nils-herrmann/clean_scirepeval huggingface dataset:
A Critical Success Factors Model for Enterprise Resource Planning Implementation

Enterprise Resource Planning (ERP) systems are highly integrated enterprise-wide information systems that automate core business processes. The ERP packages of vendors such as SAP, Baan, J.D. Edwards, Peoplesoft and Intentia represent more than a standard business platform, they prescribe information blueprints of how an organisation’s business processes should operate. In this paper the scale and strategic importance of ERP systems are identified and the problem of ERP implementation is defined. A Critical Success Factors (CSFs) framework is proposed to aid managers develop an ERP implementation strategy. The framework is illustrated using two case examples from a research sample of eight companies. The case analysis highlights the critical impact of legacy systems upon the implementation process, the importance of selecting an appropriate ERP strategy and identifies the importance of Business Process Change (BPC) and software configuration in addition to factors already cited in the literature. The implications of the results for managerial practice are described and future research opportunities are outlined.
CJM-ex: Goal-oriented Exploration of Customer Journey Maps using Event Logs and Data Analytics

Customer Journey Mapping (CJM), is an emerging area of research tackling issues related to customer behavior and user trajectories when consuming a service. The increasing complexity of the service industry makes this type of tools popular amongst practitioners. However, to date, it is not clear how a CJM can be used to depict hundreds or thousands of customer journeys. Inspired by process discovery techniques – borrowed from Process Mining – we present CJM-explorer (CJM-ex). CJM-ex is a web interface that uses hierarchical clustering and statistical indexes to allow interactive navigation, with or without a-priori information, through numerous journeys stored in standard event log formats. The exploration of the underlying journeys can be done in the whole set of data available or driven by user goals in order to examine events and patterns in specific areas of interest.
Collective Emotions in Conflict Situations : Societal Implications

It is well established today that emotions are an important part of most societal dynamics. The current article focuses on the role of different collective emotional elements in creating, preserving, and resolving conflicts. The main premise is that collective emotions play a pivotal role in shaping individual and societal responses to conflicting events and in contributing to the evolvement of a social context that maintains the emotional climate and collective emotional orientation that have developed. The first part of the article provides a conceptual framework to discuss the relations between conflict, context, and collective emotions. The second part uses the conceptual framework to discuss the societal implications of the articles presented in this issue. Taken together, the parts create a platform for future research on the role of collective emotions in conflict resolution and the construction of cultures of peace.
Software fault prediction: A literature review and current trends

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.10.024 E-mail addresses: cagatay.catal@bte.mam.gov.tr, ca Software engineering discipline contains several prediction approaches such as test effort prediction, correction cost prediction, fault prediction, reusability prediction, security prediction, effort prediction, and quality prediction. However, most of these prediction approaches are still in preliminary phase and more research should be conducted to reach robust models. Software fault prediction is the most popular research area in these prediction approaches and recently several research centers started new projects on this area. In this study, we investigated 90 software fault prediction papers published between year 1990 and year 2009 and then we categorized these papers according to the publication year. This paper surveys the software engineering literature on software fault prediction and both machine learning based and statistical based approaches are included in this survey. Papers explained in this article reflect the outline of what was published so far, but naturally this is not a complete review of all the papers published so far. This paper will help researchers to investigate the previous studies from metrics, methods, datasets, performance evaluation metrics, and experimental results perspectives in an easy and effective manner. Furthermore, current trends are introduced and discussed.
A Switched-Capacitor DC–DC Converter With High Voltage Gain and Reduced Component Rating and Count

This paper proposes a bidirectional switched-capacitor dc-dc converter for applications that require high voltage gain. Some of conventional switched-capacitor dc-dc converters have diverse voltage or current stresses for the switching devices in the circuit, not suitable for modular configuration or for high efficiency demand; some suffer from relatively high power loss or large device count for high voltage gain, even if the device voltage stress could be low. By contrast, the proposed dc-dc converter features low component (switching device and capacitor) power rating, small switching device count, and low output capacitance requirement. In addition to its low current stress, the combination of two short symmetric paths of charge pumps further lowers power loss. Therefore, a small and light converter with high voltage gain and high efficiency can be achieved. Simulation and experimental results of a 450-W prototype with a voltage conversion ratio of six validate the principle and features of this topology.
Forecasting with Recurrent Neural Networks: 12 Tricks

