nils-herrmann/clean_scirepeval huggingface dataset:
Deep Textured 3D Reconstruction of Human Bodies

Recovering textured 3D models of non-rigid human body shapes is challenging due to self-occlusions caused by complex body poses and shapes, clothing obstructions, lack of surface texture, background clutter, sparse set of cameras with non-overlapping fields of view, etc. Further, a calibration-free environment adds additional complexity to both reconstruction and texture recovery. In this paper, we propose a deep learning based solution for textured 3D reconstruction of human body shapes from a single view RGB image. This is achieved by first recovering the volumetric grid of the non-rigid human body given a single view RGB image followed by orthographic texture view synthesis using the respective depth projection of the reconstructed (volumetric) shape and input RGB image. We propose to co-learn the depth information readily available with affordable RGBD sensors (e.g., Kinect) while showing multiple views of the same object during the training phase. We show superior reconstruction performance in terms of quantitative and qualitative results, on both, publicly available datasets (by simulating the depth channel with virtual Kinect) as well as real RGBD data collected with our calibrated multi Kinect setup.
Integrating Perception and Planning for Autonomous Navigation of Urban Vehicles

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the coupling of two crucial robotic capabilities, namely perception and planning. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system
A Survey of Mobile Crowdsensing Techniques: A Critical Component for the Internet of Things

Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.
Artificial Intelligence and Pro-Social Behaviour

If artificial intelligence (AI) were achievable, what would the consequences be for human society?1 Perhaps surprisingly, the answer to this question is already at hand. We are achieving rapid and accelerating success in our quest to build AI. That very success — and the slowness with which both the academic community and the general public have come to recognise it — has shown how little we understand our own intelligence, and its role in our lives and culture. Here I attempt to address this problem of understanding, exploiting a variety of scientific evidence, including social simulation. I begin by reviewing current progress in AI, which is profound but underestimated. I suggest this lack of recognition is due to the mistaken belief that intelligence implies agency. I next examine the related question of human uniqueness: why do only we have language and extensive built culture? I use models and data to show that the propensities to use culture, share information and behave altruistically are neither unique to humans nor inexplicable to biology, but rather our uniqueness hinges on the extent of our capacities for communication and memory. Finally, I apply the impact of AI on extending our intelligence to these theories, to predict—and observe—consequences of AI on human societies and individual human lives. I make and support policy recommendations based on these predictions.
DeepV2D: Video to Depth with Differentiable Structure from Motion

We propose DeepV2D, an end-to-end differentiable deep learning architecture for predicting depth from a video sequence. We incorporate elements of classical Structure from Motion into an end-to-end trainable pipeline by designing a set of differentiable geometric modules. Our full system alternates between predicting depth and refining camera pose. We estimate depth by building a cost volume over learned features and apply a multi-scale 3D convolutional network for stereo matching. The predicted depth is then sent to the motion module which performs iterative pose updates by mapping optical flow to a camera motion update. We evaluate our proposed system on NYU, KITTI, and SUN3D datasets and show improved results over monocular baselines and deep and classical stereo reconstruction.
Semantic Facial Expression Editing using Autoencoded Flow

High-level manipulation of facial expressions in images — such as changing a smile to a neutral expression — is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.
Towards Haskell in the cloud

We present Cloud Haskell, a domain-specific language for developing programs for a distributed computing environment. Implemented as a shallow embedding in Haskell, it provides a message-passing communication model, inspired by Erlang, without introducing incompatibility with Haskell's established shared-memory concurrency. A key contribution is a method for serializing function closures for transmission across the network. Cloud Haskell has been implemented; we present example code and some preliminary performance measurements.
Dependency-Based Construction of Semantic Space Models

Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive science and natural language processing: semantic priming, synonymy detection, and word sense disambiguation. In all cases, our framework obtains results that are comparable or superior to the state of the art.
Pixel-level image fusion with simultaneous orthogonal matching pursuit

Pixel-level image fusion integrates the information from multiple images of one scene to get an informative image which is more suitable for human visual perception or further image-processing. Sparse representation is a new signal representation theory which explores the sparseness of natural signals. Comparing to the traditional multiscale transform coefficients, the sparse representation coefficients can more accurately represent the image information. Thus, this paper proposes a novel image fusion scheme using the signal sparse representation theory. Because image fusion depends on local information of source images, we conduct the sparse representation on overlapping patches instead of the whole image, where a small size of dictionary is needed. In addition, the simultaneous orthogonal matching pursuit technique is introduced to guarantee that different source images are sparsely decomposed into the same subset of dictionary bases, which is the key to image fusion. The proposed method is tested on several categories of images and compared with some popular image fusion methods. The experimental results show that the proposed method can provide superior fused image in terms of several quantitative fusion evaluation indexes.
Identifying At-Risk Students in Massive Open Online Courses

Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their successes, a major problem faced by MOOCs is low completion rates. In this paper, we explore the accurate early identification of students who are at risk of not completing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabilities of failure, enacted only for marginal students. To be effective, predicted probabilities must be both wellcalibrated and smoothed across weeks. Based on logistic regression, we propose two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms.
Feedback Control of Soft Robot Actuators via Commercial Flex Bend Sensors

