nils-herrmann/clean_scirepeval huggingface dataset:
Alert prioritization in Intrusion Detection Systems

Intrusion Detection Systems (IDSs) are designed to monitor user and/or network activity and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large; making the task of security analysts difficult to manage. Furthermore, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide imprecise results. In this paper, we propose a fuzzy-logic based technique for scoring and prioritizing alerts generated by an IDS(1). In addition, we present an alert rescoring technique that leads to a further reduction of the number of alerts. The approach is validated using the 2000 DARPA intrusion detection scenario specific datasets and comparative results between the Snort IDS alert scoring and our scoring and prioritization scheme are presented.
Learning graph structure for multi-label image classification via clique generation

Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.
A Study of Integrated Prefetching and Caching Strategies

Prefetching and caching are effective techniques for improving the performance of file systems, but they have not been studied in an integrated fashion. This paper proposes four properties that optimal integrated strategies for prefetching and caching must satisfy, and then presents and studies two such integrated strategies, called aggressive and conservative. We prove that the performance of the conservative approach is within a factor of two of optimal and that the performance of the aggressive strategy is a factor significantly less than twice that of the optimal case. We have evaluated these two approaches by trace-driven simulation with a collection of file access traces. Our results show that the two integrated prefetching and caching strategies are indeed close to optimal and that these strategies can reduce the running time of applications by up to 50%.
Increase in capacity of multiuser OFDM system using dynamic subchannel allocation

This paper investigates the problem of dynamic multiuser subchannel allocation in the downlink of OFDM systems. The assumptions are that the channel model is quasi-static and that the base station has perfect channel information. In traditional TDMA or FDMA systems, resource allocation for each user is nonadaptively fixed, and the water-filling power spectrum is known to be optimal. Since the subchannel allocations among the users are not optimized, a group of users is likely to suffer from poor channel gains resulting from large path loss and random fading. To resolve this problem, we derive a multiuser convex optimization problem to find the optimal allocation of subchannels, and propose a low-complexity adaptive subchannel allocation algorithm. Simulation results show that the proposed algorithm performs almost as well as the optimal solution. Also, higher spectral efficiency is achieved for larger number of users in a cell due to the multiuser diversity.
AMS Without 4-Wise Independence on Product Domains

1 University of California Los Angeles. Supported in part by N SF grants 0716835, 0716389, 0830803, 0916574 and Lockheed Martin Corporation. E-mail address :  ̃ vova 2 Harvard School of Engineering and Applied Sciences. Suppor ted by US-Israel BSF grant 2006060 and NSF grant CNS-0831289. E-mail address : kmchung@fas.harvard.edu URL: http://people.seas.harvard.edu/ ̃ kmchung/ 3 Harvard School of Engineering and Applied Sciences. Suppor ted in part by NSF grant CNS-0721491. The work was finished during an internship in Microsoft Research Asia. E-mail address : zliu@fas.harvard.edu URL: http://people.seas.harvard.edu/ ̃ zliu/ 4 Harvard School of Engineering and Applied Sciences. Suppor ted in part by NSF grant CNS-0721491 and research grants from Yahoo!, Google, and Cisco. E-mail address : michaelm@eecs.harvard.edu URL: http://www.eecs.harvard.edu/ ̃ michaelm/ 5 University of California Los Angeles. Supported in part by I BM Faculty Award, Lockheed-Martin Corporation Research Award, Xerox Innovation Group Award, the Okawa Fou ndation Award, Intel, Teradata, NSF grants 0716835, 0716389, 0830803, 0916574 and U.C. MICRO grant. E-mail address :  ̃ rafail
MAV urban localization from Google street view data

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.
Combining strengths of circuit-based and CNF-based algorithms for a high-performance SAT solver

We propose Satisfiability Checking (SAT) techniques that lead to a consistent performance improvement of up to 3x over state-of-the-art SAT solvers like Chaff on important problem domains in VLSI CAD. We observe that in circuit oriented applications like ATPG and verification, different software engineering techniques are required for the portions of the formula corresponding to learnt clauses compared to the original formula. We demonstrate that by employing the same innovations as in advanced CNF-based SAT solvers, but in a hybrid approach where these two portions of the formula are represented differently and processed separately, it is possible to obtain the consistently highest performing SAT solver for circuit oriented problem domains. We also present controlled experiments to highlight where these gains come from. Once it is established that the hybrid approach is faster, it becomes possible to apply low overhead circuit-based heuristics that would be unavailable in the CNF domain for greater speedup.
Optimal Target Assignment and Path Finding for Teams of Agents

