nils-herrmann/clean_scirepeval huggingface dataset:
Predicting stock market index using fusion of machine learning techniques

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion approach involving Support Vector Regression (SVR) in the first stage. The second stage of the fusion approach uses Artificial Neural Network (ANN), Random Forest (RF) and SVR resulting into SVR–ANN, SVR–RF and SVR–SVR fusion prediction models. The prediction performance of these hybrid models is compared with the single stage scenarios where ANN, RF and SVR are used single-handedly. Ten technical indicators are selected as the inputs to each of the prediction models.
Simulating educational physical experiments in augmented reality

We present an augmented reality application for mechanics education. It utilizes a recent physics engine developed for the PC gaming market to simulate physical experiments in the domain of mechanics in real time. Students are enabled to actively build own experiments and study them in a three-dimensional virtual world. A variety of tools are provided to analyze forces, mass, paths and other properties of objects before, during and after experiments. Innovative teaching content is presented that exploits the strengths of our immersive virtual environment. PhysicsPlayground serves as an example of how current technologies can be combined to deliver a new quality in physics education.
Program Comprehension During Software Maintenance and Evolution

Computer P rogram understanding is a major factor in providing effective software maintenance and enabling successful evolution of computer systems. For years, researchers have tried to understand how programmers comprehend programs during software maintenance and evolution. Five types of tasks are commonly associated with software maintenance and evolution: adaptive, perfective, and corrective maintenance; reuse; and code leverage. Each task type typically involves certain activities (see Table 1). Some activities, such as understanding the system or problem, are common to several tasks. To analyze the cognitive processes behind these tasks, researchers have developed several models (see Table 2). In this article, we describe common elements of six cognition models and compare them based on their scope and experimental support. All models use existing knowledge to build new knowledge about the mental model of the software that is under consideration. Many of these models are based on exploratory experiments, and some of those models have been validated. Programmers employ various strategies and use cues in code or documentation as guidance. However, their level of expertise greatly influences how efficiently they understand the code. Because of limited knowledge about specialized cognition needs for some maintenance tasks, the code cognition models do not represent every single task listed in Table 1. Most models assume that the objective is to understand all of the code rather than a particular purpose, such as debugging. While these general models can foster a complete understanding of a piece of code, they may not always apply to specialized tasks that more efficiently employ strategies geared toward partial understanding. We identify open questions, particularly considering maintenance and evolution of large-scale code. These questions relate to scalability of existing experimental results with small programs, validity and credibility of results based on experimental procedure, and challenges of data availability.
Algorithmic Game Theory

The payof matrix of the number­distance game is as folow. We know that each player is invariant to the diferent actions in her support. First we guessed that al of the actions are in supports for both players. Let x,y,z be the probability that the first players plays ­1,0,2 respectively and Let p,q,r be the probability that the second players plays ­1,0,2 respectively. For the first player we have: 0*p+1*q+3*r = 1*p+0*q+2*r = 3*p+2*q+0*r p+q+r=1
Vineyard computing: sensor networks in agricultural production

Using ethnographic research methods, the authors studied the structure of the needs and priorities of people working in a vineyard to gain a better understanding of the potential for sensor networks in agriculture. We discuss an extended study of vineyard workers and their work practices to assess the potential for sensor network systems to aid work in this environment. The major purpose is to find new directions and new topics that pervasive computing and sensor networks might address in designing technologies to support a broader range of users and activities.
A Wideband Circularly Polarized Antenna for Microwave and Millimeter-Wave Applications

A wideband circularly polarized antenna with a single feed is achieved by adjusting the shapes and dimensions of a linearly polarized magneto-electric (ME) dipole antenna. Experimentally, an antenna prototype operating at around 2 GHz exhibits a wide impedance bandwidth of 73.3% for SWR ≤ 2 and 46.6% for SWR ≤ 1.5, a 3-dB axial ratio bandwidth of 47.7% and an antenna gain of 6.8 ± 1.8 dBic. Based on the design operating at lower microwave frequencies, a millimeter-wave antenna is designed on a dielectric substrate. The antenna has a wide impedance bandwidth (SWR ≤ 2) of 56.7% covering 38.5 to 69 GHz and a 3-dB axial ratio bandwidth of 41% covering 45.8 to 69.4 GHz, over which the antenna boresight gain varies from 5 to 9.9 dBic. This validates that the design can be realized on a single-layer printed circuit board.
Cube2Video: Navigate Between Cubic Panoramas in Real-Time

