nils-herrmann/clean_scirepeval huggingface dataset:
The Need for Design Thinking in Business Schools

The demands placed on today’s organizations and their managers suggest that we have to develop pedagogies combining analytic reasoning with a more exploratory skill set that design practitioners have embraced and business schools have traditionally neglected. Design thinking is an iterative, exploratory process involving visualizing, experimenting, creating, and prototyping of models, and gathering feedback. It is a particularly apt method for addressing innovation and messy, ill-structured situations. We discuss key characteristics of design thinking, link design-thinking characteristics to recent studies of cognition, and note how the repertoire of skills and methods that embody design thinking can address deficits in business school education. ........................................................................................................................................................................
Study on equivalent consumption minimization strategy for fuel cell hybrid tramway

Fuel cell hybrid tramway has gained increasing attention recently and energy management strategy (EMS) is one of its key technologies. A hybrid tramway power system consisting of proton exchange membrane fuel cell (PEMFC) and battery is designed in the MATLAB /SIMULINK software as basic for the energy management strategy research. An equivalent consumption minimization strategy (ECMS) for hybrid tramway is proposed and embedded into the aforementioned hybrid model. In order to evaluate the proposed energy management, a real tramway driving cycle is adopted to simulate in RT-LAB platform. The simulation results prove the effectiveness of the proposed EMS.
An Empirical Analysis of Network Externalities in Peer-to-Peer Music-Sharing Networks

P (P2P) file sharing networks are an important medium for the distribution of information goods. However, there is little empirical research into the optimal design of these networks under real-world conditions. Early speculation about the behavior of P2P networks has focused on the role that positive network externalities play in improving performance as the network grows. However, negative network externalities also arise in P2P networks because of the consumption of scarce network resources or an increased propensity of users to free ride in larger networks, and the impact of these negative network externalities—while potentially important—has received far less attention. Our research addresses this gap in understanding by measuring the impact of both positive and negative network externalities on the optimal size of P2P networks. Our research uses a unique dataset collected from the six most popular OpenNap P2P networks between December 19, 2000, and April 22, 2001. We find that users contribute additional value to the network at a decreasing rate and impose costs on the network at an increasing rate, while the network increases in size. Our results also suggest that users are less likely to contribute resources to the network as the network size increases. Together, these results suggest that the optimal size of these centralized P2P networks is bounded—At some point the costs that a marginal user imposes on the network will exceed the value they provide to the network. This finding is in contrast to early predictions that larger P2P networks would always provide more value to users than smaller networks. Finally, these results also highlight the importance of considering user incentives—an important determinant of resource sharing in P2P networks—in network design.
Adult PTSD and Its Treatment With EMDR : A Review of Controversies , Evidence , and Theoretical Knowledge

117 Experiencing trauma is an essential part of being human; history is written in blood” (van der Kolk & McFarlane, 1996, p. 3). As humans, however, we do have an extraordinary ability to adapt to trauma, and resilience is our most common response (Bonanno, 2005). Nonetheless, traumatic experiences can alter one’s social, psychological, and biological equilibrium, and for years memories of the event can taint experiences in the present. Despite advances in our knowledge of posttraumatic stress disorder (PTSD) and the development of psychosocial treatments, almost half of those who engage in treatment for PTSD fail to fully recover (Bradley, Greene, Russ, Dutra, & Westen, 2005). Furthermore, no theory as yet provides an adequate account of all the complex phenomena and processes involved in PTSD, and our understanding of the mechanisms that underlie effective treatment, such as eye movement desensitization and reprocessing (EMDR) and exposure therapy remains unclear. Historical Overview of PTSD
Social Networks Under Stress

Social network research has begun to take advantage of finegrained communications regarding coordination, decisionmaking, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network “turtles up”. It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.
Learning Biological Processes with Global Constraints

Biological processes are complex phenomena involving a series of events that are related to one another through various relationships. Systems that can understand and reason over biological processes would dramatically improve the performance of semantic applications involving inference such as question answering (QA) – specifically “How?” and “Why?” questions. In this paper, we present the task of process extraction, in which events within a process and the relations between the events are automatically extracted from text. We represent processes by graphs whose edges describe a set of temporal, causal and co-reference event-event relations, and characterize the structural properties of these graphs (e.g., the graphs are connected). Then, we present a method for extracting relations between the events, which exploits these structural properties by performing joint inference over the set of extracted relations. On a novel dataset containing 148 descriptions of biological processes (released with this paper), we show significant improvement comparing to baselines that disregard process structure.
The pharmacology of psilocybin.

