nils-herrmann/clean_scirepeval huggingface dataset:
Passwords and Perceptions

The security of many computer systems hinges on the secrecy of a single word – if an adversary obtains knowledge of a password, they will gain access to the resources controlled by this password. Human users are the ‘weakest link’ in password control, due to our propensity to reuse passwords and to create weak ones. Policies which forbid such unsafe password practices are often violated, even if these policies are well-advertised. We have studied how users perceive their accounts and their passwords. Our participants mentally classified their accounts and passwords into a few groups, based on a small number of perceived similarities. Our participants used stronger passwords, and reused passwords less, in account groups which they considered more important. Our participants thus demonstrated awareness of the basic tenets of password safety, but they did not behave safely in all respects. Almost half of our participants reused at least one of the passwords in their high-importance accounts. Our findings add to the body of evidence that a typical computer user suffers from ‘password overload’. Our concepts of password and account grouping point the way toward more intuitive user interfaces for passwordand account-management systems. .
InP Single-Ended Transimpedance Amplifier with 92-GHz Transimpedance Bandwidth

A single-ended InP transimpedance amplifier (TIA) for next generation high-bandwidth optical fiber communication systems is presented. The TIA exhibits 48 dB-Omega transimpedance and has a 3-dB bandwidth of 92 GHz. The input-referred current noise is 20 pA/radicHz and the transimpedance group delay is below 10 ps over the entire measured frequency range.
Building detection from aerial images using invariant color features and shadow information

Robust detection of buildings is an important part of the automated aerial image interpretation problem. Automatic detection of buildings enables creation of maps, detecting changes, and monitoring urbanization. Due to the complexity and uncontrolled appearance of the scene, an intelligent fusion of different methods gives better results. In this study, we present a novel approach for building detection using multiple cues. We benefit from segmentation of aerial images using invariant color features. Besides, we use the edge and shadow information for building detection. We also determine the shape of the building by a novel method.
Cognitive neuroscience of emotional memory

Emotional events often attain a privileged status in memory. Cognitive neuroscientists have begun to elucidate the psychological and neural mechanisms underlying emotional retention advantages in the human brain. The amygdala is a brain structure that directly mediates aspects of emotional learning and facilitates memory operations in other regions, including the hippocampus and prefrontal cortex. Emotion–memory interactions occur at various stages of information processing, from the initial encoding and consolidation of memory traces to their long-term retrieval. Recent advances are revealing new insights into the reactivation of latent emotional associations and the recollection of personal episodes from the remote past.
AODV routing protocol implementation design

To date, the majority of ad hoc routing protocol research has been done using simulation only. One of the most motivating reasons to use simulation is the difficulty of creating a real implementation. In a simulator, the code is contained within a single logical component, which is clearly defined and accessible. On the other hand, creating an implementation requires use of a system with many components, including many that have little or no documentation. The implementation developer must understand not only the routing protocol, but all the system components and their complex interactions. Further, since ad hoc routing protocols are significantly different from traditional routing protocols, a new set of features must be introduced to support the routing protocol. In this paper we describe the event triggers required for AODV operation, the design possibilities and the decisions for our ad hoc on-demand distance vector (AODV) routing protocol implementation, AODV-UCSB. This paper is meant to aid researchers in developing their own on-demand ad hoc routing protocols and assist users in determining the implementation design that best fits their needs.
Single-Neuron Responses in Humans during Execution and Observation of Actions

Direct recordings in monkeys have demonstrated that neurons in frontal and parietal areas discharge during execution and perception of actions [1-8]. Because these discharges "reflect" the perceptual aspects of actions of others onto the motor repertoire of the perceiver, these cells have been called mirror neurons. Their overlapping sensory-motor representations have been implicated in observational learning and imitation, two important forms of learning . In humans, indirect measures of neural activity support the existence of sensory-motor mirroring mechanisms in homolog frontal and parietal areas [10, 11], other motor regions [12-15], and also the existence of multisensory mirroring mechanisms in nonmotor regions [16-19]. We recorded extracellular activity from 1177 cells in human medial frontal and temporal cortices while patients executed or observed hand grasping actions and facial emotional expressions. A significant proportion of neurons in supplementary motor area, and hippocampus and environs, responded to both observation and execution of these actions. A subset of these neurons demonstrated excitation during action-execution and inhibition during action-observation. These findings suggest that multiple systems in humans may be endowed with neural mechanisms of mirroring for both the integration and differentiation of perceptual and motor aspects of actions performed by self and others.
A common criteria based security requirements engineering process for the development of secure information systems

