nils-herrmann/clean_scirepeval huggingface dataset:
The organisation of the elderly connectome

Investigations of the human connectome have elucidated core features of adult structural networks, particularly the crucial role of hub-regions. However, little is known regarding network organisation of the healthy elderly connectome, a crucial prelude to the systematic study of neurodegenerative disorders. Here, whole-brain probabilistic tractography was performed on high-angular diffusion-weighted images acquired from 115 healthy elderly subjects (age 76-94 years; 65 females). Structural networks were reconstructed between 512 cortical and subcortical brain regions. We sought to investigate the architectural features of hub-regions, as well as left-right asymmetries, and sexual dimorphisms. We observed that the topology of hub-regions is consistent with a young adult population, and previously published adult connectomic data. More importantly, the architectural features of hub connections reflect their ongoing vital role in network communication. We also found substantial sexual dimorphisms, with females exhibiting stronger inter-hemispheric connections between cingulate and prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized subnetworks consistent with the neural circuitry specialised for language and executive functions, whilst rightward subnetworks were dominant in visual and visuospatial streams. These findings provide insights into healthy brain ageing and provide a benchmark for the study of neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD).
Going Spear Phishing: Exploring Embedded Training and Awareness

To explore the effectiveness of embedded training, researchers conducted a large-scale experiment that tracked workers' reactions to a series of carefully crafted spear phishing emails and a variety of immediate training and awareness activities. Based on behavioral science findings, the experiment included four different training conditions, each of which used a different type of message framing. The results from three trials showed that framing had no significant effect on the likelihood that a participant would click a subsequent spear phishing email and that many participants either clicked all links or none regardless of whether they received training. The study was unable to determine whether the embedded training materials created framing changes on susceptibility to spear phishing attacks because employees failed to read the training materials.
Modeling Radicalization Phenomena in Heterogeneous Populations

The phenomenon of radicalization is investigated within a mixed population composed of core and sensitive subpopulations. The latest includes first to third generation immigrants. Respective ways of life may be partially incompatible. In case of a conflict core agents behave as inflexible about the issue. In contrast, sensitive agents can decide either to live peacefully adjusting their way of life to the core one, or to oppose it with eventually joining violent activities. The interplay dynamics between peaceful and opponent sensitive agents is driven by pairwise interactions. These interactions occur both within the sensitive population and by mixing with core agents. The update process is monitored using a Lotka-Volterra-like Ordinary Differential Equation. Given an initial tiny minority of opponents that coexist with both inflexible and peaceful agents, we investigate implications on the emergence of radicalization. Opponents try to turn peaceful agents to opponents driving radicalization. However, inflexible core agents may step in to bring back opponents to a peaceful choice thus weakening the phenomenon. The required minimum individual core involvement to actually curb radicalization is calculated. It is found to be a function of both the majority or minority status of the sensitive subpopulation with respect to the core subpopulation and the degree of activeness of opponents. The results highlight the instrumental role core agents can have to hinder radicalization within the sensitive subpopulation. Some hints are outlined to favor novel public policies towards social integration.
Low latency MEC framework for SDN-based LTE/LTE-A networks

Mobile Edge Computing (MEC) consists of deploying computing resources (CPU, storage) at the edge of mobile networks; typically near or with eNodeBs. Besides easing the deployment of applications and services requiring low access to the remote server, such as Virtual Reality and Vehicular IoT, MEC will enable the development of context-aware and context-optimized applications, thanks to the Radio API (e.g. information on user channel quality) exposed by eNodeBs. Although ETSI is defining the architecture specifications, solutions to integrate MEC to the current 3GPP architecture are still open. In this paper, we fill this gap by proposing and implementing a Software Defined Networking (SDN)-based MEC framework, compliant with both ETSI and 3GPP architectures. It provides the required data-plane flexibility and programmability, which can on-the-fly improve the latency as a function of the network deployment and conditions. To illustrate the benefit of using SDN concept for the MEC framework, we present the details of software architecture as well as performance evaluations.
The Theory of Culture Care Diversity and Universality

Understanding the why of culture care differences and similarities among and between cultures would offer explanatory power to support nursing as an academic discipline and practice profession . . . Using the theory, then, could help to establish the nature, essence, meanings, expressions, and forms of human care or caring—a highly unique, credible, reliable, and meaningful body of knowledge for nursing. (Leininger, 1991, p. 35)
Vision aided automatic landing system for fixed wing UAV

In this paper, we present a multi-sensor system for automatic landing of fixed wing UAVs. The system is composed of a high precision aircraft controller and a vision module which is currently used for detection and tracking of runways. Designing the system we paid special attention to its robustness. The runway detection algorithm uses a maximum amount of information in images and works with high level geometrical models. It allows detecting a runway under different weather conditions even if only a small part is visible in the image. In order to increase landing reliability under sub-optimal wind conditions, an additional loop was introduced into the altitude controller. All control and image processing is performed onboard. The system has been successfully tested in flight experiments with two different fixed wing platforms at various weather conditions, in summer, fall and winter.
A model for types and levels of human interaction with automation

