nils-herrmann/clean_scirepeval huggingface dataset:
SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention

Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.
Bachelor Degree Project Hierarchical Temporal Memory Software Agent

Artificial general intelligence is not well defined, but attempts such as the recent list of “Ingredients for building machines that think and learn like humans” are a starting point for building a system considered as such . Numenta is attempting to lead the new era of machine intelligence with their research to re-engineer principles of the neocortex. It is to be explored how the ingredients are in line with the design principles of their algorithms. Inspired by Deep Minds commentary about an autonomyingredient, this project created a combination of Numentas Hierarchical Temporal Memory theory and Temporal Difference learning to solve simple tasks defined in a browser environment. An open source software, based on Numentas intelligent computing platform NUPIC and Open AIs framework Universe, was developed to allow further research of HTM based agents on customized browser tasks. The analysis and evaluation of the results show that the agent is capable of learning simple tasks and there is potential for generalization inherent to sparse representations. However, they also reveal the infancy of the algorithms, not capable of learning dynamic complex problems, and that much future research is needed to explore if they can create scalable solutions towards a more general intelligent system.
Click Through Rate Prediction for Contextual Advertisment Using Linear Regression

This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection. Click Through Rate(CTR), Contextual Advertisements, Machine Learning, Web advertisements,
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief (Dean et al., 2012) implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art and achieve 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. Our evaluations further indicate that at specific operating thresholds, the performance of the proposed system is comparable to that of human operators. To date, our system has helped us extract close to 100 million physical street numbers from Street View imagery worldwide.
Recovering human body configurations: combining segmentation and recognition

The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.
Multivariate neural biomarkers of emotional states are categorically distinct.

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories.
3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder

We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points&#x2013;local subsets of points&#x2013;and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders.  This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise, and missing data are shown.
An Overview of the Developmental Process for the Modular Prosthetic Limb

207 their natural limb. In Phase 1, the leveraging of COTS products coupled with prototypical technology development was demonstrated in the 7-degree-of-freedom (DOF) Prototype 1 system, which was fully patient tested in a clinical environment using noninvasive neural integration strategies. Prototype 2 improved upon this technological foundation to solidify subsystem design approaches and prove the viability of a highly dexterous (22+ DOF) system. At the system level, Phase 1 required the identification of advanced scientific research and engineering concepts to identify viable integration pathways to meet Phase 2 objectives. The ultimate deliverable for Phase 1 was a Systems Integration Plan to address the following topics: engineering development road maps, additional INTRODUCTION In 2005, the Defense Advanced Research Projects Agency (DARPA) started the Revolutionizing Prosthetics program in an effort to restore natural limb function to war fighters who had suffered limb amputations in the line of duty. Because prosthetic technology at the time was still rooted in concepts generated decades ago, the potential advancement in restorative function that advanced prosthetic devices could provide would be revolutionary. After two phases of development, DARPA envisioned by the end of 2009 a fully functional and neurally integrated prosthetic device that could mimic the natural function of the human limb to the extent that current technology would allow. DARPA charged that the device should look, feel, weigh, perform, and seamlessly integrate with the human user as if it were he development of the Modular Prosthetic Limb (MPL) has and continues to be the result of cutting-edge technology innovation in mechanical, electrical, and software design. Sound systems engineering practices have laid the foundation for the successes achieved during the Revolutionizing Prosthetics program. From the initial effort to prove, with Prototype 1, that an advanced prosthetic device is possible to the technology candidate elimination process undertaken for Prototype 2, the design methodology included extensive analysis of user requirements, system trade studies, and testing to engineer the MPL to meet challenging sponsor needs. To show how this remarkable technology came to be, we describe in detail the MPL development process. An Overview of the Developmental Process for the Modular Prosthetic Limb
Learning MultiGoal Inverse Kinematics in Humanoid Robot

General Inverse Kinematic (IK) solvers may not guarantee real-time control of the end-effectors in external coordinates along with maintaining stability. This work addresses this problem by using Reinforcement Learning (RL) for learning an inverse kinematics solver for reachability tasks which ensures stability and self-collision avoidance while solving for end effectors. We propose an actor-critic based algorithm to learn joint space trajectories of stable configuration for solving inverse kinematics that can operate over continuous action spaces. Our approach is based on the idea of exploring the entire workspace and learning the best possible configurations. The proposed strategy was evaluated on the highly articulated upper body of a 27 degrees of freedom (DoF) humanoid for learning multi-goal reachability tasks of both hands along with maintaining stability in double support phase. We show that the trained model was able to solve inverse kinematics for both the hands, where the articulated torso contributed to both the tasks.
Sensing Visual Attention by Sequential Patterns

