nils-herrmann/clean_scirepeval huggingface dataset:
Security Analysis of KEA Authenticated Key Exchange Protocol

KEA is a Diffie-Hellman based key-exchange protocol developed by NSA which provides mutual authentication for the parties. It became publicly available in 1998 and since then it was neither attacked nor proved to be secure. We analyze the security of KEA and find that the original protocol is susceptible to a class of attacks. On the positive side, we present a simple modification of the protocol which makes KEA secure. We prove that the modified protocol, called KEA+, satisfies the strongest security requirements for authenticated key-exchange and that it retains some security even if a secret key of a party is leaked. Our security proof is in the random oracle model and uses the Gap Diffie-Hellman assumption. Finally, we show how to add a key confirmation feature to KEA+ (we call the version with key confirmation KEA+C) and discuss the security properties of KEA+C.
IT alignment: what have we learned?

We provide a review of the alignment literature in IT, addressing questions such as: What have we learned? What is disputed? Who are contributors to the debate? The article is intended to be useful to faculty and graduate students considering conducting research on alignment, instructors preparing lectures, and practitioners seeking to assess the ‘state-of-play’. It is both informational and provocative. Challenges to the value of alignment research, divergent views, and new perspectives on alignment are presented. It is hoped that the article will spark helpful conversation on the merits of continued investigation of IT alignment.  (2007) 22, 297–315. doi:10.1057/palgrave.jit.2000109 Published online 18 September 2007
HyQ - Hydraulically actuated quadruped robot: Hopping leg prototype

This paper describes the concept, specifications and design of the biologically inspired quadruped robot HyQ, with special focus on the leg design. The main scope of this new robotic platform is to study highly dynamic tasks such as running and jumping. To meet the specifications in terms of performance and dimensions, hydraulic actuation has been chosen due to its high power to weight ratio and fast response. Guidelines on how to choose the design parameters of the hydraulic cylinders including lever length are reported. A two DOF leg prototype has been designed and constructed. The experimental test setup for the leg prototype is explained and the results of first hopping experiments are reported.
Virtual reality in engineering education: The future of creative learning

Virtual reality has achieved an adequate level of development for it to be considered in innovative applications such as education, training, and research in higher education. Virtual reality offers both opportunities and challenges for the educational sector. One of the challenges of virtual reality technology is the costs associated which have been unaffordable for educational institutes. However, in recent years, computer hardware and software development has made it more feasible to incorporate virtual reality technology into future teaching strategies. Despite the cost challenges, educational benefits of implementing virtual reality remain compelling. This paper explains virtual reality principle and describes the interactive educational environment developed at WMG, the University of Warwick. It also discusses the benefits of using state-of-the-art 3D photorealistic interactive and immersive virtual environment for engineering undergraduates and postgraduate teaching, learning and training.
Authentication in the clouds: a framework and its application to mobile users

Cloud computing is a natural fit for mobile security. Typical handsets have input constraints and practical computational and power limitations, which must be respected by mobile security technologies in order to be effective. We describe how cloud computing can address these issues. Our approach is based on a flexible framework for supporting authentication decisions we call TrustCube (to manage the authentication infrastructure) and on a behavioral authentication approach referred to as implicit authentication (to translate user behavior into authentication scores). The combination results in a new authentication paradigm for users of mobile technologies, one where an appropriate balance between usability and trust can be managed through flexible policies and dynamic tuning.
Spin: lexical semantics, transitivity, and the identification of implicit sentiment

Title of Document: SPIN: LEXICAL SEMANTICS, TRANSITIVITY, AND THE IDENTIFICATION OF IMPLICIT SENTIMENT Stephan Charles Greene Doctor of Philosophy, 2007 Directed By: Professor Philip Resnik, Department of Linguistics and Institute for Advanced Computer Studies Current interest in automatic sentiment analysis i motivated by a variety of information requirements. The vast majority of work in sentiment analysis has been specifically targeted at detecting subjective state ments and mining opinions. This dissertation focuses on a different but related pro blem that to date has received relatively little attention in NLP research: detect ing implicit sentiment , or spin, in text. This text classification task is distinguished from ther sentiment analysis work in that there is no assumption that the documents to b e classified with respect to sentiment are necessarily overt expressions of opin ion. They rather are documents that might reveal a perspective . This dissertation describes a novel approach to t e identification of implicit sentiment, motivated by ideas drawn from the literature on lexical semantics and argument structure, supported and refined through psycholinguistic experimentation. A relationship pr edictive of sentiment is established for components of meaning that are thou g t to be drivers of verbal argument selection and linking and to be arbiters o f what is foregrounded or backgrounded in discourse. In computational experim nts employing targeted lexical selection for verbs and nouns, a set of features re flective of these components of meaning is extracted for the terms. As observable p roxies for the underlying semantic components, these features are exploited using mach ine learning methods for text classification with respect to perspective. After i nitial experimentation with manually selected lexical resources, the method is generaliz d to require no manual selection or hand tuning of any kind. The robustness of this lin gu stically motivated method is demonstrated by successfully applying it to three d istinct text domains under a number of different experimental conditions, obtain ing the best classification accuracies yet reported for several sentiment class ification tasks. A novel graph-based classifier combination method is introduced which f urther improves classification accuracy by integrating statistical classifiers wit h models of inter-document relationships.
Growing closer on facebook: changes in tie strength through social network site use

