nils-herrmann/clean_scirepeval huggingface dataset:
Determinants of ERP implementation knowledge transfer

Our study examined the determinants of ERP knowledge transfer from implementation consultants (ICs) to key users (KUs), and vice versa. An integrated model was developed, positing that knowledge transfer was influenced by the knowledge-, source-, recipient-, and transfer context-related aspects. Data to test this model were collected from 85 ERP-implementation projects of firms that were mainly located in Zhejiang province, China. The results of the analysis demonstrated that all four aspects had a significant influence on ERP knowledge transfer. Furthermore, the results revealed the mediator role of the transfer activities and arduous relationship between ICs and KUs. The influence on knowledge transfer from the source’s willingness to transfer and the recipient’s willingness to accept knowledge was fully mediated by transfer activities, whereas the influence on knowledge transfer from the recipient’s ability to absorb knowledge was only partially mediated by transfer activities. The influence on knowledge transfer from the communication capability (including encoding and decoding competence) was fully mediated by arduous relationship.
Electrical performance of high bandwidth memory (HBM) interposer channel in terabyte/s bandwidth graphics module

Recently, IT trends such as big data, cloud computing, internet of things (IoT), 3D visualization, network, and so on demand terabyte/s bandwidth computer performance in a graphics card. In order to meet these performance, terabyte/s bandwidth graphics module using 2.5D-IC with high bandwidth memory (HBM) technology has been emerged. Due to the difference in scale of interconnect pitch between GPU or HBM and package substrate, the HBM interposer is certainly required for terabyte/s bandwidth graphics module. In this paper, the electrical performance of the HBM interposer channel in consideration of the manufacturing capabilities is analyzed by simulation both the frequency- and time-domain. Furthermore, although the silicon substrate is most widely employed for the HBM interposer fabrication, the organic and glass substrate are also proposed to replace the high cost and high loss silicon substrate. Therefore, comparison and analysis of the electrical performance of the HBM interposer channel using silicon, organic, and glass substrate are conducted.
Identification of Transformational Leadership Qualities : An Examination of Potential Biases

A basic ingredient in transformational leadership development consists in identifying leadership qualities via distribution of the multifactor leadership questionnaire (MLQ) to followers of the target leaders. It is vital that the MLQ yields an accurate and unbiased assessment of leaders on the various leadership dimensions. This article focuses on two sources of bias which may occur in identifying leadership qualities. First, when followers assess the strengths and weaknesses of their leaders, they may have difficulty in differentiating between the various transformational and transactional leadership behaviours. It is found that this is only the case for the transformational leadership attributes because the four transformational leadership dimensions measured by the MLQ correlate highly and cluster into one factor. MLQ ratings on the three transactional leadership dimensions are found not to be interrelated and show evidence for three distinct factors: contingent reward, active management-by-exception and passive leadership. Second, social desirability does not seem to be a strong biasing factor, although the transformational leadership scale is somewhat more socially desirable. These findings emphasize that the measurement of so-called “new” leadership qualities remains a controversial issue in leadership development. Practical implications of these findings and avenues for future research are also discussed.
Syntactic Dependency-Based N-grams as Classification Features

In this paper we introduce a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner of what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking the words as they appear in the text. Dependency trees fit directly into this idea, while in case of constituency trees some simple additional steps should be made. Sn-grams can be applied in any NLP task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. SVM classifier for several profile sizes was used. We used as baseline traditional n-grams of words, POS tags and characters. Obtained results are better when applying sn-grams.
A Kind of Design Schema of Wireless Smart Water Meter Reading System Based on Zigbee Technology

Traditional way of manual meter reading was not only waste of human and material resources, but also very inconvenient. Especially with the emergence of a number of high residential in recent years, this traditional way of water management was obviously inefficient. Cable automatic meter reading system is very vulnerable and it needs a heavy workload of construction wiring. In this paper, based on the study of existed water meters, a kind of design schema of wireless smart water meter was introduced. In the system, the main communication way is based on Zigbee technology. This kind of design schema is appropriate for the modern water management and the efficiency can be improved.
Maximum Achievable Efficiency in Near-Field Coupled Power-Transfer Systems

