nils-herrmann/clean_scirepeval huggingface dataset:
Scriptless attacks: stealing the pie without touching the sill

Due to their high practical impact, Cross-Site Scripting (XSS) attacks have attracted a lot of attention from the security community members. In the same way, a plethora of more or less effective defense techniques have been proposed, addressing the causes and effects of XSS vulnerabilities. NoScript, and disabling scripting code in non-browser applications such as e-mail clients or instant messengers.
 As a result, an adversary often can no longer inject or even execute arbitrary scripting code in several real-life scenarios.
 In this paper, we examine the attack surface that remains after XSS and similar scripting attacks are supposedly mitigated by preventing an attacker from executing JavaScript code. We address the question of whether an attacker really needs JavaScript or similar functionality to perform attacks aiming for information theft. The surprising result is that an attacker can also abuse Cascading Style Sheets (CSS) in combination with other Web techniques like plain HTML, inactive SVG images or font files. Through several case studies, we introduce the so called scriptless attacks and demonstrate that an adversary might not need to execute code to preserve his ability to extract sensitive information from well protected websites. More precisely, we show that an attacker can use seemingly benign features to build side channel attacks that measure and exfiltrate almost arbitrary data displayed on a given website.
 We conclude this paper with a discussion of potential mitigation techniques against this class of attacks. In addition, we have implemented a browser patch that enables a website to make a vital determination as to being loaded in a detached view or pop-up window. This approach proves useful for prevention of certain types of attacks we here discuss.
News contextualization with geographic and visual information

In this paper, we investigate the contextualization of news documents with geographic and visual information. We propose a matrix factorization approach to analyze the location relevance for each news document. We also propose a method to enrich the document with a set of web images. For location relevance analysis, we first perform toponym extraction and expansion to obtain a toponym list from news documents. We then propose a matrix factorization method to estimate the location-document relevance scores while simultaneously capturing the correlation of locations and documents. For image enrichment, we propose a method to generate multiple queries from each news document for image search and then employ an intelligent fusion approach to collect a set of images from the search results. Based on the location relevance analysis and image enrichment, we introduce a news browsing system named NewsMap which can support users in reading news via browsing a map and retrieving news with location queries. The news documents with the corresponding enriched images are presented to help users quickly get information. Extensive experiments demonstrate the effectiveness of our approaches.
An open framework for multi-source, cross-domain personalisation with semantic interest graphs

Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modeled as semantic interest graphs. This novel framework covers all parts of a recommender system. It includes an architecture for privacy-enabled profile exchange, a distributed and domain-agnostic user model and a cross-domain recommendation algorithm. This enables users to receive recommendations for a target domain (e.g. food) based on any kind of previous interests.
Above 500V class Superjunction MOSFETs fabricated by deep trench etching and epitaxial growth

Above 500V class superjunction (SJ) MOSFETs fabricated by deep-trench etching and epitaxial growth are investigated. These SJ-MOSFETs show the lowest specific on-resistance (RonA) of 21.3mOmegacm2 at a breakdown voltage (VB) of 540V, among reported trench-filling type of devices in the same voltage class. These RonA-VB trade-off characteristics are accomplished by optimizing doping concentrations of n- and p- column regions. In addition, low reverse biased leakage current has been achieved by filling deep trenches with defect-free single crystal silicon
Learning word embeddings efficiently with noise-contrastive estimation

Overview • Learning flexible word representations is the first step towards learning semantics. •The best current approach to learning word embeddings involves training a neural language model to predict each word in a sentence from its neighbours. – Need to use a lot of data and high-dimensional embeddings to achieve competitive performance. – More scalable methods translate to better results. •We propose a simple and scalable approach to learning word embeddings based on training lightweight models with noise-contrastive estimation. – It is simpler, faster, and produces better results than the current state-of-the art method.
Tolerating Malicious Device Drivers in Linux

This paper presents SUD, a system for running existing Linux device drivers as untrusted user-space processes. Even if the device driver is controlled by a malicious adversary, it cannot compromise the rest of the system. One significant challenge of fully isolating a driver is to confine the actions of its hardware device. SUD relies on IOMMU hardware, PCI express bridges, and messagesignaled interrupts to confine hardware devices. SUD runs unmodified Linux device drivers, by emulating a Linux kernel environment in user-space. A prototype of SUD runs drivers for Gigabit Ethernet, 802.11 wireless, sound cards, USB host controllers, and USB devices, and it is easy to add a new device class. SUD achieves the same performance as an in-kernel driver on networking benchmarks, and can saturate a Gigabit Ethernet link. SUD incurs a CPU overhead comparable to existing runtime driver isolation techniques, while providing much stronger isolation guarantees for untrusted drivers. Finally, SUD requires minimal changes to the kernel—just two kernel modules comprising 4,000 lines of code—which may at last allow the adoption of these ideas in practice.
Languages, Automata, and Logic

