nils-herrmann/clean_scirepeval huggingface dataset:
Spectrum of Variable-Random Trees

In this paper, we show that a continuous spectrum of randomis ation exists, in which most existing tree randomisations are only operating around the tw o ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a ba se le rner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span f rom the conventional deterministic trees to the complete-random trees using a probabilistic pa rameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensemb les, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new appr o ch in building better decision tree ensembles. We name this approach Coalescence, which co ales es a number of points in the random-half of the spectrum. Coalescence acts as a committe e of “ xperts” to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchm arking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Co alescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests , we have identified conditions under which one will perform better than the other.
SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets

Time series analysis, as an application for high dimensional data mining, is a common task in biochemistry, meteorology, climate research, bio-medicine or marketing. Similarity search in data with increasing dimensionality results in an exponential growth of the search space, referred to as Curse of Dimensionality. A common approach to postpone this effect is to apply approximation to reduce the dimensionality of the original data prior to indexing. However, approximation involves loss of information, which also leads to an exponential growth of the search space. Therefore, indexing an approximation with a high dimensionality, i. e. high quality, is desirable.
 We introduce Symbolic Fourier Approximation (SFA) and the SFA trie which allows for indexing of not only large datasets but also high dimensional approximations. This is done by exploiting the trade-off between the quality of the approximation and the degeneration of the index by using a variable number of dimensions to represent each approximation. Our experiments show that SFA combined with the SFA trie can scale up to a factor of 5--10 more indexed dimensions than previous approaches. Thus, it provides lower page accesses and CPU costs by a factor of 2--25 respectively 2--11 for exact similarity search using real world and synthetic data.
Support Vector Machines for Paraphrase Identification and Corpus Construction

The lack of readily-available large corpora of aligned monolingual sentence pairs is a major obstacle to the development of Statistical Machine Translation-based paraphrase models. In this paper, we describe the use of annotated datasets and Support Vector Machines to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters found on the World Wide Web. Features include: morphological variants; WordNet synonyms and hypernyms; loglikelihood-based word pairings dynamically obtained from baseline sentence alignments; and formal string features such as word-based edit distance. Use of this technique dramatically reduces the Alignment Error Rate of the extracted corpora over heuristic methods based on position of the sentences in the text.
Robust Large-Scale Machine Learning in the Cloud

The convergence behavior of many distributed machine learning (ML) algorithms can be sensitive to the number of machines being used or to changes in the computing environment. As a result, scaling to a large number of machines can be challenging. In this paper, we describe a new scalable coordinate descent (SCD) algorithm for generalized linear models whose convergence behavior is always the same, regardless of how much SCD is scaled out and regardless of the computing environment. This makes SCD highly robust and enables it to scale to massive datasets on low-cost commodity servers. Experimental results on a real advertising dataset in Google are used to demonstrate SCD's cost effectiveness and scalability. Using Google's internal cloud, we show that SCD can provide near linear scaling using thousands of cores for 1 trillion training examples on a petabyte of compressed data. This represents 10,000x more training examples than the 'large-scale' Netflix prize dataset. We also show that SCD can learn a model for 20 billion training examples in two hours for about $10.
Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects

Part of the ventral temporal lobe is thought to be critical for face perception, but what determines this specialization remains unknown. We present evidence that expertise recruits the fusiform gyrus 'face area'. Functional magnetic resonance imaging (fMRI) was used to measure changes associated with increasing expertise in brain areas selected for their face preference. Acquisition of expertise with novel objects (greebles) led to increased activation in the right hemisphere face areas for matching of upright greebles as compared to matching inverted greebles. The same areas were also more activated in experts than in novices during passive viewing of greebles. Expertise seems to be one factor that leads to specialization in the face area.
Power electronic traction transformer technology

Combining modern high-power semiconductor devices with constantly improving magnetic materials opens up the possibility to replace bulky low frequency transformers with a new medium voltage medium frequency conversion structures. While there are still challenges to be addressed related to these so called power electronic transformers, a steadily increasing development effort is evident and considered in various contexts. Traction applications seem to be the first ones where proliferation of these new galvanic isolated power electronic converters is expected. In this particular application field, substantial weight and volume reduction could be achieved while providing additional functionality at the same time. In this paper a survey of recent R&D efforts in this field is presented.
Classification of basic daily movements using a triaxial accelerometer

A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.
PLATO: Policy learning using adaptive trajectory optimization

