nils-herrmann/clean_scirepeval huggingface dataset:
The impact of work design , autonomy support , and strategy on employee outcomes : A differentiated perspective on self-determination at work

Drawing upon self-determination theory, this study tested different types of behavioral regulation as parallel mediators of the association between the job’s motivating potential, autonomy-supportive leadership, and understanding the organization’s strategy, on the one hand, and job satisfaction, turnover intention, and two types of organizational citizenship behaviors (OCB), on the other hand. In particular, intrinsic motivation and identified regulation were contrasted as idiosyncratic motivational processes. Analyses were based on data from 201 employees in the Swiss insurance industry. Results supported both types of self-determined motivation as mediators of specific antecedent-outcome relationships. Identified regulation, for example, particularly mediated the impact of contextual antecedents on both civic virtue and altruism OCB. Overall, controlled types of behavioral regulation showed comparatively weak relations to antecedents or consequences. The unique characteristics of motivational processes and potential explanations for the weak associations of controlled motivation are discussed.
Learning Syntactic Program Transformations from Examples

Automatic program transformation tools can be valuable for programmers to help them with refactoring tasks, and for Computer Science students in the form of tutoring systems that suggest repairs to programming assignments. However, manually creating catalogs of transformations is complex and time-consuming. In this paper, we present REFAZER, a technique for automatically learning program transformations. REFAZER builds on the observation that code edits performed by developers can be used as input-output examples for learning program transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for efficiently synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of code edits used by students to fix incorrect programming assignment submissions, we learn program transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive code edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 56 scenarios of repetitive edits taken from three large C# open-source projects, REFAZER learns the intended program transformation in 84% of the cases using only 2.9 examples on average.
Robot companion: A social-force based approach with human awareness-navigation in crowded environments

Robots accompanying humans is one of the core capacities every service robot deployed in urban settings should have. We present a novel robot companion approach based on the so-called Social Force Model (SFM). A new model of robot-person interaction is obtained using the SFM which is suited for our robots Tibi and Dabo. Additionally, we propose an interactive scheme for robot's human-awareness navigation using the SFM and prediction information. Moreover, we present a new metric to evaluate the robot companion performance based on vital spaces and comfortableness criteria. Also, a multimodal human feedback is proposed to enhance the behavior of the system. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.
Periodic Resource Model for Compositional Real-Time Guarantees

We address the problem of providing compositional hard real-time guarantees in a hierarchy of schedulers. We first propose a resource model to characterize a periodic resource allocation and present exact schedulability conditions for our proposed resource model under the EDF and RM algorithms. Using the exact schedulability conditions, we then provide methods to abstract the timing requirements that a set of periodic tasks demands under the EDF and RM algorithms as a single periodic task. With these abstraction methods, for a hierarchy of schedulers, we introduce a composition method that derives the timing requirements of a parent scheduler from the timing requirements of its child schedulers in a compositional manner such that the timing requirement of the parent scheduler is satisfied, if and only if, the timing requirements of its child schedulers are satisfied.  Reprinted from Proceedings of the 24th IEEE Real-Time Systems Symposium (RTSS 2003), pages 2-13. Publisher URL:http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=28040 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/25 Periodic Resource Model for Compositional Real-Time Guarantees Insik Shin and Insup Lee Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 USA ishin,lee
Improving Event Detection with Abstract Meaning Representation

Event Detection (ED) aims to identify instances of specified types of events in text, which is a crucial component in the overall task of event extraction. The commonly used features consist of lexical, syntactic, and entity information, but the knowledge encoded in the Abstract Meaning Representation (AMR) has not been utilized in this task. AMR is a semantic formalism in which the meaning of a sentence is encoded as a rooted, directed, acyclic graph. In this paper, we demonstrate the effectiveness of AMR to capture and represent the deeper semantic contexts of the trigger words in this task. Experimental results further show that adding AMR features on top of the traditional features can achieve 67.8% (with 2.1% absolute improvement) F-measure (F1), which is comparable to the state-of-the-art approaches.
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.
RULE MINING AND CLASSIFICATION OF ROAD TRAFFIC ACCIDENTS USING ADAPTIVE REGRESSION TREES

