Query: Musical training as an alternative and effective method for neuro-education and neuro-rehabilitation

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An objective and standardized test of hand function. <=SEP=> 

Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions <=SEP=> Neural correlates of the often-powerful emotional responses to music are poorly understood. Here we used positron emission tomography to examine cerebral blood flow (CBF) changes related to affective responses to music. Ten volunteers were scanned while listening to six versions of a novel musical passage varying systematically in degree of dissonance. Reciprocal CBF covariations were observed in several distinct paralimbic and neocortical regions as a function of dissonance and of perceived pleasantness/unpleasantness. The findings suggest that music may recruit neural mechanisms similar to those previously associated with pleasant/unpleasant emotional states, but different from those underlying other components of music perception, and other emotions such as fear.

Listening to polyphonic music recruits domain-general attention and working memory circuits. <=SEP=> Polyphonic music combines multiple auditory streams to create complex auditory scenes, thus providing a tool for investigating the neural mechanisms that orient attention in natural auditory contexts. Across two fMRI experiments, we varied stimuli and task demands in order to identify the cortical areas that are activated during attentive listening to real music. In individual experiments and in a conjunction analysis of the two experiments, we found bilateral blood oxygen level dependent (BOLD) signal increases in temporal (the superior temporal gyrus), parietal (the intraparietal sulcus), and frontal (the precentral sulcus, the inferior frontal sulcus and gyrus, and the frontal operculum) areas during selective and global listening, as compared with passive rest without musical stimulation. Direct comparisons of the listening conditions showed significant differences between attending to single timbres (instruments) and attending across multiple instruments, although the patterns that were observed depended on the relative demands of the tasks being compared. The overall pattern of BOLD signal increases indicated that attentive listening to music recruits neural circuits underlying multiple forms of working memory, attention, semantic processing, target detection, and motor imagery. Thus, attentive listening to music appears to be enabled by areas that serve general functions, rather than by music-specific cortical modules.

Brain organization for music processing. <=SEP=> Research on how the brain processes music is emerging as a rich and stimulating area of investigation of perception, memory, emotion, and performance. Results emanating from both lesion studies and neuroimaging techniques are reviewed and integrated for each of these musical functions. We focus our attention on the common core of musical abilities shared by musicians and nonmusicians alike. Hence, the effect of musical training on brain plasticity is examined in a separate section, after a review of the available data regarding music playing and reading skills that are typically cultivated by musicians. Finally, we address a currently debated issue regarding the putative existence of music-specific neural networks. Unfortunately, due to scarcity of research on the macrostructure of music organization and on cultural differences, the musical material under focus is at the level of the musical phrase, as typically used in Western popular music.

Taming the beast: Free and open-source massive point cloud web visualization <=SEP=> Powered by WebGL, some renderers have recently become available for the visualization of point cloud data over the web, for example Plasio or Potree. We have extended Potree to be able to visualize massive point clouds and we have successfully used it with the second national Lidar survey of the Netherlands, AHN2, with 640 billion points. In addition to the visualization, the publicly available service at http://ahn2.pointclouds.nl/ also features a multi-resolution download tool, a geographic name search bar, a measurement toolkit, a 2D orientation map with field of view depiction, a demo mode and the tuning of the visualization parameters. Potree relies on reorganizing the point cloud data into an multi-resolution octree data structure. However, this reorganization is very time consuming for massive data sets. Hence, we have used a divide and conquer approach to decrease the octree creation time. To achieve such performance improvement we divided the entire space into smaller cells, generated an octree for each of them in a distributed manner and then we merged them into a single massive octree. The merging is possible because the extent of all the nodes of the octrees is known and fixed. All the developed tools are free and open-source (FOSS) and they can be used to visualize over the web other massive point clouds.

