nils-herrmann/clean_scirepeval huggingface dataset:
Clustering stock price time series data to generate stock trading recommendations: An empirical study

Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user’s job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.
Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, we first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. An example of a CCQSP statement is “go to A through B within 30 minutes, with less than 0.001% probability of failure.” We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to enable CCQSP planning, we develop the following two capabilities in this paper: 1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a continuous domain with temporal constraints. The first capability is to ensures that the probability of failure is bounded. The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning. We demonstrate the capabilities of the p-Sulu Planner by simulations on two real-world scenarios: the path planning and scheduling of a personal aerial vehicle as well as the space rendezvous of an autonomous cargo spacecraft.
SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities

SigTur/E-Destination is a Web-based system that provides personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labeled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. The system has been fully designed and implemented in the Science and Technology Park of Tourism and Leisure. The paper presents a numerical evaluation of the correlation between the recommendations and the user’s motivations, and a qualitative evaluation performed by end users. & 2012 Elsevier Ltd. All rights reserved.
Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells

We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T -cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immunotherapy cancer treatment. Segmenting individual touching cells in cluttered regions is challenging as the feature distribution on shared borders and cell foreground are similar thus difficulting discriminating pixels into proper classes. We present two novel weight maps applied to the weighted cross entropy loss function which take into account both class imbalance and cell geometry. Binary ground truth training data is augmented so the learning model can handle not only foreground and background but also a third touching class. This framework allows training using U - N et. Experiments with our formulations have shown superior results when compared to other similar schemes, outperforming binary class models with significant improvement of boundary adequacy and instance detection. We validate our results on manually annotated microscope images of T -cells.
When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks

Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to traffic monitoring, route planning, ridesharing, taxi/Uber dispatching, etc. However, it is a very challenging problem, affected by diverse complex factors, including spatial correlations, temporal dependencies, external conditions (e.g. weather, traffic lights). Prior work usually focuses on estimating the travel times of individual road segments or sub-paths and then summing up these times, which leads to an inaccurate estimation because such approaches do not consider road intersections/traffic lights, and local errors may accumulate. To address these issues, we propose an end-to-end Deep learning framework for Travel Time Estimation (called DeepTTE) that estimates the travel time of the whole path directly. More specifically, we present a geo-convolution operation by integrating the geographic information into the classical convolution, capable of capturing spatial correlations. By stacking recurrent unit on the geo-convoluton layer, our DeepTTE can capture the temporal dependencies as well. A multi-task learning component is given on the top of DeepTTE, that learns to estimate the travel time of both the entire path and each local path simultaneously during the training phase. Extensive experiments on two trajectory datasets show our DeepTTE significantly outperforms the state-of-the-art methods.
Second Language Speech Learning Theory , Findings , and Problems

speech learning changes over the life span and to explain why "earlier is better" as far as learning to pronounce a second language (L2) is concerned. An assumption we make is that the phonetic systems used in the production and perception of vowels and consonants remain adaptive over the life span, and that phonetic systems reorganize in response to sounds encountered in an L2 through the addition of new phonetic categories, or through the modification of old ones. The chapter is organized in the following way. Several general hypotheses concerning the cause of foreign accent in L2 speech production are summarized in the introductory section. In the next section, a model of L2 speech learning that aims to account for age-related changes in L2 pronunciation is presented. The next three sections present summaries of empirical research dealing with the production and perception of L2 vowels, word-initial consonants, and word-final consonants. The final section discusses questions of general theoretical interest, with special attention to a featural (as opposed to a segmental) level of analysis. Although nonsegmental (Le., prosodic) dimensions are an important source of foreign accent, the present chapter focuses on phoneme-sized units of speech. Although many different languages are learned as an L2, the focus is on the acquisition of English.
Fitting B-Spline Curves to Point Clouds in the Presence of Obstacles ausgeführt am

We consider the computation of an approximating curve to fit a given set of points. Such a curve fitting is a common problem in CAGD and we review three different approaches based upon minimization of the squared distance function: the point distance, tangent distance and squared distance error term. We enhance the classic setup comprising a point cloud and an approximating B-Spline curve by obstacles a final solution must not penetrate. Two algorithms for solving the emerging constrained optimization problems are presented and used to enclose point clouds from inside as well as from outside and to avoid arbitrary smooth bounded obstacles. Moreover, approximations of guaranteed quality and shaking objects are examined within this context. In a next step, we extend our work and study the curve fitting problem on parametrized surfaces. The approximation is still performed in the two dimensional parameter space while geometric properties of the manifold enter the computation at the same time. Additionally, we use this new error term to approximate the borders of point clouds on manifolds. Finally, as a minimization of the squared distance function is sensitive to outliers, a curve fitting in the L norm based on the signed distance function is developed. We describe the necessary results of non-smooth optimization theory and apply this robust optimization to curve fitting in the presence of obstacles.
100-V 2-MHz isolated QSW-ZVS three-level DC-DC converter with on-chip dynamic dead-time controlled synchronous gate driver for eGaN power FETs