Technical developments in computer hardware and software now make it possible to introduce automation into virtually all aspects of human-machine systems. Given these technical capabilities, which system functions should be automated and to what extent? We outline a model for types and levels of automation that provides a framework and an objective basis for making such choices. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.
The Foundations of Cryptography

1 Summary of the review In this review I present " Foundations of Cryptography " by Oded Goldreich. This two-volume work gives an extensive description of the theoretical foundations of modern cryptography. It is beneficial to keep in mind that the two volumes are two parts of a whole, and in particular the second volume builds heavily on the first. 2 Summary of the book Modern cryptography is a very vast and inter-disciplinar subject. It combines elements from mathematics, computer science, and engineering, with the aim of designing systems for secure communication. Very rigorous and formal foundations are required to properly describe all the issues which concern cryptography; this book seeks to give a detailed treatment of all these foundations, presenting the most important tools and applications as building blocks for solving cryptographic problems. The book is divided into two volumes: the first volume features all the basic tools that are necessary to define and approach the subject. In the second volume are instead described the main applications such as encryption and signature schemes. In this volume the author concentrates his attention on the basic tools of modern cryptography. The volume is divided into just four, rather large chapters, and two appendices. Chapter 1 is an introduction, containing a high-level overview of the subject. Some background on probability and computational complexity is also presented here, as well as a motivation for needing a rigorous treatment. The first, fundamental tool is presented in Chapter 2: one-way functions. These capture the very essence of cryptography: protecting confidential information from being retrieved by a malicious user. Hence, one-way functions are functions that are easy to compute, but hard to invert. They are described as a general tool, both at a basic level (strong and weak) and in a number of variants, such as universal one-way functions, trapdoor one-way permutations, hard-core predicates etc. The author also presents the most popular candidates for one-way functions, based on the hardness of a variety of mathematical problems; some examples are integer
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

We introduce instancewise feature selection as a methodology for model interpretation. Our method is based on learning a function to extract a subset of features that are most informative for each given example. This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response variable given the input is the model to be explained. We develop an efficient variational approximation to the mutual information, and show the effectiveness of our method on a variety of synthetic and real data sets using both quantitative metrics and human evaluation.
Digital forensics: Latest challenges and response

Twenty first century has witnessed an explosive growth in use of computers and networks in every facet of life. With this explosive growth, new advancements in the field of secure computing, network technologies and systems have also been witnessed at an unprecedented rate. These developments have made existing digital forensics tools and techniques ineffective. Moreover, easy access to science of digital forensics and associated techniques has lead to use of anti-forensics measures by malicious actors to cover their traces. The paper highlights the impact of latest trends, technological advancement and use of anti-forensics measures on digital forensics along with the response of digital forensics to them. Shortfalls in the said response have also been covered along with recommended measures to address them. Moreover, historical perspective and established procedures of digital forensics are also discussed.
TR 09-004 Detecting Anomalies in a Time Series Database

We present a comprehensive evaluation of a large number of semi-supervised anomaly detection techniques for time series data. Some of these are existing techniques and some are adaptations that have never been tried before. For example, we adapt the window based discord detection technique to solve this problem. We also investigate several techniques that detect anomalies in discrete sequences, by discretizing the time series data. We evaluate these techniques on a large variety of data sets obtained from a broad spectrum of application domains. The data sets have different characteristics in terms of the nature of normal time series and the nature of anomalous time series. We evaluate the techniques on different metrics, such as accuracy in detecting the anomalous time series, sensitivity to parameters, and computational complexity, and provide useful insights regarding the effectiveness of different techniques based on the experimental evaluation.
Strategic information systems planning: too little or too much?

Strategic information systems planning (SISP) is a key concern facing top business and information systems executives. Observers have suggested that both too little and too much SISP can prove ineffective. Hypotheses examine the expected relationship between comprehensiveness and effectiveness in five SISP planning phases. They predict a nonlinear, inverted-U relationship thus suggesting the existence of an optimal level of comprehensiveness. A survey collected data from 161 US information systems executives. After an extensive validation of the constructs, the statistical analysis supported the hypothesis in a Strategy Implementation Planning phase, but not in terms of the other four SISP phases. Managers may benefit from the knowledge that both too much and too little implementation planning may hinder SISP success. Future researchers should investigate why the hypothesis was supported for that phase, but not the others.
The Physics-of-Failure approach in reliability engineering

In today's world, apart from the fact that systems and products are becoming increasingly complex, electronic technology is rapidly progressing in both miniaturization and higher complexity. Consequently, these facts are accompanied with new failures modes. Standard reliability tools cope to tackle all of the new emerging challenges. New technology and designs require adapted approaches to ensure that the products cost-effectively and timely meet desired reliability goals. The Physics-of-Failure (P-o-F) represents one approach to reliability assessment based on modeling and simulation that relies on understanding the physical processes contributing to the appearance of the critical failures. This paper outlines the classical approaches to reliability engineering and discusses advantages of the Physics-of-Failure approach. It also stresses that the P-o-F approach should be probabilistic in order to include inevitable variations of variables involved in processes contributing to the occurrence of failures in the analysis.
Conditional Kronecker Batch Normalization for Compositional Reasoning

