nils-herrmann/clean_scirepeval huggingface dataset:
Asymptotically Efficient Allocation Rules for the Multiarmed Bandit Problem with Multiple Plays-Part I : I . I .

Absrract-At each instant of time we are required to sample a fixed number rn 2 1 out of N i.i.d. processes whose distributions belong to a family suitably parameterized by a real number 8. The objective is to maximize the long run total expected value of the samples. Following Lai and Robbins, the learning loss of a sampling scheme corresponding to a configuration of parameters C = (e,, . . a , e,\,) is quantified by the regret R n ( o . This is the difference between the maximum expected reward at time n that could be achieved if C were known and the expected reward actually obtained by the sampling scheme. We provide a lower bound for the regret associated with any uniformly good scheme, and construct a scheme which attains the lower bound for every configuration C. The lower bound is given explicitly in terms of the Kullback-Liebler number between pairs of distributions. Part I1 of this paper considers the same problem when the reward processes are Markovian.
Factoring Polynomials with Rational Coefficients

In this paper we present a polynomial-time algorithm to solve the following problem: given a non-zero polynomial f e Q[X] in one variable with rational coefficients, find the decomposition of f into irreducible factors in Q[X]. It is well known that this is equivalent to factoring primitive polynomials feZ[X] into irreducible factors in Z[X]. Here we call f ~ Z[X] primitive if the greatest common divisor of its coefficients (the content of f ) is 1. Our algorithm performs well in practice, cf. . Its running time, measured in bit operations, is O(nl2+n9(log[fD3). Here f~Tl[X] is the polynomial to be factored, n = deg(f) is the degree of f, and
Community Detection for Emerging Networks

Nowadays, many new social networks offering specific services spring up overnight. In this paper, we want to detect communities for emerging networks. Community detection for emerging networks is very challenging as information in emerging networks is usually too sparse for traditional methods to calculate effective closeness scores among users and achieve good community detection results. Meanwhile, users nowadays usually join multiple social networks simultaneously, some of which are developed and can share common information with the emerging networks. Based on both link and attribution information across multiple networks, a new general closeness measure, intimacy, is introduced in this paper. With both micro and macro controls, an effective and efficient method, CAD (Cold stArt community Detector), is proposed to propagate information from developed network to calculate effective intimacy scores among users in emerging networks. Extensive experiments conducted on real-world social networks demonstrate that CAD can perform very well in addressing the emerging network community detection problem.
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines

We propose a framework for collaborative filtering based on Restricted Boltzmann Machines (RBM), which extends previous RBMbased approaches in several important directions. First, while previous RBM research has focused on modeling the correlation between item ratings, we model both user-user and item-item correlations in a unified hybrid non-IID framework. We further use real values in the visible layer as opposed to multinomial variables, thus taking advantage of the natural order between user-item ratings. Finally, we explore the potential of combining the original training data with data generated by the RBM-based model itself in a bootstrapping fashion. The evaluation on two MovieLens datasets (with 100K and 1M user-item ratings, respectively), shows that our RBM model rivals the best previouslyproposed approaches.
People counting based on head detection combining Adaboost and CNN in crowded surveillance environment

People counting is one of the key techniques in video surveillance. This task usually encounters many challenges in crowded environment, such as heavy occlusion, low resolution, imaging viewpoint variability, etc. Motivated by the success of R-CNN  on object detection, in this paper we propose a head detection based people counting method combining the Adaboost algorithm and the CNN. Unlike the R-CNN which uses the general object proposals as the inputs of CNN, our method uses the cascade Adaboost algorithm to obtain the head region proposals for CNN, which can greatly reduce the following classification time. Resorting to the strong ability of feature learning of the CNN, it is used as a feature extractor in this paper, instead of as a classifier as its commonlyused strategy. The final classification is done by a linear SVM classifier trained on the features extracted using the CNN feature extractor. Finally, the prior knowledge can be applied to post-process the detection results to increase the precision of head detection and the people count is obtained by counting the head detection results. A real classroom surveillance dataset is used to evaluate the proposed method and experimental results show that this method has good performance and outperforms the baseline methods, including deformable part model and cascade Adaboost methods. ∗Corresponding author Email address: gaocq@cqupt.edu.cn (Chenqiang Gao∗, Pei Li, Yajun Zhang, Jiang Liu, Lan Wang)  2016
Multi-dimensional search result diversification