Policy search can in principle acquire complex strategies for control of robots and other autonomous systems. When the policy is trained to process raw sensory inputs, such as images and depth maps, it can also acquire a strategy that combines perception and control. However, effectively processing such complex inputs requires an expressive policy class, such as a large neural network. These high-dimensional policies are difficult to train, especially when learning to control safety-critical systems. We propose PLATO, a continuous, reset-free reinforcement learning algorithm that trains complex control policies with supervised learning, using model-predictive control (MPC) to generate the supervision, hence never in need of running a partially trained and potentially unsafe policy. PLATO uses an adaptive training method to modify the behavior of MPC to gradually match the learned policy in order to generate training samples at states that are likely to be visited by the learned policy. PLATO also maintains the MPC cost as an objective to avoid highly undesirable actions that would result from strictly following the learned policy before it has been fully trained. We prove that this type of adaptive MPC expert produces supervision that leads to good long-horizon performance of the resulting policy. We also empirically demonstrate that MPC can still avoid dangerous on-policy actions in unexpected situations during training. Our empirical results on a set of challenging simulated aerial vehicle tasks demonstrate that, compared to prior methods, PLATO learns faster, experiences substantially fewer catastrophic failures (crashes) during training, and often converges to a better policy.
Exact Discovery of Time Series Motifs

Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.
Convolutional RNN: An enhanced model for extracting features from sequential data

Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or hidden states of the recurrent units to compute the extracted features. By doing so, we exploit the fact that a window containing a few frames of the sequential data is a sequence itself and this additional structure might encapsulate valuable information. In addition, we allow for more steps of computation in the feature extraction process, which is potentially beneficial as an affine function followed by a non-linearity can result in too simple features. Using our convolutional recurrent layers, we obtain an improvement in performance in two audio classification tasks, compared to traditional convolutional layers.
PinDr0p: using single-ended audio features to determine call provenance

The recent diversification of telephony infrastructure allows users to communicate through landlines, mobile phones and VoIP phones. However, call metadata such as Caller-ID is either not transferred or transferred without verification across these networks, allowing attackers to maliciously alter it. In this paper, we develop PinDr0p, a mechanism to assist users in determining call provenance - the source and the path taken by a call. Our techniques detect and measure single-ended audio features to identify all of the applied voice codecs, calculate packet loss and noise profiles, while remaining agnostic to characteristics of the speaker's voice (as this may legitimately change when interacting with a large organization). In the absence of verifiable call metadata, these features in combination with machine learning allow us to determine the traversal of a call through as many as three different providers (e.g., cellular, then VoIP, then PSTN and all combinations and subsets thereof) with 91.6% accuracy. Moreover, we show that once we identify and characterize the networks traversed, we can create detailed fingerprints for a call source. Using these fingerprints we show that we are able to distinguish between calls made using specific PSTN, cellular, Vonage, Skype and other hard and soft phones from locations across the world with over 90% accuracy. In so doing, we provide a first step in accurately determining the provenance of a call.
1 Bid-Response Models for Customized Pricing

In this paper, we study pricing situations where a firm provides a price quote in the presence of uncertainty in the preferences of the buyer and the competitive landscape. We introduce two customized-pricing bid-response models used in practice, which can be developed from the historical information available to the firm based on previous bidding opportunities. We show how these models may be used to exploit the differences in the market segments to generate optimal price quotes given the characteristics of the current bid opportunity. We also describe the process of evaluating competing models using an industry dataset as a test bed to measure the model fit. Finally, we test the models on the industry dataset to compare their performance and estimate the percent improvement in expected profits that may be possible from their use.
CrowdTC: Crowdsourced Taxonomy Construction

Recently, taxonomy has attracted much attention. Both automatic construction solutions and human-based computation approaches have been proposed. The automatic methods suffer from the problem of either low precision or low recall and human computation, on the other hand, is not suitable for large scale tasks. Motivated by the shortcomings of both approaches, we present a hybrid framework, which combines the power of machine-based approaches and human computation (the crowd) to construct a more complete and accurate taxonomy. Specifically, our framework consists of two steps: we first construct a complete but noisy taxonomy automatically, then crowd is introduced to adjust the entity positions in the constructed taxonomy. However, the adjustment is challenging as the budget (money) for asking the crowd is often limited. In our work, we formulate the problem of finding the optimal adjustment as an entity selection optimization (ESO) problem, which is proved to be NP-hard. We then propose an exact algorithm and a more efficient approximation algorithm with an approximation ratio of 1/2(1-1/e). We conduct extensive experiments on real datasets, the results show that our hybrid approach largely improves the recall of the taxonomy with little impairment for precision.
Skeleton Based Shape Matching and Retrieval