Online virtual navigation systems enable users to hop from one 360° panorama to another, which belong to a sparse point-to-point collection, resulting in a less pleasant viewing experience. In this paper, we present a novel method, namely Cube2Video, to support navigating between cubic panoramas in a video-viewing mode. Our method circumvents the intrinsic challenge of cubic panoramas, i.e., the discontinuities between cube faces, in an efficient way. The proposed method extends the matching-triangulation-interpolation procedure with special considerations of the spherical domain. A triangle-to-triangle homography-based warping is developed to achieve physically plausible and visually pleasant interpolation results. The temporal smoothness of the synthesized video sequence is improved by means of a compensation transformation. As experimental results demonstrate, our method can synthesize pleasant video sequences in real time, thus mimicking walking or driving navigation.
Mapping the Intel Last-Level Cache

Modern Intel processors use an undisclosed hash function to map memory lines into last-level cache slices. In this work we develop a technique for reverse-engineering the hash function. We apply the technique to a 6-core Intel processor and demonstrate that knowledge of this hash function can facilitate cache-based side channel attacks, reducing the amount of work required for profiling the cache by three orders of magnitude. We also show how using the hash function we can double the number of colours used for page-colouring techniques.
VHDL Implementation of UART with Status Register

In parallel communication the cost as well as complexity of the system increases due to simultaneous transmission of data bits on multiple wires. Serial communication alleviates this drawback and emerges as effective candidate in many applications for long distance communication as it reduces the signal distortion because of its simple structure. This paper focuses on the VHDL implementation of UART with status register which supports asynchronous serial communication. The paper presents the architecture of UART which indicates, during reception of data, parity error, framing error, overrun error and break error using status register. The whole design is functionally verified using Xilinx ISE Simulator.
Fast head modeling for animation

This paper describes an efficient method to make individual faces for animation from several possible inputs. We present a method to reconstruct 3D facial model for animation from two orthogonal pictures taken from front and side views or from range data obtained from any available resources. It is based on extracting features on a face in a semiautomatic way and modifying a generic model with detected feature points. Then the fine modifications follow if range data is available. Automatic texture mapping is employed using a composed image from the two images. The reconstructed 3Dface can be animated immediately with given expression parameters. Several faces by one methodology applied to different input data to get a final animatable face are illustrated.
Business Process Management

Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works are severely limited, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log, based on Allen’s interval algebra, as a complete temporal representation of a log, which enables simultaneous discovery of control-flow and performance information. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we develop a framework for measuring performance fitness. Under this framework, we provide guarantees that TNR-based process discovery dominates existing techniques in measuring performance characteristics of a process. To illustrate the practical value of the TNR, we evaluate the approach against three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms.
Business intelligence success: The roles of BI capabilities and decision environments

This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing needs of the organization. Our findings suggest that technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success, regardless of the decision environment. However, the decision environment does influence the relationship between BI success and capabilities, such as the extent to which BI supports flexibility and risk in decision making.  Corresponding author.  E-mail addresses: oyku.isik@vlerick.com (Ö. Işık), mary.jones@unt.edu (M.C. Jones), anna.sidorova@unt.edu (A. Sidorova).
Metric Rectification of Curved Document Images