Scientists debate whether people grow closer to their friends through social networking sites like Facebook, whether those sites displace more meaningful interaction, or whether they simply reflect existing ties. Combining server log analysis and longitudinal surveys of 3,649 Facebook users reporting on relationships with 26,134 friends, we find that communication on the site is associated with changes in reported relationship closeness, over and above effects attributable to their face-to-face, phone, and email contact. Tie strength increases with both one-on-one communication, such as posts, comments, and messages, and through reading friends' broadcasted content, such as status updates and photos. The effect is greater for composed pieces, such as comments, posts, and messages than for 'one-click' actions such as 'likes.' Facebook has a greater impact on non-family relationships and ties who do not frequently communicate via other channels.
Bregman Iterative Algorithms for \ell1-Minimization with Applications to Compressed Sensing

We propose simple and extremely efficient methods for solving the Basis Pursuit problem min{‖u‖1 : Au = f, u ∈ R}, which is used in compressed sensing. Our methods are based on Bregman iterative regularization and they give a very accurate solution after solving only a very small number of instances of the unconstrained problem min u∈Rn μ‖u‖1 + 1 2 ‖Au− f‖2, for given matrix A and vector fk. We show analytically that this iterative approach yields exact solutions in a finite number of steps, and present numerical results that demonstrate that as few as two to six iterations are sufficient in most cases. Our approach is especially useful for many compressed sensing applications where matrix-vector operations involving A and A> can be computed by fast transforms. Utilizing a fast fixed-point continuation solver that is solely based on such operations for solving the above unconstrained sub-problem, we were able to solve huge instances of compressed sensing problems quickly on a standard PC.
Formulaic Language in Native and Second Language Speakers : Psycholinguistics , Corpus Linguistics , and TESOL

Natural language makes considerable use of recurrent formulaic patterns of words. This article triangulates the construct of formula from corpus linguistic, psycholinguistic, and educational perspectives. It describes the corpus linguistic extraction of pedagogically useful formulaic sequences for academic speech and writing. It determines English as a second language (ESL) and English for academic purposes (EAP) instructors’ evaluations of their pedagogical importance. It summarizes three experiments which show that different aspects of formulaicity affect the accuracy and fluency of processing of these formulas in native speakers and in advanced L2 learners of English. The language processing tasks were selected to sample an ecologically valid range of language processing skills: spoken and written, production and comprehension. Processing in all experiments was affected by various corpus-derived metrics: length, frequency, and mutual information (MI), but to different degrees in the different populations. For native speakers, it is predominantly the MI of the formula which determines processability; for nonnative learners of the language, it is predominantly the frequency of the formula. The implications of these findings are discussed for (a) the psycholinguistic validity of corpus-derived formulas, (b) a model of their acquisition, (c) ESL and EAP instruction and the prioritization of which formulas to teach.
Mining Top-K Sequential Rules

Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine-tuning the parameters can be very time-consuming. In this paper, we address this problem by proposing TopSeqRules, an efficient algorithm for mining the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user. Experimental results on real-life datasets show that the algorithm has excellent performance and scalability.
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.
Learning a Deep Model for Human Action Recognition from Novel Viewpoints

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art.
On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference

We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. Specifically, a natural relaxation of the dual formulation gives rise to exact iterative solutions to the finite and infinite horizon stochastic optimal control problem, while direct application of Bayesian inference methods yields instances of risk sensitive control. We furthermore study corresponding formulations in the reinforcement learning setting and present model free algorithms for problems with both discrete and continuous state and action spaces. Evaluation of the proposed methods on the standard Gridworld and Cart-Pole benchmarks verifies the theoretical insights and shows that the proposed methods improve upon current approaches.
A Non-Inclusive Memory Permissions architecture for protection against cross-layer attacks

Protecting modern computer systems and complex software stacks against the growing range of possible attacks is becoming increasingly difficult. The architecture of modern commodity systems allows attackers to subvert privileged system software often using a single exploit. Once the system is compromised, inclusive permissions used by current architectures and operating systems easily allow a compromised high-privileged software layer to perform arbitrary malicious activities, even on behalf of other software layers. This paper presents a hardware-supported page permission scheme for the physical pages that is based on the concept of non-inclusive sets of memory permissions for different layers of system software such as hypervisors, operating systems, and user-level applications. Instead of viewing privilege levels as an ordered hierarchy with each successive level being more privileged, we view them as distinct levels each with its own set of permissions. Such a permission mechanism, implemented as part of a processor architecture, provides a common framework for defending against a range of recent attacks. We demonstrate that such a protection can be achieved with negligible performance overhead, low hardware complexity and minimal changes to the commodity OS and hypervisor code.
Classification of malicious web code by machine learning