Most existing search result diversification algorithms diversify search results in terms of a specific dimension. In this paper, we argue that search results should be diversified in a multi-dimensional way, as queries are usually ambiguous at different levels and dimensions. We first explore mining subtopics from four types of data sources, including anchor texts, query logs, search result clusters, and web sites. Then we propose a general framework that explicitly diversifies search results based on multiple dimensions of subtopics. It balances the relevance of documents with respect to the query and the novelty of documents by measuring the coverage of subtopics. Experimental results on the TREC 2009 Web track dataset indicate that combining multiple types of subtopics do help better understand user intents. By incorporating multiple types of subtopics, our models improve the diversity of search results over the sole use of one of them, and outperform two state-of-the-art models.
DDoS defense system for web services in a cloud environment

Recently, a new kind of vulnerability has surfaced: application layer Denial-of-Service (DoS) attacks targeting web services. These attacks aim at consuming resources by sending Simple Object Access Protocol (SOAP) requests that contain malicious XML content. These requests cannot be detected on the network or transportation (TCP/IP) layer, as they appear as legitimate packets. Until now, there is no web service security specification that addresses this problem. Moreover, the current WS-Security standard induces crucial additional vulnerabilities threatening the availability of certain web service implementations. First, this paper introduces an attack-generating tool to test and confirm previously reported vulnerabilities. The results indicate that the attacks have a devastating impact on theweb service availability, even whilst utilizing an absolute minimum of attack resources. Since these highly effective attacks can be mounted with relative ease, it is clear that defending against them is essential, looking at the growth of cloud andweb services. Second, this paper proposes an intelligent, fast and adaptive system for detecting against XML and HTTP application layer attacks. The intelligent system works by extracting several features and using them to construct a model for typical requests. Finally, outlier detection can be used to detect malicious requests. Furthermore, the intelligent defense system is capable of detecting spoofing and regular flooding attacks. The system is designed to be inserted in a cloud environmentwhere it can transparently protect the cloud broker and even cloud providers. For testing its effectiveness, the defense systemwas deployed to protect web services running onWSO2 with Axis2: the defacto standard for open source web service deployment. The proposed defense system demonstrates its capability to effectively filter out the malicious requests, whilst generating a minimal amount of overhead for the total response time.
A Wireless and Batteryless 10-Bit Implantable Blood Pressure Sensing Microsystem With Adaptive RF Powering for Real-Time Laboratory Mice Monitoring

An implantable real-time blood pressure monitoring microsystem for laboratory mice has been demonstrated. The system achieves a 10-bit blood pressure sensing resolution and can wirelessly transmit the pressure information to an external unit. The implantable device is operated in a batteryless manner, powered by an external RF power source. The received RF power level can be sensed and wirelessly transmitted along with blood pressure signal for feedback control of the external RF power. The microsystem employs an instrumented silicone cuff, wrapped around a blood vessel with a diameter of approximately 200 ¿m, for blood pressure monitoring. The cuff is filled by low-viscosity silicone oil with an immersed MEMS capacitive pressure sensor and integrated electronic system to detect a down-scaled vessel blood pressure waveform with a scaling factor of approximately 0.1. The integrated electronic system, consisting of a capacitance-to-voltage converter, an 11-bit ADC, an adaptive RF powering system, an oscillator-based 433 MHz FSK transmitter and digital control circuitry, is fabricated in a 1.5 ¿m CMOS process and dissipates a power of 300 ¿W. The packaged microsystem weighs 130 milligram and achieves a capacitive sensing resolution of 75 aF over 1 kHz bandwidth, equivalent to a pressure sensing resolution of 1 mmHg inside an animal vessel, with a dynamic range of 60 dB. Untethered laboratory animal in vivo evaluation demonstrates that the microsystem can capture real-time blood pressure information with a high fidelity under an adaptive RF powering and wireless data telemetry condition.
Validity Issues in the Use of Social Network Analysis with Digital Trace Data

There is an exciting natural match between Social Network Analysis (SNA) and the growth of social interaction through digital platforms and technologies, from online communities to corporate information systems. This convergence offers a combination of exciting domains, interesting research questions, appropriate analysis techniques and the availability of copious data. Agarwal et al. (2008) put it thus, "Most transactions and conversations in these online groups leave a digital trace ... this research data makes visible social processes that are much more difficult to study in conventional organizational settings." The availability of such data, together with exciting domains and an appropriate analysis technique, form a golden opportunity for research, perhaps even a “21 Century Science” (Watts, 2007).
Software Vulnerability Discovery Techniques: A Survey