Recurrent neural networks (RNNs) are typically considered as relatively simple architectures, which come along with complicated learning algorithms. This paper has a different view: We start from the fact that RNNs can model any high dimensional, nonlinear dynamical system. Rather than focusing on learning algorithms, we concentrate on the design of network architectures. Unfolding in time is a well-known example of this modeling philosophy. Here a temporal algorithm is transferred into an architectural framework such that the learning can be performed by an extension of standard error backpropagation. We introduce 12 tricks that not only provide deeper insights in the functioning of RNNs but also improve the identification of underlying dynamical system from data.
Overview of Beyond-CMOS Devices and a Uniform Methodology for Their Benchmarking

Multiple logic devices are presently under study within the Nanoelectronic Research Initiative (NRI) to carry the development of integrated circuits beyond the complementary metal-oxide-semiconductor (CMOS) roadmap. Structure and operational principles of these devices are described. Theories used for benchmarking these devices are overviewed, and a general methodology is described for consistent estimates of the circuit area, switching time, and energy. The results of the comparison of the NRI logic devices using these benchmarks are presented.
Implementing moving target IPv6 defense to secure 6LoWPAN in the internet of things and smart grid

The growing momentum of the Internet of Things (IoT) has shown an increase in attack vectors within the security research community. We propose adapting a recent new approach of frequently changing IPv6 address assignment to add an additional layer of security to the Internet of Things. We examine implementing Moving Target IPv6 Defense (MT6D) in IPv6 over Low-Powered Wireless Personal Area Networks (6LoWPAN); a protocol that is being used in wireless sensors found in home automation systems and smart meters. 6LoWPAN allows the Internet of Things to extend into the world of wireless sensor networks. We propose adapting Moving-Target IPv6 Defense for use with 6LoWPAN in order to defend against network-side attacks such as Denial-of-Service and Man-In-The-Middle while maintaining anonymity of client-server communications. This research aims in providing a moving-target defense for wireless sensor networks while maintaining power efficiency within the network.
Metamaterial-Based Low-Profile Broadband Aperture-Coupled Grid-Slotted Patch Antenna

A metamaterial-based broadband low-profile grid-slotted patch antenna is presented. By slotting the radiating patch, a periodic array of series capacitor loaded metamaterial patch cells is formed, and excited through the coupling aperture in a ground plane right underneath and parallel to the slot at the center of the patch. By exciting two adjacent resonant modes simultaneously, broadband impedance matching and consistent radiation are achieved. The dispersion relation of the capacitor-loaded patch cell is applied in the mode analysis. The proposed grid-slotted patch antenna with a low profile of 0.06 λ0 (λ0 is the center operating wavelength in free space) achieves a measured bandwidth of 28% for the |S11| less than -10 dB and maximum gain of 9.8 dBi.
The Mechanical Design and Experiments of HIT/DLR Prosthetic Hand

Designed based on the underactuated mechanism, HIT/DLR Prosthetic Hand is a multi-sensory flve-flngered bio- prosthetic hand. Similarly with adult's hand, it is simple constructed and comprises 13 joints. Three motors actuate the thumb, index finger and the other three fingers each. Actuated by a motor, the thumb can move along cone surface, which resembles human thumb and is superior in the appearance. Driven by another motor and transmitted by springs, the mid finger, ring finger and little finger can envelop objects with complex shape. The appearance designation and sensory system are introduced. The grasp experiments are presented in detail. The hand has been greatly improved from HIT-ARhand. It was verified from experimentations, the hand has strong capability of self adaptation, can accomplish precise and power grasp for objects with complex shape.
Semi-convolutional Operators for Instance Segmentation

Object detection and instance segmentation are dominated by region-based methods such as Mask RCNN. However, there is a growing interest in reducing these problems to pixel labeling tasks, as the latter could be more efficient, could be integrated seamlessly in imageto-image network architectures as used in many other tasks, and could be more accurate for objects that are not well approximated by bounding boxes. In this paper we show theoretically and empirically that constructing dense pixel embeddings that can separate object instances cannot be easily achieved using convolutional operators. At the same time, we show that simple modifications, which we call semi-convolutional, have a much better chance of succeeding at this task. We use the latter to show a connection to Hough voting as well as to a variant of the bilateral kernel that is spatially steered by a convolutional network. We demonstrate that these operators can also be used to improve approaches such as Mask RCNN, demonstrating better segmentation of complex biological shapes and PASCAL VOC categories than achievable by Mask RCNN alone.
A Cloud-Based Trajectory Data Management System