In this paper, we propose a metric rectification method to restore an image from a single camera-captured document image. The core idea is to construct an isometric image mesh by exploiting the geometry of page surface and camera. Our method uses a general cylindrical surface (GCS) to model the curved page shape. Under a few proper assumptions, the printed horizontal text lines are shown to be line convergent symmetric. This property is then used to constrain the estimation of various model parameters under perspective projection. We also introduce a paraperspective projection to approximate the nonlinear perspective projection. A set of close-form formulas is thus derived for the estimate of GCS directrix and document aspect ratio. Our method provides a straightforward framework for image metric rectification. It is insensitive to camera positions, viewing angles, and the shapes of document pages. To evaluate the proposed method, we implemented comprehensive experiments on both synthetic and real-captured images. The results demonstrate the efficiency of our method. We also carried out a comparative experiment on the public CBDAR2007 data set. The experimental results show that our method outperforms the state-of-the-art methods in terms of OCR accuracy and rectification errors.
#CHImoney: financial interactions, digital cash, capital exchange and mobile money

Interactions around money and financial services are a critical part of our lives on and off-line. New technologies and new ways of interacting with these technologies are of huge interest; they enable new business models and ways of making sense of this most important aspect of our everyday lives. At the same time, money is an essential element in HCI research and design. This workshop is intended to bring together researchers and practitioners involved in the design and use of systems that combine digital and new media with monetary and financial interactions to build on an understanding of these technologies and their impacts on users' behaviors. The workshop will focus on social, technical, and economic aspects around everyday user interactions with money and emerging financial technologies and systems.
Designing and Evaluating Explanations for Recommender Systems

This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.
TopicTiling: A Text Segmentation Algorithm based on LDA

This work presents a Text Segmentation algorithm called TopicTiling. This algorithm is based on the well-known TextTiling algorithm, and segments documents using the Latent Dirichlet Allocation (LDA) topic model. We show that using the mode topic ID assigned during the inference method of LDA, used to annotate unseen documents, improves performance by stabilizing the obtained topics. We show significant improvements over state of the art segmentation algorithms on two standard datasets. As an additional benefit, TopicTiling performs the segmentation in linear time and thus is computationally less expensive than other LDA-based segmentation methods.
A semisupervised learning method to merge search engine results

The proliferation of searchable text databases on local area networks and the Internet causes the problem of finding information that may be distributed among many disjoint text databases (distributed information retrieval). How to merge the results returned by selected databases is an important subproblem of the distributed information retrieval task. Previous research assumed that either resource providers cooperate to provide normalizing statistics or search clients download all retrieved documents and compute normalized scores without cooperation from resource providers.This article presents a semisupervised learning solution to the result merging problem. The key contribution is the observation that information used to create resource descriptions for resource selection can also be used to create a centralized sample database to guide the normalization of document scores returned by different databases. At retrieval time, the query is sent to the selected databases, which return database-specific document scores, and to a centralized sample database, which returns database-independent document scores. Documents that have both a database-specific score and a database-independent score serve as training data for learning to normalize the scores of other documents. An extensive set of experiments demonstrates that this method is more effective than the well-known CORI result-merging algorithm under a variety of conditions.
28 GHz Angle of Arrival and Angle of Departure Analysis for Outdoor Cellular Communications Using Steerable Beam Antennas in New York City

Propagation measurements at 28 GHz were conducted in outdoor urban environments in New York City using four different transmitter locations and 83 receiver locations with distances of up to 500 m. A 400 mega- chip per second channel sounder with steerable 24.5 dBi horn antennas at the transmitter and receiver was used to measure the angular distributions of received multipath power over a wide range of propagation distances and urban settings. Measurements were also made to study the small-scale fading of closely-spaced power delay profiles recorded at half-wavelength (5.35 mm) increments along a small-scale linear track (10 wavelengths, or 107 mm) at two different receiver locations. Our measurements indicate that power levels for small- scale fading do not significantly fluctuate from the mean power level at a fixed angle of arrival. We propose here a new lobe modeling technique that can be used to create a statistical channel model for lobe path loss and shadow fading, and we provide many model statistics as a function of transmitter- receiver separation distance. Our work shows that New York City is a multipath-rich environment when using highly directional steerable horn antennas, and that an average of 2.5 signal lobes exists at any receiver location, where each lobe has an average total angle spread of 40.3° and an RMS angle spread of 7.8°. This work aims to create a 28 GHz statistical spatial channel model for future 5G cellular networks.
Fuzzy logic based sentiment analysis of product review documents

