nils-herrmann/clean_scirepeval huggingface dataset:
MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks

Human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects. Inspired by the success of convolutional neural networks (CNN) for image classification, recent attempts have been made to learn 3D CNNs for recognizing human actions in videos. However, partly due to the high complexity of training 3D convolution kernels and the need for large quantities of training videos, only limited success has been reported. This has triggered us to investigate in this paper a new deep architecture which can handle 3D signals more effectively. Specifically, we propose factorized spatio-temporal convolutional networks (FstCN) that factorize the original 3D convolution kernel learning as a sequential process of learning 2D spatial kernels in the lower layers (called spatial convolutional layers), followed by learning 1D temporal kernels in the upper layers (called temporal convolutional layers). We introduce a novel transformation and permutation operator to make factorization in FstCN possible. Moreover, to address the issue of sequence alignment, we propose an effective training and inference strategy based on sampling multiple video clips from a given action video sequence. We have tested FstCN on two commonly used benchmark datasets (UCF-101 and HMDB-51). Without using auxiliary training videos to boost the performance, FstCN outperforms existing CNN based methods and achieves comparable performance with a recent method that benefits from using auxiliary training videos.
Open Source Software and the "Private-Collective" Innovation Model: Issues for Organization Science

Currently two models of innovation are prevalent in organization science. The "private investment" model assumes returns to the innovator results from private goods and efficient regimes of intellectual property protection. The "collective action" model assumes that under conditions of market failure, innovators collaborate in order to produce a public good. The phenomenon of open source software development shows that users program to solve their own as well as shared technical problems, and freely reveal their innovations without appropriating private returns from selling the software. In this paper we propose that open source software development is an exemplar of a compound model of innovation that contains elements of both the private investment and the collective action models. We describe a new set of research questions this model raises for scholars in organization science. We offer some details regarding the types of data available for open source projects in order to ease access for researchers who are unfamiliar with these, and also offer some advice on conducting empirical studies on open source software development processes.
Deriving Verb Predicates By Clustering Verbs with Arguments

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other hand, can give clusters with much larger coverage, and can be adapted to specific corpora such as Twitter. We present a method for clustering the outputs of VerbKB: verbs with their multiple argument types, e.g.“marry(person, person)”, “feel(person, emotion).” We make use of a novel lowdimensional embedding of verbs and their arguments to produce high quality clusters in which the same verb can be in different clusters depending on its argument type. The resulting verb clusters do a better job than hand-built clusters of predicting sarcasm, sentiment, and locus of control in tweets.
Will it hurt ? MacroeconoMic effects of fiscal consolidation

3 chapter This chapter examines the effects of fiscal consolidation —tax hikes and government spending cuts—on economic activity. Based on a historical analysis of fiscal consolidation in advanced economies, and on simulations of the IMF's Global Integrated Monetary and Fiscal Model (GIMF), it finds that fiscal consolidation typically reduces output and raises unemployment in the short term. At the same time, interest rate cuts, a fall in the value of the currency, and a rise in net exports usually soften the contractionary impact. Consolidation is more painful when it relies primarily on tax hikes; this occurs largely because central banks typically provide less monetary stimulus during such episodes, particularly when they involve indirect tax hikes that raise inflation. Also, fiscal consolidation is more costly when the perceived risk of sovereign default is low. These findings suggest that budget deficit cuts are likely to be more painful if they occur simultaneously across many countries, and if monetary policy is not in a position to offset them. Over the long term, reducing government debt is likely to raise output, as real interest rates decline and the lighter burden of interest payments permits cuts to distortionary taxes. Budget deficits and government debt soared during the Great Recession. In 2009, the budget deficit averaged about 9 percent of GDP in advanced economies, up from only 1 percent of GDP in 2007. 1 By the end of 2010, government debt is expected to reach about 100 percent of GDP—its highest level in 50 years. Looking ahead, population aging could create even more serious problems for public finances. In response to these worrisome developments, virtually all advanced economies will face the challenge of fiscal consolidation. Indeed, many governments are already undertaking or planning The main authors of this chapter are Daniel Leigh (team leader), Advanced economies are defined as the 33 economies so designated based on the World Economic Outlook classification described in the Statistical Appendix. large spending cuts and tax hikes. An important and timely question is, therefore, whether fiscal retrenchment will hurt economic performance. Although there is widespread agreement that reducing debt has important long-term benefits, there is no consensus regarding the short-term effects of fiscal austerity. On the one hand, the conventional Keynesian view is that cutting spending or raising taxes reduces economic activity in the short term. On the other hand, a number of studies present evidence that cutting budget deficits can …
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems. .
The ligament anatomy of the deltoid complex of the ankle: a qualitative and quantitative anatomical study.

The deltoid ligament has both superficial and deep layers and consists of up to six ligamentous bands. The prevalence of the individual bands is variable, and no consensus as to which bands are constant or variable exists. Although other studies have looked at the variance in the deltoid anatomy, none have quantified the distance to relevant osseous landmarks.