We study the TAPF (combined target-assignment and pathfinding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multiagent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collisionfree paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (ConflictBased Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent pathfinding algorithms. On the low level, CBM uses a mincost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.
Pose from Flow and Flow from Pose

Human pose detectors, although successful in localising faces and torsos of people, often fail with lower arms. With fast movements body motion estimation is often inaccurate. We build a segmentation-detection algorithm that mediates the information between body parts recognition, and multi-frame motion grouping to improve both pose detection and tracking. Motion of body parts, though not accurate, is often sufficient to segment them from their backgrounds. Such segmentations are crucial for extracting hard to detect body parts out of their interior body clutter. By matching these segments to exemplars we obtain pose labeled body segments. The pose labeled segments and corresponding articulated joints are used to improve the motion flow fields by proposing kinematically constrained affine displacements on body parts. The pose-based articulated motion model is shown to handle large limb rotations and displacements. Our algorithm can detect people under rare poses, frequently missed by pose detectors, showing the benefits of jointly reasoning about pose, segmentation and motion in videos.
The Fastest Pedestrian Detector in the West

In many applications, detection speed is as important as accuracy. (a) A standard pipeline for performing modern multiscale detection is to create a densely sampled image pyramid, compute features at each scale, and finally perform sliding window classification (with a fixed scale model). Although effective; the creation of the feature pyramid can dominate the cost of detection, leading to slow multiscale detection. (b) Viola and Jones  utilized shift and scale invariant features, allowing a trained detector to be placed at any location and scale without relying on an image pyramid. Constructing such a classifier pyramid results in fast multiscale detection; unfortunately, most features are not scale invariant, including gradient histograms, significantly limiting the generality of this scheme. (c) We propose a fast method for approximating features at multiple scales using a sparsely sampled image pyramid with a step size of an entire octave and within each octave we use a classifier pyramid. The proposed approach achieves nearly the same accuracy as using densely sampled image pyramids, with nearly the same speed as using a classifier pyramid applied to an image at a single scale.
Differentially Private Smart Metering with Battery Recharging

The energy industry has recently begun using smart meters to take fine-grained readings of energy usage. These smart meters enable flexible timeof-use billing, forecasting, and demand response, but they also raise serious user privacy concerns. We propose a novel technique for provably hiding sensitive power consumption information in the overall power consumption stream. Our technique relies on a rechargeable battery that is connected to the household’s power supply. This battery is used to modify the household’s power consumption by adding or subtracting noise (i.e., increasing or decreasing power consumption), in order to establish strong privacy guarantees in the sense of differential privacy. To achieve these privacy guarantees in realistic settings, we first investigate the influence of, and the interplay between, capacity and throughput bounds that batteries face in reality. We then propose an integrated method based on noise cascading that allows for recharging the battery on-the-fly so that differential privacy is retained, while adhering to capacity and throughput constraints, and while keeping the additional consumption of energy induced by our technique to a minimum.
Revisiting Traffic Anomaly Detection Using Software Defined Networking

Despite their exponential growth, home and small office/home office networks continue to be poorly managed. Consequently, security of hosts in most home networks is easily compromised and these hosts are in turn used for largescale malicious activities without the home users’ knowledge. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and contain network security problems in home and home office networks. We show how four prominent traffic anomaly detection algorithms can be implemented in an SDN context using Openflow compliant switches and NOX as a controller. Our experiments indicate that these algorithms are significantly more accurate in identifying malicious activities in the home networks as compared to the ISP. Furthermore, the efficiency analysis of our SDN implementations on a programmable home network router indicates that the anomaly detectors can operate at line rates without introducing any performance penalties for the home network traffic.
One millisecond face alignment with an ensemble of regression trees

This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.
Whitening Black-Box Neural Networks

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.
Understanding tools: Task-oriented object modeling, learning and recognition

