nils-herrmann/clean_scirepeval huggingface dataset:
Fine-grained Visual Categorization using PAIRS: Pose and Appearance Integration for Recognizing Subcategories

In Fine-grained Visual Categorization (FGVC), the differences between similar categories are often highly localized to a small number of object parts , and significant pose variation therefore constitutes a great challenge for identification. To address this, we propose extracting image patches using pairs of predicted keypoint locations as anchor points. The benefits of this approach are two-fold: (1) it achieves explicit top-down visual attention on object parts(see Figure 5), and (2) the extracted patches are pose-aligned and thus contain stable appearance features. We employ the popular Stacked Hourglass Network [39] to predict keypoint locations, reporting stateof-the-art keypoint localization results on the challenging CUB-200-2011  dataset. Anchored by these predicted keypoints, an overcomplete basis of pose-aligned patches is extracted and a specialized appearance classification network is trained for each patch. An aggregating network is then applied to combine the patch networks’ individual predictions, producing a final classification score. Our PAIRS algorithm attains an accuracy of 88.6%, an increase of 1.1% over the current state-of-the-art. Enhancing the base PAIRS model with single-keypoint patches produces a further improvement, yielding a new state-of-the-art accuracy of 89.2% on the CUB dataset and clearly demonstrating the power of integrating pose and appearance features.
Ad Injection at Scale: Assessing Deceptive Advertisement Modifications

Today, web injection manifests in many forms, but fundamentally occurs when malicious and unwanted actors tamper directly with browser sessions for their own profit. In this work we illuminate the scope and negative impact of one of these forms, ad injection, in which users have ads imposed on them in addition to, or different from, those that websites originally sent them. We develop a multi-staged pipeline that identifies ad injection in the wild and captures its distribution and revenue chains. We find that ad injection has entrenched itself as a cross-browser monetization platform impacting more than 5% of unique daily IP addresses accessing Google -- tens of millions of users around the globe. Injected ads arrive on a client's machine through multiple vectors: our measurements identify 50,870 Chrome extensions and 34,407 Windows binaries, 38% and 17% of which are explicitly malicious. A small number of software developers support the vast majority of these injectors who in turn syndicate from the larger ad ecosystem. We have contacted the Chrome Web Store and the advertisers targeted by ad injectors to alert each of the deceptive practices involved.
On the write energy of non-volatile resistive crossbar arrays with selectors

Crossbar arrays based on non-volatile resistive devices such as resistive RAM and phase change memory have become an important technology due to the applications to memory systems. The energy consumption of integrated circuits has become a primary issue due to thermal constraints in high performance systems and limited battery time in mobile and IoT applications. In this paper, the energy efficiency of a crossbar array of a one-selector-one-resistor (1S1R) configuration during a write operation is explored for the V/2 and V/3 bias schemes. The characteristics that affect the most energy efficient bias scheme are demystified. The write energy of a crossbar array is modeled in terms of the array size, number of selected cells, and the nonlinearity factor. For a specific array size and selector technology, the number of selected cells during a write operation can affect the choice of bias scheme. Moreover, the effect of leakage current due to partially biased unselected cells is explored.
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees

We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach.
The Word Tree, an Interactive Visual Concordance

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.
Learning to map between ontologies on the semantic web

Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web.We describe glue, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology glue finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that glue can work with all of them. This is in contrast to most existing approaches, which deal with a single similarity measure. Another key feature of glue is that it uses multiple learning strategies, each of which exploits a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend glue to incorporate commonsense knowledge and domain constraints into the matching process. For this purpose, we show that relaxation labeling, a well-known constraint optimization technique used in computer vision and other fields, can be adapted to work efficiently in our context. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that glue proposes highly accurate semantic mappings.
Network localization and navigation via cooperation

Network localization and navigation give rise to a new paradigm for communications and contextual data collection, enabling a variety of new applications that rely on position information of mobile nodes (agents). The performance of such networks can be significantly improved via the use of cooperation. Therefore, a deep understanding of information exchange and cooperation in the network is crucial for the design of location-aware networks. This article presents an exploration of cooperative network localization and navigation from a theoretical foundation to applications, covering technologies and spatiotemporal cooperative algorithms.
Safe, Strong, and Tractable Relevance Analysis for Planning

In large and complex planning problems, there will almost inevitably be aspects that are not relevant to a specific problem instance. Thus, identifying and removing irrelevant parts from an instance is one of the most important techniques for scaling up automated planning. We examine the path-based relevance analysis method, which is safe (preserves plan existence and cost) and powerful but has exponential time complexity, and show how to make it run in polynomial time with only a minimal loss of pruning power.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems

Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision. We efficiently solve the specialized problem of stereo matching under active illumination using a new learning-based algorithm. This type of active stereo i.e. stereo matching where scene texture is augmented by an active light projector is proving compelling for designing depth cameras, largely due to improved robustness when compared to time of flight or traditional structured light techniques. Our algorithm uses an unsupervised greedy optimization scheme that learns features that are discriminative for estimating correspondences in infrared images. The proposed method optimizes a series of sparse hyperplanes that are used at test time to remap all the image patches into a compact binary representation in O(1). The proposed algorithm is cast in a PatchMatch Stereo-like framework, producing depth maps at 500Hz. In contrast to standard structured light methods, our approach generalizes to different scenes, does not require tedious per camera calibration procedures and is not adversely affected by interference from overlapping sensors. Extensive evaluations show we surpass the quality and overcome the limitations of current depth sensing technologies.
Extreme gigantomastia in pregnancy: case report—my experience with two cases in last 5 years

We present an extreme case of gigantomastia in pregnancy during the second gemelar pregnancy of a 30-year-old woman. Her first pregnancy was 8 years ago, was also gemelar and she delivered with caesarean section. From the beginning of her current pregnancy, the patient noted steady growth of both of her breasts that reached enormous dimensions at the end of the pregnancy. This kind of breast changes did not occur during her first pregnancy. The patient also suffered from myasthenia gravis that was in remission during this pregnancy, without any therapy. The patient was in the 38 weeks of gestation, and a delivery with caesarean section was performed in line with the reduction of her breasts. The main reasons that led me to perform these two interventions as one act were the fact that puerperal mastitis could develop on these enormous breasts, further the small regression of these huge breasts during the bromocriptine treatment, as well as the intention to avoid other operative traumas, considering possibility of exacerbation of myasthenia gravis. I had already performed bilateral reduction mammaplasty with free areola-nipple graft, when a tissue with total weight of 20 kg (2 × 10 kg) was removed. The patient had an excellent post-operation recovery course.
Semi-supervised Learning with Regularized Laplacian

We study a semi-supervised learning method based on the similarity graph and Regularized Laplacian. We give convenient optimization formulation of the Regularized Laplacian method and establish its various properties. In particular, we show that the kernel of the method can be interpreted in terms of discrete and continuous time random walks and possesses several important properties of proximity measures. Both optimization and linear algebra methods can be used for efficient computation of the classification functions. We demonstrate on numerical examples that the Regularized Laplacian method is competitive with respect to the other state of the art semi-supervised learning methods.  Semi-supervised learning, Graph-based learning, Regularized Laplacian, Proximity measure, Wikipedia article classification ∗ Corresponding author. K. Avrachenkov is with Inria Sophia Antipolis, 2004 Route des Lucioles, 06902, Sophia Antipolis, France k.avrachenkov@inria.fr † P. Chebotarev is with Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, 65 Profsoyuznaya Str., Moscow, 117997, Russia ‡ A. Mishenin is with St. Petersburg State University, Faculty of Applied Mathematics and Control Processes, Peterhof, 198504, Russia § This work was partially supported by Campus France, Alcatel-Lucent Inria Joint Lab, EU Project Congas FP7-ICT-2011-8-317672, and RFBR grant No. 13-07-00990. L’Apprentissage Semi-supervisé avec Laplacian Régularisé Résumé : Nous étudions une méthode d’apprentissage semi-supervisé, basé sur le graphe de similarité et Laplacian régularisé. Nous formalisons la méthode comme un problème d’optimisation convexe et quadratique et nous établissons ses diverses propriétés. En particulier, nous montrons que le noyau de la méthode peut être interprété en termes des marches aléatoires en temps discret et continu et possède plusieurs propriétés importantes des mesures de proximité. Les techniques d’optimisation ainsi que les techniques d’algébre linéaire peuvent être utilisé pour un calcul efficace des fonctions de classification. Nous démontrons sur des exemples numériques que la méthode de Laplacian régularisé est concurrentiel par rapport aux autres état de l’art méthodes d’apprentissage semi-supervisé. Mots-clés : Apprentissage Semi-supervisé, Apprentissage basé sur le graphe de similarité, Laplacian régularisé, mesure de proximité, classification des articles Wikipedia Semi-supervised Learning with Regularized Laplacian 3
Compiling Packet Programs to Reconfigurable Switches

Programmable switching chips are becoming more commonplace, along with new packet processing languages to configure the forwarding behavior. Our paper explores the design of a compiler for such switching chips, in particular how to map logical lookup tables to physical tables, while meeting data and control dependencies in the program. We study the interplay between Integer Linear Programming (ILP) and greedy algorithms to generate solutions optimized for latency, pipeline occupancy, or power consumption. ILP is slower but more likely to fit hard cases; further, ILP can be used to suggest the best greedy approach. We compile benchmarks from real production networks to two different programmable switch architectures: RMT and Intel’s FlexPipe. Greedy solutions can fail to fit and can require up to 38% more stages, 42% more cycles, or 45% more power for some benchmarks. Our analysis also identifies critical resources in chips. For a complicated use case, doubling the TCAM per stage reduces the minimum number of stages needed by 12.5%.
A swarm optimization algorithm inspired in the behavior of the social-spider

