nils-herrmann/clean_scirepeval huggingface dataset:
Advanced social engineering attacks

Social engineering has emerged as a serious threat in virtual communities and is an effective means to attack information systems. The services used by today’s knowledge workers prepare the ground for sophisticated social engineering attacks. The growing trend towards BYOD (bring your own device) policies and the use of online communication and collaboration tools in private and business environments aggravate the problem. In globally acting companies, teams are no longer geographically co-located, but staffed just-in-time. The decrease in personal interaction combined with a plethora of tools used for communication (e-mail, IM, Skype, Dropbox, LinkedIn, Lync, etc.) create new attack vectors for social engineering attacks. Recent attacks on companies such as the New York Times and RSA have shown that targeted spear-phishing attacks are an effective, evolutionary step of social engineering attacks. Combined with zero-day-exploits, they become a dangerous weapon that is often used by advanced persistent threats. This paper provides a taxonomy of well-known social engineering attacks as well as a comprehensive overview of advanced social engineering attacks on the knowledge worker.
Automatic Pain Intensity Estimation with Heteroscedastic Conditional Ordinal Random Fields

Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit model in the CORF to change depending on the input features, resulting in the model able to adapt to the pain expressiveness level specific to each subject. Our experimental results on the UNBC Shoulder Pain Database show that modeling heterogeneity in the subjects with the framework of CORFs improves the pain intensity estimation attained by the standard CORF model, and the other commonly used classification models.
Forensic evidence findings in prepubertal victims of sexual assault.

The American Academy of Pediatrics recommends forensic evidence collection when sexual abuse has occurred within 72 hours, or when there is bleeding or acute injury. It is not known whether these recommendations are appropriate for prepubertal children, because few data exist regarding the utility of forensic evidence collection in cases of child sexual assault. This study describes the epidemiology of forensic evidence findings in prepubertal victims of sexual assault.



The medical records of 273 children <10 years old who were evaluated in hospital emergency departments in Philadelphia, Pennsylvania, and had forensic evidence processed by the Philadelphia Police Criminalistics Laboratory were retrospectively reviewed for history, physical examination findings, forensic evidence collection, and forensic results.



Some form of forensic evidence was identified in 24.9% of children, all of whom were examined within 44 hours of their assault. Over 90% of children with positive forensic evidence findings were seen within 24 hours of their assault. The majority of forensic evidence (64%) was found on clothing and linens, yet only 35% of children had clothing collected for analysis. After 24 hours, all evidence, with the exception of 1 pubic hair, was recovered from clothing or linens. No swabs taken from the child's body were positive for blood after 13 hours or sperm/semen after 9 hours. A minority of children (23%) had genital injuries. Genital injury and a history of ejaculation provided by the child were associated with an increased likelihood of identifying forensic evidence, but several children had forensic evidence found that was unanticipated by the child's history.



The general guidelines for forensic evidence collection in cases of acute sexual assault are not well-suited for prepubertal victims. The decision to collect evidence is best made by the timing of the examination. Swabbing the child's body for evidence is unnecessary after 24 hours. Clothing and linens yield the majority of evidence and should be pursued vigorously for analysis.
Extra-Linguistic Constraints on Stance Recognition in Ideological Debates

Determining the stance expressed by an author from a post written for a twosided debate in an online debate forum is a relatively new problem. We seek to improve Anand et al.’s (2011) approach to debate stance classification by modeling two types of soft extra-linguistic constraints on the stance labels of debate posts, user-interaction constraints and ideology constraints. Experimental results on four datasets demonstrate the effectiveness of these inter-post constraints in improving debate stance classification.
Efficient, transparent, and comprehensive runtime code manipulation

This thesis addresses the challenges of building a software system for general-purpose runtime code manipulation. Modern applications, with dynamically-loaded modules and dynamicallygenerated code, are assembled at runtime. While it was once feasible at compile time to observe and manipulate every instruction — which is critical for program analysis, instrumentation, trace gathering, optimization, and similar tools — it can now only be done at runtime. Existing runtime tools are successful at inserting instrumentation calls, but no general framework has been developed for fine-grained and comprehensive code observation and modification without high overheads. This thesis demonstrates the feasibility of building such a system in software. We present DynamoRIO, a fully-implemented runtime code manipulation system that supports code transformations on any part of a program, while it executes. DynamoRIO uses code caching technology to provide efficient, transparent, and comprehensive manipulation of an unmodified application running on a stock operating system and commodity hardware. DynamoRIO executes large, complex, modern applications with dynamically-loaded, generated, or even modified code. Despite the formidable obstacles inherent in the IA-32 architecture, DynamoRIO provides these capabilities efficiently, with zero to thirty percent time and memory overhead on both Windows and Linux. DynamoRIO exports an interface for building custom runtime code manipulation tools of all types. It has been used by many researchers, with several hundred downloads of our public release, and is being commercialized in a product for protection against remote security exploits, one of numerous applications of runtime code manipulation.  Associate Professor of Electrical Engineering and Computer Science
Towards Networked Learning Analytics - A concept and a tool