Development and Validation of a Surgical Workload Measure: The Surgery Task Load Index (SURG-TLX) <=SEP=> The purpose of the present study was to develop and validate a multidimensional, surgery-specific workload measure (the SURG-TLX), and to determine its utility in providing diagnostic information about the impact of various sources of stress on the perceived demands of trained surgical operators. As a wide range of stressors have been identified for surgeons in the operating room, the current approach of considering stress as a unidimensional construct may not only limit the degree to which underlying mechanisms may be understood but also the degree to which training interventions may be successfully matched to particular sources of stress. The dimensions of the SURG-TLX were based on two current multidimensional workload measures and developed via focus group discussion. The six dimensions were defined as mental demands, physical demands, temporal demands, task complexity, situational stress, and distractions. Thirty novices were trained on the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task and then completed the task under various conditions designed to manipulate the degree and source of stress experienced: task novelty, physical fatigue, time pressure, evaluation apprehension, multitasking, and distraction. The results were supportive of the discriminant sensitivity of the SURG-TLX to different sources of stress. The sub-factors loaded on the relevant stressors as hypothesized, although the evaluation pressure manipulation was not strong enough to cause a significant rise in situational stress. The present study provides support for the validity of the SURG-TLX instrument and also highlights the importance of considering how different stressors may load surgeons. Implications for categorizing the difficulty of certain procedures, the implementation of new technology in the operating room (man–machine interface issues), and the targeting of stress training strategies to the sources of demand are discussed. Modifications to the scale to enhance clinical utility are also suggested.

SMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams <=SEP=> Data collection is required to be safe and efficient considering both data privacy and system performance. In this paper, we study a new problem: distributed data sharing with privacy-preserving requirements. Given a data demander requesting data from multiple distributed data providers, the objective is to enable the data demander to access the distributed data without knowing the privacy of any individual provider. The problem is challenged by two questions: how to transmit the data safely and accurately; and how to efficiently handle data streams? As the first study, we propose a practical method, Shadow Coding, to preserve the privacy in data transmission and ensure the recovery in data collection, which achieves privacy preserving computation in a data-recoverable, efficient, and scalable way. We also provide practical techniques to make Shadow Coding efficient and safe in data streams. Extensive experimental study on a large-scale real-life dataset offers insight into the performance of our schema. The proposed schema is also implemented as a pilot system in a city to collect distributed mobile phone data.

HMM-based Address Parsing with Massive Synthetic Training Data Generation <=SEP=> Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches of synthetic training data generation to build robust models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses.

Copysets: Reducing the Frequency of Data Loss in Cloud Storage <=SEP=> Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. We present Copyset Replication, a novel generalpurpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node’s data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99% to 0.15%. For Facebook’s HDFS cluster, it reduces the probability from 22.8% to 0.78%.

Bend, stretch, and touch: Locating a finger on an actively deformed transparent sensor array <=SEP=> The development of bendable, stretchable, and transparent touch sensors is an emerging technological goal in a variety of fields, including electronic skin, wearables, and flexible handheld devices. Although transparent tactile sensors based on metal mesh, carbon nanotubes, and silver nanowires demonstrate operation in bent configurations, we present a technology that extends the operation modes to the sensing of finger proximity including light touch during active bending and even stretching. This is accomplished using stretchable and ionically conductive hydrogel electrodes, which project electric field above the sensor to couple with and sense a finger. The polyacrylamide electrodes are embedded in silicone. These two widely available, low-cost, transparent materials are combined in a three-step manufacturing technique that is amenable to large-area fabrication. The approach is demonstrated using a proof-of-concept 4 × 4 cross-grid sensor array with a 5-mm pitch. The approach of a finger hovering a few centimeters above the array is readily detectable. Light touch produces a localized decrease in capacitance of 15%. The movement of a finger can be followed across the array, and the location of multiple fingers can be detected. Touch is detectable during bending and stretch, an important feature of any wearable device. The capacitive sensor design can be made more or less sensitive to bending by shifting it relative to the neutral axis. Ultimately, the approach is adaptable to the detection of proximity, touch, pressure, and even the conformation of the sensor surface.

A Survey of Distributed Mining of Data Streams <=SEP=> With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.