Psilocybin (4-phosphoryloxy-N,N-dimethyltryptamine) is the major psychoactive alkaloid of some species of mushrooms distributed worldwide. These mushrooms represent a growing problem regarding hallucinogenic drug abuse. Despite its experimental medical use in the 1960s, only very few pharmacological data about psilocybin were known until recently. Because of its still growing capacity for abuse and the widely dispersed data this review presents all the available pharmacological data about psilocybin.
Self-Attentive Residual Decoder for Neural Machine Translation

Baseline NMT decoder Self-attentive residual decoder p(yt|y1, ..., yt−1, ct) ≈ g(ht, ct, yt−1) p(yt|y1, ..., yt−1, ct) ≈ g(ht, ct, dt) ht = f (ht−1, yt−1) ht = f (ht−1, yt−1) dt = fa(y1, ..., yt−1) •The baseline NMT decoder uses a residual connection to the previously predicted word yt−1 •We propose to use residual connections from all previously translated words y1, ..., yt−1 with a summary vector dt.
A scientometric review of emerging trends and new developments in recommendation systems

Recommendation systems have drawn an increasingly broad range of interest since early 1990s. Recently, a search with the query of “recommendation systems” on Google Scholar found over 32,000 documents. As the volume of the literature grows rapidly, thus, a systematic review of the diverse research field and its current challenges becomes essential. This study surveys the literature of recommendation systems between 1992 and 2014. The overall structure of its intellectual landscape is illustrated in terms of thematic concentrations of co-cited references and emerging trends of bursting keywords and citations to references. Our review is based on two sets of bibliographic records retrieved from the Web of Science. The core dataset, obtained through a topic search, contains 2573 original research and review articles. The expanded dataset, consisting of 12,916 articles and reviews, was collected by citation expansion. We identified intellectual landscapes, landmark articles and bursting keywords of the domain in core and broader perspectives. We found that a number of landmark studies in 1980s and 1990s and techniques such as LDA, pLSI, and matrix factorization have tremendously influenced the development of the recommendation systems research. Furthermore, our study reveals that the field of recommendation systems is still evolving and developing. Thematic trends in recommendation systems research reflect the development of a wide variety of information systems such as the World Wide Web and social media. Finally, collaborative filtering has been a dominant research concept of the field. Recent emerging topics focus on enhancing the effectiveness of recommendation systems by addressing diverse challenges.
IEC 61850 Communication Networks and Systems In Substations : An Overview for Users

Over the last decade, the “digitization” of the electron enterprise has grown at exponential rates. Utility, industrial, commercial, and even residential consumers are transforming all aspects of their lives into the digital domain. Moving forward, it is expected that every piece of equipment, every receptacle, every switch, and even every light bulb will possess some type of setting, monitoring and/or control. In order to be able to manage the large number of devices and to enable the various devices to communicate with one another, a new communication model was needed. That model has been developed and standardized as IEC61850 – Communication Networks and Systems in Substations. This paper looks at the needs of next generation communication systems and provides an overview of the IEC61850 protocol and how it meets these needs. I. Communication System Needs Communication has always played a critical role in the real-time operation of the power system. In the beginning, the telephone was used to communicate line loadings back to the control center as well as to dispatch operators to perform switching operations at substations. Telephoneswitching based remote control units were available as early as the 1930’s and were able to provide status and control for a few points. As digital communications became a viable option in the 1960’s, data acquisition systems (DAS) were installed to automatically collect measurement data from the substations. Since bandwidth was limited, DAS communication protocols were optimized to operate over low-bandwidth communication channels. The “cost” of this optimization was the time it took to configure, map, and document the location of the various data bits received by the protocol. As we move into the digital age, literally thousands of analog and digital data points are available in a single Intelligent Electronic Device (IED) and communication bandwidth is no longer a limiting factor. Substation to master communication data paths operating at 64,000 bits per second are becoming commonplace with an obvious migration path to much high rates. With this migration in technology, the “cost” component of a data acquisition system has now become the configuration and documentation component. Consequently, a key component of a communication system is the ability to describe themselves from both a data and services (communication functions that an IED performs) perspective. Other “key” requirements include: • High-speed IED to IED communication
Design and implementation of a CUDA-compatible GPU-based core for gapped BLAST algorithm

