nils-herrmann/clean_scirepeval huggingface dataset:
3D Human pose estimation: A review of the literature and analysis of covariates

Estimating the pose of a human in 3D given an image or a video has recently received significant attention from the scientific community. The main reasons for this trend are the ever increasing new range of applications (e.g., humanrobot interaction, gaming, sports performance analysis) which are driven by current technological advances. Although recent approaches have dealt with several challenges and have reported remarkable results, 3D pose estimation remains a largely unsolved problem because real-life applications impose several challenges which are not fully addressed by existing methods. For example, estimating the 3D pose of multiple people in an outdoor environment remains a largely unsolved problem. In this paper, we review the recent advances in 3D human pose estimation from RGB images or image sequences. We propose a taxonomy of the approaches based on the input (e.g., single image or video, monocular or multi-view) and in each case we categorize the methods according to their key characteristics. To provide an overview of the current capabilities, we conducted an extensive experimental evaluation of state-of-the-art approaches in a synthetic dataset created specifically for this task, which along with its ground truth is made publicly available for research purposes. Finally, we provide an in-depth discussion of the insights obtained from reviewing the literature and the results of our experiments. Future directions and challenges are identified.
Robust fitting of multiple structures: The statistical learning approach

We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. The Mercer kernel permits the application of well-grounded statistical learning methods, among which nonlinear dimensionality reduction, principal component analysis and spectral clustering are applied for robust fitting. Our method can remove gross outliers and in parallel discover the multiple structures present. It functions well under severe outliers (more than 90% of the data) and considerable inlier noise without requiring elaborate manual tuning or unrealistic prior information. Experiments on synthetic and real problems illustrate the superiority of the proposed idea over previous methods.
Coca-Cola Enterprises Optimizes Vehicle Routes for Efficient Product Delivery

In 2004 and 2005, Coca-Cola Enterprises (CCE)—the world’s largest bottler and distributor of Coca-Cola products—implemented ORTEC’s vehicle-routing software. Today, over 300 CCE dispatchers use this software daily to plan the routes of approximately 10,000 trucks. In addition to handling nonstandard constraints, the implementation is notable for its progressive transition from the prior business practice. CCE has realized an annual cost saving of $45 million and major improvements in customer service. This approach has been so successful that Coca-Cola has extended it beyond CCE to other Coca-Cola bottling companies and beer distributors.
Designing electrical contacts to MoS2 monolayers: a computational study.

Studying the reason why single-layer molybdenum disulfide (MoS2) appears to fall short of its promising potential in flexible nanoelectronics, we find that the nature of contacts plays a more important role than the semiconductor itself. In order to understand the nature of MoS2/metal contacts, we perform ab initio density functional theory calculations for the geometry, bonding, and electronic structure of the contact region. We find that the most common contact metal (Au) is rather inefficient for electron injection into single-layer MoS2 and propose Ti as a representative example of suitable alternative electrode materials.
Augmented reality in the smart factory: Supporting workers in an industry 4.0. environment

We present an augmented reality system that supports human workers in a rapidly changing production environment. By providing spatially registered information on the task directly in the user's field of view the system can guide the user through unfamiliar tasks (e.g. assembly of new products) and visualize information directly in the spatial context were it is relevant. In the first version we present the user with picking and assembly instructions in an assembly application. In this paper we present the initial experience with this system, which has already been used successfully by several hundred users who had no previous experience in the assembly task.
Image ranking and retrieval based on multi-attribute queries

We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.
Probabilistic Novelty Detection With Support Vector Machines

Novelty detection, or one-class classification, is of particular use in the analysis of high-integrity systems, in which examples of failure are rare in comparison with the number of examples of stable behaviour, such that a conventional multi-class classification approach cannot be taken. Support Vector Machines (SVMs) are a popular means of performing novelty detection, and it is conventional practice to use a train-validate-test approach, often involving cross-validation, to train the one-class SVM, and then select appropriate values for its parameters. An alternative method, used with multi-class SVMs, is to calibrate the SVM output into conditional class probabilities. A probabilistic approach offers many advantages over the conventional method, including the facility to select automatically a probabilistic novelty threshold. The contributions of this paper are (i) the development of a probabilistic calibration technique for one-class SVMs, such that on-line novelty detection may be performed in a probabilistic manner; and (ii) the demonstration of the advantages of the proposed method (in comparison to the conventional one-class SVM methodology) using case studies, in which one-class probabilistic SVMs are used to perform condition monitoring of a high-integrity industrial combustion plant, and in detecting deterioration in patient physiological condition during patient vital-sign monitoring.
Web of Things: Description, Discovery and Integration

