nils-herrmann/clean_scirepeval huggingface dataset:
Insights into User Behavior in Dealing with Internet Attacks

The Internet is a lucrative medium for criminals targeting Internet users. Most common Internet attacks require some form of user interaction such as clicking on an exploit link. Hence, the problem at hand is not only a technical one, but it also has a strong human aspect. Although the security community has proposed many technical solutions to common attacks, the behavior of users when they face current threats, and the way they evaluate the security implications of their actions remain largely unexplored. In this paper we describe an online experiment platform we built for testing the behavior of users when they are confronted with prevalent, concrete attack scenarios such as reflected cross-site scripting, session fixation, and file sharing scams. We conducted experiments with 164 Internet users with diverse backgrounds. Our findings suggest that many non-technical users can exhibit performance comparable to security experts at averting relatively simple threats that they are frequently exposed to in everyday life. They can do so solely by following their intuition, without actually perceiving the severity of the threat. However, when facing more sophisticated attacks, these non-technical users often rely on misleading cues such as the “size” and “length” of artifacts (e.g., URLs), and hence, fail to protect themselves. We also show that trick banners that are common in file sharing websites and shortened URLs have high success rates of deceiving non-technical users, thus posing a severe security risk.
SCNN: An accelerator for compressed-sparse convolutional neural networks

Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.
Design and Implementation of a Learning Analytics Toolkit for Teachers

Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students’ performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics Toolkit, which enables teachers to explore and correlate learning object usage, user properties, user behavior, as well as assessment results based on graphical indicators. The primary aim of the development of eLAT is to process large data sets in microseconds with regard to individual data analysis interests of teachers and data privacy issues, in order to help them to self-reflect on their technology-enhanced teaching and learning scenarios and to identify opportunities for interventions and improvements.
A World-Championship-Level Othello Program

Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, Iago. The work described here includes: (1) a task analysis of Othello; (2) the implemenation of a program based on this analysis and state-of-the-art AI gameplaying techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play. This research was sponsored by the Defense Advanced Research Projects Agency (DOD) , ARPA Order N o . 3597, monitored by the Air Force Avionics Laboratory Under Contract F33615-78-C-1551. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government .
Point-Set Topological Spatial Relations

Practical needs in the realm of Geographic Information Systems (GISs) have driven the efforts to investigate formal and sound methods to describe spatial relations. After an introduction of the basic ideas and notions of topology, a novel theory of topological spatial relations between sets is developed in which the relations are defined in terms of the intersections of the boundaries and interiors of two sets. By considering and as the values of the intersections, a total of sixteen topological spatial relations are described, each of which can be realized in IR . This set is reduced to nine relations if the sets are restricted to spatial regions, a fairly broad class of subsets of a connected topological space having application to GIS. It is shown that these relations correspond to some of the standard set-theoretic and topological spatial relations between sets such as equality, disjointness, and containment in the interior.
Pioneer Networks: Progressively Growing Generative Autoencoder

We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory highquality results. Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achieves high-quality reconstruction with 128×128 images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder–generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks.
Nearest prototype classifier designs: An experimental study

We compare eleven methods for finding prototypes upon which to base the nearest Ž prototype classifier. Four methods for prototype selection are discussed: Wilson Hart a . condensation error-editing method , and three types of combinatorial search random search, genetic algorithm, and tabu search. Seven methods for prototype extraction are discussed: unsupervised vector quantization, supervised learning vector quantization Ž . with and without training counters , decision surface mapping, a fuzzy version of vector quantization, c-means clustering, and bootstrap editing. These eleven methods can be usefully divided two other ways: by whether they employ preor postsupervision; and by whether the number of prototypes found is user-defined or ‘‘automatic.’’ Generalization error rates of the 11 methods are estimated on two synthetic and two real data sets. Offering the usual disclaimer that these are just a limited set of experiments, we feel confident in asserting that presupervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Finally, our calculations do not suggest that methods which find the ‘‘best’’ number of prototypes ‘‘automatically’’ are superior to methods for which the user simply specifies the number of prototypes.
What is Gab: A Bastion of Free Speech or an Alt-Right Echo Chamber

Over the past few years, a number of new “fringe” communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web’s information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting “people and free speech first”, welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan’s Politically Incorrect board.
Modified post-filter to recover modulation spectrum for HMM-based speech synthesis