Software vulnerabilities are the root cause of computer security problem. How people can quickly discover vulnerabilities existing in a certain software has always been the focus of information security field. This paper has done research on software vulnerability techniques, including static analysis, Fuzzing, penetration testing. Besides, the authors also take vulnerability discovery models as an example of software vulnerability analysis methods which go hand in hand with vulnerability discovery techniques. The ending part of the paper analyses the advantages and disadvantages of each technique introduced here and talks about the future direction of this field.
Modern approaches in deep learning for SAR ATR

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior performance for object recognition in image processing applications. This paper discusses modern training approaches adopted from the image processing literature and shows how those approaches enable significantly improved performance for synthetic aperture radar (SAR) automatic target recognition (ATR). In particular, we show how a set of novel enhancements to the learning algorithm, based on new stochastic gradient descent approaches, generate significant classification improvement over previously published results on a standard dataset called MSTAR.
Handwriting beautification using token means

In this paper, we propose a general purpose approach to handwriting beautification using online input from a stylus. Given a sample of writings, drawings, or sketches from the same user, our method improves a user's strokes in real-time as they are drawn. Our approach relies on one main insight. The appearance of the average of multiple instances of the same written word or shape is better than most of the individual instances. We utilize this observation using a two-stage approach. First, we propose an efficient real-time method for finding matching sets of stroke samples called tokens in a potentially large database of writings from a user. Second, we refine the user's most recently written strokes by averaging them with the matching tokens. Our approach works without handwriting recognition, and does not require a database of predefined letters, words, or shapes. Our results show improved results for a wide range of writing styles and drawings.
Distributed receding horizon control for multi-vehicle formation stabilization

We consider the control of interacting subsystems whose dynamics and constraints are decoupled, but whose state vectors are coupled non-separably in a single cost function of a finite horizon optimal control problem. For a given cost structure, we generate distributed optimal control problems for each subsystem and establish that a distributed receding horizon control implementation is stabilizing to a neighborhood of the objective state. The implementation requires synchronous updates and the exchange of the most recent optimal control trajectory between coupled subsystems prior to each update. The key requirements for stability are that each subsystem not deviate too far from the previous open-loop state trajectory, and that the receding horizon updates happen sufficiently fast. The venue of multi-vehicle formation stabilization is used to demonstrate the distributed implementation.
WebPatrol: automated collection and replay of web-based malware scenarios

Traditional remote-server-exploiting malware is quickly evolving and adapting to the new web-centric computing paradigm. By leveraging the large population of (insecure) web sites and exploiting the vulnerabilities at client-side modern (complex) browsers (and their extensions), web-based malware becomes one of the most severe and common infection vectors nowadays. While traditional malware collection and analysis are mainly focusing on binaries, it is important to develop new techniques and tools for collecting and analyzing web-based malware, which should include a complete web-based malicious logic to reflect the dynamic, distributed, multi-step, and multi-path web infection trails, instead of just the binaries executed at end hosts. This paper is a first attempt in this direction to automatically collect web-based malware scenarios (including complete web infection trails) to enable fine-grained analysis. Based on the collections, we provide the capability for offline "live" replay, i.e., an end user (e.g., an analyst) can faithfully experience the original infection trail based on her current client environment, even when the original malicious web pages are not available or already cleaned. Our evaluation shows that WebPatrol can collect/cover much more complete infection trails than state-of-the-art honeypot systems such as PHoneyC [11] and Capture-HPC . We also provide several case studies on the analysis of web-based malware scenarios we have collected from a large national education and research network, which contains around 35,000 web sites.
Extending Full Text Search for Legal Document Collections Using Word Embeddings

Traditional full text search allows fast search for exact matches. However, full text search is not optimal to deal with synonyms or semantically related terms and phrases. In this paper we explore a novel method that provides the ability to find not only exact matches, but also semantically similar parts for arbitrary length search queries. We achieve this without the application of ontologies, but base our approach on Word Embeddings. Recently, Word Embeddings have been applied successfully for many natural language processing tasks. We argue that our method is well suited for legal document collections and examine its applicability for two different use cases: We conduct a case study on a stand-alone law, in particular the EU Data Protection Directive 94/46/EC (EU-DPD) in order to extract obligations. Secondly, from a collection of publicly available templates for German rental contracts we retrieve similar provisions.
Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment

Microwave power transfer (MPT) delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. This provides mobiles practically infinite battery lives and eliminates the need of power cords and chargers. To enable MPT for mobile recharging, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. The deployment of the hybrid network under an outage constraint on data links is investigated based on a stochastic-geometry model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes (PPPs) with densities λ<sub>b</sub> and λ<sub>p</sub>, respectively, and single-antenna mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, mobiles and PBs fix their transmission power at p and q, respectively; a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is used to derive the tradeoffs between the network parameters (p, λ<sub>b</sub>, q, λ<sub>p</sub>) under the outage constraint. First, consider the deployment of the cellular network. It is proved that the outage constraint is satisfied so long as the product pλ<sub>b</sub><sup>α/2</sup> is above a given threshold where α is the path-loss exponent. Next, consider the deployment of the hybrid network assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product qλ<sub>p</sub>λ<sub>b</sub><sup>α/2</sup> has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, z<sub>m</sub>qλ<sub>p</sub>λ<sub>b</sub><sup>α/2</sup> with z<sub>m</sub> denoting the array gain should exceed a different threshold to ensure short distances between PBs and their target mobiles. Furthermore, similar results are derived for the case of mobiles having small energy storage.
Modeling of planar transformer parasitics using design of experiment methodology

Planar transformer parasitics are difficult to model due to the complex winding geometry and their nonlinear and multivariate nature. This paper provides parametric models for planar transformer parasitics based on finite element simulations of a variety of winding design parameters using the Design of Experiments (DoE) methodology. A rotatable Central Composite Design (CCD) is employed based on a full 24 factorial design to obtain equations for the leakage inductance, inter and intra-winding capacitances, resistance, output current, and output voltage. Using only 25 runs, the final results can be used to replace time-intensive finite element simulations for a wide range of planar transformer winding design options. Validation simulations were performed to confirm the accuracy of the model. Results are presented using a planar E18/4/10 core set and can be extended to a variety of core shapes and sizes.
The Number Field Sieve

The number field sieve is an algorithm to factor integers of the form r e ± s for small positive r and s . This note is intended as a ‘report on work in progress’ on this algorithm. We informally describe the algorithm, discuss several implementation related aspects, and present some of the factorizations obtained so far. We also mention some solutions to the problems encountered when generalizing the algorithm to general integers using an idea of Buhler and Pomerance. It is not unlikely that this leads to a general purpose factoring algorithm that is asymptotically substantially faster than the fastest factoring algorithms known so far, like the multiple polynomial quadratic sieve.
SpanDex: Secure Password Tracking for Android

This paper presents SpanDex, a set of extensions to Android’s Dalvik virtual machine that ensures apps do not leak users’ passwords. The primary technical challenge addressed by SpanDex is precise, sound, and efficient handling of implicit information flows (e.g., information transferred by a program’s control flow). SpanDex handles implicit flows by borrowing techniques from symbolic execution to precisely quantify the amount of information a process’ control flow reveals about a secret. To apply these techniques at runtime without sacrificing performance, SpanDex runs untrusted code in a data-flow sensitive sandbox, which limits the mix of operations that an app can perform on sensitive data. Experiments with a SpanDex prototype using 50 popular Android apps and an analysis of a large list of leaked passwords predicts that for 90% of users, an attacker would need over 80 login attempts to guess their password. Today the same attacker would need only one attempt for all users.
Reach for A*: Efficient Point-to-Point Shortest Path Algorithms

We study the point-to-point shortest path problem in a setting where preprocessing is allowed. We improve the reach-based approach of Gutman [17] in several ways. In particular, we introduce a bidirectional version of the algorithm that uses implicit lower bounds and we add shortcut arcs to reduce vertex reaches. Our modifications greatly improve both preprocessing and query times. The resulting algorithm is as fast as the best previous method, due to Sanders and Schultes . However, our algorithm is simpler and combines in a natural way with A search, which yields significantly better query times.
Fourier tags: Smoothly degradable fiducial markers for use in human-robot interaction

In this paper we introduce the Fourier tag, a synthetic fiducial marker used to visually encode information and provide controllable positioning. The Fourier tag is a synthetic target akin to a bar-code that specifies multi-bit information which can be efficiently and robustly detected in an image. Moreover, the Fourier tag has the beneficial property that the bit string it encodes has variable length as a function of the distance between the camera and the target. This follows from the fact that the effective resolution decreases as an effect of perspective. This paper introduces the Fourier tag, describes its design, and illustrates its properties experimentally.