In order to develop security critical Information Systems, specifying security quality requirements is vitally important, although it is a very difficult task. Fortunately, there are several security standards, like the Common Criteria (ISO/IEC 15408), which help us handle security requirements. This article will present a Common Criteria centred and reuse-based process that deals with security requirements at the early stages of software development in a systematic and intuitive way, by providing a security resources repository as well as integrating the Common Criteria into the software lifecycle, so that it unifies the concepts of requirements engineering and security engineering.
Using Universal Linguistic Knowledge to Guide Grammar Induction

We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies between pairs of syntactic categories that commonly occur across languages. During inference of the probabilistic model, we use posterior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also automatically refine the syntactic categories given in our coarsely tagged input. Across six languages our approach outperforms state-of-theart unsupervised methods by a significant margin.1
Edge-based recognizer for Arabic sign language alphabet (ArS2V-Arabic sign to voice)

This paper introduces a new hand gesture recognition technique to recognize Arabic sign language alphabet and converts it into voice correspondences to enable Arabian deaf people to interact with normal people. The proposed technique captures a color image for the hand gesture and converts it into YCbCr color space that provides an efficient and accurate way to extract skin regions from colored images under various illumination changes. Prewitt edge detector is used to extract the edges of the segmented hand gesture. Principal Component Analysis algorithm is applied to the extracted edges to form the predefined feature vectors for signs and gestures library. The Euclidean distance is used to measure the similarity between the signs feature vectors. The nearest sign is selected and the corresponding sound clip is played. The proposed technique is used to recognize Arabic sign language alphabets and the most common Arabic gestures. Specifically, we applied the technique to more than 150 signs and gestures with accuracy near to 97% at real time test for three different signers. The detailed of the proposed technique and the experimental results are discussed in this paper.
Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms. 2006 Pattern Recognition Society.
Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.
Evaluation methods for topic models

A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean method and empirical likelihood method. In this paper, we demonstrate experimentally that commonly-used methods are unlikely to accurately estimate the probability of held-out documents, and propose two alternative methods that are both accurate and efficient.
Analysis and characterization of large‐scale Web server access patterns and performance

In this paper we develop a general methodology for characterizing the access patterns of Web server requests based on a time‐series analysis of finite collections of observed data from real systems. Our approach is used together with the access logs from the IBM Web site for the Olympic Games to demonstrate some of its advantages over previous methods and to construct a particular class of benchmarks for large‐scale heavily‐accessed Web server environments. We then apply an instance of this class of benchmarks to analyze aspects of large‐scale Web server performance, demonstrating some additional problems with methods commonly used to evaluate Web server performance at different request traffic intensities.
Search-based inference of polynomial metamorphic relations

Metamorphic testing (MT) is an effective methodology for testing those so-called ``non-testable'' programs (e.g., scientific programs), where it is sometimes very difficult for testers to know whether the outputs are correct. In metamorphic testing, metamorphic relations (MRs) (which specify how particular changes to the input of the program under test would change the output) play an essential role. However, testers may typically have to obtain MRs manually.
 In this paper, we propose a search-based approach to automatic inference of polynomial MRs for a program under test. In particular, we use a set of parameters to represent a particular class of MRs, which we refer to as polynomial MRs, and turn the problem of inferring MRs into a problem of searching for suitable values of the parameters. We then dynamically analyze multiple executions of the program, and use particle swarm optimization to solve the search problem. To improve the quality of inferred MRs, we further use MR filtering to remove some inferred MRs.
 We also conducted three empirical studies to evaluate our approach using four scientific libraries (including 189 scientific functions). From our empirical results, our approach is able to infer many high-quality MRs in acceptable time (i.e., from 9.87 seconds to 1231.16 seconds), which are effective in detecting faults with no false detection.
Fault based cryptanalysis of the Advanced Encryption Standard