This paper proposes a modified post-filter to recover a Modulation Spectrum (MS) in HMM-based speech synthesis. To alleviate the over-smoothing effect which is one of the major problems in HMM-based speech synthesis, the MS-based post-filter has been proposed. It recovers the utterance-level MS of the generated speech trajectory, and we have reported its benefit to the quality improvement. However, this post-filter is not applicable to various lengths of speech parameter trajectories, such as phrases or segments, which are shorter than an utterance. To address this problem, we propose two modified post-filters, (1) the time-invariant filter with a simplified conversion form and (2) the segment-level post-filter which applicable to a short-term parameter sequence. Furthermore, we also propose (3) the post-filter to recover the phoneme-level MS of HMM-state duration. Experimental results show that the modified post-filters also yield significant quality improvements in synthetic speech as yielded by the conventional post-filter.
Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation

Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children's lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.
Architecture for an Intelligent Tutoring System that Considers Learning Styles

In this paper we propose the architecture of an Intelligent Tutoring System that considers the student's learning style and the competency-based education. We also describe the processes that have been implemented so far. Our architecture presents innovations in the representation of the tutor module and in the knowledge module; the tutor module incorporates a selector agent, which will choose the content to show, considering the teaching strategies that support the student's learning style.
A fault tolerant, area efficient architecture for Shor's factoring algorithm

We optimize the area and latency of Shor's factoring while simultaneously improving fault tolerance through: (1) balancing the use of ancilla generators, (2) aggressive optimization of error correction, and (3) tuning the core adder circuits. Our custom CAD flow produces detailed layouts of the physical components and utilizes simulation to analyze circuits in terms of area, latency, and success probability. We introduce a metric, called ADCR, which is the probabilistic equivalent of the classic Area-Delay product. Our error correction optimization can reduce ADCR by order of magnitude or more. Contrary to conventional wisdom, we show that the area of an optimized quantum circuit is not dominated exclusively by error
 correction. Further, our adder evaluation shows that quantum carry-lookahead adders (QCLA) beat ripple-carry adders in ADCR, despite being larger and more complex. We conclude with what we believe is one of most accurate estimates of the area and latency required for 1024-bit Shor's factorization: 7659 mm2 for the smallest circuit and 6 x 108 seconds for the fastest circuit.
Towards Further Automation in AutoML

Even though recent AutoML systems have been successful in various applications, they introduce new hyper-hyperparameters of their own, including the choice of the evaluation strategy used in the loss function, time budgets to use and the optimization strategy with its hyper-hyperparameters. We study whether it is possible to make these choices in a data-driven way for a dataset at hand. Using 437 datasets from OpenML, we demonstrate the possibility of automating these choices, that this improves over picking a fixed strategy and that for different time horizons different strategies are necessary.
The max-min hill-climbing Bayesian network structure learning algorithm

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.
Using Argumentative Zones for Extractive Summarization of Scientific Articles

Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information structure has been found useful for important Natural Language Processing (NLP) tasks, such as information retrieval and extraction. Since scientific articles typically follow a certain discourse structure describing the prior work, problem being solved, methods used, and so forth, it could also be useful for summarization of these articles. In this work we focus on a scheme of information structure called Argumentative Zoning (AZ), and investigate whether its categories could support extractive text summarization in a scientific domain. We develop a summarization system that uses AZ categories (i) as features and (ii) in the final sentence selection process. We evaluate the system directly as well as using task-based evaluation. The results show that AZ can support both full document and customized summarization. We report a statistically significant improvement in summarization performance against a competitive baseline that uses journal section labels instead of AZ information.  IN MANDARIN 一种根据“论证结构”自动摘录科技文献的方法 信息结构是指作者组织语句陈述信息的方式。信息结构例如科技文献的篇章结构包含丰富 的语言信息,有助于解决自然语言处理领域的一些重要问题例如信息检索和信息提取等。 科技文献通常使用特定的篇章结构来陈述以往的研究,阐述研究问题以及研究方法等等, 这些篇章结构可以被用于文献的自动摘录。本文着眼于一类特定的信息结构—“论证结构 ”,研究其是否有助于更好地摘录科技文献。在本文开发的摘录系统中,“论证结构”有 两种用途:一是作为特征供机器学习,二是用于最终的语句筛选过程。本文对该系统进行 了直接和间接的评测,测试结果显示“论证结构”有助于更好地对全文或指定信息进行摘 录。基于“论证结构”的摘录系统显著性优于基于章节标题的摘录系统。
A GENETIC ALGORITHM FOR STRUCTURAL OPTIMIZATION OF STEEL TRUSS ROOFS

