giraph, GPS, Mizan, and GraphLab, all of which have appeared in the past two years. To gain an understanding of how Pregel-like systems perform, we conduct a study to experimentally compare Giraph, GPS, Mizan, and GraphLab on equal ground by considering graph and algorithm agnostic optimizations and by using several metrics. The systems are compared with four different algorithms (PageRank, single source shortest path, weakly connected components, and distributed minimum spanning tree) on up to 128 Amazon EC2 machines. We find that the system optimizations present in Giraph and GraphLab allow them to perform well. Our evaluation also shows Giraph 1.0.0’s considerable improvement since Giraph 0.1 and identifies areas of improvement for all systems.
And Lead Us (Not) into Persuasion…? Persuasive Technology and the Ethics of Communication

The paper develops ethical guidelines for the development and usage of persuasive technologies (PT) that can be derived from applying discourse ethics to this type of technologies. The application of discourse ethics is of particular interest for PT, since 'persuasion' refers to an act of communication that might be interpreted as holding the middle between 'manipulation' and 'convincing'. One can distinguish two elements of discourse ethics that prove fruitful when applied to PT: the analysis of the inherent normativity of acts of communication ('speech acts') and the Habermasian distinction between 'communicative' and 'strategic rationality' and their broader societal interpretation. This essay investigates what consequences can be drawn if one applies these two elements of discourse ethics to PT.
Hunting for Spammers: Detecting Evolved Spammers on Twitter

Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.
An Information Retrieval Approach For Automatically Constructing Software Libraries

Although software reuse presents clear advantages for programmer productivit.y and code reliability, it is not practiced enough. One of the reasons for the only moderate success of reuse is the lack of software libr'lries that facilitate the actual locating and understanding of reusable components. This paper dpscrihes II technology for automatically I\Ssembling large softwar~ libraries that promote softwarp rpuse by helping the user locate the components closest to hfO'r/his neeels Softwarp libraries are aut.omatically assemblfO'd from a set. of Ilnorganizpd component.s by using informat.ion rptripval techniques. The constrllction of the library is done in !.wo steps. First, attribut.es ;up alJtornal.ically pxtracted from nat.urlll language docllmpntat.ion by IIsing a npIV inJexing scheme ba~fO'd an t.he nntinns of lexical affinities anci quantity of inforrnllt.ion. Then, a hierarchy for browsing is alJtomat.iclllly generaterl using a clustering techniq1le that draws only nn the informlltion provided by the attribulps. Thanks to the frfO'p-!.pxt inrlexing se-heme, tools following this approach can accept free-style nat'Jral language 'luprips. This tpChllOlogy h1\.S bfO'en impll"ment .. d in the GURU ~ystpm, whirh h<l5 bpen applied 1.0 construct an organized library of Alx utilities. An exppriment. was rondllrtpd in orrlpr to pvalullte the n·t.rievll\ effectivl"nfO'ss of GURU 1\.5 compl\red to INFOExPLORER a hypprt.pxt lihrary syslplll for Alx 3 on the IBM RISe System/6000 series. We followpr\ Ihe usual evaluation prOf"pr\lJr!' us .. .! in informat.ion rptrifO'vl\l, hased upon recall and precision ml"a5IJrPS, and d"t.erminpd t 1111 I. nllr syst.f'Tll pr>rforms l!i% better on a random test set, while being milch le~s pxpensive to build than INFOEXl'LORF.R.
Perceived robot capability

Robotics research often focuses on increasing robot capability. If end users do not perceive these increases, however, user acceptance may not improve. In this work, we explore the idea of perceived capability and how it relates to true capability, differentiating between physical and social capabilities. We present a framework that outlines their potential relationships, along with two user studies, on robot speed and speech, exploring these relationships. Our studies identify two possible consequences of the disconnect between the true and perceived capability: (1) under-perception: true improvements in capability may not lead to perceived improvements and (2) over-perception: true improvements in capability may lead to additional perceived improvements that do not actually exist.
Inferring Strategies for Sentence Ordering in Multidocument News Summarization

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.
LASER: a scalable response prediction platform for online advertising

We describe LASER, a scalable response prediction platform currently used as part of a social network advertising system. LASER enables the familiar logistic regression model to be applied to very large scale response prediction problems, including ones beyond advertising. Though the underlying model is well understood, we apply a whole-system approach to address model accuracy, scalability, explore-exploit, and real-time inference. To facilitate training with both large numbers of training examples and high dimensional features on commodity clustered hardware, we employ the Alternating Direction Method of Multipliers (ADMM). Because online advertising applications are much less static than classical presentations of response prediction, LASER employs a number of techniques that allows it to adapt in real time. LASER models can be divided into components with different re-training frequencies, allowing us to learn from changes in ad campaign performance frequently without incurring the cost of retraining larger, more stable sections of the model. Thompson sampling during online inference further helps by efficiently balancing exploration of new ads with exploitation of long running ones. To enable predictions made with the most recent feature data, we employ a range of techniques, including extensive caching and lazy evaluation, to permit real time, low latency scoring. LASER models are defined using a configuration language that ties together the training, validation, and inference pieces and permits even non-programming analysts to experiment with different model structures without modifications to code or interruptions to running servers. Finally, we show via extensive offline experiments and online A/B tests that this system provides significant benefits to prediction accuracy, gains in revenue and CTR, and reductions in system latency.
An efficient genetic algorithm for the traveling salesman problem with precedence constraints

