nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Context-awareness in Business Process Design

In recent years, flexibility has emerged as an important guiding principle in the design of business processes. However, research on process flexibility has traditionally been solely focused on ways of how the demand for process flexibility can be satisfied by advanced process modelling techniques, i.e., issues intrinsic to the process. This paper proposes to extent existing research by studying the extrinsic drivers for process flexibility, i.e. an analysis of the root causes that drive the demand for flexible processes. These drivers can be found in the context of the process, which may include among others time, location, weather, legislation, culture or performance requirements. We argue for a stronger and more explicit consideration of these contextual factors in the design and modelling of business processes. Based on a real case study, we discuss how context can be conceptualized and integrated with existing approaches to business process design. These extensions are an essential foundation for the definition and implementation of agile processes and as such of high practical and theoretical value.
Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s finegrained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce ‘‘somewhat novel’’ articles to
A complete analytical solution to the inverse kinematics of the Pioneer 2 robotic arm

For robotic manipulators that are redundant or with high degrees of freedom (dof ), an analytical solution to the inverse kinematics is very difficult or impossible. Pioneer 2 robotic arm (P2Arm) is a recently developed and widely used 5-dof manipulator. There is no effective solution to its inverse kinematics to date. This paper presents a first complete analytical solution to the inverse kinematics of the P2Arm, which makes it possible to control the arm to any reachable position in an unstructured environment. The strategies developed in this paper could also be useful for solving the inverse kinematics problem of other types of robotic arms.
Novel Multi-Stage Stochastic DG Investment Planning with Recourse

This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.
Synthesizing, correcting and improving code, using model checking-based genetic programming

We show here how the use of genetic programming in combination of model checking provides a powerful way to synthesize programs. Whereas classical algorithmic synthesis provides alarming high complexity and undecidability results, the genetic approach provides a surprisingly successful heuristics. We describe several versions of a method for synthesizing sequential and concurrent systems. We show several examples where we used our approach to synthesize, improve and correct code.
Control method of robot suit HAL working as operator's muscle using biological and dynamical information

For assisting human motion, assistive devices working as muscles would be useful. A robot suit HAL (hybrid assistive limb) has been developed as an assistive device for lower limbs. Human can appropriately produce muscle contraction torque and control joint viscoelasticity by muscle effort such as co-contraction. Thus, to implement functions equivalent to human muscles using HAL, it is necessary to control viscoelasticity of HAL as well as to produce torque in accordance with operator's intention. Therefore the purpose of this study is to propose a control method of HAL using biological and motion information. In this method, HAL produces torque corresponding to muscle contraction torque by referring to the myoelectricity that is biological information to control operator's muscles. In addition, the viscoelasticities of HAL are adjusted in proportion to operator's viscoelasticity that is estimated from motion information by using an on-line parameter identification method. To evaluate the effectiveness of the proposed method, the method was applied to a swinging motion of a lower leg. When this method was applied, HAL could work like operator's muscles in the swinging motion, and as a consequence, the muscle activities of the operator were reduced. As a result of this experiment, we confirmed the effectiveness of the proposed method.
Relative Building-Block Fitness and the Building Block Hypothesis

The building-block hypothesis states that the GA works well when short, low-order, highly-fit schemas recombine to form even more highly fit higher-order schemas. The ability to produce fitter and fitter partial solutions by combining building blocks is believed to be a primary source of the GA’s search power, but the GA research community currently lacks precise and quantitative descriptions of how schema processing actually takes place during the typical evolution of a GA search. Another open problem is to characterize in detail the types of fitness landscapes for which crossover will be an effective operator. In this paper we first describe a class of fitness landscapes (the “Royal Road” functions) that we have designed to investigate these questions. We then present some unexpected experimental results concerning the GA’s performance on simple instances of these landscapes, in which we vary the strength of reinforcement from “stepping stones”—fit intermediate-order schemas obtained by recombining fit low-order schemas. Finally, we compare the performance of the GA on these functions with that of three commonly used hill-climbing schemes, Current address: Santa Fe Institute, 1660 Old Pecos Trail, Suite A, Santa Fe, NM 87501 and find that one of them, “random-mutation hill-climbing”, significantly outperforms the GA on these functions.
Text Mining of News Articles for Stock Price Predictions