Wireless power transfer is commonly realized by means of near-field inductive coupling and is critical to many existing and emerging applications in biomedical engineering. This paper presents a closed form analytical solution for the optimum load that achieves the maximum possible power efficiency under arbitrary input impedance conditions based on the general two-port parameters of the network. The two-port approach allows one to predict the power transfer efficiency at any frequency, any type of coil geometry and through any type of media surrounding the coils. Moreover, the results are applicable to any form of passive power transfer such as provided by inductive or capacitive coupling. Our results generalize several well-known special cases. The formulation allows the design of an optimized wireless power transfer link through biological media using readily available EM simulation software. The proposed method effectively decouples the design of the inductive coupling two-port from the problem of loading and power amplifier design. Several case studies are provided for typical applications.
Unsupervised Morphology Induction Using Word Embeddings

We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in highdimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages.
Size Reduction of a Low-Profile Spiral Antenna Using Inductive and Dielectric Loading

This letter discusses a miniature low profile ultrawideband (UWB) spiral. The antenna is miniaturized using a combination of dielectric and inductive loading. In addition, a ferrite coated ground plane is adopted in place of the traditional metallic ground plane for profile reduction. Using full-wave simulations and measurements, it is shown that the miniaturized spiral can achieve similar performance to a traditional planar spiral twice its size.
Applications of Deep Learning and Reinforcement Learning to Biological Data

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)–machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.
On Plenoptic Multiplexing and Reconstruction

Photography has been striving to capture an ever increasing amount of visual information in a single image. Digital sensors, however, are limited to recording a small subset of the desired information at each pixel. A common approach to overcoming the limitations of sensing hardware is the optical multiplexing of high-dimensional data into a photograph. While this is a well-studied topic for imaging with color filter arrays, we develop a mathematical framework that generalizes multiplexed imaging to all dimensions of the plenoptic function. This framework unifies a wide variety of existing approaches to analyze and reconstruct multiplexed data in either the spatial or the frequency domain. We demonstrate many practical applications of our framework including high-quality light field reconstruction, the first comparative noise analysis of light field attenuation masks, and an analysis of aliasing in multiplexing applications.
Anatomy: Simple and Effective Privacy Preservation

This paper presents a novel technique, anatomy, for publishing sensitive data. Anatomy releases all the quasi-identifier and sensitive values directly in two separate tables. Combined with a grouping mechanism, this approach protects privacy, and captures a large amount of correlation in the microdata. We develop a linear-time algorithm for computing anatomized tables that obey the l-diversity privacy requirement, and minimize the error of reconstructing the microdata. Extensive experiments confirm that our technique allows significantly more effective data analysis than the conventional publication method based on generalization. Specifically, anatomy permits aggregate reasoning with average error below 10%, which is lower than the error obtained from a generalized table by orders of magnitude.
Simple statistical gradient-following algorithms for connectionist reinforcement learning

This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which such estimates could be computed. Specific examples of such algorithms are presented, some of which bear a close relationship to certain existing algorithms while others are novel but potentially interesting in their own right. Also given are results that show how such algorithms can be naturally integrated with backpropagation. We close with a brief discussion of a number of additional issues surrounding the use of such algorithms, including what is known about their limiting behaviors as well as further considerations that might be used to help develop similar but potentially more powerful reinforcement learning algorithms.
A study on exception detecton and handling using aspect-oriented programming

Aspect-Oriented Programming (AOP) is intended to ease situations that involve many kinds of code tangling. This paper reports on a study to investigate AOP's ability to ease tangling related to exception detection and handling. We took an existing framework written in Java™, the JWAM framework, and partially reengineered its exception detection and handling aspects using AspectJ™, an aspect-oriented programming extension to Java.
We found that AspectJ supported implementations that drastically reduced the portion of the code related to exception detection and handling. In one scenario, we were able to reduce that code by a factor of 4. We also found that, with respect to the original implementation in plain Java, AspectJ provided better support for different configurations of exceptional behaviors, more tolerance for changes in the specifications of exceptional behaviors, better support for incremental development, better reuse, automatic enforcement of contracts in applications that use the framework, and cleaner program texts. We also found some weaknesses of AspectJ that should be addressed in the future.
Money Laundering Detection using Synthetic Data