This paper presents the first ever reported implementation of the Gapped Basic Local Alignment Search Tool (Gapped BLAST) for biological sequence alignment, with the Two-Hit method, on CUDA (compute unified device architecture)-compatible Graphic Processing Units (GPUs). The latter have recently emerged as relatively low cost and easy to program high performance platforms for general purpose computing. Our Gapped BLAST implementation on an NVIDIA Geforce 8800 GTX GPU is up to 2.7x quicker than the most optimized CPU-based implementation, namely NCBI BLAST, running on a Pentium4 3.4 GHz desktop computer with 2GB RAM.
Stiffer double-stranded DNA in two-dimensional confinement due to bending anisotropy.

Using analytical approach and Monte Carlo (MC) simulations, we study the elastic behavior of the intrinsically twisted elastic ribbons with bending anisotropy, such as double-stranded DNA (dsDNA), in two-dimensional (2D) confinement. We show that, due to the bending anisotropy, the persistence length of dsDNA in 2D conformations is always greater than three-dimensional (3D) conformations. This result is in consistence with the measured values for DNA persistence length in 2D and 3D in equal biological conditions. We also show that in two dimensions, an anisotropic, intrinsically twisted polymer exhibits an implicit twist-bend coupling, which leads to the transient curvature increasing with a half helical turn periodicity along the bent polymer.
A Distributed Access Control System for Cloud Federations

Cloud federations are a new collaboration paradigm where organizations share data across their private cloud infrastructures. However, the adoption of cloud federations is hindered by federated organizations' concerns on potential risks of data leakage and data misuse. For cloud federations to be viable, federated organizations' privacy concerns should be alleviated by providing mechanisms that allow organizations to control which users from other federated organizations can access which data. We propose a novel identity and access management system for cloud federations. The system allows federated organizations to enforce attribute-based access control policies on their data in a privacy-preserving fashion. Users are granted access to federated data when their identity attributes match the policies, but without revealing their attributes to the federated organization owning data. The system also guarantees the integrity of the policy evaluation process by using block chain technology and Intel SGX trusted hardware. It uses block chain to ensure that users identity attributes and access control policies cannot be modified by a malicious user, while Intel SGX protects the integrity and confidentiality of the policy enforcement process. We present the access control protocol, the system architecture and discuss future extensions.
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion

Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links. Facts can however also be represented using pairwise embeddings, i.e. embeddings for pairs of entities and relations. In this paper we explore such bigram embeddings with a flexible Factorization Machine model and several ablations from it. We investigate the relevance of various bigram types on the fb15k237 dataset and find relative improvements compared to a compositional model.
Exploring many-core architecture design space for parallel discrete event simulation

As multicore and manycore processor architectures are emerging and the core counts per chip continue to increase, it is important to evaluate and understand the performance and scalability of Parallel Discrete Event Simulation (PDES) on these platforms. Most existing architectures are still limited to a modest number of cores, feature simple designs and do not exhibit heterogeneity, making it impossible to perform comprehensive analysis and evaluations of PDES on these platforms. Instead, in this paper we evaluate PDES using a full-system cycle-accurate simulator of a multicore processor and memory subsystem. With this approach, it is possible to flexibly configure the simulator and perform exploration of the impact of architecture design choices on the performance of PDES. In particular, we answer the following four questions with respect to PDES performance and scalability: (1) For the same total chip area, what is the best design point in terms of the number of cores and the size of the on-chip cache? (2) What is the impact of using in-order vs. out-of-order cores? (3) What is the impact of a heterogeneous system with a mix of in-order and out-of-order cores? (4) What is the impact of object partitioning on PDES performance in heterogeneous systems? To answer these questions, we use MARSSx86 simulator for evaluating performance, and rely on Cacti and McPAT tools to derive the area and latency estimates for cores and caches.
PrDOS: prediction of disordered protein regions from amino acid sequence

PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.
A Fuzzy Approach to Social Network Analysis