The deltoid ligaments from fourteen non-paired, fresh-frozen cadaveric specimens were isolated and the ligamentous bands were identified. The lengths, footprint areas, orientations, and distances from relevant osseous landmarks were measured with a three-dimensional coordinate measurement device.



In all specimens, the tibionavicular, tibiospring, and deep posterior tibiotalar ligaments were identified. Three additional bands were variable in our specimen cohort: the tibiocalcaneal, superficial posterior tibiotalar, and deep anterior tibiotalar ligaments. The deep posterior tibiotalar ligament was the largest band of the deltoid ligament. The origins from the distal center of the intercollicular groove were 16.1 mm (95% confidence interval, 14.7 to 17.5 mm) for the tibionavicular ligament, 13.1 mm (95% confidence interval, 11.1 to 15.1 mm) for the tibiospring ligament, and 7.6 mm (95% confidence interval, 6.7 to 8.5 mm) for the deep posterior tibiotalar ligament. Relevant to other pertinent osseous landmarks, the tibionavicular ligament inserted at 9.7 mm (95% confidence interval, 8.4 to 11.0 mm) from the tuberosity of the navicular, the tibiospring inserted at 35% (95% confidence interval, 33.4% to 36.6%) of the spring ligament's posteroanterior distance, and the deep posterior tibiotalar ligament inserted at 17.8 mm (95% confidence interval, 16.3 to 19.3 mm) from the posteromedial talar tubercle.



The tibionavicular, tibiospring, and deep posterior tibiotalar ligament bands were constant components of the deltoid ligament. The deep posterior tibiotalar ligament was the largest band of the deltoid ligament.



The anatomical data regarding the deltoid ligament bands in this study will help to guide anatomical placement of repairs and reconstructions for deltoid ligament injury or instability.
Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection

In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. It was also applied successfully to malware detection. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. So far successful attacks have been proved to be very effective especially in the domains of images and speech, where small perturbations to the input signal do not change how it is perceived by humans but greatly affect the classification of the model under attack. Our goal is to modify a malicious binary so it would be detected as benign while preserving its original functionality. In contrast to images or speech, small modifications to bytes of the binary lead to significant changes in the functionality. We introduce a novel approach to generating adversarial example for attacking a whole-binary malware detector. We append to the binary file a small section, which contains a selected sequence of bytes that steers the prediction of the network from malicious to be benign with high confidence. We applied this approach to a CNNbased malware detection model and showed extremely high rates of success in the attack.
Long short-term memory neural network for network traffic prediction

This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model. And after considering the autocorrelation features, the neural network of LSTM and DNN has certain advantages in the accuracy of the large granularity data sets. Several experiments were held using real-world data to show effectivity of LSTM model and accuracy were improve with autocorrelation considered.
DieHarder: Securing the Heap

Heap-based attacks depend on a combination of memory management error and an exploitable memory allocator. Many allocators include ad hoc countermeasures against particular exploits but their effectiveness against future exploits has been uncertain. This paper presents the first formal treatment of the impact of allocator design on security. It analyzes a range of widely-deployed memory allocators, including those used by Windows, Linux, FreeBSD and OpenBSD, and shows that they remain vulnerable to attack. It them presents DieHarder, a new allocator whose design was guided by this analysis. DieHarder provides the highest degree of security from heap-based attacks of any practical allocator of which we are aware while imposing modest performance overhead. In particular, the Firefox web browser runs as fast with DieHarder as with the Linux allocator.
Stress control on polymeric outdoor insulators using Zinc oxide microvaristor composites

An approach to high field control, particularly in the areas near the high voltage (HV) and ground terminals of an outdoor insulator, is proposed using a nonlinear grading material; Zinc Oxide (ZnO) microvaristors compounded with other polymeric materials to obtain the required properties and allow easy application. The electrical properties of the microvaristor compounds are characterised by a nonlinear field-dependent conductivity. This paper describes the principles of the proposed field-control solution and demonstrates the effectiveness of the proposed approach in controlling the electric field along insulator profiles. A case study is carried out for a typical 11 kV polymeric insulator design to highlight the merits of the grading approach. Analysis of electric potential and field distributions on the insulator surface is described under dry clean and uniformly contaminated surface conditions for both standard and microvaristor-graded insulators. The grading and optimisation principles to allow better performance are investigated to improve the performance of the insulator both under steady state operation and under surge conditions. Furthermore, the dissipated power and associated heat are derived to examine surface heating and losses in the grading regions and for the complete insulator. Preliminary tests on inhouse prototype insulators have confirmed better flashover performance of the proposed graded insulator with a 21 % increase in flashover voltage.
The reinterpretation of dreams: an evolutionary hypothesis of the function of dreaming.