Criminals use money laundering to make the proceeds from their illegal activities look legitimate in the eyes of the rest of society. Current countermeasures taken by financial organizations are based on legal requirements and very basic statistical analysis. Machine Learning offers a number of ways to detect anomalous transactions. These methods can be based on supervised and unsupervised learning algorithms that improve the performance of detection of such criminal activity. In this study we present an analysis of the difficulties and considerations of applying machine learning techniques to this problem. We discuss the pros and cons of using synthetic data and problems and advantages inherent in the generation of such a data set. We do this using a case study and suggest an approach based on Multi-Agent Based Simulations (MABS).
Incentive Mechanisms for Crowdsourcing Platforms

Crowdsourcing emerged with the development of Web 2.0 technologies as a distributed online practice that harnesses the collective aptitudes and skills of the crowd in order to reach specific goals. The success of crowdsourcing systems is influenced by the users’ levels of participation and interactions on the platform. Therefore, there is a need for the incorporation of appropriate incentive mechanisms that would lead to sustained user engagement and quality contributions. Accordingly, the aim of the particular paper is threefold: first, to provide an overview of user motives and incentives, second, to present the corresponding incentive mechanisms used to trigger these motives, alongside with some indicative examples of successful crowdsourcing platforms that incorporate these incentive mechanisms, and third, to provide recommendations on their careful design in order to cater to the context and goal of the platform.
Simultaneous Authentication of Equals: A Secure, Password-Based Key Exchange for Mesh Networks

We propose a simple protocol for authentication using only a password. The result of the protocol is a cryptographically strong shared secret for securing other data - e.g. network communication. SAE is resistant to passive attack, active attack, and dictionary attack. It provides a secure alternative to using certificates or when a centralized authority is not available. It is a peer-to-peer protocol, has no asymmetry, and supports simultaneous initiation. It is therefore well-suited for use in mesh networks. It supports the ability to tradeoff speed for strength of the resulting shared key. SAE has been implemented for 802.11-based mesh networks and can easily be adapted to other wireless mesh technology.
Auditing disclosure by relevance ranking

Numerous widely publicized cases of theft and misuse of private information underscore the need for audit technology to identify the sources of unauthorized disclosure. We present an auditing methodology that ranks potential disclosure sources according to their proximity to the leaked records. Given a sensitive table that contains the disclosed data, our methodology prioritizes by relevance the past queries to the database that could have potentially been used to produce the sensitive table. We provide three conceptually different measures of proximity between the sensitive table and a query result. One measure is inspired by information retrieval in text processing, another is based on statistical record linkage, and the third computes the derivation probability of the sensitive table in a tree-based generative model. We also analyze the characteristics of the three measures and the corresponding ranking algorithms.
Grasp planning: how to choose a suitable task wrench space

For the evaluation of grasp quality, different measures have been proposed that are based on wrench spaces. Almost all of them have drawbacks that derive from the non-uniformity of the wrench space, composed of force and torque dimensions. Moreover, many of these approaches are computationally expensive. We address the problem of choosing a proper task wrench space to overcome the problems of the non-uniform wrench space and show how to integrate it in a well-known, high precision and extremely fast computable grasp quality measure.
A Survey on Intrusion Detection in Mobile Ad Hoc Networks

In recent years, the use of mobile ad hoc networks (MANETs) has been widespread in many applications, including some mission critical applications, and as such security has become one of the major concerns in MANETs. Due to some unique characteristics of MANETs, prevention methods alone are not sufficient to make them secure; therefore, detection should be added as another defense before an attacker can breach the system. In general, the intrusion detection techniques for traditional wireless networks are not well suited for MANETs. In this paper, we classify the architectures for intrusion detection systems (IDS) that have been introduced for MANETs. Current IDS’s corresponding to those architectures are also reviewed and compared. We then provide some directions for future research.
Everyday robotics: robots as everyday objects

