nils-herrmann/clean_scirepeval huggingface dataset:
Verena: End-to-End Integrity Protection for Web Applications

Web applications rely on web servers to protect the integrity of sensitive information. However, an attacker gaining access to web servers can tamper with the data and query computation results, and thus serve corrupted web pages to the user. Violating the integrity of the web page can have serious consequences, affecting application functionality and decision-making processes. Worse yet, data integrity violation may affect physical safety, as in the case of medical web applications which enable physicians to assign treatment to patients based on diagnostic information stored at the web server. This paper presents Verena, a web application platform that provides end-to-end integrity guarantees against attackers that have full access to the web and database servers. In Verena, a client's browser can verify the integrity of a web page by verifying the results of queries on data stored at the server. Verena provides strong integrity properties such as freshness, completeness, and correctness for a common set of database queries, by relying on a small trusted computing base. In a setting where there can be many users with different write permissions, Verena allows a developer to specify an integrity policy for query results based on our notion of trust contexts, and then enforces this policy efficiently. We implemented and evaluated Verena on top of the Meteor framework. Our results show that Verena can support real applications with modest overhead.
College students' text messaging, use of textese and literacy skills

In this study, I examined reported frequency of text messaging, use of textese and literacy skills (reading accuracy, spelling and reading fluency) in a sample of American college students. Participants reported using text messaging, social networking sites and textese more often than was reported in previous (2009) research, and their frequency of textese use varied across contexts. Correlational analyses revealed significant, positive relationships between text messaging frequency and literacy skills (spelling and reading fluency), but significant, negative relationships between textese usage in certain contexts (on social networking sites such as MySpaceTM and FacebookTM and in emails to professors) and literacy (reading accuracy). These findings differ from findings reported in recent studies withAustralian college students, British schoolchildren and American college students. Explanations for these differences are discussed, and future directions for research are presented.
Address Oblivious Code Reuse: On the Effectiveness of Leakage Resilient Diversity

Memory corruption vulnerabilities not only allow modification of control data and injection of malicious payloads; they also allow adversaries to reconnoiter a diversified program, customize a payload, and ultimately bypass code randomization defenses. In response, researchers have proposed and built various leakage-resilient defenses against code reuse. Leakage-resilient defenses use memory protection techniques to prevent adversaries from directly reading code as well as pointer indirection or encryption techniques to decouple code pointers from the randomized code layout, avoiding indirect leakage. In this paper, we show that although current code pointer protections do prevent leakage per se, they are fundamentally unable to stop code reuse. Specifically, we demonstrate a new class of attacks we call address-oblivious code reuse that bypasses state-of-the-art leakage-resilience techniques by profiling and reusing protected code pointers, without leaking the code layout. We show that an attacker can accurately identify protected code pointers of interest and mount code-reuse attacks at the abstraction level of pointers without requiring any knowledge of code addresses. We analyze the prevalence of opportunities for such attacks in popular code bases and build three real-world exploits against Nginx and Apache to demonstrate their practicality. We analyze recently proposed leakage resilient defenses and show that they are vulnerable to address oblivious code reuse. Our findings indicate that because of the prevalence of code pointers in realistic programs and the fundamental need to expose them to “read” operations (even indirectly), diversity defenses face a fundamental design challenge in mitigating such attacks.  Approved for public release: distribution
Approaches for Enriching and Improving Textual Knowledge Bases

Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added statements. Statements can be any arbitrary piece of text, ranging from a sentence up to a paragraph. However, in many cases, citations are either outdated, missing, or link to non-existing references (e.g. dead URL, moved content etc.). In total, 20% of the cases such citations refer to news articles and represent the second most cited source. Even in cases where citations are provided, there are no explicit indicators for the span of a citation for a given piece of text. In addition to issues related with the verifiability principle, many Wikipedia entity pages are incomplete, with relevant information that is already available in online news sources missing. Even for the already existing citations, there is often a delay between the news publication time and the reference time. In this thesis, we address the aforementioned issues and propose automated approaches that enforce the verifiability principle in Wikipedia, and suggest relevant and missing news references for further enriching Wikipedia entity pages. To this end we make the following contributions as part of this thesis [1, 2, 3, 4]
Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization

We present a design framework for neuromorphic architectures in the nano-CMOS era. Our approach to the design of spiking neurons and STDP learning circuits relies on parallel computational structures where neurons are abstracted as digital arithmetic logic units and communication processors. Using this approach, we have developed arrays of silicon neurons that scale to millions of neurons in a single state-of-the-art Field Programmable Gate Array (FPGA). We demonstrate the validity of the design methodology through the implementation of cortical development in a circuit of spiking neurons, STDP synapses, and neural architecture optimization.
Position Control of a Permanent Magnet DC Motor by Model Reference Adaptive Control

