nils-herrmann/clean_scirepeval huggingface dataset:
Profiling grounded theory approaches in information systems research

3 October 2009  22 June 2011  3 July 2011  Studies claiming to use the Grounded theory methodology (GTM) have been quite prevalent in information systems (IS) literature. A cursory review of this literature reveals conflict in the understanding of GTM, with a variety of grounded theory approaches apparent. The purpose of this investigation was to establish what alternative grounded theory approaches have been employed in IS, and to what extent each has been used. In order to accomplish this goal, a comprehensive set of IS articles that claimed to have followed a grounded theory approach were reviewed. The articles chosen were those published in the widely acknowledged top eight IS-centric journals, since these journals most closely represent exemplar IS research. Articles for the period 1985-2008 were examined. The analysis revealed four main grounded theory approaches in use, namely (1) the classic grounded theory approach, (2) the evolved grounded theory approach, (3) the use of the grounded theory approach as part of a mixed methodology, and (4) the application of grounded theory techniques, typically for data analysis purposes. The latter has been the most common approach in IS research. The classic approach was the least often employed, with many studies opting for an evolved or mixed method approach. These and other findings are discussed and implications drawn.  (2013) 22, 119–129. doi:10.1057/ejis.2011.35; published online 30 August 2011
HUMAN RESOURCE MANAGEMENT PRACTICES AND FIRM PERFORMANCE : THE MODERATING ROLES OF STRATEGIES AND ENVIRONMENTAL UNCERTAINTIES by NORMALA

Over the last decade, much research has been conducted in the field of human resource management (HRM) and its associations with firm performance. Prior studies have found substantial positive evidence for statistical associations between HRM practices and improved firm performance. The purpose of this study is to investigate the relationships between HRM practices and firm performance with business strategy and environmental uncertainty as moderators. This study examines the relationships among HRM practices, environmental uncertainty, business strategy and firm performance. It was hypothesized that HRM practices could positively influenced profitability and growth and negatively influenced employee turnover. Data were collected using mail questionnaire sent to human resource managers in manufacturing firms in Malaysia. A total of 162 useable responses were obtained and used for the purpose of analysis. Results of hierarchical regression used to test the relationships among the variables indicated that (1) human resource planning has a relationship with profitability and growth; (2) performance-based pay has a relationship with profitability and growth; (3) skills development has a relationship with involuntary employee turnover; (4) environmental uncertainty as a moderator influence the relationship between human resource planning, performance-based pay and profitability; and (5) business strategy as a moderator influences the relationship between performance-based pay and growth. The findings can form the basis for useful recommendations for Malaysian managers in encouraging the practice of human resource management and for employees who are
TGF-β: the master regulator of fibrosis

Transforming growth factor-β (TGF-β) is the primary factor that drives fibrosis in most, if not all, forms of chronic kidney disease (CKD). Inhibition of the TGF-β isoform, TGF-β1, or its downstream signalling pathways substantially limits renal fibrosis in a wide range of disease models whereas overexpression of TGF-β1 induces renal fibrosis. TGF-β1 can induce renal fibrosis via activation of both canonical (Smad-based) and non-canonical (non-Smad-based) signalling pathways, which result in activation of myofibroblasts, excessive production of extracellular matrix (ECM) and inhibition of ECM degradation. The role of Smad proteins in the regulation of fibrosis is complex, with competing profibrotic and antifibrotic actions (including in the regulation of mesenchymal transitioning), and with complex interplay between TGF-β/Smads and other signalling pathways. Studies over the past 5 years have identified additional mechanisms that regulate the action of TGF-β1/Smad signalling in fibrosis, including short and long noncoding RNA molecules and epigenetic modifications of DNA and histone proteins. Although direct targeting of TGF-β1 is unlikely to yield a viable antifibrotic therapy due to the involvement of TGF-β1 in other processes, greater understanding of the various pathways by which TGF-β1 controls fibrosis has identified alternative targets for the development of novel therapeutics to halt this most damaging process in CKD.
Public Key Encryption with keyword Search