Why are we not living yet with robots? If robots are not common everyday objects, it is maybe because we have looked for robotic applications without considering with sufficient attention what could be the experience of interacting with a robot. This article introduces the idea of a value profile, a notion intended to capture the general evolution of our experience with different kinds of objects. After discussing value profiles of commonly used objects, it offers a rapid outline of the challenging issues that must be investigated concerning immediate, short-term and long-term experience with robots. Beyond science-fiction classical archetypes, the picture emerging from this analysis is the one of versatile everyday robots, autonomously developing in interaction with humans, communicating with one another, changing shape and body in order to be adapted to their various context of use. To become everyday objects, robots will not necessary have to be useful, but they will have to be at the origins of radically new forms of experiences.
Modelling mobility in disaster area scenarios

This paper provides a model that realistically represents the movements in a disaster area scenario. The model is based on an analysis of tactical issues of civil protection. This analysis provides characteristics influencing network performance in public safety communication networks like heterogeneous area-based movement, obstacles, and joining/leaving of nodes. As these characteristics cannot be modelled with existing mobility models, we introduce a new disaster area mobility model. To examine the impact of our more realistic modelling, we compare it to existing ones (modelling the same scenario) using different pure movement and link based metrics. The new model shows specific characteristics like heterogeneous node density. Finally, the impact of the new model is evaluated in an exemplary simulative network performance analysis. The simulations show that the new model discloses new information and has a significant impact on performance analysis.
Audio-visual deep learning for noise robust speech recognition

Deep belief networks (DBN) have shown impressive improvements over Gaussian mixture models for automatic speech recognition. In this work we use DBNs for audio-visual speech recognition; in particular, we use deep learning from audio and visual features for noise robust speech recognition. We test two methods for using DBNs in a multimodal setting: a conventional decision fusion method that combines scores from single-modality DBNs, and a novel feature fusion method that operates on mid-level features learned by the single-modality DBNs. On a continuously spoken digit recognition task, our experiments show that these methods can reduce word error rate by as much as 21% relative over a baseline multi-stream audio-visual GMM/HMM system.
The serverless trilemma: function composition for serverless computing

The field of serverless computing has recently emerged in support of highly scalable, event-driven applications. A serverless application is a set of stateless functions, along with the events that should trigger their activation. A serverless runtime allocates resources as events arrive, avoiding the need for costly pre-allocated or dedicated hardware. 
While an attractive economic proposition, serverless computing currently lags behind the state of the art when it comes to function composition. This paper addresses the challenge of programming a composition of functions, where the composition is itself a serverless function. 
We demonstrate that engineering function composition into a serverless application is possible, but requires a careful evaluation of trade-offs. To help in evaluating these trade-offs, we identify three competing constraints: functions should be considered as black boxes; function composition should obey a substitution principle with respect to synchronous invocation; and invocations should not be double-billed. 
Furthermore, we argue that, if the serverless runtime is limited to a reactive core, i.e. one that deals only with dispatching functions in response to events, then these constraints form the serverless trilemma. Without specific runtime support, compositions-as-functions must violate at least one of the three constraints. 
Finally, we demonstrate an extension to the reactive core of an open-source serverless runtime that enables the sequential composition of functions in a trilemma-satisfying way. We conjecture that this technique could be generalized to support other combinations of functions.
Processing directed acyclic graphs with recursive neural networks

Recursive neural networks are conceived for processing graphs and extend the well-known recurrent model for processing sequences. In Frasconi et al. (1998), recursive neural networks can deal only with directed ordered acyclic graphs (DOAGs), in which the children of any given node are ordered. While this assumption is reasonable in some applications, it introduces unnecessary constraints in others. In this paper, it is shown that the constraint on the ordering can be relaxed by using an appropriate weight sharing, that guarantees the independence of the network output with respect to the permutations of the arcs leaving from each node. The method can be used with graphs having low connectivity and, in particular, few outcoming arcs. Some theoretical properties of the proposed architecture are given. They guarantee that the approximation capabilities are maintained, despite the weight sharing.