This paper is a survey on logical aspects of nite automata. Central points are the connection between nite automata and monadic second-order logic, the Ehrenfeucht-Fra ss e technique in the context of formal language theory, nite automata on !-words and their determinization, and a self-contained proof of the \Rabin Tree Theorem". Sections 5 and 6 contain material presented in a lecture series to the \Final Winter School of AMICS" (Palermo, February 1996). A modiied version of the paper will be a chapter of the \Handbook of Formal Language Theory", edited by G. Rozenberg and A. Salomaa, to appear in Springer-Verlag.
Simultaneous wireless information and power transfer in modern communication systems

Energy harvesting for wireless communication networks is a new paradigm that allows terminals to recharge their batteries from external energy sources in the surrounding environment. A promising energy harvesting technology is wireless power transfer where terminals harvest energy from electromagnetic radiation. Thereby, the energy may be harvested opportunistically from ambient electromagnetic sources or from sources that intentionally transmit electromagnetic energy for energy harvesting purposes. A particularly interesting and challenging scenario arises when sources perform simultaneous wireless information and power transfer (SWIPT), as strong signals not only increase power transfer but also interference. This article provides an overview of SWIPT systems with a particular focus on the hardware realization of rectenna circuits and practical techniques that achieve SWIPT in the domains of time, power, antennas, and space. The article also discusses the benefits of a potential integration of SWIPT technologies in modern communication networks in the context of resource allocation and cooperative cognitive radio networks.
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelise on GPUs. Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion. The resulting PyraMiD-LSTM is easy to parallelise, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12).
MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinionlevel Sentiment Intensity dataset (MOSI). The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features. Furthermore, we present baselines for future studies in this direction as well as a new multimodal fusion approach that jointly models spoken words and visual gestures.
Generalization and Exploration via Randomized Value Functions

We propose randomized least-squares value iteration (RLSVI) – a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized value functions. We explain why versions of least-squares value iteration that use Boltzmann or -greedy exploration can be highly inefficient, and we present computational results that demonstrate dramatic efficiency gains enjoyed by RLSVI. Further, we establish an upper bound on the expected regret of RLSVI that demonstrates nearoptimality in a tabula rasa learning context. More broadly, our results suggest that randomized value functions offer a promising approach to tackling a critical challenge in reinforcement learning: synthesizing efficient exploration and effective generalization.
AdSentry: comprehensive and flexible confinement of JavaScript-based advertisements

Internet advertising is one of the most popular online business models. JavaScript-based advertisements (ads) are often directly embedded in a web publisher's page to display ads relevant to users (e.g., by checking the user's browser environment and page content). However, as third-party code, the ads pose a significant threat to user privacy. Worse, malicious ads can exploit browser vulnerabilities to compromise users' machines and install malware. To protect users from these threats, we propose AdSentry, a comprehensive confinement solution for JavaScript-based advertisements. The crux of our approach is to use a shadow JavaScript engine to sandbox untrusted ads. In addition, AdSentry enables flexible regulation on ad script behaviors by completely mediating its access to the web page (including its DOM) without limiting the JavaScript functionality exposed to the ads. Our solution allows both web publishers and end users to specify access control policies to confine ads' behaviors. We have implemented a proof-of-concept prototype of AdSentry that transparently supports the Mozilla Firefox browser. Our experiments with a number of ads-related attacks successfully demonstrate its practicality and effectiveness. The performance measurement indicates that our system incurs a small performance overhead.
Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.
Acute symptomatic calcific discitis in adults: a case report and review of literature

Symptomatic calcific discitis has been reported in the paediatric population but is a rare entity in adults with only eight cases reported in the English literature. We present a case of adult calcific discitis presenting with acute onset back pain. Radiographs and CT demonstrated central T11–T12 disc calcification with diffuse marrow oedema on subsequent MRI. The patient was referred to our spinal oncology unit due to the extensive marrow oedema as a possible underlying primary bone tumour. Review of the CT confirmed an end-plate defect with herniated calcific nucleus pulposus with no underlying bone lesion. Features were in keeping with acute calcific discitis. The patient was treated symptomatically and made an uneventful recovery. We discuss the characteristic imaging features seen on radiograph, CT and MRI and review the current literature. Calcific discitis is a self-limiting pathology requiring symptomatic management only. Radiologists need to be aware of this rare entity as it can occur in adults and may be mistaken for a more sinister pathology such as infective discitis or a bone tumour and lead to further unnecessary imaging or invasive procedures.
Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.
Traumatic asphyxia: a rare syndrome in trauma patients

