Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructs in the Mist: The Lost World of the IT Artifact

Recent years have seen much discussion in the literature about the core of the IS field. While extreme positions in this debate see IS research either as purely technical or purely behavioral work, we believe that one area of competence and contribution for IS researchers lies at the boundary of technology and individual human psychology. In addressing this question, IS researchers frequently invoke psychological constructs at a high level of abstraction, in order to achieve theories that allow wide knowledge claims. We contend that this fails to provide operationalizable and actionable linkages between the IT artifact and the psychological user model: exactly that area that should represent the contribution of IS research. This paper addresses these shortcomings by urging researchers to focus on the "forgotten" constructs on the "left hand side" of the model, characteristics of the artifact that serve as antecedents to user behavior. We propose a theory template that can be used to instantiate specific theories. We illustrate this template by examining how it can be used to instantiate existing theories and develop new theories.
Time-Dependent Selection of an Optimal Set of Sources to Define a Stable Celestial Reference Frame

Temporal statistical position stability is required for VLBI sources to define a stable Celestial Reference Frame (CRF) and has been studied in many recent papers. This study analyzes the sources from the latest realization of the International Celestial Reference Frame (ICRF2) with the Allan variance, in addition to taking into account the apparent linear motions of the sources. Focusing on the 295 defining sources shows how they are a good compromise of different criteria, such as statistical stability and sky distribution, as well as having a sufficient number of sources, despite the fact that the most stable sources of the entire ICRF2 are mostly in the Northern Hemisphere. Nevertheless, the selection of a stable set is not unique: studying different solutions (GSF005a and AUG24 from GSFC and OPA from the Paris Observatory) over different time periods (1989.5 to 2009.5 and 1999.5 to 2009.5) leads to selections that can differ in up to 20% of the sources. Observing, recording, and network improvement are some of the causes, showing better stability for the CRF over the last decade than the last twenty years. But this may also be explained by the assumption of stationarity that is not necessarily right for some sources.
Fischkrankheiten in der Ostsee

Ein 'Überblick über einige ältere Arbeiten, die sich mit Krankheiten von Fischen verschiedener Regionen der Ostsee befassen, fmdet sich in Tabelle I. Danach waren die am häufigsten erkrankten Arten Dorsch (Gadlls morhua). Aal (Angllilla angllilla), Flunder (Platichthys jleslIs) und Hering (Cllipea harenglls). Die häufigsten Erkrankungen waren Ulcerationen und Skelettdeformationen des Dor;ches, Salzwasseraalrotseuche des Aales und Lymphocystis der Flunder. Dethlefsen und Watermann (1982) berichteten über eigene Untersuchungen in den Jahren 197R bis 1982, bei denen Dorsche, Flundern und Klieschen (Limanda limanda) auf Stationen von der Kieler Bucht bis südöstlich Gotland einbezogen waren. Ergebnisse über das Auftreten von Krankheiten des
Evaluation of Tone Mapping Operators for HDR‐Video

Eleven tone‐mapping operators intended for video processing are analyzed and evaluated with camera‐captured and computer‐generated high‐dynamic‐range content. After optimizing the parameters of the operators in a formal experiment, we inspect and rate the artifacts (flickering, ghosting, temporal color consistency) and color rendition problems (brightness, contrast and color saturation) they produce. This allows us to identify major problems and challenges that video tone‐mapping needs to address. Then, we compare the tone‐mapping results in a pair‐wise comparison experiment to identify the operators that, on average, can be expected to perform better than the others and to assess the magnitude of differences between the best performing operators.
Leadership in online knowledge networks: Challenges and coping strategies in an network of practice

In this paper we explore the challenges and coping strategies for leading online intraorganizational Networks of Practice (NOPs). The research indicates that coordinating distributed knowledge in NOPs poses a leadership challenge that is not yet addressed in the literature on knowledge management in general and is unique when comparing intraorganizational NOPs to research on leadership in other types of online knowledge networks. This challenge entails creating and maintaining a balance between the interests of the formal organization and the interests of the informal network, and shows that coordinating informal knowledge sharing in a formal context involves a management dilemma thereby contributing to theory on coordinating distributed knowledge
Modeling and verification of web service composition based interorganizational workflows