Model reference adaptive control (MRAC) is one of the various techniques of solving the control problem when the parameters of the controlled process are poorly known or vary during normal operation. To understand the dynamic behavior of a dc motor it is required to know its parameters; armature inductance and resistance (La, Ra), inertia of the rotor (Jm), motor constant (Km), friction coefficient (Bm), etc. To identify these parameters, some experiments should be performed. However, motor parameters change under operation according to several conditions. Therefore, the performance of controller, which has been designed considering constant motor parameters becomes poorer. For this reason, a model reference adaptive control method is proposed to control the position of a dc motor without requiring fixed motor parameters. Experimental results show how well this method controls the position of a permanent magnet dc motor.
An effective GPU implementation of breadth-first search

Breadth-first search (BFS) has wide applications in electronic design automation (EDA) as well as in other fields. Researchers have tried to accelerate BFS on the GPU, but the two published works are both asymptotically slower than the fastest CPU implementation. In this paper, we present a new GPU implementation of BFS that uses a hierarchical queue management technique and a three-layer kernel arrangement strategy. It guarantees the same computational complexity as the fastest sequential version and can achieve up to 10 times speedup.
A Logit Model of Brand Choice Calibrated on Scanner Data

Optical scanning of the Universal Product Code in supermarkets provides a new level of detail and completeness in household panel data and makes possible the construction of more comprehensive brand choice models than hitherto possible. A multinomial logit model calibrated on 32 weeks of purchases of regular ground coffee by 100 households shows high statistical significance for the explanatory variables of brand loyalty, size loyalty, presence/absence of store promotion, regular shelf price and promotional price cut. The model is quite parsimonious in that the coefficients of these variables are modeled to be the same for all coffee brand-sizes. Considering its parsimony, the calibrated model predicts surprisingly well the share of purchases by brand-size in a hold-out sample of 100 households over the 32 week calibration period and a subsequent 20 week forecast period. Discrepencies in prediction are conjectured to be due in part to missing variables. Three short term market response measures are calculated from the model: regular shelf price elasticity of share, percent share increase from a promotion with a median price cut, and promotional price cut elasticity of share. Responsiveness varies across brand-sizes in a systematic way with large share brand-sizes showing less responsiveness in percentage terms. On the basis of the model a quantitative picture emerges of groups of loyal customers who are relatively insensitive to marketing actions and a pool of switchers who are quite responsive.
Managing congenitally missing lateral incisors. Part I: Canine substitution.

Dentists often encounter patients with missing or malformed teeth. The maxillary lateral incisor is the second most common congenitally absent tooth. There are three treatment options that exist for replacing missing lateral incisors. They include canine substitution, a tooth-supported restoration, or a single-tooth implant. Selecting the appropriate option depends on the malocclusion, specific space requirements, tooth-size relationship, and size and shape of the canine. The ideal treatment is the most conservative option that satisfies individual esthetic and functional requirements. Often the ideal option is canine substitution. Although the orthodontist positions the canine in the most esthetic and functional location, the restorative dentist often needs to place a porcelain veneer or crown to re-create normal lateral incisor shape and color. This article closely examines patient selection and illustrates the importance of interdisciplinary treatment planning to achieve optimal esthetics. It is the first in a three-part series discussing the three treatment alternatives for replacing missing lateral incisors.



Patients with congenitally missing lateral incisors often raise difficult treatment planning issues. Therefore, to produce the most predictable esthetic results, it is important to choose the treatment that will best address the initial diagnosis. This article is the first in a three-part series that describes the different treatments available for patients with congenitally missing lateral incisors. This first article focuses on canine substitution as a method of tooth replacement for these missing teeth. The general dentist will learn to evaluate specific patient selection criteria and determine whether canine substitution is an appropriate treatment alternative for replacing missing lateral incisors. The orthodontist will understand how to position the canines to satisfy functional requirements and achieve proper esthetics. Finally, the importance of interdisciplinary team treatment planning is emphasized as a requirement for achieving optimal final esthetics.
A Model of a Localized Cross-Border E-Commerce

By the explosive growth of B2B e-commerce transactions in international supply chains and the rapid increase of business documents in Iran’s cross-border trading, effective management of trade processes over borders is vital in B2B e-commerce systems. Structure of the localized model in this paper is based on three major layers of a B2B e-commerce infrastructure, which are messaging layer, business process layer and content layer. For each of these layers proper standards and solutions are chosen due to Iran’s e-commerce requirements. As it is needed to move smoothly towards electronic documents in Iran, UNedocs standard is suggested to support the contents of both paper and electronic documents. The verification of the suggested model is done by presenting a four phase scenario through case study method. The localized model in this paper tries to make a strategic view of business documents exchange in trade processes, and getting closer to the key target of regional single windows establishment in global trade e-supply chains.
PrefMiner: mining user's preferences for intelligent mobile notification management

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, also for this reason, they are considered disruptive and potentially annoying.
 In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users' preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a "black-box" solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.
Pharmaceutical cold chain management: Platform based on a distributed ledger

In the process of distributing pharmaceutical products multiple stages are involved until products are delivered to patient. A reliable way for validating and verifying that products had been maintained within a licensed range is required. The paper presents a solution for pharmaceutical cold chain management using distributed ledger technologies. An application framework is proposed for shipment tracking which will deliver information to all stakeholders during the distribution phase of pharmaceutical products. The solution is based on Hyperledger Sawtooth distributed ledger framework, which has been extended with a custom transactions family and a sensors gateway for automatically collecting data from temperature tracking devices. The focus of the paper is to describe the data model and how entities of the system communicate.
Big data for transport and logistics: A review