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.
Interactive multiresolution mesh editing

We describe a multiresolution representation for meshes based on subdivision, which is a natural extension of the existing patch-based surface representations. Combining subdivision and the smoothing algorithms of Taubin [26] allows us to construct a set of algorithms for interactive multiresolution editing of complex hierarchical meshes of arbitrary topology. The simplicity of the underlying algorithms for refinement and coarsification enables us to make them local and adaptive, thereby considerably improving their efficiency. We have built a scalable interactive multiresolution editing system based on such algorithms.
Toward an SDN-enabled NFV architecture

This article presents the progressive evolution of NFV from the initial SDN-agnostic initiative to a fully SDN-enabled NFV solution, where SDN is not only used as infrastructure support but also influences how virtual network functions (VNFs) are designed. In the latest approach, when possible, stateless processing in the VNF shifts from the computing element to the networking element. To support these claims, the article presents the implementation of a flow-based network access control solution, with an SDN-enabled VNF built on IEEE 802.1x, which establishes services as sets of flow definitions that are authorized as the result of an end user authentication process. Enforcing the access to the network is done at the network element, while the authentication and authorization state is maintained at the compute element. The application of this proposal allows the performance to be enhanced, while traffic in the control channel is reduced to a minimum. The SDN-enabled NFV approach sets the foundation to increase the areas of application of NFV, in particular in those areas where massive stateless processing of packets is expected.
An Experiment on DES Statistical Cryptanalysis

Linear cryptanalysis and differential cryptanalysis are the most important methods of attack against block ciphers. Their efficiency have been demonstrated against several ciphers, including the Data Encryption Standard. We prove that both of them can be considered, improved and joined in a more general statistical framework. We also show that the very same results as those obtained in the case of DES can be found without any linear analysis and we slightly improve them into an attack with theoretical complexity 242 ·9 . We can apply another statistical attack the xcryptanalysis on the same characteristics without a definite idea of what happens in the encryption process. It appears to be roughly as efficient as both differential and linear cryptanalysis. We propose a new heuristic method to find good characteristics. It has found an attack against DES absolutely equivalent to Matsui's one by following a distinct path.
Automatic knowledge extraction from OCR documents using hierarchical document analysis

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format [1, 11, 18, 26]. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF.
Tracking systems for satellite communications

Satellites in nominally geostationary orbits possess diurnal motion which causes the apparent position of the spacecraft to wander in the sky as seen by an earth station. If uncompensated this causes a variation in the performance of the communications link. The motion of the satellite therefore has to be tracked by the earth station antenna. During the past twenty years or so a considerable amount of work has gone into the development of tracking schemes for satellite communication systems. Here a review of the various techniques is presented. For each method used the principle of operation is described and the various salient features extracted. Comparisons are made where appropriate and practical implementations of the systems discussed. The application of intelligent control algorithms to tracking is also considered. The areas covered include, orbit determination, optimal estimation techniques, performance, and practical implementations. Recently introduced techniques are considered and likely future developments projected.
Sequential Decision Problems and Neural Networks

c. J. C. H. Watkins 25B Framfield Highbury, London N51UU Decision making tasks that involve delayed consequences are very common yet difficult to address with supervised learning methods. If there is an accurate model of the underlying dynamical system, then these tasks can be formulated as sequential decision problems and solved by Dynamic Programming. This paper discusses reinforcement learning in terms of the sequential decision framework and shows how a learning algorithm similar to the one implemented by the Adaptive Critic Element used in the pole-balancer of Barto, Sutton, and Anderson (1983), and further developed by Sutton (1984), fits into this framework. Adaptive neural networks can play significant roles as modules for approximating the functions required for solving sequential decision problems.
Multilevel Inverters for Electric Vehicle Applications

 This paper presents multilevel inverters as an application for all-electric vehicle (EV) and hybrid-electric vehicle (HEV) motor drives. Diode-clamped inverters and cascaded H-bridge inverters, (1) can generate near-sinusoidal voltages with only fundamental frequency switching; (2) have almost no electromagnetic interference (EMI) and commonmode voltage; and (3) make an EV more accessible/safer and open wiring possible for most of an EV’s power system. This paper explores the benefits and discusses control schemes of the cascade inverter for use as an EV motor drive or a parallel HEV drive and the diode-clamped inverter as a series HEV motor drive. Analytical, simulated, and experimental results show the superiority of these multilevel inverters for this new niche.