1 Problem Description This work is focused on the relationship between the news articles (breaking news) and stock prices. The student will design and develop methods to analyze how and when the news articles influence the stock market. News articles about Norwegian oil related companies and stock prices from " BW Offshore Limited " (BWO), " DNO International " (DNO), " Frontline " (FRO), " Petroleum Geo-Services " (PGS), " Seadrill " (SDRL), " Sevan Marine " (SEVAN), " Siem Offshore " (SIOFF), " Statoil " (STL) and " TGS-NOPEC Geophysical Company " (TGS) will be crawled, preprocessed and the important features in the text will be extracted to effectively represent the news in a form that allows the application of computational techniques. This data will then be used to train text sense classifiers. A prototype system that employs such classifiers will be developed to support the trader in taking sell/buy decisions. Methods will be developed for automaticall sense-labeling of news that are informed by the correlation between the changes in the stock prices and the breaking news. Performance of the prototype decision support system will be compared with a chosen baseline method for trade-related decision making.  This thesis investigates the prediction of possible stock price changes immediately after news article publications. This is done by automatic analysis of these news articles. Some background information about financial trading theory and text mining is given in addition to an overview of earlier related research in the field of automatic news article analyzes with the purpose of predicting future stock prices. In this thesis a system is designed and implemented to predict stock price trends for the time immediately after the publication of news articles. This system consists mainly of four components. The first component gathers news articles and stock prices automatically from internet. The second component prepares the news articles by sending them to some document preprocessing steps and finding relevant features before they are sent to a document representation process. The third component categorizes the news articles into predefined categories, and finally the fourth component applies appropriate trading strategies depending on the category of the news article. This system requires a labeled data set to train the categorization component. This data set is labeled automatically on the basis of the price trends directly after the news article publication. An additional label refining step using clustering is added in an …
It won't happen to me: Promoting secure behaviour among internet users

Fraudulent activity on the Internet, in particular the practice known as ‘Phishing’, is on the increase. Although a number of technology focussed counter measures have been explored user behaviour remains fundamental to increased online security. Encouraging users to engage in secure online behaviour is difficult with a number of different barriers to change. Guided by a model adapted from health psychology this paper reports on a study designed to encourage secure behaviour online. The study aimed to investigate the effects of education via a training program and the effects of risk level manipulation on subsequent self-reported behaviour online. The training program ‘Anti-Phishing Phil’ informed users of the common types of phishing threats and how to identify them whilst the risk level manipulation randomly allocated participants to either high risk or low risk of becoming a victim of online fraud. Sixty-four participants took part in the study, which comprised of 9 males and 55 females with an age range of 18– 43 years. Participants were randomly allocated to one of four experimental groups. High threat information and/or the provision of phishing education were expected to increase self-reports of secure behaviour. Secure behaviour was measured at three stages, a baseline measure stage, an intention measure stage, and a 7-day follow-up measure stage. The results showed that offering a seemingly tailored risk message increased users’ intentions to act in a secure manner online regardless of whether the risk message indicated they were at high or low risk of fraud. There was no effect of the training programme on secure behaviour in general. The findings are discussed in relation to the model of behaviour change, information provision and the transferability of training.
A Multilevel Mixture-of-Experts Framework for Pedestrian Classification

Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level, we represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level, we consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers. Experiments are performed on a unique real-world multi-modality dataset captured from a moving vehicle in urban traffic. This dataset has been made public for research purposes. Our results show a significant performance boost of up to a factor of 42 in reduction of false positives at constant detection rates of our approach compared to a baseline intensity-only HOG/linSVM approach.
A folded dipole antenna for RFID

Radio frequency identification (RFID) of objects or people has become very popular in many services in industry, distribution logistics, manufacturing companies and goods flow systems. When RFID frequency rises into the microwave region, the tag antenna must be carefully designed to match the free space and to the following ASIC. In this paper, we present a novel folded dipole antenna with a very simple configuration. The required input impedance can be achieved easily by choosing suitable geometry parameters.
Raspberry PI based stereo vision for small size ASVs

This paper presents an approach to stereovision applied to small water vehicles. By using a small low-cost computer and inexpensive off-the-shelf components, we were able to develop an autonomous driving system capable of following other vehicle and moving along paths delimited by coloured buoys. A pair of webcams was used and, with an ultrasound sensor, we were also able to implement a basic frontal obstacle avoidance system. With the help of the stereoscopic system, we inferred the position of specific objects that serve as references to the ASV guidance. The final system is capable of identifying and following targets in a distance of over 5 meters. This system was integrated with the framework already existent and shared by all the vehicles used in the OceanSys research group at INESC - DEEC/FEUP.
Robust Fake Iris Detection Based on Variation of the Reflectance Ratio Between the IRIS and the Sclera

In this paper, we propose a new fake iris detection method based on the changes in the reflectance ratio between the iris and the sclera. The proposed method has four advantages over previous works. First, it is possible to detect fake iris images with high accuracy. Second, our method does not cause inconvenience to users since it can detect fake iris images at a very fast speed. Third, it is possible to show the theoretical background of using the variation of the reflectance ratio between the iris and the sclera. To compare fake iris images with live ones, three types of fake iris images were produced: a printed iris, an artificial eye, and a fake contact lens. In the experiments, we prove that the proposed fake iris detection method achieves high performance when distinguishing between live and fake iris.
The affect heuristic

InT. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 397-420).  Cambridge University Press,2002. This chapter introduces a theoretical framework that describes the importance of affect in guiding judgments and decisions. As used here,. affect means the specific quality of "goodness" or "badness" (1) experienced as a feeling state (with or without consciousness) and (2) demarcating a positive or negative quality of a stimulus. Affective responses occur rapidly and automatically note how quickly you sense the feelings as.sociated with the stimulus words treasure or hate. We argue that reliance on such feelings can be characterized as the affect heuristic. In this chapter, we trace the development of the affect heuristic across a variety of research paths followed by ourselves and many others. We also discuss some of the important practical implications resulting from ways that this heuristic impacts our daily lives.
Comparison of Access Methods for Time-Evolving Data

