nils-herrmann/clean_scirepeval huggingface dataset:
Attention and Visual Memory in Visualization and Computer Graphics

A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see” details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.
Detecting Danger: The Dendritic Cell Algorithm

The Dendritic Cell Algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, and abstract model of DC behaviour is developed and subsequently used to form an algorithm, the DCA. The abstraction process was facilitated through close collaboration with laboratorybased immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population based algorithm, with each agent in the system represented as an ‘artificial DC’. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous. In this chapter the abstraction process and details of the resultant algorithm are given. The algorithm is applied to numerous intrusion detection problems in computer security including the detection of port scans and botnets, where it has produced impressive results with relatively low rates of false positives.
InterAxis: Steering Scatterplot Axes via Observation-Level Interaction

Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes, from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios.
Understanding DevOps & bridging the gap from continuous integration to continuous delivery

As part of Agile transformation in past few years we have seen IT organizations adopting continuous integration principles in their software delivery lifecycle, which has improved the efficiency of development teams. With the time it has been realized that this optimization as part of continuous integration - alone - is just not helping to make the entire delivery lifecycle efficient or is not driving the organization efficiency. Unless all the pieces of a software delivery lifecycle work like a well oiled machine - efficiency of organization to optimize the delivery lifecycle can not be met. This is the problem which DevOps tries to address. This paper tries to cover all aspects of Devops applicable to various phases of SDLC and specifically talks about business need, ways to move from continuous integration to continuous delivery and its benefits. Continuous delivery transformation in this paper is explained with a real life case study that how infrastructure can be maintained just in form of code (IAAC). Finally this paper touches upon various considerations one must evaluate before adopting DevOps and what kind of benefits one can expect.
A deep learning network for right ventricle segmentation in short-axis MRI

The segmentation of the right ventricle (RV) myocardium on MRI is a prerequisite step for the evaluation of RV structure and function, which is of great importance in the diagnose of most cardiac diseases, such as pulmonary hypertension, congenital heart disease, coronary heart disease, and dysplasia. However, RV segmentation is considered challenging, mainly because of the complex crescent shape of the RV across slices and phases. Hence this study aims to propose a new approach to segment RV endocardium and epicardium based on deep learning. The proposed method contains two subtasks: (1) localizing the region of interest (ROI), the biventricular region which contains more meaningful features and can facilitate the RV segmentation, and (2) segmenting the RV myocardium based on the localization. The two subtasks are integrated into a joint task learning framework, in which each task is solved via two multilayer convolutional neural networks. The experiments results show that the proposed method has big potential to be further researched and applied in clinical diagnosis.
A survey of DHT security techniques

Peer-to-peer networks based on distributed hash tables (DHTs) have received considerable attention ever since their introduction in 2001. Unfortunately, DHT-based systems have been shown to be notoriously difficult to protect against security attacks. Various reports have been published that discuss or classify general security issues, but so far a comprehensive survey describing the various proposed defenses has been lacking. In this article, we present an overview of techniques reported in the literature for making DHT-based systems resistant to the three most important attacks that can be launched by malicious nodes participating in the DHT: (1) the Sybil attack, (2) the Eclipse attack, and (3) routing and storage attacks. We review the advantages and disadvantages of the proposed solutions and, in doing so, confirm how difficult it is to secure DHT-based systems in an adversarial environment.
Hormones and endocrine-disrupting chemicals: low-dose effects and nonmonotonic dose responses.

For decades, studies of endocrine-disrupting chemicals (EDCs) have challenged traditional concepts in toxicology, in particular the dogma of "the dose makes the poison," because EDCs can have effects at low doses that are not predicted by effects at higher doses. Here, we review two major concepts in EDC studies: low dose and nonmonotonicity. Low-dose effects were defined by the National Toxicology Program as those that occur in the range of human exposures or effects observed at doses below those used for traditional toxicological studies. We review the mechanistic data for low-dose effects and use a weight-of-evidence approach to analyze five examples from the EDC literature. Additionally, we explore nonmonotonic dose-response curves, defined as a nonlinear relationship between dose and effect where the slope of the curve changes sign somewhere within the range of doses examined. We provide a detailed discussion of the mechanisms responsible for generating these phenomena, plus hundreds of examples from the cell culture, animal, and epidemiology literature. We illustrate that nonmonotonic responses and low-dose effects are remarkably common in studies of natural hormones and EDCs. Whether low doses of EDCs influence certain human disorders is no longer conjecture, because epidemiological studies show that environmental exposures to EDCs are associated with human diseases and disabilities. We conclude that when nonmonotonic dose-response curves occur, the effects of low doses cannot be predicted by the effects observed at high doses. Thus, fundamental changes in chemical testing and safety determination are needed to protect human health.
Learning Representations for Log Data in Cybersecurity