We study the problem of searching on data that is encrypted using a public key system. Consider user Bob who sends email to user Alice encrypted under Alice’s public key. An email gateway wants to test whether the email contains the keyword “urgent” so that it could route the email accordingly. Alice, on the other hand does not wish to give the gateway the ability to decrypt all her messages. We define and construct a mechanism that enables Alice to provide a key to the gateway that enables the gateway to test whether the word “urgent” is a keyword in the email without learning anything else about the email. We refer to this mechanism as Public Key Encryption with keyword Search. As another example, consider a mail server that stores various messages publicly encrypted for Alice by others. Using our mechanism Alice can send the mail server a key that will enable the server to identify all messages containing some specific keyword, but learn nothing else. We define the concept of public key encryption with keyword search and give several constructions.
Can Work Be Fun? Improving Task Motivation and Help-Seeking Through Game Mechanics

Gamification has become an increasingly important design paradigm for information systems, impacting both research and practice. Effective gamification requires designers to tap into intrinsic motivations by framing tasks in ways that evoke positive attitudes through challenge and friendly competition. These positive attitudes can lead to greater engagement and encourage help-seeking behavior, a key goal of developing motivated learners. The objective of this paper is to understand the conditions and types of mechanisms that that encourage participants to become engaged in a task and seek help to improve. Based on the literature on the game element hierarchy and goal orientation, we propose a theoretical model to explain how different types of learners will respond to different game mechanisms. We outline the design an experiment to test
Social learning theory and the Health Belief Model.

The Health Belief Model, social learning theory (recently relabelled social cognitive theory), self-efficacy, and locus of control have all been applied with varying success to problems of explaining, predicting, and influencing behavior. Yet, there is conceptual confusion among researchers and practitioners about the interrelationships of these theories and variables. This article attempts to show how these explanatory factors may be related, and in so doing, posits a revised explanatory model which incorporates self-efficacy into the Health Belief Model. Specifically, self-efficacy is proposed as a separate independent variable along with the traditional health belief variables of perceived susceptibility, severity, benefits, and barriers. Incentive to behave (health motivation) is also a component of the model. Locus of control is not included explicitly because it is believed to be incorporated within other elements of the model. It is predicted that the new formulation will more fully account for health-related behavior than did earlier formulations, and will suggest more effective behavioral interventions than have hitherto been available to health educators.
Secondary metabolism in cannabis

Cannabis sativa L. is an annual dioecious plant from Central Asia. Cannabinoids, flavonoids, stilbenoids, terpenoids, alkaloids and lignans are some of the secondary metabolites present in C. sativa. Earlier reviews were focused on isolation and identification of more than 480 chemical compounds; this review deals with the biosynthesis of the secondary metabolites present in this plant. Cannabinoid biosynthesis and some closely related pathways that involve the same precursors are disscused.
A Mood-Based Music Classification and Exploration System by

Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.  Professor of Media Arts and Sciences, Program in Media Arts and Sciences
Text-guided Attention Model for Image Captioning

Visual attention plays an important role to understand images and demonstrates its effectiveness in generating natural language descriptions of images. On the other hand, recent studies show that language associated with an image can steer visual attention in the scene during our cognitive process. Inspired by this, we introduce a text-guided attention model for image captioning, which learns to drive visual attention using associated captions. For this model, we propose an exemplarbased learning approach that retrieves from training data associated captions with each image, and use them to learn attention on visual features. Our attention model enables to describe a detailed state of scenes by distinguishing small or confusable objects effectively. We validate our model on MSCOCO Captioning benchmark and achieve the state-of-theart performance in standard metrics.
Customers Churn Prediction and Attribute Selection in Telecom Industry Using Kernelized Extreme Learning Machine and Bat Algorithms