Soft robotics is an emerging field that takes advantage of compliant materials and makes use of nonstandard actuators. Flexible fluid actuators (FFAs) use fluid pressure to produce high deformation of elastomeric-based structures. However, closed-loop control of such actuators is still very challenging due to the lack of robust, reliable, and inexpensive sensors that can be integrated onto highly deformable actuator structures, involving very low cost materials and manufacturing. This paper presents a systematic approach to implement the feedback control of FFA-based soft robotic bending modules by using commercial flex bend sensors. A flex bend sensor detects the module curvature in one direction, and its response is processed by an on board microcontroller and sent to the central control system. Such sensor integration enables the closed-loop control of modular robotic architectures, often used in soft robotics. Once integrated with the soft module, the sensor response was calibrated by the use of a ground truth electro-magnetic tracking system in order to characterize its behavior when combined with the relative FFA. A feedback control using a low-pass filter and a proportional-integral controller was designed and used to evaluate the dynamic response and the position accuracy of the integrated module. With such closed-loop control, the module tip is positioned with less than 1 mm accuracy, which can be considered a relevant result in the soft robotics field.
Detecting Student Misuse of Intelligent Tutoring Systems

Recent research has indicated that misuse of intelligent tutoring software is correlated with substantially lower learning. Students who frequently engage in behavior termed “gaming the system” (behavior aimed at obtaining correct answers and advancing within the tutoring curriculum by systematically taking advantage of regularities in the software’s feedback and help) learn only 2/3 as much as similar students who do not engage in such behaviors. We present a machine-learned Latent Response Model that can identify if a student is gaming the system in a way that leads to poor learning. We believe this model will be useful both for re-designing tutors to respond appropriately to gaming, and for understanding the phenomenon of gaming better.
Towards Efficient Cryptographic Group Access Control Systems

Encrypted cloud storage systems allow end-users to cryptographically protect their data locally before sending it to the storage spaces. Therefore, groups of users performing collaborative operations, such as data sharing, need to rely on cryptographic mechanisms as well. This PhD research proposal describes a wide spectrum of research challenges and hypothesis within the context of cryptographic group sharing systems, such as zero-knowledge membership operations, the scalability and anonymity of group access control, the scalability of active revocation, and a multi-admin approach to access control.
Evidence of phytohormones and phenolic acids variability in garden-waste-derived vermicompost leachate, a well-known plant growth stimulant

Cytokinins, auxins, abscisic acid, gibberellins (GAs) and brassinosteroids (BRs) as well as the phenolic acid content in three batches of vermicompost leachate (VCL) were quantified using ultra high performance liquid chromatography–tandem mass spectrometry. N 6-isopentenyladenine formed the major (60 %) proportion of the CK content while dihydrozeatin had the lowest (<0.02 %) concentration. Indole-3-acetic acid ranged from approximately 0.55–0.77 pmol/mL. A total of 18 GAs including bioactive forms and metabolic end products were observed in the VCL samples. Cathasterone had the highest (2,500–3,200 fg/mL) concentration while brassinolide was the lowest (1–5 fg/mL) abundant BRs found. Phenolic acids quantified were protocatechuic acid (3–3.6 µg/mL), p-hydroxybenzoic acid (2.5–2.8 µg/mL), p-coumaric acid (1–1.7 µg/mL) and ferulic acid (0–4 µg/mL). These results provide an indication of the rich diversity in natural PGRs and phytochemicals in VCL which may inevitably contribute to the numerous favorable physiological responses elicited by VCL application to plants.
Application of phylogenetic networks in evolutionary studies.

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.
The Improvisation Effect: A Case Study of User Improvisation and Its Effects on Information System Evolution

Few studies have examined interactions between IT change and organizational change during information systems evolution (ISE). We propose a dynamic model of ISE where change dynamics are captured in four dimensions: planned change, improvised change, organizational change and IT change. This inductively-generated model yields a rich account of ISE and its drivers by integrating the four change dimensions. The model shows how incremental adjustments in IT and organizational processes often grow into a profound change as users improvise. We demonstrate the value of the dynamic model by illustrating ISE processes in the context of two manufacturing organizations implementing the same system over a study period of five years. This paper makes its contribution by holistically characterizing improvisation in the context of IT and organizational change. Our ISE model moves research in organizational and IT change towards a common framing by showing how each affects the other’s form, function and evolution.
Empirical research through design

This paper describes the empirical research through design method (ERDM), which differs from current approaches to research through design by enforcing the need for the designer, after a series of pilot prototype based studies, to a-priori develop a number of testable interaction design hypothesis which are then embedded in a working prototype and tested in context with target users. The approach builds on contextual enquiry methods such as context mapping leading to a verifiable working prototype while contributing to fundamental design knowledge. A case study is described to illustrate the application of the ERDM method.
Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multidomain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently Multi-Instance Multi-Label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and Multi-Label learning algorithms.
Capability Model for Open Data: An Empirical Analysis

Creating superior competitiveness is central to open data organization's survivability in the fast changing and competitive open data market. In their quest to develop and increase competitiveness and survivability, many of these organizations are moving towards developing open data capabilities. Research-based knowledge on open data capabilities and how they relate to each other remains sparse, however, with most of the open data literature focusing on social and economic value of open data, not capabilities required. By exploring the related literature on business and organizational capabilities and linking the findings to the empirical evidence collected through the survey of 49 open data organizations around the world, this study develops an open data capability model. The model emerged from our deductive research process improves both theoretical and practical understanding of open data capabilities and their relationships required to help increase competitiveness and survivability of these types of organizations.
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).
Swarm Intelligence Approaches for Grid Load Balancing

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.