With the rapid development of location-acquisition techniques, massive trajectories are continuously generated. Many urban applications rely heavily on the data mining/analysis results of massive trajectory data. This demo presents a holistic data management system for both historical and real-time trajectory records based on a cloud platform, such as Microsoft Azure. The proposed system is able to efficiently support a variety of trajectory queries, including ID-Temporal query, Spatio-Temporal query, and Path-Temporal query. With these queries, we demonstrate that different urban applications can be realized in a much easier way.
Sharp-Rejection Ultra-Wide Bandstop Filters

In this letter, a modified optimum bandstop filter (BSF) design is presented. Broad stopband and sharp rejection characteristics are achieved by realizing three transmission zeros near the passband edges. Stopband rejection depth and bandwidth can be controlled by the impedances of the configuration. A lossless transmission line model analysis is used to derive design equations. Further, a compact geometry is chosen by replacing the low impedance section with its equivalent dual-high impedance lines, which facilitates convenient folding. To validate the theoretical prediction, a single unit sharp-rejecting prototype BSF having a 20 dB rejection bandwidth of 126% at 1.5 GHz has been fabricated.
Aligning Business and IT Strategies in Multi-Business Organizations

The alignment of business and information technology (IT) strategies is an important and enduring theoretical challenge for the information systems discipline, remaining a top issue in practice over the past 20 years. Multi-business organizations (MBOs) present a particular alignment challenge because business strategies are developed at the corporate level, within individual strategic business units and across the corporate investment cycle. In contrast, the extant literature implicitly assumes that IT strategy is aligned with a single business strategy at a single point in time. This paper draws on resource-based theory and path dependence to model functional, structural, and temporal IT strategic alignment in MBOs. Drawing on Makadok’s theory of profit, we show how each form of alignment creates value through the three strategic drivers of competence, governance, and flexibility, respectively. We illustrate the model with examples from a case study on the Commonwealth Bank of Australia. We also explore the model’s implications for existing IT alignment models, providing alternative theoretical explanations for how IT alignment creates value.  (2015) 30, 101–118. doi:10.1057/jit.2015.1; published online 24 March 2015
Modeling Steel Frame Buildings in Three Dimensions-Part II : Elastofiber Beam Element and Examples

This is the second of two papers describing a procedure for the three dimensional nonlinear timehistory analysis of steel framed buildings. An overview of the procedure and the theory for the panel zone element and the plastic hinge beam element are presented in Part I. In this paper, the theory for an efficient new element for modeling beams and columns in steel frames called the elastofiber element is presented, along with four illustrative examples. The elastofiber beam element is divided into three segments two end nonlinear segments and an interior elastic segment. The cross-sections of the end segments are subdivided into fibers. Associated with each fiber is a nonlinear hysteretic stress-strain law for axial stress and strain. This accounts for coupling of nonlinear material behavior between bending about the major and minor axes of the cross-section and axial deformation. Examples presented include large deflection of an elastic cantilever, cyclic loading of a cantilever beam, pushover analysis of a 20-story steel moment-frame building to collapse, and strong ground motion analysis of a 2-story unsymmetric steel moment-frame building. 1Post-Doctoral Scholar, Seismological Laboratory, MC 252-21, California Institute of Technology, Pasadena, CA91125. Email: krishnan@caltech.edu 2Professor, Civil Engineering and Applied Mechanics, MC 104-44, California Institute of Technology, Pasadena,
Using of Jaccard Coefficient for Keywords Similarity

—Presently, information retrieval can be accomplished simply and rapidly with the use of search engines. This allows users to specify the search criteria as well as specific keywords to obtain the required results. Additionally, an index of search engines has to be updated on most recent information as it is constantly changed over time. Particularly, information retrieval results as documents are typically too extensive, which affect on accessibility of the required results for searchers. Consequently, a similarity measurement between keywords and index terms is essentially performed to facilitate searchers in accessing the required results promptly. Thus, this paper proposed the similarity measurement method between words by deploying Jaccard Coefficient. Technically, we developed a measure of similarity Jaccard with Prolog programming language to compare similarity between sets of data. Furthermore, the performance of this proposed similarity measurement method was accomplished by employing precision, recall, and F-measure. Precisely, the test results demonstrated the awareness of advantage and disadvantages of the measurement which were adapted and applied to a search for meaning by using Jaccard similarity coefficient.
Looking Around the Corner using Ultrafast Transient Imaging