Networked learning is much more ambitious than previous approaches of ICT-support in education. It is therefore more difficult to evaluate the effectiveness and efficiency of the networked learning activities. Evaluation of learners’ interactions in networked learning environments is a difficult, resource and expertise demanding task. Educators participating in online learning environments, have very little support by integrated tools to evaluate students’ activities and identify learners’ online browsing behavior and interactions. As a consequence, educators are in need for non-intrusive and automatic ways to get feedback from learners’ progress in order to better follow their learning process and appraise the online course effectiveness. They also need specialized tools for authoring, delivering, gathering and analysing data for evaluating the learning effectiveness of networked learning courses. Thus, the aim of this paper is to propose a new set of services for the evaluator and lecturer so that he/she can easily evaluate the learners’ progress and produce evaluation reports based on learners’ behaviour within a Learning Management System. These services allow the evaluator to easily track down the learners’ online behavior at specific milestones set up, gather feedback in an automatic way and present them in a comprehensive way. The innovation of the proposed set of services lies on the effort to adopt/adapt some of the web usage mining techniques combining them with the use of semantic description of networked learning tasks
Convolutional Neural Networks for Distant Speech Recognition

We investigate convolutional neural networks (CNNs) for large vocabulary distant speech recognition, trained using speech recorded from a single distant microphone (SDM) and multiple distant microphones (MDM). In the MDM case we explore a beamformed signal input representation compared with the direct use of multiple acoustic channels as a parallel input to the CNN. We have explored different weight sharing approaches, and propose a channel-wise convolution with two-way pooling. Our experiments, using the AMI meeting corpus, found that CNNs improve the word error rate (WER) by 6.5% relative compared to conventional deep neural network (DNN) models and 15.7% over a discriminatively trained Gaussian mixture model (GMM) baseline. For cross-channel CNN training, the WER improves by 3.5% relative over the comparable DNN structure. Compared with the best beamformed GMM system, cross-channel convolution reduces the WER by 9.7% relative, and matches the accuracy of a beamformed DNN.
Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services

Web services are becoming business-critical components that must provide a non-vulnerable interface to the client applications. However, previous research and practice show that many web services are deployed with critical vulnerabilities. SQL Injection vulnerabilities are particularly relevant, as web services frequently access a relational database using SQL commands. Penetration testing and static code analysis are two well-know techniques often used for the detection of security vulnerabilities. In this work we compare how effective these two techniques are on the detection of SQL Injection vulnerabilities in web services code. To understand the strengths and limitations of these techniques, we used several commercial and open source tools to detect vulnerabilities in a set of vulnerable services. Results suggest that, in general, static code analyzers are able to detect more SQL Injection vulnerabilities than penetration testing tools. Another key observation is that tools implementing the same detection approach frequently detect different vulnerabilities. Finally, many tools provide a low coverage and a high false positives rate, making them a bad option for programmers.
Index modulation techniques for 5G wireless networks

The ambitious goals set for 5G wireless networks, which are expected to be introduced around 2020, require dramatic changes in the design of different layers for next generation communications systems. Massive MIMO systems, filter bank multi-carrier modulation, relaying technologies, and millimeter-wave communications have been considered as some of the strong candidates for the physical layer design of 5G networks. In this article, we shed light on the potential and implementation of IM techniques for MIMO and multi-carrier communications systems, which are expected to be two of the key technologies for 5G systems. Specifically, we focus on two promising applications of IM: spatial modulation and orthogonal frequency-division multiplexing with IM, and discuss the recent advances and future research directions in IM technologies toward spectrum- and energy-efficient 5G wireless networks.
Distributed neural representation of expected value.

Anticipated reward magnitude and probability comprise dual components of expected value (EV), a cornerstone of economic and psychological theory. However, the neural mechanisms that compute EV have not been characterized. Using event-related functional magnetic resonance imaging, we examined neural activation as subjects anticipated monetary gains and losses that varied in magnitude and probability. Group analyses indicated that, although the subcortical nucleus accumbens (NAcc) activated proportional to anticipated gain magnitude, the cortical mesial prefrontal cortex (MPFC) additionally activated according to anticipated gain probability. Individual difference analyses indicated that, although NAcc activation correlated with self-reported positive arousal, MPFC activation correlated with probability estimates. These findings suggest that mesolimbic brain regions support the computation of EV in an ascending and distributed manner: whereas subcortical regions represent an affective component, cortical regions also represent a probabilistic component, and, furthermore, may integrate the two.
Big data for Natural Language Processing: A streaming approach

Requirements in computational power have grown dramatically in recent years. This is also the case in many language processing tasks, due to the overwhelming and ever increasing amount of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the Natural Language Processing field. In this paper we present a new distributed architecture and technology for scaling up text analysis running a complete chain of linguistic processors on several virtual machines. Furthermore, we also describe a series of experiments carried out with the goal of analyzing the scaling capabilities of the language processing pipeline used in this setting. We explore the use of Storm in a new approach for scalable distributed language processing across multiple machines and evaluate its effectiveness and efficiency when processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding language processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing
How significant is human error as a cause of privacy breaches? An empirical study and a framework for error management

