nils-herrmann/clean_scirepeval huggingface dataset:
Visual Interaction Networks

From just a glance, humans can make rich predictions about the future state of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, and graphics are often restricted to narrow domains and require direct measurements of the underlying states. We introduce the Visual Interaction Network, a general-purpose model for learning the dynamics of a physical system from raw visual observations. Our model consists of a perceptual front-end based on convolutional neural networks and a dynamics predictor based on interaction networks. Through joint training, the perceptual front-end learns to parse a dynamic visual scene into a set of factored latent object representations. The dynamics predictor learns to roll these states forward in time by computing their interactions and dynamics, producing a predicted physical trajectory of arbitrary length. We found that from just six input video frames the Visual Interaction Network can generate accurate future trajectories of hundreds of time steps on a wide range of physical systems. Our model can also be applied to scenes with invisible objects, inferring their future states from their effects on the visible objects, and can implicitly infer the unknown mass of objects. Our results demonstrate that the perceptual module and the object-based dynamics predictor module can induce factored latent representations that support accurate dynamical predictions. This work opens new opportunities for model-based decision-making and planning from raw sensory observations in complex physical environments.
The impacts of climate change on water resources and agriculture in China

China is the world’s most populous country and a major emitter of greenhouse gases. Consequently, much research has focused on China’s influence on climate change but somewhat less has been written about the impact of climate change on China. China experienced explosive economic growth in recent decades, but with only 7% of the world’s arable land available to feed 22% of the world’s population, China's economy may be vulnerable to climate change itself. We find, however, that notwithstanding the clear warming that has occurred in China in recent decades, current understanding does not allow a clear assessment of the impact of anthropogenic climate change on China’s water resources and agriculture and therefore China’s ability to feed its people. To reach a more definitive conclusion, future work must improve regional climate simulations—especially of precipitation—and develop a better understanding of the managed and unmanaged responses of crops to changes in climate, diseases, pests and atmospheric constituents.
Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter

We present three new machine learning based artificial intelligence (AI) techniques, which we have added to our parameterized computational painterly rendering framework and show their benefits in computational creativity and non-photorealistic rendering (NPR) of portraits. Traditional portrait artists use a specific but open human creativity, vision, technical and perception methodologies to create a painterly portrait of a live or photographed sitter. By incorporating more open ended creative, semantic and concept blending techniques, these new neural network based AI techniques allow us to better model the creative cognitive thinking process that human painters employ. We analyse these AI based methods for their operating principles and outputs that together along with our parameterized NPR modules can be relevant to the field of computational creativity research and computational painterly rendering.
An Overview of Graph-Based Keyword Extraction Methods and Approaches

The paper surveys methods and approaches for the task of keyword extraction. The systematic review of methods was gathered which resulted in a comprehensive review of existing approaches. Work related to keyword extraction is elaborated for supervised and unsupervised methods, with a special emphasis on graph-based methods. Various graph-based methods are analyzed and compared. The paper provides guidelines for future research plans and encourages the development of new graph-based approaches for keyword extraction.
Analysis of NB-IoT Deployment in LTE Guard-Band

In 3GPP Rel-13, a narrowband wide-area cellular system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, can be deployed in three operation modes - in-band within LTE carrier, in the guard-band of LTE carrier, and stand-alone. Guard-band deployment allows the operator to support NB-IoT without requiring new spectrum and with minimal impact to LTE. This paper provides an overview and presents a study of NB-IoT deployment in LTE guard band. An analysis of coexistence of NB-IoT with LTE is presented and demonstrates that while there is mutual interference between NB-IoT and LTE on the uplink, the impact on either system is small. A performance analysis of NB-IoT is also presented. The link budget analysis demonstrates how the coverage target can be achieved for the uplink and downlink data channels. Illustrative uplink and downlink link-level simulation results are provided. The analysis in the paper shows that guard-band operation of NB-IoT is feasible and can provide support for massive number of low-throughput IoT devices.
Choosing Among Alternative Service Delivery Modes : An Investigation of Customer Trial of Self-Service Technologies