We introduce a framework for exploring and learning representations of log data generated by enterprise-grade security devices with the goal of detecting advanced persistent threats (APTs) spanning over several weeks. The presented framework uses a divide-and-conquer strategy combining behavioral analytics, time series modeling and representation learning algorithms to model large volumes of data. In addition, given that we have access to human-engineered features, we analyze the capability of a series of representation learning algorithms to complement human-engineered features in a variety of classification approaches. We demonstrate the approach with a novel dataset extracted from 3 billion log lines generated at an enterprise network boundaries with reported command and control communications. The presented results validate our approach, achieving an area under the ROC curve of 0.943 and 95 true positives out of the Top 100 ranked instances on the test data set.
Robust discrete optimization and network flows

We propose an approach to address data uncertainty for discrete optimization and network flow problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation. When only the cost coefficients are subject to uncertainty and the problem is a 0 − 1 discrete optimization problem on n variables, then we solve the robust counterpart by solving at most n + 1 instances of the original problem. Thus, the robust counterpart of a polynomially solvable 0 − 1 discrete optimization problem remains polynomially solvable. In particular, robust matching, spanning tree, shortest path, matroid intersection, etc. are polynomially solvable. We also show that the robust counterpart of an NP -hard α-approximable 0 − 1 discrete optimization problem, remains α-approximable. Finally, we propose an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.
Grain: a stream cipher for constrained environments

A new stream cipher, Grain, is proposed. The design targets hardware environments where gate count, power consumption and memory is very limited. It is based on two shift registers and a nonlinear output function. The cipher has the additional feature that the speed can be increased at the expense of extra hardware. The key size is 80 bits and no attack faster than exhaustive key search has been identified. The hardware complexity and throughput compares favourably to other hardware oriented stream ciphers like E0 and A5/1.
Deconstructing and restyling D3 visualizations

The D3 JavaScript library has become a ubiquitous tool for developing visualizations on the Web. Yet, once a D3 visualization is published online its visual style is difficult to change. We present a pair of tools for deconstructing and restyling existing D3 visualizations. Our deconstruction tool analyzes a D3 visualization to extract the data, the marks and the mappings between them. Our restyling tool lets users modify the visual attributes of the marks as well as the mappings from the data to these attributes. Together our tools allow users to easily modify D3 visualizations without examining the underlying code and we show how they can be used to deconstruct and restyle a variety of D3 visualizations.
Internet scale string attribute publish/subscribe data networks

With this work we aim to make a three-fold contribution. We first address the issue of supporting efficiently queries over string-attributes involving prefix, suffix, containment, and equality operators in large-scale data networks. Our first design decision is to employ distributed hash tables (DHTs) for the data network's topology, harnessing their desirable properties. Our next design decision is to derive DHT-independent solutions, treating DHT as a black box. Second, we exploit this infrastructure to develop efficient content based publish/subscribe systems. The main contribution here are algorithms for the efficient processing of queries (subscriptions) and events (publications). Specifically, we show that our subscription processing algorithms require O(logN) messages for a N-node network, and our event processing algorithms require O(l x logN) messages (with l being the average string length).Third, we develop algorithms for optimizing the processing of multi-dimensional events, involving several string attributes. Further to our analysis, we provide simulation-based experiments showing promising performance results in terms of number of messages, required bandwidth, load balancing, and response times.
Nouns and verbs in the brain: A review of behavioural, electrophysiological, neuropsychological and imaging studies

In the past 30 years there has been a growing body of research using different methods (behavioural, electrophysiological, neuropsychological, TMS and imaging studies) asking whether processing words from different grammatical classes (especially nouns and verbs) engage different neural systems. To date, however, each line of investigation has provided conflicting results. Here we present a review of this literature, showing that once we take into account the confounding in most studies between semantic distinctions (objects vs. actions) and grammatical distinction (nouns vs. verbs), and the conflation between studies concerned with mechanisms of single word processing and those studies concerned with sentence integration, the emerging picture is relatively clear-cut: clear neural separability is observed between the processing of object words (nouns) and action words (typically verbs), grammatical class effects emerge or become stronger for tasks and languages imposing greater processing demands. These findings indicate that grammatical class per se is not an organisational principle of knowledge in the brain; rather, all the findings we review are compatible with two general principles described by typological linguistics as underlying grammatical class membership across languages: semantic/pragmatic, and distributional cues in language that distinguish nouns from verbs. These two general principles are incorporated within an emergentist view which takes these constraints into account.
Nested LSTMs