To face the worldwide competition and meet the recent requirements in the new information technologies era, digitalization and adoption of new information techniques have become a must for all transport and logistics companies and organizations to improve their activities. This digital transformation of transport and logistics sectors is giving birth to huge and increasingly growing sets of voluminous data with high velocity and varied data sources, also known as Big Data. With new manipulation and management infrastructures, as well as more real-time analysis and techniques, these enormous datasets can be efficiently harvested to carry out valuable operational improvements and create new business values in the transport and logistics domains. This paper provides a review of the Big Data in the transport and logistic fields, discusses the current research challenges and identifies some of the promising directions for future research.
Improved Inference for Unlexicalized Parsing

We present several improvements to unlexicalized parsing with hierarchically state-split PCFGs. First, we present a novel coarse-to-fine method in which a grammar’s own hierarchical projections are used for incremental pruning, including a method for efficiently computing projections of a grammar without a treebank. In our experiments, hierarchical pruning greatly accelerates parsing with no loss in empirical accuracy. Second, we compare various inference procedures for state-split PCFGs from the standpoint of risk minimization, paying particular attention to their practical tradeoffs. Finally, we present multilingual experiments which show that parsing with hierarchical state-splitting is fast and accurate in multiple languages and domains, even without any language-specific tuning.
Towards Text Knowledge Engineering

We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.
Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.
Learning user interaction models for predicting web search result preferences

Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate modeling and interpretation of user behavior has important applications to ranking, click spam detection, web search personalization, and other tasks. Our key insight to improving robustness of interpreting implicit feedback is to model query-dependent deviations from the expected "noisy" user behavior. We show that our model of clickthrough interpretation improves prediction accuracy over state-of-the-art clickthrough methods. We generalize our approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone. We report results of a large-scale experimental evaluation that show substantial improvements over published implicit feedback interpretation methods.
Gaze Direction based Mobile Application for Quadriplegia Wheelchair Control System

People with quadriplegia recruit the interest of researchers in introducing automated movement systems for adopted special purpose wheelchairs. These systems were introduced for easing the movement of such type of disabled people independently. This paper proposed a comprehensive control system that can control the movement of Quadriplegia wheelchairs using gaze direction and blink detection. The presented system includes two main parts. The first part consists of a smartphone that applies the propose gaze direction detection based mobile application. It then sends the direction commands to the second part via Wi-Fi connection. The second part is a prototype representing the quadriplegia wheelchair that contains robotic car (two-wheel driving car), Raspberry Pi III and ultrasound sensors. The gaze direction commands, sent from the smartphone, are received by the Raspberry Pi for processing and producing the control signals. The ultrasound sensors are fixed at the front and back of the car for performing the emergency stop when obstacles are detected. The proposed system is based on gaze tracking and direction detection without the requirement of calibration with additional sensors and instruments. The obtained results show the superior performance of the proposed system that proves the claim of authors. The accuracy ratio is ranged between 66% and 82% depending on the environment (indoor and outdoor) and surrounding lighting as well as the smart phone type. Gaze direction detection; mobile application; obstacle detection; quadriplegia; Raspberry Pi microcomputer
Survey on Independent Component Analysis

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA.
Dilated convolutions for image classification and object localization

Yu et ] showed that dilated convolutions are very effective in dense prediction problems such as semantic segmentation. In this work, we propose a new ResNet[2] based convolutional neural network model using dilated convolutions and show that this model can achieve lower error rate for image classification than ResNet with reduction of the number of the parameters of the network by 94% and that this model has high ability to localize objects despite being trained on image-level labels. We evaluated this model on ImageNet[5] which has 50 class labels randomly selected from 1000 class labels.
Distributed Learning in Multi-Armed Bandit With Multiple Players

We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N independent arms. Each arm, when played, offers i.i.d. reward according to a distribution with an unknown parameter. At each time, each player chooses one arm to play without exchanging observations or any information with other players. Players choosing the same arm collide, and, depending on the collision model, either no one receives reward or the colliding players share the reward in an arbitrary way. We show that the minimum system regret of the decentralized MAB grows with time at the same logarithmic order as in the centralized counterpart where players act collectively as a single entity by exchanging observations and making decisions jointly. A decentralized policy is constructed to achieve this optimal order while ensuring fairness among players and without assuming any pre-agreement or information exchange among players. Based on a time-division fair sharing (TDFS) of the M best arms, the proposed policy is constructed and its order optimality is proven under a general reward model. Furthermore, the basic structure of the TDFS policy can be used with any order-optimal single-player policy to achieve order optimality in the decentralized setting. We also establish a lower bound on the system regret for a general class of decentralized polices, to which the proposed policy belongs. This problem finds potential applications in cognitive radio networks, multi-channel communication systems, multi-agent systems, web search and advertising, and social networks.