nils-herrmann/clean_scirepeval huggingface dataset:
Oxymoron: Making Fine-Grained Memory Randomization Practical by Allowing Code Sharing

The latest effective defense against code reuse attacks is fine-grained, per-process memory randomization. However, such process randomization prevents code sharing since there is no longer any identical code to share between processes. Without shared libraries, however, tremendous memory savings are forfeit. This drawback may hinder the adoption of fine-grained memory randomization. We present Oxymoron, a secure fine-grained memory randomization technique on a per-process level that does not interfere with code sharing. Executables and libraries built with Oxymoron feature ‘memory-layout-agnostic code’, which runs on a commodity Linux. Our theoretical and practical evaluations show that Oxymoron is the first solution to be secure against just-in-time code reuse attacks and demonstrate that fine-grained memory randomization is feasible without forfeiting the enormous memory savings of shared libraries.
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts

Generating captions for images is a task that has recently received considerable attention. In this work we focus on caption generation for abstract scenes, or object layouts where the only information provided is a set of objects and their locations. We propose OBJ2TEXT, a sequence-tosequence model that encodes a set of objects and their locations as an input sequence using an LSTM network, and decodes this representation using an LSTM language model. We show that our model, despite encoding object layouts as a sequence, can represent spatial relationships between objects, and generate descriptions that are globally coherent and semantically relevant. We test our approach in a task of object-layout captioning by using only object annotations as inputs. We additionally show that our model, combined with a state-of-the-art object detector, improves an image captioning model from 0.863 to 0.950 (CIDEr score) in the test benchmark of the standard MS-COCO Captioning task.
Urban channel models for smart city IoT-networks based on empirical measurements of LoRa-links at 433 and 868 MHz

The vision of Smart City is to enable new use cases being overall connected. Considering a large number of Internet of Things sensors and challenging channel characteristics, new technologies evolve facing these new challenges. A promising solution to meet such requirements are Low Power Wide Area Networks. In this paper, a LoRa availability analysis is carried out to analyze performance of a Low Power Wide Area Networks representative in the 433 MHz and 868 MHz ISM bands considering a Smart City Internet of Things scenario, resulting in signal ranges up to 5.8 km. Additional reliability measurements show that indoor and outdoor LoRa nodes are able to achieve a Packet Delivery Rate of over 99% within a range of 4.8 km, depending on their installation side. The results of these measurements are compared with established empirical channel models. Due to the insufficient prediction accuracy of the established models in the examined Smart City area of Dortmund, Germany, two new path loss models for urban areas are presented for both the 868 MHz and 433 MHz frequency bands.
Analyzing an automotive testing process with evidence-based software engineering

Evidence-based software engineering (EBSE) provides a process for solving practical problems based on a rigorous research approach. The primary focus so far was on mapping and aggregating evidence through systematic reviews.  We extend existing work on evidence-based software engineering by using the EBSE process in an industrial case to help an organization to improve its automotive testing process. With this we contribute in (1) providing experiences on using evidence based processes to analyze a real world automotive test process; and (2) provide evidence of challenges and related solutions for automotive software testing processes.  In this study we perform an in-depth investigation of an automotive test process using an extended EBSE process including case study research (gain an understanding of practical questions to define a research scope), systematic literature review (identify solutions through systematic literature), and value stream mapping (map out an improved automotive test process based on the current situation and improvement suggestions identified). These are followed by reflections on the EBSE process used.  In the first step of the EBSE process we identified 10 challenge areas with a total of 26 individual challenges. For 15 out of those 26 challenges our domain specific systematic literature review identified solutions. Based on the input from the challenges and the solutions, we created a value stream map of the current and future process.  Overall, we found that the evidence-based process as presented in this study helps in technology transfer of research results to industry, but at the same time some challenges lie ahead (e.g. scoping systematic reviews to focus more on concrete industry problems, and understanding strategies of conducting EBSE with respect to effort and quality of the evidence).
Discriminative Gaifman Models

We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally connected and bounded-size regions of knowledge bases (KBs). Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing. Gaifman models sample neighborhoods of knowledge bases so as to make the learned relational models more robust to missing objects and relations which is a common situation in open-world KBs. We present the core ideas of Gaifman models and apply them to large-scale relational learning problems. We also discuss the ways in which Gaifman models relate to some existing relational machine learning approaches.
Learning Neural Programs To Parse Programs

In this work, we study an important problem: learning programs from input-output examples. We propose a novel method to learn a neural program operating a domain-specific non-differentiable machine, and demonstrate that this method can be applied to learn programs that are significantly more complex than the ones synthesized before: programming language parsers from input-output pairs without knowing the underlying grammar. The main challenge is to train the neural program without supervision on execution traces. To tackle it, we propose: (1) LL machines and neural programs operating them to effectively regularize the space of the learned programs; and (2) a two-phase reinforcement learning-based search technique to train the model. Our evaluation demonstrates that our approach can successfully learn to parse programs in both an imperative language and a functional language, and achieve 100% test accuracy, while existing approaches’ accuracies are almost 0%. This is the first successful demonstration of applying reinforcement learning to train a neural program operating a non-differentiable machine that can fully generalize to test sets on a non-trivial task.
Lipschitz Properties for Deep Convolutional Networks