This paper presents techniques for high-voltage converters to achieve high power efficiency at high switching frequency. A quasi-square-wave zero-voltage switching isolated three-level half-bridge architecture is proposed to minimize the converter switching loss under high-voltage high-frequency conditions. A synchronous three-level gate driver with dynamic dead-time control is also developed to ensure reliability of all eGaN power FETs, automatically generate appropriate dead-time for all power FETs to achieve ZVS with minimal reverse bias behavior, and provide fast propagation delays for high-frequency converter operation. Implemented in a 0.5-μm HV CMOS process, the proposed gate driver achieves 15ns propagation delays and enables a 100V 35W isolated three-level half-bridge converter to achieve the peak power efficiencies of 95.2% and 90.7% at 1MHz and 2MHz, respectively.
THE POSITIVE FALSE DISCOVERY RATE: A BAYESIAN INTERPRETATION AND THE q-VALUE1

Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all significant hypotheses. The FDR is especially appropriate for exploratory analyses in which one is interested in finding several significant results among many tests. In this work, we introduce a modified version of the FDR called the “positive false discovery rate” (pFDR). We discuss the advantages and disadvantages of the pFDR and investigate its statistical properties. When assuming the test statistics follow a mixture distribution, we show that the pFDR can be written as a Bayesian posterior probability and can be connected to classification theory. These properties remain asymptotically true under fairly general conditions, even under certain forms of dependence. Also, a new quantity called the “q-value” is introduced and investigated, which is a natural “Bayesian posterior p-value,” or rather the pFDR analogue of the p-value.
A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers

We present here a new InSAR persistent scatterer (PS) method for analyzing episodic crustal deformation in non-urban environments, with application to volcanic settings. Our method for identifying PS pixels in a series of interferograms is based primarily on phase characteristics and finds low-amplitude pixels with phase stability that are not identified by the existing amplitude-based algorithm. Our method also uses the spatial correlation of the phases rather than a well-defined phase history so that we can observe temporally-variable processes, e.g., volcanic deformation. The algorithm involves removing the residual topographic component of flattened interferogram phase for each PS, then unwrapping the PS phases both spatially and temporally. Our method finds scatterers with stable phase characteristics independent of amplitudes associated with man-made objects, and is applicable to areas where conventional InSAR fails due to complete decorrelation of the majority of scatterers, yet a few stable scatterers are present.
Predicting Ego-Vehicle Paths from Environmental Observations with a Deep Neural Network

Ahstract- Advanced driver assistance systems allow for increasing user comfort and safety by sensing the environment and anticipating upcoming hazards. Often, this requires to accurately predict how situations will change. Recent approaches make simplifying assumptions on the predictive model of the Ego-Vehicle motion or assume prior knowledge, such as road topologies, to be available. However, in many urban areas this assumption is not satisfied. Furthermore, temporary changes (e.g. construction areas, vehicles parked on the street) are not considered by such models. Since many cars observe the environment with several different sensors, predictive models can benefit from them by considering environmental properties. In this work, we present an approach for an Ego-Vehicle path prediction from such sensor measurements of the static vehicle environment. Besides proposing a learned model for predicting the driver's multi-modal future path as a grid-based prediction, we derive an approach for extracting paths from it. In driver assistance systems both can be used to solve varying assistance tasks. The proposed approach is evaluated on real driving data and outperforms several baseline approaches.
Mobile learning and integration of mobile technologies in education

Mobile technologies have a huge potential to transform education provided these technologies are designed and implemented in such a way that they are relevant to the social and cultural context of learning. Clearly, the application, implementation, and design of mobile technology in the global educational context pose technological and socio-cultural challenges. Specifically, technology tools and applications that are familiar in developed nations may pose unique challenges in developing countries, so ‘one size fits all’ or ‘one technology for all contexts’ does not practically work. Therefore, the authors provide case studies focusing on the pedagogical benefits of mobile technologies when used as educational tools. Ideally, this article is intended to stimulate reflections on the subject of m-learning as well as the integration of mobile technologies in education.
Analyzing Biases in Human Perception of User Age and Gender from Text