Interorganisationale Workflows sind Arbeitsablaufe, welche die Grenzen einer Organisation verlassen und einen Rahmen fur Kooperationen der verschiedenen autonomen Organisationen zur Verfugung stellen. Ein wichtiger Punkt fur den Entwurf solcher Workflows ist die Balance zwischen Offenheit und Abgrenzung, wobei erstere fur Kooperationen und letztere die fur den Schutz von Know-how benotigt wird. Workflow Sichten stellen ein effizientes Werkzeug fur diesen Zweck zur Verfugung. Durch Offenlegung von bestimmten Teilen eines Prozesses, konnen Organisationen sowohl kooperieren als auch das Know-how schutzen. Diese Dissertation prasentiert nun eine Methode fur die korrekte Konstruktion von Workflow Sichten. Es wird angenommen, dass Organisationen Web Service orientierte Technologien zur Modellierung und Implementierung von interorganisationalen Workflows verwenden. Die Anwendung von Web Services bietet Organisationen viele Vorteile. Den eigentlichen Mehrwert von Web Services stellt aber die Kompositionsfahigkeit dar. Verfugbare Web Services konnen dadurch von anderen Choreographien und Orchestrationen (wieder-)verwendet werden. Die Notwendigkeit der Implementierung von Systemen von Null weg kann minimiert werden. Die zentralen Anforderungen sind einerseits eine Architektur mit adaquatem Potential, andererseits die Verifikation der Korrektheit. Diese Dissertation prasentiert nun eine Architektur zur Modellierung von Web Service Composition basierten interorganisationalen Workflows, genannt foderierte Choreographien, die verglichen mit anderen Architekturen verschiedene Vorteile anbieten. Daruber hinaus werden Algorithmen und Techniken zur Verifikation der strukturellen und temporalen Korrektheit vorgestellt. Strukturelle Korrektheit pruft, ob die Strukturen der beteiligten Prozesse zusammenpassen. Temporale Korrektheit uberpruft, ob ein interorganisationaler Workflow, der aus mehreren Choreographien und Orchestrationen besteht hinsichtlich der lokalen und globalen Bedingungen fehlerfrei ist. Mit Hilfe dieser Techniken kann die strukturelle und temporale Konformitat des Modells zur Designzeit uberpruft werden. Falls das Modell nicht strukturell oder temporal konform ist, konnen notige Anderungen durchgefuhrt werden, sodass die korrekte Ausfuhrung zur Laufzeit garantiert werden kann. Die Uberprufung der Konformitat zur Designzeit reduziert die Prozesskosten vor allem wegen den folgenden zwei Grunden: Erstens, die entdeckten Fehler zur Designzeit sind normalerweise billiger als jene, die zur Laufzeit entdeckt werden und zweitens, Fehlerbehandlungsmechanismen konnen verhindert werden, die wiederum Zusatzkosten verursachen. Zusatzlich zu der vorgestellten Architektur wird eine allgemeinere Architektur zusammen mit den passenden Konformitatsprufungsalgorithmen prasentiert. Der Ansatz ist Platform- und sprachunabhangig und die Algorithmen sind verteilt.
Classification in Networked Data 0 : A toolkit and a univariate case study

This paper is about classifying entities that are interlink ed with entities for which the class is known. After surveying prior work, we present NetKit, a mo dular toolkit for classification in networked data, and a case-study of its application to netwo rked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective infere nce procedure. Various existing node-centric relational learning algorithms can be instantiated with ap pro riate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information u sed is the structure of class linkage in the network (i.e., only links and some class labels). To our k nowledge, no work previously has evaluated systematically the power of class-linkage alone f r classification in machine learning benchmark data sets. The results demonstrate that very simp le network-classification models perform quite well—well enough that they should be used regular ly s baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes—i.e., Gaussian-field classifiers, Hopfield networks, and relation l-neighbor classifiers. The case study also shows that there are two sets of techniques that are pref rable in different situations, namely when few versus many labels are known initially. We also demo nstrate that link selection plays an important role similar to traditional feature selection.
Supporting meaningful social networks

Recent years have seen exponential growth of social network sites (SNSs) such as Friendster, MySpace and Facebook. SNSs flatten the real-world social network by making personal information and social structure visible to users outside the ego-centric networks. They provide a new basis of trust and credibility upon the Internet and Web infrastructure for users to communicate and share information. For the vast majority of social networks, it takes only a few clicks to befriend other members. People's dynamic ever-changing real-world connections are translated to static links which, once formed, are permanent -- thus entailing zero maintenance. The existence of static links as public exhibition of private connections causes the problem of friendship inflation, which refers to the online practice that users will usually acquire much more "friends" on SNSs than they can actually maintain in the real world. There is mounting evidence both in social science and statistical analysis to support the idea that there have been an inflated number of digital friendship connections on most SNSs. The theory of friendship inflation is also evidenced by our nearly 3-year observation on Facebook users in the University of Southampton. Friendship inflation can devalue the social graph and eventually lead to the decline of a social network site. From Sixdegrees.com to Facebook.com, there have been rise and fall of many social networks. We argue that friendship inflation is one of the main forces driving this move. Despite the gravity of the issue, there is surprisingly little academic research carried out to address the problems. The thesis proposes a novel algorithm, called ActiveLink, to identify meaningful online social connections. The innovation of the algorithm lies in the combination of preferential attachment and assortativity. The algorithm can identify long-range connections which may not be captured by simple reciprocity algorithms. We have tested the key ideas of the algorithms on the data set of 22,553 Facebook users in the network of University of Southampton. To better support the development of SNSs, we discuss an SNS model called RealSpace, a social network architecture based on active links. The system introduces three other algorithms: social connectivity, proximity index and community structure detection. Finally, we look at the problems relating to improving the network model and social network systems.
Discrete Differential Forms for Computational Modeling

