nils-herrmann/clean_scirepeval huggingface dataset:
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural networks in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm — the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.
Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread.
 Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure.
 We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads.
Local shape feature fusion for improved matching, pose estimation and 3D object recognition.

We provide new insights to the problem of shape feature description and matching, techniques that are often applied within 3D object recognition pipelines. We subject several state of the art features to systematic evaluations based on multiple datasets from different sources in a uniform manner. We have carefully prepared and performed a neutral test on the datasets for which the descriptors have shown good recognition performance. Our results expose an important fallacy of previous results, namely that the performance of the recognition system does not correlate well with the performance of the descriptor employed by the recognition system. In addition to this, we evaluate several aspects of the matching task, including the efficiency of the different features, and the potential in using dimension reduction. To arrive at better generalization properties, we introduce a method for fusing several feature matches with a limited processing overhead. Our fused feature matches provide a significant increase in matching accuracy, which is consistent over all tested datasets. Finally, we benchmark all features in a 3D object recognition setting, providing further evidence of the advantage of fused features, both in terms of accuracy and efficiency.
Data Consistency Properties and the Trade-offs in Commercial Cloud Storage: the Consumers' Perspective

A new class of data storage systems, called NoSQL (Not Only SQL), have emerged to complement traditional database systems, with rejection of general ACID transactions as one common feature. Different platforms, and indeed different primitives within one NoSQL platform, can offer various consistency properties, from Eventual Consistency to single-entity ACID. For the platform provider, weaker consistency should allow better availability, lower latency, and other benefits. This paper investigates what consumers observe of the consistency and performance properties of various offerings. We find that many platforms seem in practice to offer more consistency than they promise; we also find cases where the platform offers consumers a choice between stronger and weaker consistency, but there is no observed benefit from accepting weaker consistency properties.
Critical event prediction for proactive management in large-scale computer clusters

As the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. The core of autonomic computing, a recently proposed initiative towards next-generation IT-systems capable of 'self-healing', is the ability to analyze data in real-time and to predict potential problems. The goal is to avoid catastrophic failures through prompt execution of remedial actions.This paper describes an attempt to build a proactive prediction and control system for large clusters. We collected event logs containing various system reliability, availability and serviceability (RAS) events, and system activity reports (SARs) from a 350-node cluster system for a period of one year. The 'raw' system health measurements contain a great deal of redundant event data, which is either repetitive in nature or misaligned with respect to time. We applied a filtering technique and modeled the data into a set of primary and derived variables. These variables used probabilistic networks for establishing event correlations through prediction algorithms. We also evaluated the role of time-series methods, rule-based classification algorithms and Bayesian network models in event prediction.Based on historical data, our results suggest that it is feasible to predict system performance parameters (SARs) with a high degree of accuracy using time-series models. Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70% accuracy.
Security analysis of the constrained application protocol in the Internet of Things

The concept of Internet of Things involves huge number of constrained devices such as wireless sensors to communicate in a machine-to-machine pattern. Based on the implementation scenario, such communication might take place over a public network such as the Internet, which is based on the TCP/IP stack. However, different research working groups argue that some of these stack protocols such as the Hyper Text Transfer Protocol (HTTP) might not be suitable for constrained devices. Therefore, the IETF Constrained RESTful Environments (CoRE) WG has proposed the Constrained Application Protocol (CoAP); an application layer protocol for constrained devices in the IoTs. The CoRE WG proposed using IPSec or DTLS to secure the CoAP communication at different levels of the protocol stack. However, to investigate the feasibility of such a proposal, we use the X.805 security standard to analyze the security aspects of such implementation. The analysis highlights the main security drawbacks and hence argues of the need for a new integrated security solution.
Analyzing features for activity recognition

Human activity is one of the most important ingredients of context information. In wearable computing scenarios, activities such as walking, standing and sitting can be inferred from data provided by body-worn acceleration sensors. In such settings, most approaches use a single set of features, regardless of which activity to be recognized. In this paper we show that recognition rates can be improved by careful selection of individual features for each activity. We present a systematic analysis of features computed from a real-world data set and show how the choice of feature and the window length over which the feature is computed affects the recognition rates for different activities. Finally, we give a recommendation of suitable features and window lengths for a set of common activities.
A Systematic Comparison of Various Statistical Alignment Models

We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer (1993), the hidden Markov alignment model, smoothing techniques, and refinements. These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignment models. As evaluation criterion, we use the quality of the resulting Viterbi alignment compared to a manually produced reference alignment. We evaluate the models on the German-English Verbmobil task and the French-English Hansards task. We perform a detailed analysis of various design decisions of our statistical alignment system and evaluate these on training corpora of various sizes. An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models. In the Appendix, we present an efficient training algorithm for the alignment models presented.
Semantic Analysis of Military Relevant Texts for Intelligence Purposes

The current deployments of the German Federal Armed Forces cause the necessity to analyze large quantities of intelligence reports and other documents written in different languages. To efficiently handle these tasks natural language processing techniques (NLP) can be applied. The ZENON project makes use of an information extraction approach for the (partial) content analysis of English HUMINT reports. It has further been extended to do multilingual information extraction, i.e., processing Dari and Tajik texts. The focus of this paper is on the improvement of ZENON’s English semantic analysis. Intelligence reports are characterized by a large topical and linguistic variety. In order to extend the system’s coverage when performing content analysis we realized a semantic role labeling approach. In this paper, after a short introduction, the ZENON system and its information extraction functionalities are explained. Then our semantic role labeling approach and the architecture of the implemented application are described in detail.
From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph

We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous.
 We introduce a novel method that is based on the content of the page visited, rather than on past browsing patterns as in previous literature. Our content-based approach can be used even for previously unseen pages.
 We represent the topics of a page by the set of Wikipedia entities extracted from it. To obtain useful query suggestions for these entities, we exploit a novel graph model that we call EQGraph (Entity-Query Graph), containing entities, queries, and transitions between entities, between queries, as well as from entities to queries. We perform Personalized PageRank computation on such a graph to expand the set of entities extracted from a page into a richer set of entities, and to associate these entities with relevant query suggestions. We develop an efficient implementation to deal with large graph instances and suggest queries from a large and diverse pool.
 We perform a user study that shows that our method produces relevant and interesting recommendations, and outperforms an alternative method based on reverse IR.
Terra: a virtual machine-based platform for trusted computing

We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a general-purpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an "open box," i.e. a general-purpose hardware platform like today's PCs and workstations, or a "closed box," an opaque special-purpose platform that protects the privacy and integrity of its contents like today's game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.
A Field Guide to Boxology: Preliminary Classification of Architectural Styles for Software Systems

Software architects use a number of commonly-recognized “styles” to guide their design of system structures. Each of these is appropriate for some classes of problems, but none is suitable for all problems. How, then, does a software designer choose an architecture suitable for the problem at hand? Two kinds of information are required: (1) careful discrimination among the candidate architectures and (2) design guidance on how to make appropriate choices. Here we support careful discrimination with a preliminary classification of styles. We use a two-dimensional classification strategy with control and data issues as the dominant organizing axes. We position the major styles within this space and use finer-grained discriminations to elaborate variations on the styles. This provides a framework for organizing design guidance , which we partially flesh out with rules of thumb.
Emotion and moral judgment.

Research in psychology and cognitive science has consistently demonstrated the importance of emotion in a wide range of everyday judgments, including moral judgment. Most current accounts of moral judgment hold that emotion plays an important role, but the nature and extent of this role are still debated. We outline three increasingly strong claims about the role of emotion in moral judgment and assess the evidence for each. According to the first and least controversial claim, emotions follow from moral judgments, such that witnessing immorality can lead to negative emotions and witnessing moral virtue can lead to positive ones. According to the second claim, emotions amplify moral judgments, for instance, by making immoral acts seem even more immoral. Finally, on the last claim, emotions can actually moralize nonmoral behaviors-that is, they give nonmoral acts a moral status. Although this claim seems to be the most intriguing one theoretically, empirical support for it is still very limited. In this review, we discuss research findings that are in line with each of these views, we highlight recurring themes across these three categories of evidence, and we identify some open questions and areas for future research.
Environmental sound classification with convolutional neural networks

This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban recordings. The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.
AmbiguityVis: Visualization of Ambiguity in Graph Layouts

Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graph layout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteria simultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attention to these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visual ambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguity in edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in community structures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others are then displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualization approaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a user to explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectiveness of the technique is demonstrated through case studies and expert reviews.
A Graph-based System for Network-vulnerability Analysis

This paper presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The graph-based tool can identify the set of attack paths that have a high probability of success (or a low "effort" cost) for the attacker. The system could be used to test the effectiveness of making configuration changes, implementing an intrusion detection system, etc. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is "matched" with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.
R 3 2 Learning to Think : The Challenges of Teaching Thinking

The idea that thinking can be taught, or at least productively nurtured along its way, is ancient. Beginning with the efforts of Plato and the introduction of Socratic dialog, we see attention to improving intelligence and promoting effective thinking as a recurring educational trend throughout the ages. Early in the twentieth century, Dewey (1933) again focused North American’s attention on the importance of thinking as an educational aim. At the same time, Selz (1935) was advocating the idea of learnable intelligence in Europe. In the 1970s and 1980s, specific programs designed to teach thinking took shape, many of which continue in schools today. Efforts to teach thinking have proliferated in the new millennium, often becoming less programmatic in nature and more integrated within the fabric of schools. Despite this long history of concern with thinking, one reasonably might ask: Why do we need to “teach” thinking anyway? After all, given reasonable access to a rich cultural surround, individuals readily engage in situated problem solving, observing, classifying, organizing, informal theory building and testing, and so on, without much prompting or even support. Indeed, neurological findings suggest that the brain is hard-wired for just such activities as a basic mechanism for facilitating language development, socialization, and general environmental survival. Furthermore, it might be assumed that these basic thinking skills are already enhanced through the regular processes of schooling, as students encounter the work of past thinkers, engage in some debate, write essays, and so on. Why, then, should we concern ourselves with the teaching and learning of thinking? Addressing these issues entails looking more closely at a fuller range of thinking, particularly what might be called high-end thinking, as well as examining the role education plays in promoting thinking. Although it is true that the human mind comes readily equipped for a wide variety of thinking tasks, it is equally true that some kinds of thinking run against these natural tendencies. For example, probabilistic thinking is often counterintuitive in nature or doesn’t fit well with our experience (Tversky & Kahneman,1993 ; also see Kahneman & Frederick, Chap. 1 2). We have a natural
Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control

There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on real-time perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing suboptimality). The use of any parametric trajectory model converts the optimal control formulation into an equivalent nonlinear programming problem. In this paper, curvature polynomials of arbitrary order are used as the assumed form of solution. Polynomials sacrifice little in terms of spanning the set of feasible controls while permitting an expression of the general solution to the system dynamics in terms of decoupled quadratures. These quadratures are then readily linearized to express the necessary conditions for optimality. Resulting trajectories are convenient to manipulate and execute in vehicle controllers and they can be computed with a straightforward numerical procedure in real time. KEY WORDS—mobile robots, car-like robots, trajectory generation, curve generation, nonholonomic, clothoid, cornu spiral, optimal control
Verb Semantics and Lexical Selection

This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.