Toward Detecting Malicious Links in Online Social Networks through User Behavior <=SEP=> It is becoming increasingly difficult to ignore the importance of using online social networks (OSNs) for various purposes such as marketing, education, entertainment, and business. However, OSNs also open the door for harmful activities and behaviors. Committing financial fraud and propagating malware and spam advertisements are very common criminal actions that people engage in by accessing uniform resource locators (URLs). It has been reported that advanced attackers tend to exploit human flaws rather than system flaws, thus, users are targeted in social media threats by hour. This research aims to understand the state of literature on detecting malicious URLs in OSNs, with a focus on two major aspects: URL and OSN objects. Although the literature presents these two aspects in a different context, this paper will mainly focus on their features in relation to malicious URL detection using classification methods. We firstly review three features of URLs: lexical features, hosts, and domains, then we discuss their limitations. We then introduce social spam analytics and detection models using combined features from both URLs and OSNs, particularly the use of user profiles and posts together with URL features, to enhance the detection of malicious behavior. This combination can help in understanding the interests of the user either explicitly, by stating choices in the profile, or implicitly, by analyzing the post behavior, as the spammers do not maintain a regular interest and tend to exploit events or top trend topics.

Side channels in cloud services , the case of deduplication in cloud storage <=SEP=> Cloud storage services commonly use deduplication, which eliminates redundant data by storing only a single copy of each file or block. Deduplication reduces the space and bandwidth requirements of data storage services, and is most effective when applied across multiple users, a common practice by cloud storage offerings. We study the privacy implications of cross-user deduplication. We demonstrate how deduplication can be used as a side channel which reveals information about the contents of files of other users. In a different scenario, deduplication can be used as a covert channel by which malicious software can communicate with its control center, regardless of any firewall settings at the attacked machine. Due to the high savings offered by cross-user deduplication, cloud storage providers are unlikely to stop using this technology. We therefore propose simple mechanisms that enable cross-user deduplication while greatly reducing the risk of data leakage.

Real-Time Deep Learning Method for Abandoned Luggage Detection in Video <=SEP=> Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). To train our neural networks we provide two types of examples: images collected from the Internet and realistic examples generated by imposing various suitcases and bags over the scene's background. We present empirical results demonstrating that our approach yields better performance than a strong CNN baseline method.

Authenticated storage using small trusted hardware <=SEP=> A major security concern with outsourcing data storage to third-party providers is authenticating the integrity and freshness of data. State-of-the-art software-based approaches require clients to maintain state and cannot immediately detect forking attacks, while approaches that introduce limited trusted hardware (e.g., a monotonic counter) at the storage server achieve low throughput. This paper proposes a new design for authenticating data storage using a small piece of high-performance trusted hardware attached to an untrusted server. The proposed design achieves significantly higher throughput than previous designs. The server-side trusted hardware allows clients to authenticate data integrity and freshness without keeping any mutable client-side state. Our design achieves high performance by parallelizing server-side authentication operations and permitting the untrusted server to maintain caches and schedule disk writes, while enforcing precise crash recovery and write access control.

An introduction to modern missing data analyses. <=SEP=> A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.

The immune contexture in human tumours: impact on clinical outcome <=SEP=> Tumours grow within an intricate network of epithelial cells, vascular and lymphatic vessels, cytokines and chemokines, and infiltrating immune cells. Different types of infiltrating immune cells have different effects on tumour progression, which can vary according to cancer type. In this Opinion article we discuss how the context-specific nature of infiltrating immune cells can affect the prognosis of patients.

A study of injection locking and pulling in oscillators <=SEP=> Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.

Resilience to natural hazards : How useful is this concept ? <=SEP=> Resilience is widely seen as a desirable system property in environmental management. This paper explores the concept of resilience to natural hazards, using weather-related hazards in coastal megacities as an example. The paper draws on the wide literature on megacities, coastal hazards, hazard risk reduction strategies, and resilience within environmental management. Some analysts define resilience as a system attribute, whilst others use it as an umbrella concept for a range of system attributes deemed desirable. These umbrella concepts have not been made operational to support planning or management. It is recommended that resilience only be used in a restricted sense to describe specific system attributes concerning (i) the amount of disturbance a system can absorb and still remain within the same state or domain of attraction and (ii) the degree to which the system is capable of selforganisation. The concept of adaptive capacity, which has emerged in the context of climate change, can then be adopted as the umbrella concept, where resilience will be one factor influencing adaptive capacity. This improvement to conceptual clarity would foster much-needed communication between the natural hazards and the climate change communities and, more importantly, offers greater potential in application, especially when attempting to move away from disaster recovery to hazard prediction, disaster prevention, and preparedness. r 2004 Elsevier Ltd. All rights reserved.

