nils-herrmann/clean_scirepeval huggingface dataset:
The Sky Is Not the Limit: Multitasking Across GitHub Projects

Software development has always inherently required multitasking: developers switch between coding, reviewing, testing, designing, and meeting with colleagues. The advent of software ecosystems like GitHub has enabled something new: the ability to easily switch between projects. Developers also have social incentives to contribute to many projects; prolific contributors gain social recognition and (eventually) economic rewards. Multitasking, however, comes at a cognitive cost: frequent context-switches can lead to distraction, sub-standard work, and even greater stress. In this paper, we gather ecosystem-level data on a group of programmers working on a large collection of projects. We develop models and methods for measuring the rate and breadth of a developers' context-switching behavior, and we study how context-switching affects their productivity. We also survey developers to understand the reasons for and perceptions of multitasking. We find that the most common reason for multitasking is interrelationships and dependencies between projects. Notably, we find that the rate of switching and breadth (number of projects) of a developer's work matter. Developers who work on many projects have higher productivity if they focus on few projects per day. Developers that switch projects too much during the course of a day have lower productivity as they work on more projects overall. Despite these findings, developers perceptions of the benefits of multitasking are varied.
A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO(2), Cu(2)O, Ga(2)O(3), Fe(2)O(3), In(2)O(3), CdO, CeO(2), and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area.
Terrain rendering in frostbite using procedural shader splatting

For the detail mapping there are multiple methods that can be used. In Battlefield 2, a 256 m patch of the terrain could have up to six different tiling detail maps that were blended together using one or two three-component unique detail mask textures (Figure 4) that controlled the visibility of the individual detail maps. Artists would paint or generate the detail masks just as for the color map.
Information Flow Analysis of Android Applications in DroidSafe

We present DroidSafe, a static information flow analysis tool that reports potential leaks of sensitive information in Android applications. DroidSafe combines a comprehensive, accurate, and precise model of the Android runtime with static analysis design decisions that enable the DroidSafe analyses to scale to analyze this model. This combination is enabled by accurate analysis stubs, a technique that enables the effective analysis of code whose complete semantics lies outside the scope of Java, and by a combination of analyses that together can statically resolve communication targets identified by dynamically constructed values such as strings and class designators. Our experimental results demonstrate that 1) DroidSafe achieves unprecedented precision and accuracy for Android information flow analysis (as measured on a standard previously published set of benchmark applications) and 2) DroidSafe detects all malicious information flow leaks inserted into 24 real-world Android applications by three independent, hostile Red-Team organizations. The previous state-of-the art analysis, in contrast, detects less than 10% of these malicious flows.
Enactive artificial intelligence: Investigating the systemic organization of life and mind

The embodied and situated approach to artificial intelligence (AI) has matured and become a viable alternative to traditional computationalist approaches with respect to the practical goal of building artificial agents, which can behave in a robust and flexible manner under changing real-world conditions. Nevertheless, some concerns have recently been raised with regard to the sufficiency of current embodied AI for advancing our scientific understanding of intentional agency. While from an engineering or computer science perspective this limitation might not be relevant, it is of course highly relevant for AI researchers striving to build accurate models of natural cognition. We argue that the biological foundations of enactive cognitive science can provide the conceptual tools that are needed to diagnose more clearly the shortcomings of current embodied AI. In particular, taking an enactive perspective points to the need for AI to take seriously the organismic roots of autonomous agency and sense-making. We identify two necessary systemic requirements, namely constitutive autonomy and adaptivity, which lead us to introduce two design principles of enactive AI. It is argued that the development of such enactive AI poses a significant challenge to current methodologies. However, it also provides a promising way of eventually overcoming the current limitations of embodied AI, especially in terms of providing fuller models of natural embodied cognition. Finally, some practical implications and examples of the two design principles of enactive AI are also discussed.
Your attention please: designing security-decision UIs to make genuine risks harder to ignore

We designed and tested attractors for computer security dialogs: user-interface modifications used to draw users' attention to the most important information for making decisions. Some of these modifications were purely visual, while others temporarily inhibited potentially-dangerous behaviors to redirect users' attention to salient information. We conducted three between-subjects experiments to test the effectiveness of the attractors.
 In the first two experiments, we sent participants to perform a task on what appeared to be a third-party site that required installation of a browser plugin. We presented them with what appeared to be an installation dialog from their operating system. Participants who saw dialogs that employed inhibitive attractors were significantly less likely than those in the control group to ignore clues that installing this software might be harmful.
 In the third experiment, we attempted to habituate participants to dialogs that they knew were part of the experiment. We used attractors to highlight a field that was of no value during habituation trials and contained critical information after the habituation period. Participants exposed to inhibitive attractors were two to three times more likely to make an informed decision than those in the control condition.
Insulin signalling and the regulation of glucose and lipid metabolism