Privacy breaches and their regulatory implications have attracted corporate attention in recent times. An often overlooked cause of privacy breaches is human error. In this study, we first apply a model based on the widely accepted GEMS error typology to analyze publicly reported privacy breach incidents within the U.S. Then, based on an examination of the causes of the reported privacy breach incidents, we propose a defense-in-depth solution strategy founded on error avoidance, error interception, and error correction. Finally, we illustrate the application of the proposed strategy to managing human error in the case of the two leading causes of privacy breach incidents. This study finds that mistakes in the information processing stage constitute the most cases of human errorrelated privacy breach incidents, clearly highlighting the need for effective policies and their enforcement in organizations. a 2008 Elsevier Ltd. All rights reserved.
Brain Intelligence: Go Beyond Artificial Intelligence

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing nextgeneration artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.
On complexity and optimization of expensive queries in complex event processing

Pattern queries are widely used in complex event processing (CEP) systems. Existing pattern matching techniques, however, can provide only limited performance for expensive queries in real-world applications, which may involve Kleene closure patterns, flexible event selection strategies, and events with imprecise timestamps. To support these expensive queries with high performance, we begin our study by analyzing the complexity of pattern queries, with a focus on the fundamental understanding of which features make pattern queries more expressive and at the same time more computationally expensive. This analysis allows us to identify performance bottlenecks in processing those expensive queries, and provides key insights for us to develop a series of optimizations to mitigate those bottlenecks. Microbenchmark results show superior performance of our system for expensive pattern queries while most state-of-the-art systems suffer from poor performance. A thorough case study on Hadoop cluster monitoring further demonstrates the efficiency and effectiveness of our proposed techniques.
Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.
An Analysis and Survey of the Development of Mutation Testing

Testing is a fault-based software testing technique that has been widely studied for over three decades. The literature on Mutation Testing has contributed a set of approaches, tools, developments, and empirical results. This paper provides a comprehensive analysis and survey of Mutation Testing. The paper also presents the results of several development trend analyses. These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest.
A soft wearable robotic device for active knee motions using flat pneumatic artificial muscles

We present the design of a soft wearable robotic device composed of elastomeric artificial muscle actuators and soft fabric sleeves, for active assistance of knee motions. A key feature of the device is the two-dimensional design of the elastomer muscles that not only allows the compactness of the device, but also significantly simplifies the manufacturing process. In addition, the fabric sleeves make the device lightweight and easily wearable. The elastomer muscles were characterized and demonstrated an initial contraction force of 38N and maximum contraction of 18mm with 104kPa input pressure, approximately. Four elastomer muscles were employed for assisted knee extension and flexion. The robotic device was tested on a 3D printed leg model with an articulated knee joint. Experiments were conducted to examine the relation between systematic change in air pressure and knee extension-flexion. The results showed maximum extension and flexion angles of 95° and 37°, respectively. However, these angles are highly dependent on underlying leg mechanics and positions. The device was also able to generate maximum extension and flexion forces of 3.5N and 7N, respectively.
Surrogate time series

Before we apply nonlinear techniques, e.g. those inspired by chaos theory, to dynamical phenomena occurring in nature, it is necessary to first ask if the use of such advanced techniques is justified by the data. While many processes in nature seem very unlikely a priori to be linear, the possible nonlinear nature might not be evident in specific aspects of their dynamics. The method of surrogate data has become a very popular tool to address such a question. However, while it was meant to provide a statistically rigorous, foolproof framework, some limitations and caveats have shown up in its practical use. In this paper, recent efforts to understand the caveats, avoid the pitfalls, and to overcome some of the limitations, are reviewed and augmented by new material. In particular, we will discuss specific as well as more general approaches to constrained randomisation, providing a full range of examples. New algorithms will be introduced for unevenly sampled and multivariate data and for surrogate spike trains. The main limitation, which lies in the interpretability of the test results, will be illustrated through instructive case studies. We will also discuss some implementational aspects of the realisation of these methods in the TISEAN software package.
Magnetic Resonant Coupling As a Potential Means for Wireless Power Transfer to Multiple Small Receivers

Wireless power transfer via magnetic resonant coupling is experimentally demonstrated in a system with a large source coil and either one or two small receivers. Resonance between source and load coils is achieved with lumped capacitors terminating the coils. A circuit model is developed to describe the system with a single receiver, and extended to describe the system with two receivers. With parameter values chosen to obtain good fits, the circuit models yield transfer frequency responses that are in good agreement with experimental measurements over a range of frequencies that span the resonance. Resonant frequency splitting is observed experimentally and described theoretically for the multiple receiver system. In the single receiver system at resonance, more than 50% of the power that is supplied by the actual source is delivered to the load. In a multiple receiver system, a means for tracking frequency shifts and continuously retuning the lumped capacitances that terminate each receiver coil so as to maximize efficiency is a key issue for future work.