A rating approach based on sentiment analysis <=SEP=> With the advent to social media the number of reviews for any particular product is in millions, as there exist thousand of websites where that particular product exists. As the numbers of reviews are very high the user ends up spending a lot of time for searching best product based on the experiences shared by review writers. This paper presents a sentiment based rating approach for food recipes which sorts food recipes present on various website on the basis of sentiments of review writers. The results are shown with the help of a mobile application: Foodoholic. The output of the application is an ordered list of recipes with user input as their core ingredient.

Poisson surface reconstruction <=SEP=> We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.

A study of count-based exploration and bonus for reinforcement learning <=SEP=> In order to better balance exploration and exploitation and solve the problem of sparse reward in reinforcement learning, this paper focus on changing the traditional exploration strategy, such as ε-greedy strategy, by introducing the count for state-action visitation into the Boltzmann distribution method as a new behavioral exploration strategy, and adding count-based exploration bonus to guide the agent to exploration, proposes a method of count-based exploration and bonus for reinforcement learning. Selecting Sarsa(λ) learning algorithm as the basis, the effectiveness of the count-based Sarsa(λ) learning algorithm is verified by carrying out comparative experiments of the Q learning algorithm based on the ε-greedy strategy, the Sarsa(λ) learning algorithm based on the ε-greedy strategy, and the count-based Sarsa(λ) learning algorithm in the tank combat simulation problem. Due to its simplicity, it provides an easy yet powerful baseline for solving MDPs that require informed exploration.

From Social Media to GeoSocial Intelligence: Crowdsourcing Civic Co-management for Flood Response in Jakarta, Indonesia <=SEP=> Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management. From now on there is an interconnection, an intertwining, even a symbiosis of technologies, exchanges, movements, which makes it so that a flood—for instance—wherever it may occur, must necessarily involve relationships with any number of technical, social, economic, political intricacies that keep us from regarding it simply as a misadventure or a misfortune whose consequences can be more or less easily circumscribed. — Jean-Luc Nancy (2015, 3–4) * E. Turpin & T. Holderness SMART Infrastructure Facility, University of Wollongong, Australia e-mail: eturpin@uow.edu.au | tomas@uow.edu.au Chapter 6 in Social Media for Government Services, Springer 2016 (preprint version)

Development of a Compact Switched-Reluctance Motor Drive for EV Propulsion With Voltage-Boosting and PFC Charging Capabilities <=SEP=> This paper presents a compact battery-powered switched-reluctance motor (SRM) drive for an electric vehicle with voltage-boosting and on-board power-factor-corrected-charging capabilities. Although the boost-type front-end DC/DC converter is externally equipped, the on-board charger is formed by the embedded components of SRM windings and converter. In the driving mode, the DC/DC boost converter is controlled to establish boostable well-regulated DC-link voltage for the SRM drive from the battery. During demagnetization of each stroke, the winding stored energy is automatically recovered back to the battery. Moreover, the regenerative braking is achieved by properly setting the SRM in the regenerating mode. The controls of two power stages are properly designed and digitally realized using a common digital signal processor. Good winding current and speed dynamic responses of the SRM drive are obtained. In addition, the static operation characteristics are also improved. In the charging mode, the power devices that were embedded in the SRM converter and the three motor-phase windings are used to form a buck-boost switch-mode rectifier to charge the battery from utility with good power quality. All constituted circuit components of the charger are placed on board, and only the insertion of power cords is needed.

On the Mathematics of Flat Origamis∗ <=SEP=> Origami is the art of folding pieces of paper into works of sculpture without the aid of scissors or glue. Modern advancements in the complexity of origami (e.g., the work of Montroll and Maekawa) reveal a rich geometric structure governing the possibilities of paperfolding. In this paper we initiate a mathematical study of this “origami geometry” and explore the possibilities of a graph theoretic model. In particular, we study the properties of origami models which fold flat (i.e., can be pressed in a book without crumpling). Necessary and sufficient conditions are given for an origami model to locally fold flat, and the problems encountered in trying to extend these results globally are discussed.