A common structural design problem is the weight minimization of structures which is formulated selecting a set of design variables that represent the structural and architectural configuration of the system. The structures are usually subject to stress and displacement constraints and the design variables can be continuous or discrete. In practice, it is often desirable to choose design variables (such as cross-sectional areas) from commercially available sizes. The use of a continuous optimization procedure – although usually more straightforward – will lead to non-available sizes and any attempt to substitute those values by the closest available commercial sizes can make the design unfeasible or unnecessarily heavier. In this paper a genetic algorithm is proposed to evolve the structural configuration for weight minimization of industrial buildings, with rectangular geometry projection, made up of uniform planar structures (steel truss roofs) along the longitudinal direction which are interconnected by purlins. The planar structures are trusses and their number, shape, and topology are allowed to change during the optimization process.
Potential Vulnerabilities of Neuronal Reward, Risk, and Decision Mechanisms to Addictive Drugs

How do addictive drugs hijack the brain's reward system? This review speculates how normal, physiological reward processes may be affected by addictive drugs. Addictive drugs affect acute responses and plasticity in dopamine neurons and postsynaptic structures. These effects reduce reward discrimination, increase the effects of reward prediction error signals, and enhance neuronal responses to reward-predicting stimuli, which may contribute to compulsion. Addictive drugs steepen neuronal temporal reward discounting and create temporal myopia that impairs the control of drug taking. Tonically enhanced dopamine levels may disturb working memory mechanisms necessary for assessing background rewards and thus may generate inaccurate neuronal reward predictions. Drug-induced working memory deficits may impair neuronal risk signaling, promote risky behaviors, and facilitate preaddictive drug use. Malfunctioning adaptive reward coding may lead to overvaluation of drug rewards. Many of these malfunctions may result in inadequate neuronal decision mechanisms and lead to choices biased toward drug rewards.
A new approach for sparse matrix vector product on NVIDIA GPUs

The sparse matrix vector product (SpMV) is a key operation in engineering and scientific computing and, hence, it has been subjected to intense research for a long time. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for NVIDIA GPUs have already appeared on the scene. This work proposes and evaluates a new implementation of SpMV for NVIDIA GPUs based on a new format, ELLPACK-R, that allows storage of the sparse matrix in a regular manner. A comparative evaluation against a variety of storage formats previously proposed has been carried out based on a representative set of test matrices. The results show that, although the performance strongly depends on the specific pattern of the matrix, the implementation based on ELLPACK-R achieves higher overall performance. Moreover, a comparison with standard state-of-the-art superscalar processors reveals that significant speedup factors are achieved with GPUs.
User Modeling in Large Social Networks

This proposal aims to harness the power of data, social, and network sciences to model user behavior in social networks. Specifically, we focus on individual users and investigate the interplay between their behavior and subsequently emergent social phenomena. Work in this proposal unveils the significant social strategies that are used by people to satisfy their social needs. We apply computational methods to address user modeling problems, including demographic inference, link recommendation, and social impact prediction. The proposed research work can be translated into applications in large social systems, such as mobile communication, online social media, and academic collaboration.
Mock-up: An Aid in the Different Steps in Aesthetic Dental Treatment

Today, in restorative and prosthodontic dental treatments, the focus is on aesthetics. This involves meeting the needs and expectations of the patient. Failures occur not only as a result of technical problems, but also as a result of poor communication with the patient. To avoid these scenarios that may obligate us to repeat the restoration from the beginning, we can obtain help by using Mock-up. Despite communication with the diagnostic and treatment planning step, Mock-up enables us to control the function of the end result without even beginning the work. It also enables us to do a minimal invasive tooth preparation. The aim of this study is to use Mock-up to show its effectiveness in communication, planning, and preparation to achieve the demanded aesthetic dental treatment.  We prepared mock-up according to wax-up in the second meetingthe treatment planning step. We used a minimal invasive, mock-up guided, preparation technique from the Mock-up accepted by us and the patient.  This easy and practical method enabled us to avoid the intervention in the ultimate restoration. Our result is in accordance with those of other authors: we had minimal preparation, even some unprepared surfaces and satisfied patient.  The use of Mock-up is a simple technique in aesthetic dentistry. Broadly in prosthodontics, it assures us that the work will be functionally and aesthetically successful.