A 6-year-old boy was admitted to the emergency department (ED) suffering from petechiae and purpura on his face caused by a farming accident. He got his T-shirt caught in a rotating shaft at the back of a tractor. The T-shirt wrapped around his thorax and compressed him. He did not lose his consciousness during the incident. His score on the Glasgow Coma Scale was 15 and his initial vital signs were stable upon arrival at the ED. On physical examination, diffuse petechiae and purpura were noted on the face and neck although there was not any sign of the direct trauma (Figs. 1 and 2). The patient denied suffering head trauma. Examination for abdominal and thoracic organ injury was negative. Traumatic asphyxia is a rare condition presenting with cervicofacial cyanosis and edema, subconjunctival hemorrhage, and petechial hemorrhages of the face, neck, and upper chest that occurs due to a compressive force to the thoracoabdominal region . Although the exact mechanism is controversial, it is probably due to thoracoabdominal compression causing increased intrathoracic pressure just at the moment of the event. The fear response, which is characterized by taking and holding a deep breath and closure of the glottis, also contributes to this process [1, 2]. This back pressure is transmitted ultimately to the head and neck veins and capillaries, with stasis and rupture producing characteristic petechial and subconjunctival hemorrhages . The skin of the face, neck, and upper torso may appear blue-red to blue-black but it blanches over time. The discoloration and petechiae are often more prominent on the eyelids, nose, and lips . In patients with traumatic asphyxia, injuries associated with other systems may also accompany the condition. Jongewaard et al. reported chest wall and intrathoracic injuries in 11 patients, loss of consciousness in 8, prolonged confusion in 5, seizures in 2, and visual disturbances in 2 of 14 patients with traumatic asphyxia . Pulmonary contusion, hemothorax, pneumothorax, prolonged loss of consciousness,  (2009) 2:255–256 DOI 10.1007/s12245-009-0115-x
CoinParty: Secure Multi-Party Mixing of Bitcoins

Bitcoin is a digital currency that uses anonymous cryptographic identities to achieve financial privacy. However, Bitcoin's promise of anonymity is broken as recent work shows how Bitcoin's blockchain exposes users to reidentification and linking attacks. In consequence, different mixing services have emerged which promise to randomly mix a user's Bitcoins with other users' coins to provide anonymity based on the unlinkability of the mixing. However, proposed approaches suffer either from weak security guarantees and single points of failure, or small anonymity sets and missing deniability. In this paper, we propose CoinParty a novel, decentralized mixing service for Bitcoin based on a combination of decryption mixnets with threshold signatures. CoinParty is secure against malicious adversaries and the evaluation of our prototype shows that it scales easily to a large number of participants in real-world network settings. By the application of threshold signatures to Bitcoin mixing, CoinParty achieves anonymity by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain and is first among related approaches to provide plausible deniability.
CUNY-UIUC-SRI TAC-KBP2011 Entity Linking System Description

In this paper we describe a joint effort by the City University of New York (CUNY), University of Illinois at Urbana-Champaign (UIUC) and SRI International at participating in the mono-lingual entity linking (MLEL) and cross-lingual entity linking (CLEL) tasks for the NIST Text Analysis Conference (TAC) Knowledge Base Population (KBP2011) track. The MLEL system is based on a simple combination of two published systems by CUNY (Chen and Ji, 2011) and UIUC (Ratinov et al., 2011). Therefore, we mainly focus on describing our new CLEL system. In addition to a baseline system based on name translation, machine translation and MLEL, we propose two novel approaches. One is based on a cross-lingual name similarity matrix, iteratively updated based on monolingual co-occurrence, and the other uses topic modeling to enhance performance. Our best systems placed 4th in mono-lingual track and 2nd in cross-lingual track.
The Number of Scholarly Documents on the Public Web

The number of scholarly documents available on the web is estimated using capture/recapture methods by studying the coverage of two major academic search engines: Google Scholar and Microsoft Academic Search. Our estimates show that at least 114 million English-language scholarly documents are accessible on the web, of which Google Scholar has nearly 100 million. Of these, we estimate that at least 27 million (24%) are freely available since they do not require a subscription or payment of any kind. In addition, at a finer scale, we also estimate the number of scholarly documents on the web for fifteen fields: Agricultural Science, Arts and Humanities, Biology, Chemistry, Computer Science, Economics and Business, Engineering, Environmental Sciences, Geosciences,  In addition, we show that among these fields the percentage of documents defined as freely available varies significantly, i.e., from 12 to 50%.
The ties that lead : A social network approach to leadership

This article investigates, for leadership research, the implications of new directions in social network theory that emphasize networks as both cognitive structures in the minds of organizational members and opportunity structures that facilitate and constrain action. We introduce the four core ideas at the heart of the network research program: the importance of relations, actors' embeddedness, the social utility of connections, and the structural patterning of social life. Then we present a theoretical model of how network cognitions in the minds of leaders affect three types of networks: the direct ties surrounding leaders, the pattern of direct and indirect ties within which leaders are embedded in the whole organization and the interorganizational linkages formed by leaders as representatives of organizations. We suggest that these patterns of ties can contribute to leader effectiveness.
Some map matching algorithms for personal navigation assistants

Third-generation personal navigation assistants (PNAs) (i.e., those that provide a map, the user's current location, and directions) must be able to reconcile the user's location with the underlying map. This process is known as map matching. Most existing research has focused on map matching when both the user's location and the map are known with a high degree of accuracy. However, there are many situations in which this is unlikely to be the case. Hence, this paper considers map matching algorithms that can be used to reconcile inaccurate locational data with an inaccurate map/network. Ó 2000