Road traffic accidents are among the top leading causes of deaths and injuries of various levels. Ethiopia is experiencing highest rate of such accidents resulting in fatalities and various levels of injuries. Addis Ababa, the capital city of Ethiopia, takes the lion’s share of the risk having higher number of vehicles and traffic and the cost of these fatalities and injuries has a great impact on the socio-economic development of a society. This research is focused on developing adaptive regression trees to build a decision support system to handle road traffic accident analysis for Addis Ababa city traffic office. The study focused on injury severity levels resulting from an accident using real data obtained from the Addis Ababa traffic office. Empirical results show that the developed models could classify accidents within reasonable accuracy.
Design principles of massive, robust prediction systems

Most data mining research is concerned with building high-quality classification models in isolation. In massive production systems, however, the ability to monitor and maintain performance over time while growing in size and scope is equally important. Many external factors may degrade classification performance including changes in data distribution, noise or bias in the source data, and the evolution of the system itself. A well-functioning system must gracefully handle all of these. This paper lays out a set of design principles for large-scale autonomous data mining systems and then demonstrates our application of these principles within the m6d automated ad targeting system. We demonstrate a comprehensive set of quality control processes that allow us monitor and maintain thousands of distinct classification models automatically, and to add new models, take on new data, and correct poorly-performing models without manual intervention or system disruption.
Simple PPDB: A Paraphrase Database for Simplification

We release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains 4.5 million paraphrase rules, making it the largest available resource for lexical simplification.
Data-driven chance constrained stochastic program

Chance constrained programming is an effective and convenient approach to control risk in decision making under uncertainty. However, due to unknown probability distributions of random parameters, the solution obtained from a chance constrained optimization problem can be biased. In addition, instead of knowing the true distributions of random parameters, in practice, only a series of historical data, which can be considered as samples taken from the true (while ambiguous) distribution, can be observed and stored. In this paper, we derive stochastic programs with data-driven chance constraints (DCCs) to tackle these problems and develop equivalent reformulations. For a given historical data set, we construct two types of confidence sets for the ambiguous distribution through nonparametric statistical estimation of its moments and density functions, depending on the amount of available data. We then formulate DCCs from the perspective of robust feasibility, by allowing the ambiguous distribution to run adversely within its confidence set. After deriving equivalent reformulations, we provide exact and approximate solution approaches for stochastic programs with DCCs under both momentbased and density-based confidence sets. In addition, we derive the relationship between the conservatism of DCCs and the sample size of historical data, which shows quantitatively what we call the value of data.
Multi-objective optimization based location and social network aware recommendation

Social networks, personal blog pages, on-line transaction web-sites, expertise web pages and location based social networks provide an attractive platform for millions of users to share opinions, comments, ratings, etc. Having this kind of diverse and comprehensive information leads to difficulties for users to reach the most appropriate and reliable conclusions. Recommendation systems form one of the solutions to deal with the information overload problem by providing personalized services. Using spatial, temporal and social information on recommender systems is a recent trend that increases the performance. Also, taking into account more than one criterion can improve the performance of the recommender systems. In this paper, a location and social network aware recommender system enhanced with multi objective filtering is proposed and described. The results show that the proposed method reaches high coverage while preserving precision. Besides, the proposed method is not affected by the range of ratings and provides persistent results in different settings.
AppInk: watermarking android apps for repackaging deterrence

With increased popularity and wide adoption of smartphones and mobile devices, recent years have seen a new burgeoning economy model centered around mobile apps. However, app repackaging, among many other threats, brings tremendous risk to the ecosystem, including app developers, app market operators, and end users. To mitigate such threat, we propose and develop a watermarking mechanism for Android apps. First, towards automatic watermark embedding and extraction, we introduce the novel concept of manifest app, which is a companion of a target Android app under protection. We then design and develop a tool named AppInk, which takes the source code of an app as input to automatically generate a new app with a transparently-embedded watermark and the associated manifest app. The manifest app can be later used to reliably recognize embedded watermark with zero user intervention. To demonstrate the effectiveness of AppInk in preventing app repackaging, we analyze its robustness in defending against distortive, subtractive, and additive attacks, and then evaluate its resistance against two open source repackaging tools. Our results show that AppInk is easy to use, effective in defending against current known repackaging threats on Android platform, and introduces small performance overhead.
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations

Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users’ replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.
Assessing data mining results via swap randomization