Web applications make life more convenient through on the activities. Many web applications have several kind of user input (e.g. personal information, a user's comment of commercial goods, etc.) for the activities. However, there are various vulnerabilities in input functions of web applications. It is possible to try malicious actions using free accessibility of the web applications. The attacks by exploitation of these input vulnerabilities enable to be performed by injecting malicious web code; it enables one to perform various illegal actions, such as SQL Injection Attacks (SQLIAs) and Cross Site Scripting (XSS). These actions come down to theft, replacing personal information, or phishing. Many solutions have devised for the malicious web code, such as AMNESIA  and SQL Check [2], etc. The methods use parser for the code, and limited to fixed and very small patterns, and are difficult to adapt to variations. Machine learning method can give leverage to cover far broader range of malicious web code and is easy to adapt to variations and changes. Therefore, we suggests adaptable classification of malicious web code by machine learning approach such as Support Vector Machine (SVM)[3], Naïve-Bayes[4], and k-Nearest Neighbor Algorithm[5] for detecting the exploitation user inputs.
Generalized BackPropagation, Étude De Cas: Orthogonality

This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network. In particular, we make use of the Riemannian geometry and optimization techniques on matrix manifolds to step outside of normal practice in training deep networks, equipping the network with structures such as orthogonality or positive definiteness. Based on our development, we make another contribution by introducing the Stiefel layer, a layer with orthogonal weights. Among various applications, Stiefel layers can be used to design orthogonal filter banks, perform dimensionality reduction and feature extraction. We demonstrate the benefits of having orthogonality in deep networks through a broad set of experiments, ranging from unsupervised feature learning to fine-grained image classification.
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario

In this report, we provide a comparative analysis of different techniques for user intent classification towards the task of app recommendation. We analyse the performance of different models and architectures for multi-label classification over a dataset with a relative large number of classes and only a handful examples of each class. We focus, in particular, on memory network architectures, and compare how well the different versions perform under the task constraints. Since the classifier is meant to serve as a module in a practical dialog system, it needs to be able to work with limited training data and incorporate new data on the fly. We devise a 1-shot learning task to test the models under the above constraint. We conclude that relatively simple versions of memory networks perform better than other approaches. Although, for tasks with very limited data, simple non-parametric methods perform comparably, without needing the extra training data.
Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells

The simplicity of programming the CRISPR (clustered regularly interspaced short palindromic repeats)–associated nuclease Cas9 to modify specific genomic loci suggests a new way to interrogate gene function on a genome-wide scale. We show that lentiviral delivery of a genome-scale CRISPR-Cas9 knockout (GeCKO) library targeting 18,080 genes with 64,751 unique guide sequences enables both negative and positive selection screening in human cells. First, we used the GeCKO library to identify genes essential for cell viability in cancer and pluripotent stem cells. Next, in a melanoma model, we screened for genes whose loss is involved in resistance to vemurafenib, a therapeutic RAF inhibitor. Our highest-ranking candidates include previously validated genes NF1 and MED12, as well as novel hits NF2, CUL3, TADA2B, and TADA1. We observe a high level of consistency between independent guide RNAs targeting the same gene and a high rate of hit confirmation, demonstrating the promise of genome-scale screening with Cas9.
Fuzzy portfolio optimization under downside risk measures

This paper presents two fuzzy portfolio selection models where the objective is to minimize the downside risk constrained by a given expected return. We assume that the rates of returns on securities are approximated as LR-fuzzy numbers of the same shape, and that the expected return and risk are evaluated by interval-valued means. We establish the relationship between those mean-interval definitions for a given fuzzy portfolio by using suitable ordering relations. Finally, we formulate the portfolio selection problem as a linear program when the returns on the assets are of trapezoidal form.
On clustering heterogeneous social media objects with outlier links

The clustering of social media objects provides intrinsic understanding of the similarity relationships between documents, images, and their contextual sources. Both content and link structure provide important cues for an effective clustering algorithm of the underlying objects. While link information provides useful hints for improving the clustering process, it also contains a significant amount of noisy information. Therefore, a robust clustering algorithm is required to reduce the impact of noisy links. In order to address the aforementioned problems, we propose heterogeneous random fields to model the structure and content of social media networks. We design a probability measure on the social media networks which output a configuration of clusters that are consistent with both content and link structure. Furthermore, noisy links can also be detected, and their impact on the clustering algorithm can be significantly reduced. We conduct experiments on a real social media network and show the advantage of the method over other state-of-the-art algorithms.
End-to-End Neural Sentence Ordering Using Pointer Network

Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pairwise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes1 and dataset2 of this paper are available.