Sentiment Analysis is the process of determining subjectivity, polarity and polarity strength of a piece of text. Survey shows that 81% of Internet users have done on-line research on a product at least once. Manual analysis of this reviews is difficult. To mine the overall sentiment or opinion polarity, sentiment analysis can be used to mine the overall sentiment or opinion polarity of the review. The objective of this paper is to extract features from the product reviews and classify reviews into positive, negative and neutral. Feature based sentiment classification approach is used to extract sentiments. It involves preprocessing to remove noise, extraction of features and corresponding descriptors and tagging their polarity. The proposed technique extends the feature based classification approach to incorporate the effect of various linguistic hedges. This approach uses fuzzy functions to emulate the effect of modifiers, concentrators and dilators. The system was evaluated with SFU corpus and the results suggest that sentiment analysis using fuzzy logic performs remarkably well.
Modeling and design of multilayer transmission line transformers and baluns

A transmission line balun and a transmission line impedance transformer are described. Both transformers were realized as part of a four-layer laminate PCB using a planar, quasi-coaxial transmission line structure. Approximately a decade bandwidth was achieved, with operating frequencies in the lower and sub Gigahertz range. Both transformers employ the same planar coil geometry, covering an area of less then 0.6 cm/sup 2/. As their power handling capability is merely limited by that of the planar transmission lines, the transformers can be used in medium power applications.
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances. State-of-the-art region proposal methods usually need several thousand proposals to get high recall, thus hurting the detection efficiency. Although the latest Region Proposal Network method gets promising detection accuracy with several hundred proposals, it still struggles in small-size object detection and precise localization (e.g., large IoU thresholds), mainly due to the coarseness of its feature maps. In this paper, we present a deep hierarchical network, namely HyperNet, for handling region proposal generation and object detection jointly. Our HyperNet is primarily based on an elaborately designed Hyper Feature which aggregates hierarchical feature maps first and then compresses them into a uniform space. The Hyper Features well incorporate deep but highly semantic, intermediate but really complementary, and shallow but naturally high-resolution features of the image, thus enabling us to construct HyperNet by sharing them both in generating proposals and detecting objects via an end-to-end joint training strategy. For the deep VGG16 model, our method achieves completely leading recall and state-of-the-art object detection accuracy on PASCAL VOC 2007 and 2012 using only 100 proposals per image. It runs with a speed of 5 fps (including all steps) on a GPU, thus having the potential for real-time processing.
River mapping from a flying robot: state estimation, river detection, and obstacle mapping

Accurately mapping the course and vegetation along a river is challenging, since overhanging trees block GPS at ground level and occlude the shore line when viewed from higher altitudes. We present a multimodal perception system for the active exploration and mapping of a river from a small rotorcraft. We describe three key components that use computer vision, laser scanning, inertial sensing and intermittant GPS to estimate the motion of the rotorcraft, detect the river without a prior map, and create a 3D map of the riverine environment. Our hardware and software approach is cognizant of the need to perform multi-kilometer missions below tree level with size, weight and power constraints. We present experimental results along a 2 km loop of river using a surrogate perception payload. Overall we can build an S. Scherer ( ) · S. Achar · H. Cover · A. Chambers · S. Nuske · S. Singh Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA e-mail: basti@cmu.edu S. Achar e-mail: supreeth@cmu.edu H. Cover e-mail: hcover@cmu.edu A. Chambers e-mail: achambers@cmu.edu S. Nuske e-mail: nuske@cmu.edu S. Singh e-mail: ssingh@cmu.edu J. Rehder Institute of Control Systems, Hamburg University of Technology, Eissendorfer Strasse 40, 21073 Hamburg, Germany e-mail: joern.rehder@tuhh.de accurate 3D obstacle map and a 2D map of the river course and width from light onboard sensing.