Adjacency relations for social network analysis have usually been tackled in their bidimensional form, in the sense that relations are computed over pairs of objects. Nevertheless, this paper considers the bidimensional case as restrictive and it proposes an approach where the dimension of the analysis is not limited to binary relations. With the aid of fuzzy logic and OWA operators, it is showed that the interpretation of m-ary adjacency relations is the same of binary relations and therefore they can consistently be employed in social network analysis and some novel results be derived. Besides justifying the use of m-ary relations, the paper proposes a way to characterize them and, eventually, it will provide the reader with an example section.
Direct control method applied for improved incremental conductance mppt using SEPIC converter

Solar energy forms the major alternative for the generation of power keeping in mind the sustainable development with reduced greenhouse emission. For improved efficiency of the MPPT which uses solar energy in photovoltaic systems(PV), this paper presents a technique utilizing improved incremental conductance(Inc Cond) MPPT with direct control method using SEPIC converter. Several improvements in the existing technique is proposed which includes converter design aspects, system simulation & DSP programming. For the control part dsPIC30F2010 is programmed accordingly to get the maximum power point for different illuminations. DSP controller also forms the interfacing of PV array with the load. Now the improved Inc Cond helps to get point to point values accurately to track MPP's under different atmospheric conditions. MATLAB and Simulink were employed for simulation studies validation of the proposed technique. Experiment result proves the improvement from existing method.
Evolution as a constraint on theories of syntax : the case against minimalism

This thesis investigates the evolutionary plausibility of the Minimalist Program. Is such a theory of language reasonable given the assumption that the human linguistic capacity has been subject to the usual forces and processes of evolution? More generally, this thesis is a comment on the manner in which theories of language can and should be constrained. What are the constraints that must be taken into account when constructing a theory of language? These questions are addressed by applying evidence gathered in evolutionary biology to data from linguistics. The development of generative syntactic theorising in the late 20th century has led to a much redesigned conception of the human language faculty. The driving question ‘why is language the way it is?’ has prompted assumptions of simplicity, perfection, optimality, and economy for language; a minimal system operating in an economic fashion to fit into the larger cognitive architecture in a perfect manner. Studies in evolutionary linguistics, on the other hand, have been keen to demonstrate that language is complex, redundant, and adaptive, Pinker & Bloom’s (1990) seminal paper being perhaps the prime example of this. The question is whether these opposing views can be married in any way. Interdisciplinary evidence is brought to bear on this problem, demonstrating that any reconciliation is impossible. Evolutionary biology shows that perfection, simplicity, and economy do not arise in typically evolving systems, yet the Minimalist Program attaches these characteristics to language. It shows that evolvable systems exhibit degeneracy, modularity, and robustness, yet the Minimalist Program must rule these features out for language. It shows that evolution exhibits a trend towards complexity, yet the Minimalist Program excludes such a depiction of language.
Impact of structural and psychological empowerment on job strain in nursing work settings: expanding Kanter's model.

In this study, we tested an expanded model of Kanter's structural empowerment, which specified the relationships among structural and psychological empowerment, job strain, and work satisfaction.



Strategies proposed in Kanter's empowerment theory have the potential to reduce job strain and improve employee work satisfaction and performance in current restructured healthcare settings. The addition to the model of psychological empowerment as an outcome of structural empowerment provides an understanding of the intervening mechanisms between structural work conditions and important organizational outcomes.



A predictive, nonexperimental design was used to test the model in a random sample of 404 Canadian staff nurses. The Conditions of Work Effectiveness Questionnaire, the Psychological Empowerment Questionnaire, the Job Content Questionnaire, and the Global Satisfaction Scale were used to measure the major study variables.



Structural equation modelling analyses revealed a good fit of the hypothesized model to the data based on various fit indices (chi 2 = 1140, df = 545, chi 2/df ratio = 2.09, CFI = 0.986, RMSEA = 0.050). The amount of variance accounted for in the model was 58%. Staff nurses felt that structural empowerment in their workplace resulted in higher levels of psychological empowerment. These heightened feelings of psychological empowerment in turn strongly influenced job strain and work satisfaction. However, job strain did not have a direct effect on work satisfaction.



These results provide initial support for an expanded model of organizational empowerment and offer a broader understanding of the empowerment process.