With the fast development of digital systems and concomitant information technologies, there is certainly an incipient spirit in the extensive overall economy to put together digital Customer Relationship Management (CRM) systems. This slanting is further more palpable in the telecommunications industry, in which businesses turn out to be increasingly digitalized. Customer churn prediction is a foremost aspect of a contemporary telecom CRM system. Churn prediction model leads the customer relationship management to retain the customers who will be possible to give up. Currently scenario, a lot of outfit and monitored classifiers and data mining techniques are employed to model the churn prediction in telecom. Within this paper, Kernelized Extreme Learning Machine (KELM) algorithm is proposed to categorize customer churn patterns in telecom industry. The primary strategy of proposed work is organized the data from telecommunication mobile customer’s dataset. The data preparation is conducted by using preprocessing with Expectation Maximization (EM) clustering algorithm. After that, customer churn behavior is examined by using Naive Bayes Classifier (NBC) in accordance with the four conditions like customer dissatisfaction (H1), switching costs (H2), service usage (H3) and customer status (H4). The attributes originate from call details and customer profiles which is enhanced the precision of customer churn prediction in the telecom industry. The attributes are measured using BAT algorithm and KELM algorithm used for churn prediction. The experimental results prove that proposed model is better than AdaBoost and Hybrid Support Vector Machine (HSVM) models in terms of the performance of ROC, sensitivity, specificity, accuracy and processing time.
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study

Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of base-
Featherweight Java: a minimal core calculus for Java and GJ

Several recent studies have introduced lightweight versions of Java: reduced languages in which complex features like threads and reflection are dropped to enable rigorous arguments about key properties such as type safety. We carry this process a step further, omitting almost all features of the full language (including interfaces and even assignment) to obtain a small calculus, Featherweight Java, for which rigorous proofs are not only possible but easy. Featherweight Java bears a similar relation to Java as the lambda-calculus does to languages such as ML and Haskell. It offers a similar computational "feel," providing classes, methods, fields, inheritance, and dynamic typecasts with a semantics closely following Java's. A proof of type safety for Featherweight Java thus illustrates many of the interesting features of a safety proof for the full language, while remaining pleasingly compact. The minimal syntax, typing rules, and operational semantics of Featherweight Java make it a handy tool for studying the consequences of extensions and variations. As an illustration of its utility in this regard, we extend Featherweight Java with generic classes in the style of GJ (Bracha, Odersky, Stoutamire, and Wadler) and give a detailed proof of type safety. The extended system formalizes for the first time some of the key features of GJ.
YALMIP : a toolbox for modeling and optimization in MATLAB

The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, developed initially to model SDPs and solve these by interfacing eternal solvers. The toolbox makes development of optimization problems in general, and control oriented SDP problems in particular, extremely simple. In fact, learning 3 YALMIP commands is enough for most users to model and solve the optimization problems
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions

Knowledge graph embedding represents entities and relations in knowledge graph as low-dimensional, continuous vectors, and thus enables knowledge graph compatible with machine learning models. Though there have been a variety of models for knowledge graph embedding, most methods merely concentrate on the fact triples, while supplementary textual descriptions of entities and relations have not been fully employed. To this end, this paper proposes the semantic space projection (SSP) model which jointly learns from the symbolic triples and textual descriptions. Our model builds interaction between the two information sources, and employs textual descriptions to discover semantic relevance and offer precise semantic embedding. Extensive experiments show that our method achieves substantial improvements against baselines on the tasks of knowledge graph completion and entity classification. Papers, Posters, Slides, Datasets and Codes: http://www.ibookman.net/conference.html
The SAP HANA Database -- An Architecture Overview

Requirements of enterprise applications have become much more demanding. They require the computation of complex reports on transactional data while thousands of users may read or update records of the same data. The goal of the SAP HANA database is the integration of transactional and analytical workload within the same database management system. To achieve this, a columnar engine exploits modern hardware (multiple CPU cores, large main memory, and caches), compression of database content, maximum parallelization in the database kernel, and database extensions required by enterprise applications, e.g., specialized data structures for hierarchies or support for domain specific languages. In this paper we highlight the architectural concepts employed in the SAP HANA database. We also report on insights gathered with the SAP HANA database in real-world enterprise application scenarios.
A novel Maximum Power Point tracking control of photovoltaic system under partial and rapidly fluctuating shadow conditions using Differential Evolution