Improving Back-Propagation by Adding an Adversarial Gradient <=SEP=> The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a ”maxout” network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. This paper shows that adversarial training has a regularizing effect also in networks with logistic, hyperbolic tangent and rectified linear units. A simple extension to the back-propagation method is proposed, that adds an adversarial gradient to the training. The extension requires an additional forward and backward pass to calculate a modified input sample, or mini batch, used as input for standard back-propagation learning. The first experimental results on MNIST show that the ”adversarial back-propagation” method increases the resistance to adversarial examples and boosts the classification performance. The extension reduces the classification error on the permutation invariant MNIST from 1.60% to 0.95% in a logistic network, and from 1.40% to 0.78% in a network with rectified linear units. Based on these promising results, adversarial back-propagation is proposed as a stand-alone regularizing method that should be further investigated.

Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. <=SEP=> The ability of drugs of abuse to increase dopamine in nucleus accumbens underlies their reinforcing effects. However, preclinical studies have shown that with repeated drug exposure neutral stimuli paired with the drug (conditioned stimuli) start to increase dopamine by themselves, which is an effect that could underlie drug-seeking behavior. Here we test whether dopamine increases occur to conditioned stimuli in human subjects addicted to cocaine and whether this is associated with drug craving. We tested eighteen cocaine-addicted subjects using positron emission tomography and [11C]raclopride (dopamine D2 receptor radioligand sensitive to competition with endogenous dopamine). We measured changes in dopamine by comparing the specific binding of [11C]raclopride when subjects watched a neutral video (nature scenes) versus when they watched a cocaine-cue video (scenes of subjects smoking cocaine). The specific binding of [11C]raclopride in dorsal (caudate and putamen) but not in ventral striatum (in which nucleus accumbens is located) was significantly reduced in the cocaine-cue condition and the magnitude of this reduction correlated with self-reports of craving. Moreover, subjects with the highest scores on measures of withdrawal symptoms and of addiction severity that have been shown to predict treatment outcomes, had the largest dopamine changes in dorsal striatum. This provides evidence that dopamine in the dorsal striatum (region implicated in habit learning and in action initiation) is involved with craving and is a fundamental component of addiction. Because craving is a key contributor to relapse, strategies aimed at inhibiting dopamine increases from conditioned responses are likely to be therapeutically beneficial in cocaine addiction.

Detection of data injection attack in industrial control system using long short term memory recurrent neural network <=SEP=> In 2010, the outbreak of Stuxnet sounded a warning in the field of industrial control.security. As the major attack form of Stuxnet, data injection attack is characterized by high concealment and great destructiveness. This paper proposes a new method to detect data injection attack in Industrial Control Systems (ICS), in which Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is a temporal sequences predictor. We then use the Euclidean detector to identify attacks in a model of a chemical plant. With simulation and evaluation in Tennessee Eastman (TE) process, we show that this method is able to detect various types of data injection attacks.

Theorieentwicklung in der Akzeptanzforschung: Entwicklung eines Modells auf Basis einer qualitativen Studie <=SEP=> Die Untersuchung der Akzeptanz von Technologien im Allgemeinen und Software im Besonderen ist ein fruchtbares Feld in der angloamerikanischen Forschung der Disziplinen (Management) Information Systems und der deutschen Wirtschaftsinformatik. Trotz zahlreicher Untersuchungen, die ihren Ursprung in dem Technologie-Akzeptanzmodell und verwandten Theorien haben, mehren sich Beiträge, welche die Defizite bisherigerer Studien und Forschungsansätze herausstellen. Eine wesentliche Ursache ist Fokussierung auf quantitative Forschungsmethoden, wie wir anhand von Metastudien und einer eigenen Literaturrecherche aufzeigen. Während quantitative Verfahren sich in der Regel gut für eine Überprüfung gegebener Theorien eignen, ist ihr Beitrag für die Bildung neuer Theorien begrenzt. Im vorliegenden Beitrag wird aufgezeigt, wie ein qualitatives Verfahren zur besseren Theoriebildung genutzt werden kann. Am Beispiel der Untersuchung der Akzeptanz von Projektmanagement-Software (PMS) kann aufgezeigt werden, dass dieses Vorgehen zu neuen Konstrukten führt, während einige Konstrukte bestehender Akzeptanz-Theorien nicht bestätigt werden konnten.