A method for sensing human visual attention is proposed. The method is based on the analysis of sequential image patterns of faces and irises observed at regular time intervals. The basic concept is to represent the set of image patterns produced by the action of gazing at a certain area as a nonlinear subspace in a high-dimensional pattern vector space. Such a space is called an attention subspace. In this framework, an input subspace from an unknown action is classified into an attention subspace of gazing at a certain area or into attention subspaces of gazing at other areas (named non-attention subspaces) by measuring the canonical angles between the input subspace and pre-computed dictionary subspaces. To maintain performance even in the presence of head movement, two mechanisms are introduced: 1) the kernel orthogonal mutual subspace method, which is suitable for classifying sets of multiple images, and 2) a kernel function for considering the head position in addition to a kernel function for pixel values. The stable performance of the proposed method including situations with head movements is demonstrated through experiments.
Complex Event Processing

Complex Event Processing (CEP) has evolved into the paradigm of choice for the development of monitoring and reactive applications. It also has a strong impact on future information systems and the way we subscribe to and consume information. Besides being a highly active research field, CEP already plays an important role in many application areas like logistics, energy management, finance, or manufacturing processes. Its importance for information systems is expected to grow further with the increasing number of decentralized information sources, such as blogs, and with the deployment of tagging and sensing technology as well as its integration in real-world objects. CEP addresses two crucial prerequisites to build highly scalable and dynamic systems. First, it decouples providers and receivers of information. Neither the providers need knowledge about the set of relevant receivers, nor do receivers need to know the set of relevant data or event sources. Second, CEP-systems do not only mediate information in form of events between providers and consumers , but support the detection of relationships among events, for instance, temporal relations that can be specified by definition of correlation rules (often called Event Patterns). Through aggregation and composition new complex events can be generated and used subsequently to derive more abstract events. CEP provides a natural de-coupling between basic events with a strong relationship to the semantics of the underlying technology (e. g. sensor readings) and complex events closer to the semantics of the application. Therefore, it enables information systems to perform independent reconfigurations at the technical and application level. Furthermore, the stepwise correlation of events can help to reduce the message load and thus contributes towards a highly scalable information system. Business applications are increasingly interconnected and can impose a massive event load to be processed by current CEP systems. Moreover, the importance of sensing devices in applications is expected to grow. In the future, the Internet of Things may comprise billions of sensing devices. CEP will be a tool to derive understandable information on the basis of a large number of events. In this context it is important to support the distribution of event correlation in the presence of highly dynamic systems and support mechanisms for self-organization. Guaranteeing non-functional properties, such as, reliability , availability, performance, and security pose major challenges on the technical infrastructure, while expres-siveness of event languages, event derivation and usability are challenges to make CEP accessible to a broader user community. …
Cyberguide: A mobile context-aware tour guide

Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to o er greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of di erent hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.
Producing sustainable competitive advantage through the effective management of people *

Executive Overview Achieving competitive success through people involves fundamentally altering how we think about the workforce and the employment relationship. It means achieving success by working with people, not by replacing them or limiting the scope of their activities. It entails seeing the workforce as a source of strategic advantage, not just as a cost to be minimized or avoided. Firms that take this different perspective are often able to successfully outmaneuver and outperform their rivals.
Undefined By Data: A Survey of Big Data Definitions

The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.
Hierarchical Disentangled Representations

Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.
Hotspot localization and prediction in wireless cellular networks via spatial traffic fitting

With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.
PRISM-games: A Model Checker for Stochastic Multi-Player Games

We present PRISM-games, a model checker for stochastic multi-player games, which supports modelling, automated verification and strategy synthesis for probabilistic systems with competitive or cooperative behaviour. Models are described in a probabilistic extension of the Reactive Modules language and properties are expressed using rPATL, which extends the well-known logic ATL with operators to reason about probabilities, various reward-based measures, quantitative properties and precise bounds. The tool is based on the probabilistic model checker PRISM, benefiting from its existing user interface and simulator, whilst adding novel model checking algorithms for stochastic games, as well as functionality to synthesise optimal player strategies, explore or export them, and verify other properties under the specified strategy.
Multi-view Scene Flow Estimation: A View Centered Variational Approach

We present a novel method for recovering the 3D structure and scene flow from calibrated multi-view sequences. We propose a 3D point cloud parametrization of the 3D structure and scene flow that allows us to directly estimate the desired unknowns. A unified global energy functional is proposed to incorporate the information from the available sequences and simultaneously recover both depth and scene flow. The functional enforces multi-view geometric consistency and imposes brightness constancy and piecewise smoothness assumptions directly on the 3D unknowns. It inherently handles the challenges of discontinuities, occlusions, and large displacements. The main contribution of this work is the fusion of a 3D representation and an advanced variational framework that directly uses the available multi-view information. This formulation allows us to advantageously bind the 3D unknowns in time and space. Different from optical flow and disparity, the proposed method results in a nonlinear mapping between the images’ coordinates, thus giving rise to additional challenges in the optimization process. Our experiments on real and synthetic data demonstrate that the proposed method successfully recovers the 3D structure and scene flow despite the complicated nonconvex optimization problem.