The vision for the "Web of Things" (WoT) aims at bringing physical objects of the world into the World Wide Web. The Web is constantly evolving and has changed over the last couple of decades and the changes have spurted new areas of growth. The primary focus of the WoT is to bridge the gap between physical and digital worlds over a common and widely used platform, which is the Web. Everyday physical "things", which are not Web-enabled, and have limited or zero computing capability, can be accommodated within the Web. As a step towards this direction, this work focuses on the specification of a thing, its descriptors and functions that could participate in the process of its discovery and operations. Besides, in this model for the WoT, we also propose a semantic Web-based architecture to integrate these things as Web resources to further demystify the realization of the WoT vision.
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem

We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. We introduce a tight asymptotic regret lower bound that is based on the information divergence. An algorithm that is inspired by the Deterministic Minimum Empirical Divergence algorithm (Honda and Takemura, 2010) is proposed, and its regret is analyzed. The proposed algorithm is found to be the first one with a regret upper bound that matches the lower bound. Experimental comparisons of dueling bandit algorithms show that the proposed algorithm significantly outperforms existing ones.
Fixed-Length Lossy Compression in the Finite Blocklength Regime

This paper studies the minimum achievable source coding rate as a function of blocklength <i>n</i> and probability ϵ that the distortion exceeds a given level <i>d</i> . Tight general achievability and converse bounds are derived that hold at arbitrary fixed blocklength. For stationary memoryless sources with separable distortion, the minimum rate achievable is shown to be closely approximated by <i>R</i>(<i>d</i>) + √<i>V</i>(<i>d</i>)/(<i>n</i>) <i>Q</i><sup>-1</sup>(ϵ), where <i>R</i>(<i>d</i>) is the rate-distortion function, <i>V</i>(<i>d</i>) is the rate dispersion, a characteristic of the source which measures its stochastic variability, and <i>Q</i><sup>-1</sup>(·) is the inverse of the standard Gaussian complementary cumulative distribution function.
Suspenser: A Story Generation System for Suspense

Interactive storytelling has been receiving a growing attention from AI and game communities and a number of computational approaches have shown promises in generating stories for games. However, there has been little research on stories evoking specific cognitive and affective responses. The goal of the work we describe here is to develop a system that produces a narrative designed specifically to arouse suspense from its reader. Our approach attempts to create stories that manipulate the reader's suspense level by elaborating on the story structure that can influence the reader's narrative comprehension at a specific point in her reading. Adapting theories developed by cognitive psychologists, our approach uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate the reader's suspense. In this paper, we describe our system implementation and empirical evaluations to test the efficacy of this system.
DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS

Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the classification problems. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis. In this paper we present an overview of the current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
A method for locating multiple sources from a frame of a large-aperture microphone array data without tracking

In this paper we present a new method for locating multiple sound sources using only a local segment of data from a large-aperture microphone array. The result of this work may be used directly or as an open-loop input to a tracking algorithm. The proposed method employs the proven- robust steered response power using the phase transform as a functional, agglomerative clustering, and low-cost global optimization (stochastic region contraction). We test the algorithm with five simultaneous "talkers" under very difficult conditions in a real room but using electrical speakers instead of human talkers to have a controlled experiment.
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks

A low precision deep neural network training technique for producing sparse, ternary neural networks is presented. The technique incorporates hardware implementation costs during training to achieve significant model compression for inference. Training involves three stages: network training using L2 regularization and a quantization threshold regularizer, quantization pruning, and finally retraining. Resulting networks achieve improved accuracy, reduced memory footprint and reduced computational complexity compared with conventional methods, on MNIST and CIFAR10 datasets. Our networks are up to 98% sparse and 5 & 11 times smaller than equivalent binary and ternary models, translating to significant resource and speed benefits for hardware implementations.
Learning Spectral Clustering

Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive a new cost function for spectral clustering based on a measure of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing this cost function with respect to the partition leads to a new spectral clustering algorithm. Minimizing with respect to the similarity matrix leads to an algorithm for learning the similarity matrix. We develop a tractable approximation of our cost function that is based on the power method of computing eigenvectors.
High-Efficient Circularly Polarized Magnetoelectric Dipole Antenna for 5G Applications Using Dual-Polarized Split-Ring Resonator Lens

A circularly polarized magnetoelectric dipole antenna with high efficiency based on printed ridge gap waveguide is presented. The antenna gain is improved by using a wideband lens in front of the antennas. The lens consists of three layers dual-polarized mu-near zero (MNZ) inclusions. Each layer consists of a <inline-formula> <tex-math notation="LaTeX">$3\times4$ </tex-math></inline-formula> MNZ unit cell. The measured results indicate that the magnitude of <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> is below −10 dB in the frequency range of 29.5–37 GHz. The resulting 3-dB axial ratio is over a frequency range of 32.5–35 GHz. The measured realized gain of the antenna is more than 10 dBi over a frequency band of 31–35 GHz achieving a radiation efficiency of 94% at 34 GHz.
The real time monitoring of water quality in IoT environment

In order to ensure the safe supply of the drinking water the quality needs to be monitor in real time. In this paper we present a design and development of a low cost system for real time monitoring of the water quality in IOT(internet of things).the system consist of several sensors is used to measuring physical and chemical parameters of the water. The parameters such as temperature, PH, turbidity, conductivity, dissolved oxygen of the water can be measured. The measured values from the sensors can be processed by the core controller. The raspberry PI B+ model can be used as a core controller. Finally, the sensor data can be viewed on internet using cloud computing.
A dynamic energy management scheme for multi-tier data centers

Multi-tier data centers have become a norm for hosting modern Internet applications because they provide a flexible, modular, scalable and high performance environment. However, these benefits come at a price of the economic dent incurred in powering and cooling these large hosting centers. Thus, energy efficiency has become a critical consideration in designing Internet data centers. In this paper, we propose a multifaceted approach, Hybrid, consisting of dynamic provisioning, frequency scaling and dynamic power management (DPM) schemes to reduce the energy consumption of multi-tier data centers, while meeting the Service Level Agreements (SLAs). We formulate a mathematical model of the energy and performance/SLA optimization problem followed by a queueing theory based approach to develop two heuristics for solving the optimization problem. The first heuristic dynamically provisions the optimal number of servers required in each tier. The second heuristic proactively decides the CPU speed and the duration of sleep states of a server to achieve further energy savings. We evaluate our heuristics using a simulator that was validated with real measurements on a prototype three-tier data center consisting of 25 servers with two multi-tier application benchmarks. Our experimental results indicate that the proposed scheme, Hybrid, can reduce the energy consumption by 50% relative to static provisioning without CPU frequency scaling and DPM. We demonstrate that Hybrid satisfies the SLAs for dynamically varying workloads. In addition, the proposed multifaceted approach is more energy efficient than the other methods such as dynamic provisioning with exploiting deep sleep states.
Image Stitching Using Structure Deformation

The aim of this paper is to achieve seamless image stitching without producing visual artifact caused by severe intensity discrepancy and structure misalignment, given that the input images are roughly aligned or globally registered. Our new approach is based on structure deformation and propagation for achieving the overall consistency in image structure and intensity. The new stitching algorithm, which has found applications in image compositing, image blending, and intensity correction, consists of the following main processes. Depending on the compatibility and distinctiveness of the 2D features detected in the image plane, single or double optimal partitions are computed subject to the constraints of intensity coherence and structure continuity. Afterwards, specific 1D features are detected along the computed optimal partitions from which a set of sparse deformation vectors is derived to encode 1D feature matching between the partitions. These sparse deformation cues are robustly propagated into the input images by solving the associated minimization problem in gradient domain, thus providing a uniform framework for the simultaneous alignment of image structure and intensity. We present results in general image compositing and blending in order to show the effectiveness of our method in producing seamless stitching results from complex input images.
Origami Robot: A Self-Folding Paper Robot With an Electrothermal Actuator Created by Printing