Electronic commerce is an increasingly popular business model with a wide range of tools available to firms. An application that is becoming more common is the use of self-service technologies (SSTs), such as telephone banking, automated hotel checkout, and online investment trading, whereby customers produce services for themselves without assistance from firm employees. Widespread introduction of SSTs is apparent across industries, yet relatively little is known about why customers decide to try SSTs and why some SSTs are more widely accepted than others. In this research, the authors explore key factors that influence the initial SST trial decision, specifically focusing on actual behavior in situations in which the consumer has a choice among delivery modes. The authors show that the consumer readiness variables of role clarity, motivation, and ability are key mediators between established adoption constructs (innovation characteristics and individual differences) and the likelihood of trial.
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.
Sustaining Superior Performance in Business Ecosystems: Evidence from Application Software Developers in the iOS and Android Smartphone Ecosystems

We study the phenomenon of business ecosystems in which a platform firm orchestrates the functioning of the ecosystem by providing a platform and setting the rules for other complementor firms to participate in it. We develop a theoretical framework to explain how the structural and evolutionary features of the ecosystem may shape the extent to which participating complementor firms can sustain their superior performance. The structural feature, which we refer to as ecosystem complexity, is a function of the number of unique components or subsystems that interact with the complementor’s product. We incorporate the evolutionary features by considering the role of generational transitions initiated by platform firms over time as well as the role of complementors’ ecosystem-specific experience. Evidence from Apple’s iOS and Google’s Android smartphone ecosystems supports our arguments that higher ecosystem complexity helps app developers sustain their superior performance, and that this effect is stronger for more experienced firms. In contrast, platform transitions initiated by Apple and Google make it more difficult for app developers to sustain their performance superiority, and that this effect is exacerbated by the extent of ecosystem complexity. The study offers a novel perspective on how the performance of complementor firms in business ecosystems may be shaped by the rules and actions of the central platform firms.
Some evidence for unconscious lie detection.

To maximize survival and reproductive success, primates evolved the tendency to tell lies and the ability to accurately detect them. Despite the obvious advantage of detecting lies accurately, conscious judgments of veracity are only slightly more accurate than chance. However, findings in forensic psychology, neuroscience, and primatology suggest that lies can be accurately detected when less-conscious mental processes (as opposed to more-conscious mental processes) are used. We predicted that observing someone tell a lie would automatically activate cognitive concepts associated with deception, and observing someone tell the truth would activate concepts associated with truth. In two experiments, we demonstrated that indirect measures of deception detection are significantly more accurate than direct measures. These findings provide a new lens through which to reconsider old questions and approach new investigations of human lie detection.
Ethical Artificial Intelligence - An Open Question

Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real-world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI-friendly environment for people and a people-friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre-set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers’ awareness on AI safety as another related research interest.
Generating Synthetic Data to Match Data Mining Patterns

Synthetic data sets can be useful in a variety of situations, including repeatable regression testing and providing realistic - but not real - data to third parties for testing new software. Researchers, engineers, and software developers can test against a safe data set without affecting or even accessing the original data, insulating them from privacy and security concerns as well as letting them generate larger data sets than would be available using only real data. Practitioners use data mining technology to discover patterns in real data sets that aren't apparent at the outset. This article explores how to combine information derived from data mining applications with the descriptive ability of synthetic data generation software. Our goal is to demonstrate that at least some data mining techniques (in particular, a decision tree) can discover patterns that we can then use to inverse map into synthetic data sets. These synthetic data sets can be of any size and will faithfully exhibit the same (decision tree) patterns. Our work builds on two technologies: synthetic data definition language and predictive model markup language.
AmbientGAN: Generative models from lossy measurements

Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest. However, current techniques for training generative models require access to fully-observed samples. In many settings, it is expensive or even impossible to obtain fullyobserved samples, but economical to obtain partial, noisy observations. We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest. We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models. Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN. On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements. Generative models trained with our method can obtain 2-4x higher inception scores than the baselines.
A new keypoint-based copy-move forgery detection for small smooth regions

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.
E-Commerce and International Tax Planning

This paper investigates whether the increased flexibility afforded by e-commerce has allowed firms to increase their tax planning activities. We specifically address the incentives of firms to use exports rather than foreign subsidiary sales. Using proxies for e-commerce activity in both company-level and country-level tests, we find evidence consistent with greater sensitivity to tax incentives when e-commerce measures are high. This research is an important first step in understanding the larger impact of e-commerce on international tax planning behavior.
Autonomous recovery of a fixed-wing UAV using a net suspended by two multirotor UAVs