In this paper, we present a new framework - task-oriented modeling, learning and recognition which aims at understanding the underlying functions, physics and causality in using objects as “tools”. Given a task, such as, cracking a nut or painting a wall, we represent each object, e.g. a hammer or brush, in a generative spatio-temporal representation consisting of four components: i) an affordances basis to be grasped by hand; ii) a functional basis to act on a target object (the nut), iii) the imagined actions with typical motion trajectories; and iv) the underlying physical concepts, e.g. force, pressure, etc. In a learning phase, our algorithm observes only one RGB-D video, in which a rational human picks up one object (i.e. tool) among a number of candidates to accomplish the task. From this example, our algorithm learns the essential physical concepts in the task (e.g. forces in cracking nuts). In an inference phase, our algorithm is given a new set of objects (daily objects or stones), and picks the best choice available together with the inferred affordance basis, functional basis, imagined human actions (sequence of poses), and the expected physical quantity that it will produce. From this new perspective, any objects can be viewed as a hammer or a shovel, and object recognition is not merely memorizing typical appearance examples for each category but reasoning the physical mechanisms in various tasks to achieve generalization.
Residential exposure to aircraft noise and hospital admissions for cardiovascular diseases: multi-airport retrospective study

To investigate whether exposure to aircraft noise increases the risk of hospitalization for cardiovascular diseases in older people (≥ 65 years) residing near airports.



Multi-airport retrospective study of approximately 6 million older people residing near airports in the United States. We superimposed contours of aircraft noise levels (in decibels, dB) for 89 airports for 2009 provided by the US Federal Aviation Administration on census block resolution population data to construct two exposure metrics applicable to zip code resolution health insurance data: population weighted noise within each zip code, and 90th centile of noise among populated census blocks within each zip code.



2218 zip codes surrounding 89 airports in the contiguous states.



6 027 363 people eligible to participate in the national medical insurance (Medicare) program (aged ≥ 65 years) residing near airports in 2009.



Percentage increase in the hospitalization admission rate for cardiovascular disease associated with a 10 dB increase in aircraft noise, for each airport and on average across airports adjusted by individual level characteristics (age, sex, race), zip code level socioeconomic status and demographics, zip code level air pollution (fine particulate matter and ozone), and roadway density.



Averaged across all airports and using the 90th centile noise exposure metric, a zip code with 10 dB higher noise exposure had a 3.5% higher (95% confidence interval 0.2% to 7.0%) cardiovascular hospital admission rate, after controlling for covariates.



Despite limitations related to potential misclassification of exposure, we found a statistically significant association between exposure to aircraft noise and risk of hospitalization for cardiovascular diseases among older people living near airports.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.
Automatic docking and recharging system for autonomous security robot

To keep autonomous mobile robot in continuously working condition, power supply is an important issue. Typically, rechargeable batteries may provide few hours of peak usage. Recharging is necessary before the power of the batteries has exhausted. In this paper, the docking station with an auto-recharging device and robot docking mechanism are implemented. We propose a docking control strategy and automatic recharging device for the security robot recharging. We also propose a power prediction algorithm to determine when the robot needs to navigate back to docking station for recharging. An artificial landmark is detected and recognized by the proposed image processing system. Then the geometrical relationship between the robot and the docking station (the depth and orientation) is estimated. The robot will move directly right in front of the docking station. Finally, the robot approaches to the docking station based on the proposed virtual spring model. In this model, we assume that the robot and the docking station are connected by a virtual spring. The compliant forces act both in the direction of the translation deformation and bending. The motion control parameters, which are velocities of the two wheels, can be derived from the model. Experimental results show that the proposed methods successfully perform the docking and automatic recharging.
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation. 
 In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice. 
 By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.
Automatic generation of destination maps

Destination maps are navigational aids designed to show anyone within a region how to reach a location (the destination). Hand-designed destination maps include only the most important roads in the region and are non-uniformly scaled to ensure that all of the important roads from the highways to the residential streets are visible. We present the first automated system for creating such destination maps based on the design principles used by mapmakers. Our system includes novel algorithms for selecting the important roads based on mental representations of road networks, and for laying out the roads based on a non-linear optimization procedure. The final layouts are labeled and rendered in a variety of styles ranging from informal to more formal map styles. The system has been used to generate over 57,000 destination maps by thousands of users. We report feedback from both a formal and informal user study, as well as provide quantitative measures of success.