A piece of paper has many useful characteristics; it is affordable, lightweight, thin, strong, and highly absorbent. These features allow inexpensive and flexible devices to be fabricated easily and rapidly. We have proposed a new field, “paper mechatronics,” which merges printed robotics and paper electronics, and to realize electronic and mechanical systems by printing. Herein, we develop a method to print an actuator and a structure on a sheet of paper. A trilayer electrothermal actuator is printed to activate a printed robot. The paper self-folds along the printed pattern to form the three-dimensional (3-D) structure of the robot body. We also investigate important factors necessary to develop a printed robot. Experiments, including finite element analysis (FEA), confirm our bimetal modeling assumption for the printed actuator and improve the locomotive ability. The key factors in self-folding are paper thickness and humidity. Our findings can improve the reliability of printed robot designs. A self-folding A7-sized paper robot demonstrates locomotion at 10 mm per step.
PhaseMax: Convex Phase Retrieval via Basis Pursuit

We consider the recovery of a (real- or complex-valued) signal from magnitude-only measurements, known as phase retrieval. We formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex methods that use semidefinite relaxation and lift the phase retrieval problem to a higher dimension, PhaseMax is a “non-lifting” relaxation that operates in the original signal dimension. We show that the dual problem to PhaseMax is basis pursuit, which implies that the phase retrieval can be performed using algorithms initially designed for sparse signal recovery. We develop sharp lower bounds on the success probability of PhaseMax for a broad range of random measurement ensembles, and we analyze the impact of measurement noise on the solution accuracy. We use numerical results to demonstrate the accuracy of our recovery guarantees, and we showcase the efficacy and limits of PhaseMax in practice.
Beat the Machine: Challenging Workers to Find the Unknown Unknowns

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare-but-important errors—most importantly, rare cases for which the model is confident of its prediction (but wrong). In this paper we present a system that, in a game-like setting, asks humans to identify cases that will cause the predictivemodel-based system to fail. Such techniques are valuable in discovering problematic cases that do not reveal themselves during the normal operation of the system, and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle, by providing a reward proportional to the magnitude of the predictive model’s error. The humans are asked to “Beat the Machine” and find cases where the automatic model (“the Machine”) is wrong. Experiments show that the humans using Beat the Machine identify more errors than traditional techniques for discovering errors in from predictive models, and indeed, they identify many more errors where the machine is confident it is correct. Further, the cases the humans identify seem to be not simply outliers, but coherent areas missed completely by the model. Beat the machine identifies the “unknown unknowns.”
Lemmatisation as a Tagging Task

We present a novel approach to the task of word lemmatisation. We formalise lemmatisation as a category tagging task, by describing how a word-to-lemma transformation rule can be encoded in a single label and how a set of such labels can be inferred for a specific language. In this way, a lemmatisation system can be trained and tested using any supervised tagging model. In contrast to previous approaches, the proposed technique allows us to easily integrate relevant contextual information. We test our approach on eight languages reaching a new state-of-the-art level for the lemmatisation task.
Density-Based 3D Shape Descriptors

We propose a novel probabilistic framework for the extraction of density-based 3D shape descriptors using kernel density estimation. Our descriptors are derived from the probability density functions (pdf) of local surface features characterizing the 3D object geometry. Assuming that the shape of the 3D object is represented as a mesh consisting of triangles with arbitrary size and shape, we provide efficient means to approximate the moments of geometric features on a triangle basis. Our framework produces a number of 3D shape descriptors that prove to be quite discriminative in retrieval applications. We test our descriptors and compare them with several other histogram-based methods on two 3D model databases, Princeton Shape Benchmark and Sculpteur, which are fundamentally different in semantic content and mesh quality. Experimental results show that our methodology not only improves the performance of existing descriptors, but also provides a rigorous framework to advance and to test new ones.