This article presents a novel recovery method for fixed-wing Unmanned Aerial Vehicles (UAVs), aimed at enabling operations from marine vessels. Instead of using the conventional method of using a fixed net on the ship deck, we propose to suspend a net under two cooperative multirotor UAVs. While keeping their relative formation, the multirotor UAVs are able to intercept the incoming fixed-wing UAV along a virtual runway over the sea, and transport it back to the ship. In addition to discussing the concept and design a control system, this paper also presents experimental validation of the proposed concept for a smallscale UAV platform.
How the Influence of Unethical Leaders on Followers Is Affected by Their Implicit Followership Theories

Our research examines the role of followers in unethical leadership. Drawing on a social–cognitive approach to leadership and recent research in the field of behavioral ethics, we focus on how leader behavior and follower information processing interact to produce unethical outcomes. In two experimental studies simulating a personnel selection context, we examine to what extent individual implicit assumptions regarding the follower role (i.e., implicit followership theories, IFTs) relate to employees’ tendency to comply with leader unethical suggestions. In Study 1, controlling for possible alternative explanations such as personal need for structure, romance of leadership, and moral disengagement, we found that the IFT Good Citizen increased and the IFT Insubordination decreased followers’ tendencies to contribute to unethical leadership. In Study 2, we varied the leader’s unethical suggestions to further investigate the conditions under which these effects occur and included authoritarianism as an additional control variable. Overall, our findings suggest that IFTs make a unique contribution to our understanding of the role of followers in unethical leadership, and that this contribution depends on the way leaders frame their unethical request. Interaction effects suggest that follower characteristics need to be considered as they are embedded in specific situational settings rather than as isolated traits.
A Novel Variational Family for Hidden Nonlinear Markov Models

Latent variable models have been widely applied for the analysis and visualization of large datasets. In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear. However, approximate inference techniques are usually necessary when dealing with nonlinear dynamics and observation functions. Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND), that is able to uncover nonlinear observation and transition functions from sequential data. The framework includes a structured approximate posterior, and an algorithm that relies on the fixed-point iteration method to find the best estimate for latent trajectories. We apply the method to several datasets and show that it is able to accurately infer the underlying dynamics of these systems, in some cases substantially outperforming state-of-the-art methods.
Strategic Management Accounting : Lots in a Name ?

Despite its thirty year history, strategic management accounting has yet to establish itself as a core element of managerial accounting. That it has the capacity to do so is immediately apparent from the term itself, which in turn explains its continuing appeal for those attracted to developing and promoting it. The purpose of this paper is to suggest that by reconceptualising strategic management accounting as ‘accounting for strategic management’ it may be possible to better realise its potential. The paper is in very large part a literature review, in the course of which the accounting for strategic management perspective is explicated. A number of precursors in the extant strategic management accounting literature are also revisited. Central to this reformulation of strategic management accounting is the understanding that the accounting aspects of accounting for strategic management will differ radically from those normally associated with that designation.
Comparative Study among the Different Formulation of Antacid Tablets by Using Acid-Base Neutralization Reaction

Antacids were the mainstay of treatment for acid-peptic disorders until the advent of Hreceptor antagonist and proton pump inhibitors. We attempted to compare the activity of the different antacid tablets formulation by using acid-base neutralization reaction studies. This experiment based on acid-base neutralization reaction studies between independent variable (sample of antacid) and dependent variable (amount of acid needed to neutralize the antacid). The tablet code no. D neutralizes highest (7 ml) of acid solution and lowest (1.2 ml) was neutralizing by antacid code no. C. The sequences of antacid according to amount of acid required to neutralization was D> E> B> A> C. This in-vitro study may help in the deepest research works such as design and discovery of new antacid formulation.
Performing perception - staging aesthetics of interaction

In interaction design for experience-oriented uses of technology, a central facet of aesthetics of interaction is rooted in the user's experience of herself “performing her perception.” By drawing on performance (theater) theory, phenomenology and sociology and with references to recent HCI-work on the relation between the system and the performer/user and the spectator's relation to this dynamic, we show how the user is simultaneously operator, performer and spectator when interacting. By engaging with the system, she continuously acts out these three roles and her awareness of them is crucial in her experience. We argue that this 3-in-1 is always already shaping the user's understanding and perception of her interaction as it is staged through her experience of the object's form and expression. Through examples ranging from everyday technologies utilizing performances of interaction to spatial contemporary artworks, digital as well as analogue, we address the notion of the performative spectator and the spectating performer. We demonstrate how perception is also performative and how focus on this aspect seems to be crucial when designing experience-oriented products, systems and services.