The problem of assessing the significance of data mining results on high-dimensional 0-1 data sets has been studied extensively in the literature. For problems such as mining frequent sets and finding correlations, significance testing can be done by, e.g., chi-square tests, or many other methods. However, the results of such tests depend only on the specific attributes and not on the dataset as a whole. Moreover, the tests are more difficult to apply to sets of patterns or other complex results of data mining. In this paper, we consider a simple randomization technique that deals with this shortcoming. The approach consists of producing random datasets that have the same row and column margins with the given dataset, computing the results of interest on the randomized instances, and comparing them against the results on the actual data. This randomization technique can be used to assess the results of many different types of data mining algorithms, such as frequent sets, clustering, and rankings. To generate random datasets with given margins, we use variations of a Markov chain approach, which is based on a simple swap operation. We give theoretical results on the efficiency of different randomization methods, and apply the swap randomization method to several well-known datasets. Our results indicate that for some datasets the structure discovered by the data mining algorithms is a random artifact, while for other datasets the discovered structure conveys meaningful information.
Perceiving temporal regularity in music

We address how listeners perceive temporal regularity in music performances, which are rich in temporal irregularities. A computational model is described in which a small system of internal self-sustained oscillations, operating at different periods with specific phase and period relations, entrains to the rhythms of music performances. Based on temporal expectancies embodied by the oscillations, the model predicts the categorization of temporally changing event intervals into discrete metrical categories, as well as the perceptual salience of deviations from these categories. The model’s predictions are tested in two experiments using piano performances of the same music with different phrase structure interpretations (Experiment 1) or different melodic interpretations (Experiment 2). The model successfully tracked temporal regularity amidst the temporal fluctuations found in the performances. The model’s sensitivity to performed deviations from its temporal expectations compared favorably with the performers’ structural (phrasal and melodic) intentions. Furthermore, the model tracked normal performances (with increased temporal variability) better than performances in which temporal fluctuations associated with individual voices were removed (with decreased variability). The small, systematic temporal irregularities characteristic of human performances (chord asynchronies) improved tracking, but randomly generated temporal irregularities did not. These findings suggest that perception of temporal regularity in complex musical sequences is based on temporal expectancies that adapt in response to temporally fluctuating input.
Enterprise resource planning systems, strategic enterprise management systems and management accounting: A Danish study

The purpose of this paper is to contribute to the body of knowledge about to what extent integrated information systems, such as ERP and SEM systems, affect the ability to solve different management accounting tasks.  The relationship between IIS and management accounting practices was investigated quantitatively. A total of 349 responses were collected using a survey, and the data were analysed using linear regression models. Findings – Analyses indicate that ERP systems support the data collection and the organisational breadth of management accounting better than SEM systems. SEM systems, on the other hand, seem to be better at supporting reporting and analysis. In addition, modern management accounting techniques involving the use of non-financial data are better supported by an SEM system. This indicates that different management accounting tasks are supported by different parts of the IIS.  The study applies the methods of quantitative research. Thus, the internal validity is threatened. Conducting in-depth studies might be able to reduce this possible shortcoming.  On the basis of the findings, there is a need to consider the potential of closer integration of ERP and SEM systems in order to solve management accounting tasks.  This paper adds to the limited body of knowledge about the relationship between IIS and management accounting practices.
Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning

We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS) to reason about both forward and backward time dependencies. Beam Search (BS) is a widely used approximate inference algorithm for decoding sequences from unidirectional neural sequence models. Interestingly, approximate inference in bidirectional models remains an open problem, despite their significant advantage in modeling information from both the past and future. To enable the use of bidirectional models, we present Bidirectional Beam Search (BiBS), an efficient algorithm for approximate bidirectional inference. To evaluate our method and as an interesting problem in its own right, we introduce a novel Fill-in-the-Blank Image Captioning task which requires reasoning about both past and future sentence structure to reconstruct sensible image descriptions. We use this task as well as the Visual Madlibs dataset to demonstrate the effectiveness of our approach, consistently outperforming all baseline methods.
Wireless battery charging: E-bike application

Nowadays, Inductive Power Transfer (IPT) represents a widely investigated issue with respect to modern battery charging methods, by providing a wireless solution. IPT is applied across a large variety of applications, from Watt to kWatt power levels. Although IPT features great benefits in terms of safety and comfort, the most significant drawback consists of a relatively poor power conversion efficiency. In this paper, a 100W wireless charging equipment for E-bikes which improves efficiency is proposed. Complete magnetic structure design, as well as transmitter and receiver efficient architectures, are deeply exposed. The efficiency of the designed solution is shown by simulation results.