How accurate are people in judging" traits of other users? • Are there systematic biases humans" are subject to? • What are the implications of using" human perception as a proxy for truth? • Which textual cues lead to a false " perception of the truth? • Which textual cues make people" more or less confident in their ratings? • Gender 2,607 authors, age – 826 authors • we use 100 tweets per author, 9 Mturk votes per author • URLs and mentions anonymized, English only filtered, duplicates eliminated, same 6 month time interval GENDER PERCEPTION
Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots

This study emphasizes the need for standardized measurement tools for human robot interaction (HRI). If we are to make progress in this field then we must be able to compare the results from different studies. A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety. The results have been distilled into five consistent questionnaires using semantic differential scales. We report reliability and validity indicators based on several empirical studies that used these questionnaires. It is our hope that these questionnaires can be used by robot developers to monitor their progress. Psychologists are invited to further develop the questionnaires by adding new concepts, and to conduct further validations where it appears necessary. C. Bartneck ( ) Department of Industrial Design, Eindhoven University of Technology, Den Dolech 2, 5600 Eindhoven, The Netherlands e-mail: c.bartneck@tue.nl D. Kulić Nakamura & Yamane Lab, Department of Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan e-mail: dana@ynl.t.u-tokyo.ac.jp E. Croft · S. Zoghbi Department of Mechanical Engineering, University of British Columbia, 6250 Applied Science Lane, Room 2054, Vancouver, V6T 1Z4, Canada E. Croft e-mail: ecroft@mech.ubc.ca S. Zoghbi e-mail: szoghbi@mech.ubc.ca
Black-box Adversarial Attacks with Limited Queries and Information

Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partialinformation setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.
Unsupervised extrinsic calibration of depth sensors in dynamic scenes

While inexpensive depth sensors are becoming increasingly ubiquitous, field of view and self-occlusion constraints limit the information a single sensor can provide. For many applications one may instead require a network of depth sensors, registered to a common world frame and synchronized in time. Historically such a setup has required a tedious manual calibration procedure, making it infeasible to deploy these networks in the wild, where spatial and temporal drift are common. In this work, we propose an entirely unsupervised procedure for calibrating the relative pose and time offsets of a pair of depth sensors. So doing, we make no use of an explicit calibration target, or any intentional activity on the part of a user. Rather, we use the unstructured motion of objects in the scene to find potential correspondences between the sensor pair. This yields a rough transform which is then refined with an occlusion-aware energy minimization. We compare our results against the standard checkerboard technique, and provide qualitative examples for scenes in which such a technique would be impossible.
Effect of perspective taking on the cognitive representation of persons: a merging of self and other.

Two experiments examined the possibility that perspective taking leads observers to create cognitive representation of others that substantially overlap with the observers' own self-representations. In Experiment 1 observers receiving role-taking instructions were more likely to ascribe traits to a novel target that they (observers) had earlier indicated were self-descriptive. This pattern was most pronounced, however for positively valenced traits. In Experiment 2 some participants received role-taking instructions but were also given a distracting memory task. In the absence of this task, role taking again produced greater overlap--primarily for positive traits--between self- and target representations. In the presence of the memory task, the degree of self-target overlap was significantly reduced for all traits, regardless of valence. Possible explanations for these findings are discussed.
Antenna-filter-antenna arrays as a class of bandpass frequency-selective surfaces

A method is introduced for designing bandpass frequency-selective surfaces (FSSs) using arrays of antenna-filter-antenna (AFA) modules. An AFA module is a filter with radiation ports, which is obtained by integrating two antennas and a nonradiating resonant structure in between. AFA modules are designed based on circuit models and microwave filter design techniques. Three types of these AFA modules are designed using microstrip antennas and coplanar-waveguide resonators, and are used to form FSSs with three- and four-pole shaped bandpass response at 35 GHz. FSS structures are formed by arraying these modules in a periodic grid with an optimal cell size. The proposed concept and the design method are validated using numerical simulation (finite-element method), as well as experimental results.
Boosting for Text Classification with Semantic Features

Current text classification systems typically use term stems for representing document content. Ontologies allow the usage of features on a higher semantic level than single words for text classification purposes. In this paper we propose such an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting, a successful machine learning technique is used for classification. Comparative experimental evaluations in three different settings support our approach through consistent improvement of the results. An analysis of the results shows that this improvement is due to two separate effects.
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative, we propose an Image-to-Image translation network that jointly maps the input image to a depth image and a facial correspondence map. This explicit pixel-based mapping can then be utilized to provide high quality reconstructions of diverse faces under extreme expressions, using a purely geometric refinement process. In the spirit of recent approaches, the network is trained only with synthetic data, and is then evaluated on “in-the-wild” facial images. Both qualitative and quantitative analyses demonstrate the accuracy and the robustness of our approach.