We propose a novel framework called transient imaging for image formation and scene understanding through impulse illumination and time images. Using time-of-flight cameras and multi-path analysis of global light transport, we pioneer new algorithms and systems for scene understanding through time images. We demonstrate that our proposed transient imaging framework allows us to accomplish tasks that are well beyond the reach of existing imaging technology. For example, one can infer the geometry of not only the visible but also the hidden parts of a scene, enabling us to look around corners. Traditional cameras estimate intensity per pixel I(x,y). Our transient imaging camera captures a 3D time-image I(x,y,t) for each pixel and uses an ultra-short pulse laser for illumination. Emerging technologies are supporting cameras with a temporal-profile per pixel at picosecond resolution, allowing us to capture an ultra-high speed time-image. This time-image contains the time profile of irradiance incident at a sensor pixel. We experimentally corroborated our theory with free space hardware experiments using a femtosecond laser and a picosecond accurate sensing device. The ability to infer the structure of hidden scene elements, unobservable by both the camera and illumination source, will create a range of new computer vision opportunities.
An automatic keyphrase extraction system for scientific documents

Automatic keyphrase extraction techniques play an important role for many tasks including indexing, categorizing, summarizing, and searching. In this paper, we develop and evaluate an automatic keyphrase extraction system for scientific documents. Compared with previous work, our system concentrates on two important issues: (1) more precise location for potential keyphrases: a new candidate phrase generation method is proposed based on the core word expansion algorithm, which can reduce the size of the candidate set by about 75% without increasing the computational complexity; (2) overlap elimination for the output list: when a phrase and its sub-phrases coexist as candidates, an inverse document frequency feature is introduced for selecting the proper granularity. Additional new features are added for phrase weighting. Experiments based on real-world datasets were carried out to evaluate the proposed system. The results show the efficiency and effectiveness of the refined candidate set and demonstrate that the new features improve the accuracy of the system. The overall performance of our system compares favorably with other state-of-the-art keyphrase extraction systems.
A statistical information extraction system for Turkish

This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the words. We have successfully applied statistical methods using both the lexical and morphological information to sentence segmentation, topic segmentation, and name tagging tasks. For sentence segmentation, we have modeled the final inflectional groups of the words and combined it with the lexical model, and decreased the error rate to 4.34%, which is 21% better than the result obtained using only the surface forms of the words. For topic segmentation, stems of the words (especially nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90% segmentation error rate on our test set according to the weighted TDT-2 segmentation cost metric. This is 32% better than the word-based baseline model. For name tagging, we used four different information sources to model names. Our first information source is based on the surface forms of the words. Then we combined the contextual cues with the lexical model, and obtained some improvement. After this, we modeled the morphological analyses of the words, and finally we modeled the tag sequence, and reached an F-Measure of 91.56%, according to the MUC evaluation criteria. Our results are important in the sense that, using linguistic information, i.e. morphological analyses of the words, and a corpus large enough to train a statistical model significantly improves these basic information extraction tasks for Turkish.
Formal Abstractions for Attested Execution Secure Processors

Realistic secure processors, including those built for academic and commercial purposes, commonly realize an “attested execution” abstraction. Despite being the de facto standard for modern secure processors, the “attested execution” abstraction has not received adequate formal treatment. We provide formal abstractions for “attested execution” secure processors and rigorously explore its expressive power. Our explorations show both the expected and the surprising. On one hand, we show that just like the common belief, attested execution is extremely powerful, and allows one to realize powerful cryptographic abstractions such as stateful obfuscation whose existence is otherwise impossible even when assuming virtual blackbox obfuscation and stateless hardware tokens. On the other hand, we show that surprisingly, realizing composable two-party computation with attested execution processors is not as straightforward as one might anticipate. Specifically, only when both parties are equipped with a secure processor can we realize composable two-party computation. If one of the parties does not have a secure processor, we show that composable two-party computation is impossible. In practice, however, it would be desirable to allow multiple legacy clients (without secure processors) to leverage a server’s secure processor to perform a multi-party computation task. We show how to introduce minimal additional setup assumptions to enable this. Finally, we show that fair multi-party computation for general functionalities is impossible if secure processors do not have trusted clocks. When secure processors have trusted clocks, we can realize fair two-party computation if both parties are equipped with a secure processor; but if only one party has a secure processor (with a trusted clock), then fairness is still impossible for general functionalities.