This paper compares different indexing techniques proposed for supporting efficient access to temporal data. The comparison is based on a collection of important performance criteria, including the space consumed, update processing, and query time for representative queries. The comparison is based on worst-case analysis, hence no assumptions on data distribution or query frequencies are made. When a number of methods have the same asymptotic worst-case behavior, features in the methods that affect average case behavior are discussed. Additional criteria examined are the pagination of an index, the ability to cluster related data together, and the ability to efficiently separate old from current data (so that larger archival storage media such as write-once optical disks can be used). The purpose of the paper is to identify the difficult problems in accessing temporal data and describe how the different methods aim to solve them. A general lower bound for answering basic temporal queries is also introduced.
A Comprehensive Feature Study for Appliance Recognition on High Frequency Energy Data

Awareness about the energy consumption of appliances can help to save energy in households. Non-intrusive Load Monitoring (NILM) is a feasible approach to provide consumption feedback at appliance level. In this paper, we evaluate a broad set of features for electrical appliance recognition, extracted from high frequency start-up events. These evaluations were applied on several existing high frequency energy datasets. To examine clean signatures, we ran all experiments on two datasets that are based on isolated appliance events; more realistic results were retrieved from two real household datasets. Our feature set consists of 36 signatures from related work including novel approaches, and from other research fields. The results of this work include a stand-alone feature ranking, promising feature combinations for appliance recognition in general and appliance-wise performances.
Relations among musical skills, phonological processing, and early reading ability in preschool children.

We examined the relations among phonological awareness, music perception skills, and early reading skills in a population of 100 4- and 5-year-old children. Music skills were found to correlate significantly with both phonological awareness and reading development. Regression analyses indicated that music perception skills contributed unique variance in predicting reading ability, even when variance due to phonological awareness and other cognitive abilities (math, digit span, and vocabulary) had been accounted for. Thus, music perception appears to tap auditory mechanisms related to reading that only partially overlap with those related to phonological awareness, suggesting that both linguistic and nonlinguistic general auditory mechanisms are involved in reading.
A Novel Compact and Polarization-Dependent Mushroom-Type EBG Using CSRR for Dual/Triple-Band Applications

In this letter, a novel compact electromagnetic band-gap (EBG) structure constructed by etching a complementary split ring resonator (CSRR) on the patch of a conventional mushroom-type EBG (CMT-EBG) is proposed. The first bandgap is defined in all directions in the surface structure. Compared to the CMT-EBG structure, the CSRR-based EBG presents a 28% size reduction in the start frequency of the first bandgap. However, asymmetrical frames of the CSRR-based EBG result in different properties at X and Y-directions. Another two tunable bandgaps in Y-direction are observed. Thus, the proposed EBG can be used for multi-band applications, such as dual/triple-band antennas. The EBGs have been constructed and measured.
A highly scalable vertical gate (VG) 3D NAND Flash with robust program disturb immunity using a novel PN diode decoding structure

A novel PN diode decoding method for 3D NAND Flash is proposed. The PN diodes are fabricated self-aligned at the source side of the Vertical Gate (VG) 3D NAND architecture. Contrary to the previous 3D NAND approaches, there is no need to fabricate plural string select (SSL) transistors inside the array, thus enabling a highly symmetrical and scalable cell structure. A novel three-step programming pulse waveform is integrated to implement the program-inhibit method, capitalizing on that the PN diodes can prevent leakage of the self-boosted channel potential. A large program-disturb-free window >5V is demonstrated.
Optimization Methods for Large-Scale Machine Learning

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations.
An Evaluation of Synthetic Speech Using the PESQ Measure

The paper presents experiments on the use of the perceptual objective measure – ITU-T Rec. P.862 Perceptual Evaluation of Speech Quality (PESQ), for the automatic evaluation of synthetic speech. The approach is based on the evaluation of the statistically significant correlation between the outputs of subjective and objective tests. We propose the following technique to evaluate the usage of the PESQ method for synthetic speech: Firstly, a list of the test words has to be defined for the entire language. Secondly, the tested synthesizers are used to generate synthetic speech signal for all the words in the list. Synthesizer engines of different quality were used for the generation of stimuli: LP synthesizer, RELP synthesizer and PSOLA synthesizer, both in female and male versions. We evaluated created stimuli by listening tests. Thirdly, the PESQ method with original human (reference) and synthesized (measured) recordings as inputs is used to evaluate the overall quality of the synthesized signals. Finally, a correlation of the resulting MOS and objective MOS scores is calculated for each voice. Our results indicate a strong correlation between the mentioned subjective and objective evaluation of the quality of synthetic speech. We plan to use the PESQ measure in automatic evaluation of new versions of synthetic voices, without a need of subjective tests. This approach can foster the life cycle of the development of new versions of synthetic voices tremendously. Using PESQ with “original voice” as reference represents a rapid and repeatable synthetic voice quality measurement technique that provides the developer with results in a few moments.