The traveling salesman problem with precedence constraints (TSPPC) is one of the most difficult combinatorial optimization problems. In this paper, an efficient genetic algorithm (GA) to solve the TSPPC is presented. The key concept of the proposed GA is a topological sort (TS), which is defined as an ordering of vertices in a directed graph. Also, a new crossover operation is developed for the proposed GA. The results of numerical experiments show that the proposed GA produces an optimal solution and shows superior performance compared to the traditional algorithms.
Multilevel resilience analysis of transportation and communication networks

For many years the research community has attempted to model the Internet in order to better understand its behaviour and improve its performance. Since much of the structural complexity of the Inter*Work performed while Egemen K. Çetinkaya, Andrew M. Peck, and Justin P. Rohrer were at the University of Kansas. Egemen K. Çetinkaya Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA cetinkayae@mst.edu, +1 573 341 6887 Mohammed J.F. Alenazi Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA malenazi@ittc.ku.edu Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia Andrew M. Peck apeck@ittc.ku.edu; andrewpeck11@gmail.com Justin P. Rohrer rohrej@ittc.ku.edu Department of Computer Science, Naval Postgraduate School, Monterey, CA 93943–5285, jprohrer@nps.edu James P.G. Sterbenz Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA https://www.ittc.ku.edu/resilinets jpgs@ittc.ku.edu, +1 508 944 3067 School of Computing and Communications, InfoLab21 Lancaster University, Lancaster, UK, jpgs@comp.lancs.ac.uk Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong jpgs@comp.polyu.edu.hk net is due to its multilevel operation, the Internet’s multilevel nature is an important and non-trivial feature that researchers must consider when developing appropriate models. In this paper, we compare the normalised Laplacian spectra of physicaland logical-level topologies of four commercial ISPs and two research networks against the US freeway topology, and show analytically that physical level communication networks are structurally similar to the US freeway topology. We also generate synthetic Gabriel graphs of physical topologies and show that while these synthetic topologies capture the grid-like structure of actual topologies, they are more expensive than the actual physical level topologies based on a network cost model. Moreover, we introduce a distinction between geographic graphs that include degree-2 nodes needed to capture the geographic paths along which physical links follow, and structural graphs that eliminate these degree-2 nodes and capture only the interconnection properties of the physical graph and its multilevel relationship to logical graph overlays. Furthermore, we develop a multilevel graph evaluation framework and analyse the resilience of single and multilevel graphs using the flow robustness metric. We then confirm that dynamic routing performed over the lower levels helps to improve the performance of a higher level service, and that adaptive challenges more severely impact the performance of the higher levels than non-adaptive challenges.
An online method for tight-tolerance insertion tasks for string and rope

This paper presents a fast tight-tolerance threading technique for string and rope. Instead of relying on simulations of these deformable objects to plan a path or compute control actions, we control the movement of the string with a virtual magnetic vector field emanating from the narrow openings we wish to thread through. We compute an approximate Jacobian to move the tip of the string through the vector field and propose a method to promote alignment of the head of the string to the opening. We also propose a method for re-grasping the string based on the relationship between the string's configuration, the orientation of the opening, and direction of gravity. This re-grasping method in conjunction with our controller can be used to thread the string through a sequence of openings. We evaluated our method in simulation (with simulated sensor noise) and on the Da Vinci surgical robot. Our results suggest that our method is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string (3.5mm) and opening (4.9mm) differ by only 1.4 mm.
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI

We present a vision-only model for gaming AI which uses a late integration deep convolutional network architecture trained in a purely supervised imitation learning context. Although state-of-the-art deep learning models for video game tasks generally rely on more complex methods such as deep-Q learning, we show that a supervised model which requires substantially fewer resources and training time can already perform well at human reaction speeds on the N64 classic game Super Smash Bros. We frame our learning task as a 30-class classification problem, and our CNN model achieves 80% top-1 and 96% top-3 validation accuracy. With slight test-time fine-tuning, our model is also competitive during live simulation with the highest-level AI built into the game. We will further show evidence through network visualizations that the network is successfully leveraging temporal information during inference to aid in decision making. Our work demonstrates that supervised CNN models can provide good performance in challenging policy prediction tasks while being significantly simpler and more lightweight than alternatives.
LCD Mura Detection Based on Accumulated Differences and Multi-resolution Background Subtraction

A mura detection approach based on the difference accumulation and background estimation is proposed to detect mura in the thin film transistor liquid crystal display (TFT-LCD) images. We were motivated by wavelet transform to derive a multi-resolution method for accelerating the detecting speed of background estimation. While the visibility of mura depends on inspecting angles and online environmental light luminance, sometimes the defects may be invisible in the observed images. Thus, we calibrate the non-uniform luminance and accumulate the continuous online images for depressing noise and enhancing the contrast between mura and background. However, the online moving speed is unstable; thus, we propose a full-automatic non-uniform displacement calibration method. We demonstrate them by applying to real and artificial online image sequences. The efficiency and effect is described in experiments. Our methods are also compared with other mura detecting methods.