In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when the inputs are from the same class. That is, we hope to see a small change in the feature vector with respect to a deformation on the input signal. This can be established mathematically, and the key step is to derive the Lipschitz properties. Further, we establish that the stability results can be extended for more general networks. We give a formula for computing the Lipschitz bound, and compare it with other methods to show it is closer to the optimal value.
Study and prototyping of practically large-scale mmWave antenna systems for 5G cellular devices

This article discusses the challenges, benefits and approaches associated with realizing largescale antenna arrays at mmWave frequency bands for future 5G cellular devices. Key design considerations are investigated to deduce a novel and practical phased array antenna solution operating at 28 GHz with near spherical coverage. The approach is further evolved into a first-of- a-kind cellular phone prototype equipped with mmWave 5G antenna arrays consisting of a total of 32 low-profile antenna elements. Indoor measurements are carried out using the presented prototype to characterize the proposed mmWave antenna system using 16-QAM modulated signals with 27.925 GHz carrier frequency. The biological implications due to the absorbed electromagnetic waves when using mmWave cellular devices are studied and compared in detail with those of 3/4G cellular devices.
Effortful control in early childhood: continuity and change, antecedents, and implications for social development.

The course, antecedents, and implications for social development of effortful control were examined in this comprehensive longitudinal study. Behavioral multitask batteries and parental ratings assessed effortful control at 22 and 33 months (N = 106). Effortful control functions encompassed delaying, slowing down motor activity, suppressing/initiating activity to signal, effortful attention, and lowering voice. Between 22 and 33 months, effortful control improved considerably, its coherence increased, it was stable, and it was higher for girls. Behavioral and parent-rated measures converged. Children's focused attention at 9 months, mothers' responsiveness at 22 months, and mothers' self-reported socialization level all predicted children's greater effortful control. Effortful control had implications for concurrent social development. Greater effortful control at 22 months was linked to more regulated anger, and at 33 months, to more regulated anger and joy and to stronger restraint.
When disfluency is--and is not--a desirable difficulty: the influence of typeface clarity on metacognitive judgments and memory.

There are many instances in which perceptual disfluency leads to improved memory performance, a phenomenon often referred to as the perceptual-interference effect (e.g., Diemand-Yauman, Oppenheimer, & Vaughn (Cognition 118:111-115, 2010); Nairne (Journal of Experimental Psychology: Learning, Memory, and Cognition 14:248-255, 1988)). In some situations, however, perceptual disfluency does not affect memory (Rhodes & Castel (Journal of Experimental Psychology: General 137:615-625, 2008)), or even impairs memory (Glass, (Psychology and Aging 22:233-238, 2007)). Because of the uncertain effects of perceptual disfluency, it is important to establish when disfluency is a "desirable difficulty" (Bjork, 1994) and when it is not, and the degree to which people's judgments of learning (JOLs) reflect the consequences of processing disfluent information. In five experiments, our participants saw multiple lists of blurred and clear words and gave JOLs after each word. The JOLs were consistently higher for the perceptually fluent items in within-subjects designs, which accurately predicted the pattern of recall performance when the presentation time was short (Exps. 1a and 2a). When the final test was recognition or when the presentation time was long, however, we found no difference in recall for clear and blurred words, although JOLs continued to be higher for clear words (Exps. 2b and 3). When fluency was manipulated between subjects, neither JOLs nor recall varied between formats (Exp. 1b). This study suggests a boundary condition for the desirable difficulty of perceptual disfluency and indicates that a visual distortion, such as blurring a word, may not always induce the deeper processing necessary to create a perceptual-interference effect.
Differentially Private Publication of Sparse Data

The problem of privately releasing data is to provide a version of a dataset without revealing sensitive information about the individuals who contribute to the data. The model of differential privacy allows such private release while providing strong guarantees on the output. A basic mechanism achieves differential privacy by adding noise to the frequency counts in the contingency tables (or, a subset of the count data cube) derived from the dataset. However, when the dataset is sparse in its underlying space, as is the case for most multi-attribute relations, then the effect of adding noise is to vastly increase the size of the published data: it implicitly creates a huge number of dummy data points to mask the true data, making it almost impossible to work with. We present techniques to overcome this roadblock and allow efficient private release of sparse data, while maintaining the guarantees of differential privacy. Our approach is to release a compact summary of the noisy data. Generating the noisy data and then summarizing it would still be very costly, so we show how to shortcut this step, and instead directly generate the summary from the input data, without materializing the vast intermediate noisy data. We instantiate this outline for a variety of sampling and filtering methods, and show how to use the resulting summary for approximate, private, query answering. Our experimental study shows that this is an effective, practical solution: in some examples we generate output that is 1000 times smaller than the naive method, in less than 1% of the time while achieving comparable and occasionally improved utility over the costly materialization approach.
Advisor 2 . 0 : A Second-Generation Advanced Vehicle Simulator for Systems Analysis