Conditional Batch Normalization (CBN) has proved to be an effective tool for visual question answering. However, previous CBN approaches fuse the linguistic information into image features via a simple affine transformation, thus they have struggled on compositional reasoning and object counting in images. In this paper, we propose a novel CBN method using the Kronecker transformation, termed as Conditional Kronecker Batch Normalization (CKBN). CKBN layer facilitates the explicit and expressive learning of compositional reasoning and robust counting in original images. Besides, we demonstrate that the Kronecker transformation in CKBN layer is a generalization of the affine transformation in prior CBN approaches. It could accelerate the fusion of visual and linguistic information, and thus the convergence of overall model. Experiment results show that our model significantly outperforms previous CBN methods (e.g. FiLM) in compositional reasoning, counting as well as the convergence speed on CLEVR dataset.
Programmer Performance and the Effects of the Workplace

Wide variation in programmer performance has been frequently reported in the literature [1, 2, 3]. In the absence of other explanation, most managers have come to accept that the variation is due to individual characteristics. The presumption that there are order-of-magnitude differences in individual performance makes accurate cost projection seem nearly impossible.
In an extensive study, 166 programmers from 35 different organizations, participated in a one-day implementation benchmarking exercise. While there were wide variations across the sample, we found evidence that characteristics of the workplace and of the organization seemed to explain a significant part of the difference.
INFERRING REWARD FUNCTIONS

Our goal is to infer reward functions from demonstrations. In order to infer the correct reward function, we must account for the systematic ways in which the demonstrator is suboptimal. Prior work in inverse reinforcement learning can account for specific, known biases, but cannot handle demonstrators with unknown biases. In this work, we explore the idea of learning the demonstrator’s planning algorithm (including their unknown biases), along with their reward function. What makes this challenging is that any demonstration could be explained either by positing a term in the reward function, or by positing a particular systematic bias. We explore what assumptions are sufficient for avoiding this impossibility result: either access to tasks with known rewards which enable estimating the planner separately, or that the demonstrator is sufficiently close to optimal that this can serve as a regularizer. In our exploration with synthetic models of human biases, we find that it is possible to adapt to different biases and perform better than assuming a fixed model of the demonstrator, such as Boltzmann rationality.
SDN enhanced campus network authentication and access control system

Attempts to secure the enterprise network, even when using strong AAA (authentication, authorization and accounting) schemes, meet the user box spoofing and security middle boxes (firewalls and other filtering tools) bypassing problems. Seeking to strengthen the network security level, the names (users, addresses) and user machines must be bound tightly to the unambiguously defined network appliances and their ports. Using traditional network' architecture these solutions are difficult to realize. The SDN framework allows solving the aforementioned problems more precise and securely. One of the possible ways to gradually implement the controllability of traffic flow in standard hierarchical networks by applying OpenFlow driven SDN architecture and commodity access switches, is described in this paper. The performance impact of the solution is also assessed.
Political Preference Formation : Competition , Deliberation , and the ( Ir ) relevance of Framing Effects

One of the most contested questions in the social sciences is whether people behave rationally. A large body of work assumes that individuals do in fact make rational economic, political, and social decisions. Yet hundreds of experiments suggest that this is not the case. Framing effects constitute one of the most stunning and influential demonstrations of irrationality. The effects not only challenge the foundational assumptions of much of the social sciences (e.g., the existence of coherent preferences or stable attitudes), but also lead many scholars to adopt alternative approaches (e.g., prospect theory). Surprisingly, virtually no work has sought to specify the political conditions under which framing effects occur. I fill this gap by offering a theory and experimental test. I show how contextual forces (e.g., elite competition, deliberation) and individual attributes (e.g., expertise) affect the success of framing. The results provide insight into when rationality assumptions apply and, also, have broad implications for political psychology and experimental methods.
Contextual String Embeddings for Sequence Labeling

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CONLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair
Extensive Tests of Autonomous Driving Technologies

This paper presents the vision of the Artificial Vision and Intelligent Systems Laboratory (VisLab) on future automated vehicles, ranging from sensor selection up to their extensive testing. VisLab's design choices are explained using the BRAiVE autonomous vehicle prototype as an example. BRAiVE, which is specifically designed to develop, test, and demonstrate advanced safety applications with different automation levels, features a high integration level and a low-cost sensor suite, which are mainly based on vision, as opposed to many other autonomous vehicle implementations based on expensive and invasive sensors. The importance of performing extensive tests to validate the design choices is considered to be a hard requirement, and different tests have been organized, including an intercontinental trip from Italy to China. This paper also presents the test, the main challenges, and the vehicles that have been specifically developed for this test, which was performed by four autonomous vehicles based on BRAiVE's architecture. This paper also includes final remarks on VisLab's perspective on future vehicles' sensor suite.