In this paper, we describe a novel method for searching and comparing 3D objects. The method encodes the geometric and topological information in the form of a skeletal graph and uses graph matching techniques to match the skeletons and to compare them. The skeletal graphs can be manually annotated to refine or restructure the search. This helps in choosing between a topological similarity and a geometric (shape) similarity. A feature of skeletal matching is the ability to perform part-matching, and its inherent intuitiveness, which helps in defining the search and in visualizing the results. Also, the matching results, which are presented in a per-node basis can be used for driving a number of registration algorithms, most of which require a good initial guess to perform registration. In this paper, we also describe a visualization tool to aid in the selection and specification of the matched objects.
77 GHz automotive radar sensors: Antenna concept for angular measurements in azimuth and elevation

A fast and precise determination of the DOA (direction of arrival) for immediate object classification becomes increasingly important for future automotive radar generations. Hereby, the elevation angle of an object is considered as a key parameter especially in complex urban environments. An antenna concept allowing the determination of object angles in azimuth and elevation is proposed and discussed in this contribution. This antenna concept consisting of a linear patch array and a cylindrical dielectric lens is implemented into a radar sensor and characterized in terms of angular accuracy and ambiguities using correlation algorithms and the CRLB (Cramer Rao Lower Bound).
Unions of balls for shape approximation in robot grasping

Typical tasks of future service robots involve grasping and manipulating a large variety of objects differing in size and shape. Generating stable grasps on 3D objects is considered to be a hard problem, since many parameters such as hand kinematics, object geometry, material properties and forces have to be taken into account. This results in a high-dimensional space of possible grasps that cannot be searched exhaustively. We believe that the key to find stable grasps in an efficient manner is to use a special representation of the object geometry that can be easily analyzed. In this paper, we present a novel grasp planning method that evaluates local symmetry properties of objects to generate only candidate grasps that are likely to be of good quality. We achieve this by computing the medial axis which represents a 3D object as a union of balls. We analyze the symmetry information contained in the medial axis and use a set of heuristics to generate geometrically and kinematically reasonable candidate grasps. These candidate grasps are tested for force-closure. We present the algorithm and show experimental results on various object models using an anthropomorphic hand of a humanoid robot in simulation.
Understanding Taxi Service Strategies From Taxi GPS Traces

Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,1 which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.
Economics and Electronic Commerce: Survey and Directions for Research

This article reviews the growing body of research on electronic commerce from the perspective of economic analysis. It begins by constructing a new framework for understanding electronic commerce research, then identifies the range of applicable theory and current research in the context of the new conceptual model. It goes on to assess the state-of-the-art of knowledge about electronic commerce phenomena in terms of the levels of analysis here proposed. And finally, it charts the directions along which useful work in this area might be developed. This survey and framework are intended to induce researchers in the field of information systems, the authors’ reference discipline, and other areas in schools of business and management to recognize that research on electronic commerce is business-school research, broadly defined. As such, developments in this research area in the next several years will occur across multiple business-school disciplines, and there will be a growing impetus for greater interdisciplinary communication and interaction.
Angle of Arrival Localization for Wireless Sensor Networks

Awareness of the physical location for each node is required by many wireless sensor network applications. The discovery of the position can be realized utilizing range measurements including received signal strength, time of arrival, time difference of arrival and angle of arrival. In this paper, we focus on localization techniques based on angle of arrival information between neighbor nodes. We propose a new localization and orientation scheme that considers beacon information multiple hops away. The scheme is derived under the assumption of noisy angle measurements. We show that the proposed method achieves very good accuracy and precision despite inaccurate angle measurements and a small number of beacons
Trace-Level Reuse

Trace-level reuse is based on the observation that some traces (dynamic sequences of instructions) are frequently repeated during the execution of a program, and in many cases, the instructions that make up such traces have the same source operand values. The execution of such traces will obviously produce the same outcome and thus, their execution can be skipped if the processor records the outcome of previous executions. This paper presents an analysis of the performance potential of trace-level reuse and discusses a preliminary realistic implementation. Like instruction-level reuse, trace-level reuse can improve performance by decreasing resource contention and the latency of some instructions. However, we show that tracelevel reuse is more effective than instruction-level reuse because the former can avoid fetching the instructions of reused traces. This has two important benefits: it reduces the fetch bandwidth requirements, and it increases the effective instruction window size since these instructions do not occupy window entries. Moreover, trace-level reuse can compute all at once the result of a chain of dependent instructions, which may allow the processor to avoid the serialization caused by data dependences and thus, to potentially exceed the dataflow limit.