The National Renewable Energy Laboratory has recently publicly released its second-generation advanced vehicle simulator called ADVISOR 2.0. This software program was initially developed four years ago, and after several years of in-house usage and evolution, this powerful tool is now available to the public through a new vehicle systems analysis World Wide Web page. ADVISOR has been applied to many different systems analysis problems, such as helping to develop the SAE J1711 test procedure for hybrid vehicles and helping to evaluate new technologies as part of the Partnership for a New Generation of Vehicles (PNGV) technology selection process. The model has been and will continue to be benchmarked and validated with other models and with real vehicle test data. After two months of being available on the Web, more than 100 users have downloaded ADVISOR. ADVISOR 2.0 has many new features, including an easy-to-use graphical user interface, a detailed exhaust aftertreatment thermal model, and complete browser-based documentation. Future work will include adding to the library of components available in ADVISOR, including optimization functionality, and linking with a more detailed fuel cell model.
Optimization based controller design and implementation for the Atlas robot in the DARPA Robotics Challenge Finals

We describe the design and hardware implementation of our walking and manipulation controllers that are based on a cascade of online optimizations. A virtual force acting at the robot's center of mass (CoM) is estimated and used to compensated for modeling errors of the CoM and unplanned external forces. The proposed controllers have been implemented on the Atlas robot, a full size humanoid robot built by Boston Dynamics, and used in the DARPA Robotics Challenge Finals, which consisted of a wide variety of locomotion and manipulation tasks.
Automated Road Lane Detection for Intelligent Vehicles

Automated road lane detection is the crucial part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this paper, we present an algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles. Initially, it converts the RGB road scene image into gray image and employs the flood-fill algorithm to label the connected components of that gray image. Afterwards, the largest connected component which is the road region is extracted from the labeled image using maximum width and no. of pixels. Eventually, the outside region is subtracted and the marks or road lane and road boundary are extracted from connected components. The experimental results show the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads.
Multi-document summarization using cluster-based link analysis

The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers.
WritingHacker: audio based eavesdropping of handwriting via mobile devices

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.
A survey of trust in social networks

Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. In addition to individuals using such networks to connect to their friends and families, governments and enterprises have started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Therefore, trust becomes an essential and important element of a successful social network. In this article, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. We then discuss recent works addressing three aspects of social trust: trust information collection, trust evaluation, and trust dissemination. Finally, we compare and contrast the literature and identify areas for further research in social trust.
Dealing with Uncertainty: A Survey of Theories and Practices

Uncertainty accompanies our life processes and covers almost all fields of scientific studies. Two general categories of uncertainty, namely, aleatory uncertainty and epistemic uncertainty, exist in the world. While aleatory uncertainty refers to the inherent randomness in nature, derived from natural variability of the physical world (e.g., random show of a flipped coin), epistemic uncertainty origins from human's lack of knowledge of the physical world, as well as ability of measuring and modeling the physical world (e.g., computation of the distance between two cities). Different kinds of uncertainty call for different handling methods. Aggarwal, Yu, Sarma, and Zhang et al. have made good surveys on uncertain database management based on the probability theory. This paper reviews multidisciplinary uncertainty processing activities in diverse fields. Beyond the dominant probability theory and fuzzy theory, we also review information-gap theory and recently derived uncertainty theory. Practices of these uncertainty handling theories in the domains of economics, engineering, ecology, and information sciences are also described. It is our hope that this study could provide insights to the database community on how uncertainty is managed in other disciplines, and further challenge and inspire database researchers to develop more advanced data management techniques and tools to cope with a variety of uncertainty issues in the real world.
Learning Visual Features from Large Weakly Supervised Data

Convolutional networks trained on large supervised dataset produce visual features which form the basis for the state-of-the-art in many computer-vision problems. Further improvements of these visual features will likely require even larger manually labeled data sets, which severely limits the pace at which progress can be made. In this paper, we explore the potential of leveraging massive, weaklylabeled image collections for learning good visual features. We train convolutional networks on a dataset of 100 million Flickr photos and captions, and show that these networks produce features that perform well in a range of vision problems. We also show that the networks appropriately capture word similarity, and learn correspondences between different languages.
A Freely Available Automatically Generated Thesaurus of Related Words

A freely available English thesaurus of related words is presented that has been automatically compiled by analyzing the distributional similarities of words in the British National Corpus. The quality of the results has been evaluated by comparison with human judgments as obtained from non-native and native speakers of English who were asked to provide rankings of word similarities. According to this measure, the results generated by our system are better than the judgments of the non-native speakers and come close to the native speakers’ performance. An advantage of our approach is that it does not require syntactic parsing and therefore can be more easily adapted to other languages. As an example, a similar thesaurus for German has already been completed.