In this paper we describe several fault attacks on the Advanced Encryption Standard (AES). First, using optical fault induction attacks as recently publicly presented by Skorobogatov and Anderson [SA], we present an implementation independent fault attack on AES. This attack is able to determine the complete 128-bit secret key of a sealed tamper-proof smartcard by generating 128 faulty cipher texts. Second, we present several implementationdependent fault attacks on AES. These attacks rely on the observation that due to the AES's known timing analysis vulnerability (as pointed out by Koeune and Quisquater [KQ]), any implementation of the AES must ensure a data independent timing behavior for the so called AES's xtime operation. We present fault attacks on AES based on various timing analysis resistant implementations of the xtime-operation. Our strongest attack in this direction uses a very liberal fault model and requires only 256 faulty encryptions to determine a 128-bit key.
DHAES: An Encryption Scheme Based on the Diffie-Hellman Problem

This paper describes a Di e-Hellman based encryption scheme, DHAES. The scheme is as e cient as ElGamal encryption, but has stronger security properties. Furthermore, these security properties are proven to hold under appropriate assumptions on the underlying primitive. We show that DHAES has not only the \basic" property of secure encryption (namely privacy under a chosen-plaintext attack) but also achieves privacy under both non-adaptive and adaptive chosenciphertext attacks. (And hence it also achieves non-malleability.) DHAES is built in a generic way from lower-level primitives: a symmetric encryption scheme, a message authentication code, group operations in an arbitrary group, and a cryptographic hash function. In particular, the underlying group may be an elliptic-curve group or the multiplicative group of integers modulo a prime number. The proofs of security are based on appropriate assumptions about the hardness of the Di e-Hellman problem and the assumption that the underlying symmetric primitives are secure. The assumptions are all standard in the sense that no random oracles are involved. We suggest that DHAES provides an attractive starting point for developing public-key encryption standards based on the Di e-Hellman assumption.
Refactoring for generalization using type constraints

Refactoring is the process of applying behavior-preserving transformations (called "refactorings") in order to improve a program's design. Associated with a refactoring is a set of preconditions that must be satisfied to guarantee that program behavior is preserved, and a set of source code modifications. An important category of refactorings is concerned with generalization (e.g., Extract Interface for re-routing the access to a class via a newly created interface, and Pull Up Members for moving members into a superclass). For these refactorings, both the preconditions and the set of allowable source code modifications depend on interprocedural relationships between types of variables. We present an approach in which type constraints are used to verify the preconditions and to determine the allowable source code modifications for a number of generalization-related refactorings. This work is implemented in the standard distribution of Eclipse (see www.eclipse.org).
Anthropomorphism influences perception of computer-animated characters' actions.

Computer-animated characters are common in popular culture and have begun to be used as experimental tools in social cognitive neurosciences. Here we investigated how appearance of these characters' influences perception of their actions. Subjects were presented with different characters animated either with motion data captured from human actors or by interpolating between poses (keyframes) designed by an animator, and were asked to categorize the motion as biological or artificial. The response bias towards 'biological', derived from the Signal Detection Theory, decreases with characters' anthropomorphism, while sensitivity is only affected by the simplest rendering style, point-light displays. fMRI showed that the response bias correlates positively with activity in the mentalizing network including left temporoparietal junction and anterior cingulate cortex, and negatively with regions sustaining motor resonance. The absence of significant effect of the characters on the brain activity suggests individual differences in the neural responses to unfamiliar artificial agents. While computer-animated characters are invaluable tools to investigate the neural bases of social cognition, further research is required to better understand how factors such as anthropomorphism affect their perception, in order to optimize their appearance for entertainment, research or therapeutic purposes.
Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.
A conceptual framework for the design and organizational control mechanisms

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ML for the working programmer

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Local word vectors guiding keyphrase extraction

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e., embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and therefore can help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.