We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell. Specifically, instead of computing the value of the (outer) memory cell as c t = ft ct−1 + it gt, NLSTM memory cells use the concatenation (ft ct−1, it gt) as input to an inner LSTM (or NLSTM) memory cell, and set c t = h inner t . Nested LSTMs outperform both stacked and single-layer LSTMs with similar numbers of parameters in our experiments on various character-level language modeling tasks, and the inner memories of an LSTM learn longer term dependencies compared with the higher-level units of a stacked LSTM.
Frankensteinian methods for evolutionary music composition

Victor Frankenstein sought to create an intelligent being imbued with the r ules of civilized human conduct, who could further learn how to behave and possibly even evolve through successive g nerations into a more perfect form. Modern human composers similarly strive to create intell igent algorithmic music composition systems that can follow prespecified rules, learn appropriate patte rns from a collection of melodies, or evolve to produce output more perfectly matched to some aesthetic criteria . H re we review recent efforts aimed at each of these three types of algorithmic composition. We focus pa rticularly on evolutionary methods, and indicate how monstrous many of the results have been. We present a ne w method that uses coevolution to create linked artificial music critics and music composers , and describe how this method can attach the separate parts of rules, learning, and evolution together in to one coherent body. “Invention, it must be humbly admitted, does not consist in creating out of void, but ou t of chaos; the materials must, in the first place, be afforded...” --Mary Shelley, Frankenstein (1831/1993, p. 299)
Dawn of the Selfie Era: The Whos, Wheres, and Hows of Selfies on Instagram

Online interactions are increasingly involving images, especially those containing human faces, which are naturally attention grabbing and more effective at conveying feelings than text. To understand this new convention of digital culture, we study the collective behavior of sharing \textit{selfies} on Instagram and present how people appear in selfies and which patterns emerge from such interactions. Analysis of millions of photos shows that the amount of selfies has increased by 900 times from 2012 to 2014. Selfies are an effective medium to grab attention; they generate on average 1.1--3.2 times more likes and comments than other types of content on Instagram. Compared to other content, interactions involving selfies exhibit variations in homophily scores (in terms of age and gender) that suggest they are becoming more widespread. Their style also varies by cultural boundaries in that the average age and majority gender seen in selfies differ from one country to another. We provide explanations of such country-wise variations based on cultural and socioeconomic contexts.
Ranking Automatically Generated Questions Using Common Human Queries

In this paper, we challenge a form of paragraph-to-question generation task. We propose a question generation system which can generate a set of comprehensive questions from a body of text. Besides the tree kernel functions to assess the grammatically of the generated questions, our goal is to rank them by using community-based question answering systems to calculate the importance of the generated questions. The main assumption behind our work is that each body of text is related to a topic of interest and it has a comprehensive information about the topic.
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the ‘Story Cloze Test’. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of 50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.
A Short Survey of Document Structure Similarity Algorithms

This paper provides a brief survey of document structural similarity algorithms, including the optimal Tree Edit Distance algorithm and various approximation algorithms. The approximation algorithms include the simple weighted tag similarity algorithm, Fourier transforms of the structure, and a new application of the shingle technique to structural similarity. We show three surprising results. First, the Fourier transform technique proves to be the least accurate of any of approximation algorithms, while also being slowest. Second, optimal Tree Edit Distance algorithms may not be the best technique for clustering pages from different sites. Third, the simplest approximation to structure may be the most effective and efficient mechanism for many applications.
Multiscale topic tomography

Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic graphical model to address this issue. The new model, which we call the Multiscale Topic Tomography Model (MTTM), employs non-homogeneous Poisson processes to model generation of word-counts. The evolution of topics is modeled through a multi-scale analysis using Haar wavelets. One of the new features of the model is its modeling the evolution of topics at various time-scales of resolution, allowing the user to zoom in and out of the time-scales. Our experiments on Science data using the new model uncovers some interesting patterns in topics. The new model is also comparable to LDA in predicting unseen data as demonstrated by our perplexity experiments.
Community detection in networks: A user guide

Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.
Walking Assistance Using Artificial Primitives: A Novel Bioinspired Framework Using Motor Primitives for Locomotion Assistance Through a Wearable Cooperative Exoskeleton

Bioinspiration in robotics deals with applying biological principles to the design of better performing devices. In this article, we propose a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. In particular, the use of motor primitives for assisting different locomotion modes (i.e., ground-level walking at several cadences and ascending and descending stairs) is explored by means of two different strategies. In the first strategy, identified motor primitives are combined through weights to directly produce the desired assistive torque profiles. In the second strategy, identified motor primitives are combined to serve as neural stimulations to a virtual model of the musculoskeletal system, which, in turn, produces the desired assistive torque profiles.