nils-herrmann/clean_scirepeval huggingface dataset:
Caribbean mangroves adjust to rising sea level through biotic controls on change in soil elevation

The long-term stability of coastal ecosystems such as mangroves and salt marshes depends upon the maintenance of soil elevations within the intertidal habitat as sea level changes. We examined the rates and processes of peat formation by mangroves of the Caribbean Region to better understand biological controls on habitat stability. Location Mangrove-dominated islands on the Caribbean coasts of Belize, Honduras and Panama were selected as study sites.  Biological processes controlling mangrove peat formation were manipulated (in Belize) by the addition of nutrients (nitrogen or phosphorus) to Rhizophora mangle (red mangrove), and the effects on the dynamics of soil elevation were determined over a 3-year period using rod surface elevation tables (RSET) and marker horizons. Peat composition and geological accretion rates were determined at all sites using radiocarbon-dated cores.  The addition of nutrients to mangroves caused significant changes in rates of mangrove root accumulation, which influenced both the rate and direction of change in elevation. Areas with low root input lost elevation and those with high rates gained elevation. These findings were consistent with peat analyses at multiple Caribbean sites showing that deposits (up to 10 m in depth) were composed primarily of mangrove root matter. Comparison of radiocarbon-dated cores at the study sites with a sea-level curve for the western Atlantic indicated a tight coupling between peat building in Caribbean mangroves and sea-level rise over the Holocene.  Mangroves common to the Caribbean region have adjusted to changing sea level mainly through subsurface accumulation of refractory mangrove roots. Without root and other organic inputs, submergence of these tidal forests is inevitable due to peat decomposition, physical compaction and eustatic sea-level rise. These findings have relevance for predicting the effects of sea-level rise and biophysical processes on tropical mangrove ecosystems.
Context Sensitive Topic Models for Author Influence in Document Networks

In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors’ interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.
Explorer Hierarchical Recurrent Neural Network for Story Segmentation

A broadcast news stream consists of a number of stories and each story consists of several sentences. We capture this structure using a hierarchical model based on a word-level Recurrent Neural Network (RNN) sentence modeling layer and a sentence-level bidirectional Long Short-Term Memory (LSTM) topic modeling layer. First, the word-level RNN layer extracts a vector embedding the sentence information from the given transcribed lexical tokens of each sentence. These sentence embedding vectors are fed into a bidirectional LSTM that models the sentence and topic transitions. A topic posterior for each sentence is estimated discriminatively and a Hidden Markov model (HMM) follows to decode the story sequence and identify story boundaries. Experiments on the topic detection and tracking (TDT2) task indicate that the hierarchical RNN topic modeling achieves the best story segmentation performance with a higher F1-measure compared to conventional state-of-the-art methods. We also compare variations of our model to infer the optimal structure for the story segmentation task.
Auditing Anti-Malware Tools by Evolving Android Malware and Dynamic Loading Technique

Although a previous paper shows that existing anti-malware tools (AMTs) may have high detection rate, the report is based on existing malware and thus it does not imply that AMTs can effectively deal with future malware. It is desirable to have an alternative way of auditing AMTs. In our previous paper, we use malware samples from android malware collection Genome to summarize a malware meta-model for modularizing the common attack behaviors and evasion techniques in reusable features. We then combine different features with an evolutionary algorithm, in which way we evolve malware for variants. Previous results have shown that the existing AMTs only exhibit detection rate of 20%–30% for 10 000 evolved malware variants. In this paper, based on the modularized attack features, we apply the dynamic code generation and loading techniques to produce malware, so that we can audit the AMTs at runtime. We implement our approach, named Mystique-S, as a service-oriented malware generation system. Mystique-S automatically selects attack features under various user scenarios and delivers the corresponding malicious payloads at runtime. Relying on dynamic code binding (via service) and loading (via reflection) techniques, Mystique-S enables dynamic execution of payloads on user devices at runtime. Experimental results on real-world devices show that existing AMTs are incapable of detecting most of our generated malware. Last, we propose the enhancements for existing AMTs.
American Economic Association Rational Decision Making in Business Organizations

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
A Dataset for Multi-Target Stance Detection

Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification.
Learning Covariant Feature Detectors

We do not know a-priori what the normalised patch looks like. But we may know the transformation Tgt between the patches: Tgt = Tʹ T = Ψ(xʹ) Ψ(x) Learning Formulation Using synthetic translations of real patches: ǁTgt Ψ(xʹ) + Ψ(x)ǁ ≅ 0 General Local Feature Detectors A general covariant framework Generalization to Affine Covariant Detectors Features are oriented ellipses and transformations are affinities.
Fractal Nature - Generating Realistic Terrains for Games