The epidemic of type 2 diabetes and impaired glucose tolerance is one of the main causes of morbidity and mortality worldwide. In both disorders, tissues such as muscle, fat and liver become less responsive or resistant to insulin. This state is also linked to other common health problems, such as obesity, polycystic ovarian disease, hyperlipidaemia, hypertension and atherosclerosis. The pathophysiology of insulin resistance involves a complex network of signalling pathways, activated by the insulin receptor, which regulates intermediary metabolism and its organization in cells. But recent studies have shown that numerous other hormones and signalling events attenuate insulin action, and are important in type 2 diabetes.
Controlled Scalable Synthesis of Uniform, High-Quality Monolayer and Few-layer MoS2 Films

Two dimensional (2D) materials with a monolayer of atoms represent an ultimate control of material dimension in the vertical direction. Molybdenum sulfide (MoS2) monolayers, with a direct bandgap of 1.8 eV, offer an unprecedented prospect of miniaturizing semiconductor science and technology down to a truly atomic scale. Recent studies have indeed demonstrated the promise of 2D MoS2 in fields including field effect transistors, low power switches, optoelectronics, and spintronics. However, device development with 2D MoS2 has been delayed by the lack of capabilities to produce large-area, uniform, and high-quality MoS2 monolayers. Here we present a self-limiting approach that can grow high quality monolayer and few-layer MoS2 films over an area of centimeters with unprecedented uniformity and controllability. This approach is compatible with the standard fabrication process in semiconductor industry. It paves the way for the development of practical devices with 2D MoS2 and opens up new avenues for fundamental research.
Statistical machine translation

Translation of natural language text using statistical machine translation (SMT) is a supervised machine learning problem. SMT algorithms are trained to learn how to translate by providing many translations produced by human language experts. The field SMT has gained momentum in recent three decades. New techniques are constantly introduced by the researchers. This is survey paper presenting an introduction of the recent developments in the field. The paper also describes the recent research for word alignment and language modelling problems in the translation process. An overview of these two sub problems is enlisted. Along the way, some challenges in machine translation are presented.
Arabic Sentiment Analysis: A Survey

Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score. Arabic Sentiment Analysis; Qualitative Analysis; Quantitative Analysis; Smoothness
A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models

We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.
Identification : Using Text Mining , Feature Engineering & Network Embedding

Authorship analysis is a challenging area that has been developed through centuries and with research done widely scattered across multiple disciples of mainly computational linguistics, text mining, data mining, stylometry and machine learning. Conventional techniques from the past relied heavily on stylometry and text-based content analysis of document text for authorship analysis. More recent developments use network embedding training focus heavily on document attributes to build a network and predict the author. We propose a system that incorporated the strengths of both text mining and network embedding methods and utilizes both the document text and document attributes fully when available. In this paper, we describe the system overview and implementation in detail and discuss how by supporting a more multi-faceted information based network embedding, it can be possible to get improved results. Finally we discuss our results and suggest some future improvements in terms of results, speed and performance for our system to handle larger corpus
An Unsupervised Model With Attention Autoencoders for Question Retrieval

Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions. Our RAMN integrates together the deep semantic representations, the shallow lexical mismatching information and the initial rank produced by an external search engine. For the first time, we propose attention autoencoders to generate semantic representations of questions. In addition, we employ lexical mismatching to capture surface matching between two questions, which is derived from the importance of each word in a question. We conduct experiments on the open CQA datasets of SemEval-2016 and SemEval-2017. The experimental results show that our unsupervised model obtains comparable performance with the state-of-the-art supervised methods in SemEval-2016 Task 3, and outperforms the best system in SemEval-2017 Task 3 by a wide margin.
Protecting Industry 4 . 0 against Advanced Persistent Threats