Photovoltaic (PV) system performance extremely depends on local insolation and temperature conditions. Under partial shading, P-I characteristics of PV systems are complicated and may have multiple local maxima. Conventional Maximum Power Point Tracking (MPPT) techniques can easily fail to track global maxima and may be trapped in local maxima under partial shading; this can be one of main causes for reduced energy yield for many PV systems. In order to solve this problem, this paper proposes a novel Maximum Power Point tracking algorithm based on Differential Evolution (DE) that is capable of tracking global MPP under partial shaded conditions. The ability of proposed algorithm and its excellent performances are evaluated with conventional and popular algorithm by means of simulation. The proposed algorithm works in conjunction with a Boost (step up) DC-DC converter to track the global peak. Moreover, this paper includes a MATLAB-based modeling and simulation scheme suitable for photovoltaic characteristics under partial shading.
MARS: Memory Attention-Aware Recommender System

In this paper, we study the problem of modeling users’ diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users’ various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep adaptive user representations. Trained in an end-toend fashion, MARS adaptively summarizes users’ interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.
A knowledge-based SWOT-analysis system as an instrument for strategic planning in small and medium sized enterprises

Good performance within a company is the result of correct interaction of business management with its internal and external environment. The recognition of internal strengths and weaknesses, as well as external opportunities and threats, takes place on the basis of a SWOT-analysis. However, despite their importance, many companies often only have vague ideas of their competitive strengths and weaknesses, opportunities and threats. In this paper, the development of a knowledge-based system is described that can assist managers of small and medium sized companies in performing a SWOT-analysis. For our research purposes, we concentrated only on the identification of internal strengths and weaknesses. q 1999 Elsevier Science B.V. All rights reserved.
A Recurrent Encoder-Decoder Network for Sequential Face Alignment

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporalvariant factors, such as pose and expression, and temporalinvariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporalvariant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.
Analysis of proximal and distal muscle activity during handwriting tasks.

In this study we sought to describe upper-extremity proximal and distal muscle activity in typically developing children during a handwriting task and to explore the relationship between muscle activity and speed and quality of writing.



We evaluated 35 third- and fourth-grade Israeli children using the Alef-Alef Ktav Yad Hebrew Handwriting Test. Simultaneously, we recorded the participants' upper trapezius and thumb muscle activity by surface electromyography. Using the coefficient of variation (standard deviation divided by mean amplitude) as a measure of variability within each muscle, we analyzed differences in muscle activity variability within and between muscles.



The proximal muscle displayed significantly less variability than the distal muscles. Decreased variability in proximal muscle activity was associated with decreased variability in distal muscle activity, and decreased variability in the distal muscles was significantly associated with faster speed of writing.



The lower amount of variability exhibited in the proximal muscle compared with the distal muscles seems to indicate that the proximal muscle functions as a stabilizer during a handwriting task. In addition, decreased variability in both proximal and distal muscle activity appears to be more economical and is related to faster writing speed. Knowledge of the type of proximal and distal muscle activity used during handwriting can help occupational therapists plan treatment for children with handwriting disabilities.
A critical appraisal of "chronic Lyme disease".

This complex infection has a number of objective manifestations, including a characteristic skin lesion called erythema migrans (the most common presentation of early Lyme disease), certain neurologic and cardiac manifestations, and pauciarticular arthritis (the most common presentation of late Lyme disease), all of which usually respond well to conventional antibiotic therapy. Despite resolution of the objective manifestations of infection after antibiotic treatment, a minority of patients have fatigue, musculoskeletal pain, difficulties with concentration or short-term memory, or all of these symptoms. In this article, we refer to these usually mild and self-limiting subjective symptoms as “post–Lyme disease symptoms,” and if they last longer than 6 months, we call them “post–Lyme disease syndrome.”