This paper presents a software tool - called Fractal Nature - that provides a set of fractal and physical based methods for creating realistic terrains called Fractal Nature. The output of the program can be used for creating content for video games and serious games. The approach for generating the terrain is based on noise filters, such as Gaussian distribution, capable of rendering highly realistic environments. It is demonstrated how a random terrain can change its shape and visual appearance containing artefacts such as steep slopes and smooth riverbeds. Moreover, two interactive erosion systems, hydraulic and thermal, were implemented. An initial evaluation with 12 expert users provided useful feedback for the applicability of the algorithms in video games as well as insights for future improvements.
Script Induction as Language Modeling

The narrative cloze is an evaluation metric commonly used for work on automatic script induction. While prior work in this area has focused on count-based methods from distributional semantics, such as pointwise mutual information, we argue that the narrative cloze can be productively reframed as a language modeling task. By training a discriminative language model for this task, we attain improvements of up to 27 percent over prior methods on standard narrative cloze metrics.
Real-time self-adaptive deep stereo

Deep convolutional neural networks trained end-toend are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system.
Inverse Optimization: A New Perspective on the Black-Litterman Model

The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.
Elliptical ASIFT Agglomeration in Class Prototype for Logo Detection

Logo localization and recognition is difficult in natural images due to perspective deformations, varying background, possible occlusions, scaling variability; furthermore, the re–branding induces changes in the logo color palette and spatial distribution. To address this task, we locate keypoints using Affine Difference of Gaussian described by SIFT elliptical features; we construct the class prototypes by analyzing the graph of homographic matching between examples of the same class. The interconnections graph is developed for each class and the representative points from the non central examples are added to the class model. Potentially, an inverted secondary model is built for classes containing color inverted logos. Finally, each class is depicted by the reunion of the suitable keypoints and descriptors. The logo integrated detection (localization and classification) system is tested on multiple databases leading to state of the art accuracy.
Renewable energy for sustainable agriculture

Agriculture is the sole provider of human food. Most farm machines are driven by fossil fuels, which contribute to greenhouse gas emissions and, in turn, accelerate climate change. Such environmental damage can be mitigated by the promotion of renewable resources such as solar, wind, biomass, tidal, geo-thermal, small-scale hydro, biofuels and wave-generated power. These renewable resources have a huge potential for the agriculture industry. The farmers should be encouraged by subsidies to use renewable energy technology. The concept of sustainable agriculture lies on a delicate balance of maximizing crop productivity and maintaining economic stability, while minimizing the utilization of finite natural resources and detrimental environmental impacts. Sustainable agriculture also depends on replenishing the soil while minimizing the use of non-renewable resources, such as natural gas, which is used in converting atmospheric nitrogen into synthetic fertilizer, and mineral ores, e.g. phosphate or fossil fuel used in diesel generators for water pumping for irrigation. Hence, there is a need for promoting use of renewable energy systems for sustainable agriculture, e.g. solar photovoltaic water pumps and electricity, greenhouse technologies, solar dryers for post-harvest processing, and solar hot water heaters. In remote agricultural lands, the underground submersible solar photovoltaic water pump is economically viable and also an environmentally-friendly option as compared with a diesel generator set. If there are adverse climatic conditions for the growth of particular plants in cold climatic zones then there is need for renewable energy technology such as greenhouses for maintaining the optimum plant ambient temperature conditions for the growth of plants and vegetables. The economics of using greenhouses for plants and vegetables, and solar photovoltaic water pumps for sustainable agriculture and the environment are presented in this article. Clean development provides industrialized countries with an incentive to invest in emission reduction projects in developing countries to achieve a reduction in CO2 emissions at the lowest cost. The mechanism of clean development is discussed in brief for the use of renewable systems for sustainable agricultural development specific to solar photovoltaic water pumps in India and the world. This article explains in detail the role of renewable energy in farming by connecting all aspects of agronomy with ecology, the environment, economics and societal change.
Fusion of heterogeneous data in convolutional networks for urban semantic labeling

In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.
Endosymbiotic gene transfer: organelle genomes forge eukaryotic chromosomes

Genome sequences reveal that a deluge of DNA from organelles has constantly been bombarding the nucleus since the origin of organelles. Recent experiments have shown that DNA is transferred from organelles to the nucleus at frequencies that were previously unimaginable. Endosymbiotic gene transfer is a ubiquitous, continuing and natural process that pervades nuclear DNA dynamics. This relentless influx of organelle DNA has abolished organelle autonomy and increased nuclear complexity.
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks

Recurrent neural networks and sequence to sequence models require a predetermined length for prediction output length. Our model addresses this by allowing the network to predict a variable length output in inference. A new loss function with a tailored gradient computation is developed that trades off prediction accuracy and output length. The model utilizes a function to determine whether a particular output at a time should be evaluated or not given a predetermined threshold. We evaluate the model on the problem of predicting the prices of securities. We find that the model makes longer predictions for more stable securities and it naturally balances prediction accuracy and length.
Learning Invariant Deep Representation for NIR-VIS Face Recognition

Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-level layers of this network are trained only on large-scale VIS data. Each convolutional layer is implemented by the simplest case of maxout operator. The highlevel layer is divided into two orthogonal subspaces that contain modality-invariant identity information and modalityvariant spectrum information respectively. Our joint formulation leads to an alternating minimization approach for deep representation at the training time and an efficient computation for heterogeneous data at the testing time. Experimental evaluations show that our method achieves 94% verification rate at FAR=0.1% on the challenging CASIA NIR-VIS 2.0 face recognition dataset. Compared with state-of-the-art methods, it reduces the error rate by 58% only with a compact 64-D representation.
Measuring the Effectiveness of Embedded Phishing Exercises

Embedded phishing exercises, which send test phishing emails, are utilized by organizations to reduce the susceptibility of its employees to this type of attack. Research studies seeking to evaluate the effectiveness of these exercises have generally been limited by small sample sizes. These studies have not been able to measure possible factors that might bias results. As a result, companies have had to create their own design and evaluation methods, with no framework to guide their efforts. Lacking such guidelines, it can often be difficult to determine whether these types of exercises are truly effective, and if reported results are statistically reliable. In this paper, we conduct a systematic analysis of data from a large real world embedded phishing exercise that involved 19,180 participants from a single organization, and utilized 115,080 test phishing emails. The first part of our study focuses on developing methodologies to correct some sources of bias, enabling sounder evaluations of the efficacy of embedded phishing exercises and training. We then use these methods to perform an analysis of the effectiveness of this embedded phishing exercise, and through our analysis, identify how the design of these exercises might be improved.
DeepKSPD: Learning Kernel-Matrix-Based SPD Representation For Fine-Grained Image Recognition

Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition. Recent study shows that kernel matrix can give considerably better representation by modelling the nonlinearity in the local descriptor set. Nevertheless, neither the descriptors nor the kernel matrix is deeply learned. Worse, they are considered separately, hindering the pursuit of an optimal SPD representation. This work proposes a deep network that jointly learns local descriptors, kernel-matrix-based SPD representation, and the classifier via an end-to-end training process. We derive the derivatives for the mapping from a local descriptor set to the SPD representation to carry out backpropagation. Also, we exploit the Daleckǐi-Krěin formula in operator theory to give a concise and unified result on differentiating SPD matrix functions, including ∗Corresponding author (leiw@uow.edu.au) 1 ar X iv :1 71 1. 04 04 7v 1 [ cs .C V ] 1 1 N ov 2 01 7 the matrix logarithm to handle the Riemannian geometry of kernel matrix. Experiments not only show the superiority of kernel-matrixbased SPD representation with deep local descriptors, but also verify the advantage of the proposed deep network in pursuing better SPD representations for fine-grained image recognition tasks.
Soft pneumatic grippers embedded with stretchable electroadhesion

Current soft pneumatic grippers cannot robustly grasp flat materials and flexible objects on curved surfaces without distorting them. Current electroadhesive grippers, on the other hand, are difficult to actively deform to complex shapes to pick up free-form surfaces or objects. An easyto-implement PneuEA gripper is proposed by the integration of an electroadhesive gripper and a two-fingered soft pneumatic gripper. The electroadhesive gripper was fabricated by segmenting a soft conductive silicon sheet into a two-part electrode design and embedding it in a soft dielectric elastomer. The two-fingered soft pneumatic gripper was manufactured using a standard soft lithography approach. This novel integration has combined the benefits of both the electroadhesive and soft pneumatic grippers. As a result, the proposed PneuEA gripper was not only able to pick-and-place flat and flexible materials such as a porous cloth but also delicate objects such as a light bulb. By combining two soft touch sensors with the electroadhesive, an intelligent and shape-adaptive PneuEA material handling system has been developed. This work is expected to widen the applications of both soft gripper and electroadhesion technologies.
Automated Behavioral Analysis of Malware: A Case Study of WannaCry Ransomware

Ransomware, a class of self-propagating malware that uses encryption to hold the victims’ data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan . Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation. We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm’s feature extraction and provide analysis of the method’s robustness to variations of input data—changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) antivirus (AV) products fail.