Several theories claim that dreaming is a random by-product of REM sleep physiology and that it does not serve any natural function. Phenomenal dream content, however, is not as disorganized as such views imply. The form and content of dreams is not random but organized and selective: during dreaming, the brain constructs a complex model of the world in which certain types of elements, when compared to waking life, are underrepresented whereas others are over represented. Furthermore, dream content is consistently and powerfully modulated by certain types of waking experiences. On the basis of this evidence, I put forward the hypothesis that the biological function of dreaming is to simulate threatening events, and to rehearse threat perception and threat avoidance. To evaluate this hypothesis, we need to consider the original evolutionary context of dreaming and the possible traces it has left in the dream content of the present human population. In the ancestral environment human life was short and full of threats. Any behavioral advantage in dealing with highly dangerous events would have increased the probability of reproductive success. A dream-production mechanism that tends to select threatening waking events and simulate them over and over again in various combinations would have been valuable for the development and maintenance of threat-avoidance skills. Empirical evidence from normative dream content, children's dreams, recurrent dreams, nightmares, post traumatic dreams, and the dreams of hunter-gatherers indicates that our dream-production mechanisms are in fact specialized in the simulation of threatening events, and thus provides support to the threat simulation hypothesis of the function of dreaming.
Concept Extraction from Student Essays, Towards Concept Map Mining

This paper presents a new approach for automatic concept extraction, using grammatical parsers and Latent Semantic Analysis. The methodology is described, also the tool used to build the benchmarkingcorpus. The results obtained on student essays shows good inter-rater agreement and promising machine extraction performance. Concept extraction is the first step to automatically extract concept maps fromstudent’s essays or Concept Map Mining.
Self-regulation of time management : mental contrasting with implementation intentions

Mental contrasting with implementation intentions (MCII) has been found to improve selfregulation across many life domains. The present research investigates whether MCII can benefit time management. In Study 1, we asked students to apply MCII to a pressing academic problem and assessed how they scheduled their time for the upcoming week. MCII participants scheduled more time than control participants who in their thoughts either reflected on similar contents using different cognitive procedures (content control group) or applied the same cognitive procedures on different contents (format control group). In Study 2, students were taught MCII as a metacognitive strategy to be used on any upcoming concerns of the subsequent week. As compared to the week prior to the training, students in the MCII (vs. format control) condition improved in self-reported time management. In Study 3, MCII (vs. format control) helped working mothers who enrolled in a vocational business program to attend classes more regularly. The findings suggest that performing MCII on one’s everyday concerns improves time management.
The Nature of Theory in Information Systems

The aim of this research essay is to examine the structural nature of theory in Information Systems. Despite the impor tance of theory, questions relating to its form and structure are neglected in comparison with questions relating to episte mology. The essay addresses issues of causality, explanation, prediction, and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, predic tion, and prescription. Five interrelated types of theory are distinguished: (I) theory for analyzing, (2) theory for ex plaining, (3) theory for predicting, (4) theory for explaining and predicting, and (5) theory for design and action. Examples illustrate the nature of each theory type. The appli cability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support Allen Lee was the accepting senior editor for this paper. M. Lynne Markus, Michael D. Myers, and Robert W. Zmud served as reviewers. is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated.
BIM Integrated Workflow Management and Monitoring System for Modular Buildings

The authors are collaborating with a manufacturer of custom built steel frame modular units which are then transported for rapid erection onsite (volumetric building system). As part of its strategy to develop modular housing, Enemetric, is taking the opportunity to develop intelligent buildings, integrating a wide range of sensors and control systems for optimising energy efficiency and directly monitoring structural health. Enemetric have recently been embracing Building Information Modeling (BIM) to improve workflow, in particular cost estimation and to simplify computer aided manufacture (CAM). By leveraging the existing data generated during the design phases, and projecting it to all other aspects of construction management, less errors are made and productivity is significantly increased. Enemetric may work on several buildings at once, and scheduling and priorities become especially important for effective workflow, and implementing Enterprise Resource Planning (ERP). The parametric nature of BIM is also very useful for improving building management, whereby real-time data collection can be logically associated with individual components of the BIM stored in a local Building Management System performing structural health monitoring and environmental monitoring and control. BIM reuse can be further employed in building simulation tools, to apply simulation assisted control strategies, in order to reduce energy consumption, and increase occupant comfort. BIM Integrated Workflow Management and Monitoring System for Modular Buildings
Machine Learning for Dialog State Tracking: A Review

Spoken dialog systems help users achieve a task using natural language. Noisy speech recognition and ambiguity in natural language motivate statistical approaches that model distributions over the user’s goal at every step in the dialog. The task of tracking these distributions, termed Dialog State Tracking, is therefore an essential component of any spoken dialog system. In recent years, the Dialog State Tracking Challenges have provided a common testbed and evaluation framework for this task, as well as labeled dialog data. As a result, a variety of machine-learned methods have been successfully applied to Dialog State Tracking. This paper reviews the machine-learning techniques that have been adapted to Dialog State Tracking, and gives an overview of published evaluations. Discriminative machine-learned methods outperform generative and rule-based methods, the previous state-of-the-art.