The SADCIP project has arisen from the need to deal with increasingly intelligent and autonomous industrial and monitoring systems, capable of collaborating with each other to meet a common objective: provide efficient and real-time manufacturing and logistics from anywhere, at any time and anyhow . However, any new condition that implies open communication with the Internet and the adaptation of heterogeneous (wireless) systems can, certainly, bring about numerous interoperability and security problems [2]. What types of problems? From a slight fault or anomaly within the operational applications, to massive and distributed attacks of a subtle and potentially damaging nature. Such problems can even have an aggressive effect on the welfare of other critical infrastructures. It is not the same to protect all those operational elements involved in the construction of each component that forms, for example, a bicycle, as the components that comprise a system of transport of greater reach, such as, a plane or a train. Therefore, it is self-evident that there is a relationship between the need to protect todays industry and the need to ensure protection, at all levels, of the rest of the dependent, critical infrastructures. In addition, this characteristic underlines the criticality degree of a new paradigm related to the Internet of Things known as Industry 4.0, which in itself, can also be considered as a critical infrastructure. Industry 4.0 (cf. Figure 1) constitutes a technological progress within the traditional industry. Here, both novel and existing systems coexist and share, in a centralized or decentralized way, resources, data and actions. As a result, novel services are enabled, and efficiency is increased. However, the nature of this context makes it difficult to trust fully on the goodness of the whole system, as multiple vulnerabilities are born mainly because of its complexity and heterogeneity. Moreover, in this particular context, one of the most dangerous threats are advanced persistent threats, or APTs. Therefore, SADCIP looks towards improving the state of the art, trying to find the necessary tools to a) monitor the technical capacities of the operational elements in the field, and b) detect relative evidence that, if applicable, should be addressed through optimal proactive response systems .
Community Recovery in Graphs with Locality

Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes rather than uniformly sampled between all nodes pairs, as in most existing models. We present an algorithm that runs nearly linearly in the number of measurements and which achieves the information theoretic limit for exact recovery.
Virtual Reality A Survival Guide for the Social Scientist

In this article, we provide the nontechnical reader with a fundamental understanding of the components of virtual reality (VR) and a thorough discussion of the role VR has played in social science. First, we provide a brief overview of the hardware and equipment used to create VR and review common elements found within the virtual environment that may be of interest to social scientists, such as virtual humans and interactive, multisensory feedback. Then, we discuss the role of VR in existing social scientific research. Specifically, we review the literature on the study of VR as an object, wherein we discuss the effects of the technology on human users; VR as an application, wherein we consider real-world applications in areas such as medicine and education; and VR as a method, wherein we provide a comprehensive outline of studies in which VR technologies are used to study phenomena that have traditionally been studied in physical settings, such as nonverbal behavior and social interaction. We then present a content analysis of the literature, tracking the trends for this research over the last two decades. Finally, we present some possibilities for future research for interested social scientists.
Extending the Use of Adaptor Grammars for Unsupervised Morphological Segmentation of Unseen Languages

We investigate using Adaptor Grammars for unsupervised morphological segmentation. Using six development languages, we investigate in detail different grammars, the use of morphological knowledge from outside sources, and the use of a cascaded architecture. Using cross-validation on our development languages, we propose a system which is language-independent. We show that it outperforms two state-of-the-art systems on 5 out of 6 languages.
Unequal Teeth Widths for Torque Ripple Reduction in Permanent Magnet Synchronous Machines With Fractional-Slot Non-Overlapping Windings

Permanent magnet synchronous machines (PMSMs) with fractional-slot non-overlapping windings, also known as tooth-coil winding PMSMs (TCW PMSM), have been under intensive research during the latest decade. There are many optimization routines explained and implemented in the literature to improve the characteristics of this machine type. This paper introduces a new technique for torque ripple minimization in TCW PMSM. The source of torque harmonics is also described. The low-order torque harmonics can be harmful for a variety of applications, such as direct drive wind generators, direct drive light vehicle electrical motors, and for some high-precision servo applications. The reduction of the torque ripple harmonics with the lowest orders (6th and 12th) is realized by machine geometry optimization technique using finite element analysis. The presented optimization technique includes the stator geometry adjustment in TCW PMSMs with rotor surface permanent magnets and with rotor embedded permanent magnets. Influence of the permanent magnet skewing on the torque ripple reduction and cogging torque elimination was also investigated. It was implemented separately and together with the stator optimization technique. As a result, the reduction of some torque ripple harmonics was attained.
GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY

Steganography is collection of methods to hide secret information (“payload”) within non-secret information (“container”). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
Pregel: a system for large-scale graph processing

Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs in some cases billions of vertices, trillions of edges poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and faulttolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
Illumination-aware faster R-CNN for robust multispectral pedestrian detection

Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF RCNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.