nils-herrmann/clean_scirepeval huggingface dataset:
On-line Boosting and Vision

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.
Velocity and acceleration estimation for optical incremental encoders q

Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%.
Deep learning with Elastic Averaging SGD

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.
A Statistical Approach to Mechanized Encoding and Searching of Literary Information

Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.
Analysis and reduction of quadrature errors in the material point method ( MPM )

The material point method (MPM) has demonstrated itself as a computationally effective particle method for solving solid mechanics problems involving large deformations and/or fragmentation of structures, which are sometimes problematic for finite element methods (FEMs). However, similar to most methods that employ mixed Lagrangian (particle) and Eulerian strategies, analysis of the method is not straightforward. The lack of an analysis framework for MPM, as is found in FEMs, makes it challenging to explain anomalies found in its employment and makes it difficult to propose methodology improvements with predictable outcomes. In this paper we present an analysis of the quadrature errors found in the computation of (material) internal force in MPM and use this analysis to direct proposed improvements. In particular, we demonstrate that lack of regularity in the grid functions used for representing the solution to the equations of motion can hamper spatial convergence of the method. We propose the use of a quadratic B-spline basis for representing solutions on the grid, and we demonstrate computationally and explain theoretically why such a small change can have a significant impact on the reduction in the internal force quadrature error (and corresponding ‘grid crossing error’) often experienced when using MPM.
Sensor-Based Mobile Web Cross-Site Input Inference Attacks and Defenses

In this paper, we investigate the accelerometer and gyroscope motion sensor-based cross-site input inference attacks that may compromise the security of many mobile Web users, and quantify the extent to which they can be effective. We formulate our attacks as a typical multi-class classification problem and build an inference framework that trains a classifier in the training phase and predicts the user’s new inputs in the attacking phase. To make our attacks effective and realistic, we design unique techniques and address major data quality and data segmentation challenges. We intensively evaluate the effectiveness of our attacks using 98 691 keystrokes collected from 20 participants. Overall, our attacks are effective, for example, they are about 10.8 times more effective than the random guessing attacks regarding inferring letters. We also perform experiments to evaluate the effect of using the data perturbation defense techniques on decreasing the accuracy of our input inference attacks. Our results demonstrate that researchers, smartphone vendors, and app developers should pay serious attention to the motion sensor-based cross-site input inference attacks that can be pervasively performed, and start to design and deploy effective defense techniques.
Unsupervised Feature Selection Using Feature Similarity

ÐIn this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
Tracking control with adaption of kites

A novel tracking paradigm for flying geometric trajectories using tethered kites is presented. It is shown how the differential-geometric notion of turning angle can be used as a one-dimensional representation of the kite trajectory, and how this leads to a single-input single-output (SISO) tracking problem. Based on this principle a Lyapunov-based nonlinear adaptive controller is developed that only needs control derivatives of the kite aerodynamic model. The resulting controller is validated using simulations with a point-mass kite model.
Concept-Based Information Retrieval Using Explicit Semantic Analysis

Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.
Adversarially Robust Generalization Requires More Data

Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high “standard” accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of “standard” learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.
Smart Shelfie -- Internet of Shelves: For Higher On-Shelf Availability

Out of stock is a great concern for Consumer Packaged Goods (CPG) and Retail organizations . They lose billions of dollars every year. If items of interest are not available on the shelves, 31% customers buy from somewhere else and 26% buy a different brand . This paper describes how the racks / shelves at a retail store can be made smarter such that they can raise alerts to replenish themselves. Smart alerts to workers in retail shop can avoid manual checks and alerts to CPG companies can expedite replenishment if retailers do not maintain the right quantity or replenishes within Service Level Agreement (SLA). While load sensors have been the most common practice [3][4], it has not succeeded as anything else on the shelves still meant 'available'. This leaves us with a challenge to track not only the quantity but also the right brand, right variant in the right position with right visibility to shoppers on the right shelf. Internet of Things can help make this thing (shelves) smarter to required extent. We have enabled shelves to take its own selfie using a simple camera and then the image processing algorithms take over to do required analytics and raise alerts with the right person. This solution scores higher over other hardware and capital intensive IoT solutions being worked upon. The paper describes in detail about the work done by our team and the impressive results achieved. The solution can be implemented using a commonly available device like a smartphone camera or even the camera in retail shops can be put to use for real time or more frequent checks.
Identity-based encryption with efficient revocation

Identity-based encryption (IBE) is an exciting alternative to public-key encryption, as IBE eliminates the need for a Public Key Infrastructure (PKI). The senders using an IBE do not need to look up the public keys and the corresponding certificates of the receivers, the identities (e.g. emails or IP addresses) of the latter are sufficient to encrypt. Any setting, PKI- or identity-based, must provide a means to revoke users from the system. Efficient revocation is a well-studied problem in the traditional PKI setting. However in the setting of IBE, there has been little work on studying the revocation mechanisms. The most practical solution requires the senders to also use time periods when encrypting, and all the receivers (regardless of whether their keys have been compromised or not) to update their private keys regularly by contacting the trusted authority. We note that this solution does not scale well -- as the number of users increases, the work on key updates becomes a bottleneck. We propose an IBE scheme that significantly improves key-update efficiency on the side of the trusted party (from linear to logarithmic in the number of users), while staying efficient for the users. Our scheme builds on the ideas of the Fuzzy IBE primitive and binary tree data structure, and is provably secure.
A distributed Newton method for Network Utility Maximization

Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of convergence properties. This work develops an alternative distributed Newton-type fast converging algorithm for solving network utility maximization problems with self-concordant utility functions. By using novel matrix splitting techniques, both primal and dual updates for the Newton step can be computed using iterative schemes in a decentralized manner with limited scalar information exchange. Similarly, the stepsize can be obtained via an iterative consensus-based averaging scheme. We show that even when the Newton direction and the stepsize in our method are computed within some error (due to finite truncation of the iterative schemes), the resulting objective function value still converges superlinearly to an explicitly characterized error neighborhood. Simulation results demonstrate significant convergence rate improvement of our algorithm relative to the existing subgradient methods based on dual decomposition.
A survey on concept drift adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Discrete Cosserat approach for soft robot dynamics: A new piece-wise constant strain model with torsion and shears

Modeling and control of soft robots is an up-to-date and exciting area of research which has been tackled with complementary approaches so far. In this paper, we modify the existing continuum Cosserat approach optimizing it for soft robot arms which can be discretized in a finite number of sections and degrees of freedom. The resulting new piece-wise constant strain model extends the existing piece-wise constant curvature model by allowing torsion and shears strains which are fundamental to cope with out-of-the-plane external forces as appearing for example during ground locomotion. A first experimental comparison has been also conducted using one fluidic actuated leg of the soft crawler FASTT.
Learning Decision Trees over Factorized Joins

This thesis investigates the problem of learning decision trees over training datasets defined as the answers of arbitrary join queries over relational databases. Existing systems do not integrate the learning and the querying components. We follow a new trend in research that does combine both. This comes with clear benefits: The flat join result, that is commonly used by state-of-the-art approaches, can have a high degree of redundancy. We instead employ a factorization of the join answer, which avoids redundancy in the representation and, most importantly, in its computation. Our approach can learn a decision tree over training data described by a query Q over a database D in O(|D|) whereas any relational approach that relies on listing the full representation of the join answer takes time O(|D|ρ(Q)). Note that fhtw(Q) ≤ ρ∗(Q) and the gap between the two quantities can be as large as the number of relations in the query Q. Thus, the speed-up can be exponential. We show our implementation can be up to 2 orders of magnitude faster than a state-of-the-art implementation (MADlib) in learning regression trees for the considered dataset.
Automatic 3D face reconstruction from single images or video

This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.
IOT in agriculture

Major challenge in Agriculture is to cultivate produce in the farm and deliver it to the end consumers with the best possible price and best possible quality. Currently all over the world, it is found that around 50% for the farm produce never reach the end consumer due to wastage and suboptimal prices. This paper provides the solution to reduce the transport cost, predictability of prices on the past data analytics and the current market conditions, reduce number of middle hops and agents between the farmer and the end consumer using IOT based solution.
Interaction and outeraction: instant messaging in action

We discuss findings from an ethnographic study of instant messaging (IM) in the workplace and its implications for media theory. We describe how instant messaging supports a variety of informal communication tasks. We document the affordances of IM that support flexible, expressive communication. We describe some unexpected uses of IM that highlight aspects of communication which are not part of current media theorizing. They pertain to communicative processes people use to connect with each other and to manage communication, rather than to information exchange. We call these processes "outeraction". We discuss how outeractional aspects of communication affect media choice and patterns of media use.
Generating Legal Arguments and Predictions from Case Texts

In this paper, we present methods for automatically finding abstract, legally relevant concepts in case texts and demonstrate how they can be used to make predictions of case outcomes, given the texts as inputs.In a set of experiments to test these methods, we focus on the open question of how best to represent legal text for finding abstract concepts. We compare different ways of representing legal case texts in order to test whether adding domain knowledge and some linguistic information can improve performance.We found that replacing individual names by roles in the case texts led to better indexing, and that adding certain syntactic and semantic information, in the form of Propositional Patterns that capture a sense of "who did what", led to better prediction. Our experiments also showed that of three learning algorithms, Nearest Neighbor worked best in learning how to identify indexing concepts in texts.In these experiments, we introduced a prototype system that can reason with text cases; it analyzes a case, predicts its outcome considering other cases in the database, and explains the prediction, all starting with a textual description of the case's facts as input.
Nrityakosha: Preserving the intangible heritage of Indian classical dance

Preservation of intangible cultural heritage, such as music and dance, requires encoding of background knowledge together with digitized records of the performances. We present an ontology-based approach for designing a cultural heritage repository for that purpose. Since dance and music are recorded in multimedia format, we use Multimedia Web Ontology Language (MOWL) to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labeled set of training data and use of the ontology to automatically annotate new instances of digital heritage artifacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have demonstrated the efficacy of our approach by constructing an ontology for the cultural heritage domain of Indian classical dance, and have developed a browsing application for semantic access to the heritage collection of Indian dance videos.