The emergence of computers as an essential tool in scientific research has shaken the very foundations of differential modeling. Indeed, the deeply-rooted abstraction of smoothness, or differentiability, seems to inherently clash with a computer’s ability of storing only finite sets of numbers. While there has been a series of computational techniques that proposed discretizations of differential equations, the geometric structures they are simulating are often lost in the process.
A perceptual model for sinusoidal audio coding based on spectral integration

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Overview of Patent Retrieval Task at NTCIR-5

In the Fifth NTCIR Workshop, we organized the Patent Retrieval Task and performed three subtasks; Document Retrieval, Passage Retrieval, and Classification. This paper describes the Document Retrieval Subtask and Passage Retrieval Subtask, both of which were intended for patent-to-patent invalidity search task. We show the evaluation results of the groups participating in those subtasks.
Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

This study uses image processing to analyze white blood cell with leukemia indicated that includes the identification, analysis of shapes and sizes, as well as white blood cell count indicated the symptoms of leukemia. A case study in this research was blood cells, from the type of leukemia Acute Myelogenous Leukemia (AML), M2 and M3 in particular. Image processing operations used for segmentation by utilizing the color conversion from RGB (Red, Green dab Blue) to obtain white blood cell candidates. Furthermore, the white blood cells candidates are separated by other cells with active contour without edge. WBC (White Blood Cell) results still have intersected or overlap condition. Watershed distance transform method can separate overlap of WBC. Furthermore, the separation of the nucleus from the cytoplasm using the HSI (Hue Saturation Intensity). The further characteristic extraction process is done by calculating the area WBC, WBC edge, roundness, the ratio of the nucleus, the mean and standard deviation of pixel intensities. The feature extraction results are used for training and testing in determining the classification of AML: M2 and M3 by using the momentum backpropagation algorithm. The classification process is done by testing the numeric data input from the feature extraction results that have been entered in the database. K-Fold validation is used to divide the amount of training data and to test the classification of AML M2 and M3. The experiment results of eight images trials, the result, was 94.285% per cell accuracy and 75% per image accuracy
A hybrid intrusion detection system

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors' accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors. Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed. This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks.
Computational Design and Automated Fabrication of Kirchho-Plateau Surfaces

Fig. 1. Our method enables users to quickly create complex Kirchho-Plateau Surfaces such as this automotive concept design. Le : the 3D shapes designed in this way are equilibrium configurations of pre-stretched fabric membranes structured with planar rod networks (inset figure). Middle: we fabricate these designs using a filament-based 3D printer that embeds rods directly into the fabric. Right : once cut out, the physical prototype deploys into the desired shape.
This paper is included in the Proceedings of the Sixteenth Symposium on Usable Privacy and Security.

Usage of weak passwords for authentication within an organization can be exploited during cyberattacks leading to unauthorized account access, denial of service, data and identity theft, sabotage etc. Such attacks could bring financial and reputational losses apart from legal consequences. Organizational password policies came into being in an attempt to encourage users to create more complex and diverse passwords. However, it has been observed that people show similar behavior in adopting those policies and end up creating passwords with similar patterns. Security training has been found to be a popular mechanism in an enterprise setting, of which, game-based trainings have shown positive impact with an added advantage of being immersive. In this paper, we present a serious game-based training on creating password security awareness among enterprise users. The training involves promoting understanding among users about various common password heuristics during password creation. This study focuses on two research questions: 1) Can a game-based password awareness training teach participants about the various password heuristics? 2) Can such a training improve the organizational password diversity? With a participation of 4,906 employees from our enterprise in the study, we were able to observe effects of game-based training on password awareness. We also found insights during the study to show that users created diverse passwords.
Handbook of the Economics of Education

The Handbooks in Economics series continues to provide the various branches of economics with handbooks which are definitive reference sources, suitable for use by professional researchers, advanced graduate students, or by those seeking a teaching supplement. With contributions from leading researchers, each Handbook presents an accurate, self-contained survey of the current state of the topic under examination. These surveys summarize the most recent discussions in journals, and elucidate new developments. Although original material is also included, the main aim of this series is the provision of comprehensive and accessible surveys
Energy levels for biphasic defibrillation

Defibrillators which deliver biphasic waveforms are rapidly replacing defibrillators which deliver monophasic waveforms. Lower energy biphasic shocks cause less myocardial injury and subsequent post-resuscitation myocardial dysfunction thus potential improving the likelihood of survival. (1-4) Recommendations of the International Liaison Committee on Resuscitation (ILCOR) state that biphasic energies less than or equal to 200 joules are as efficacious as escalating higher energy monophasic shocks. (5) However, few clinical trials in adults and none in children indicate the optimal dose of biphasic shock which would achieve maximum defibrillation rate and minimum myocardial injury. Faced with the declining availability of monophasic defibrillators and paucity of data in respect to biphasic energy levels, the Australian Resuscitation Council (ARC) makes the following recommendations.
Packet Switching in Radio Channels: Part II - The Hidden Terminal Problem in Carrier Sense Multiple-Access and the Busy-Tone Solution

We consider a population of terminals communicating with a central station over a packet-switched multiple-access radio channel. The performance of carrier sense multiple access (CSMA) [1] used as a method for multiplexing these terminals is highly dependent on the ability of each terminal to sense the carrier of any other transmission on the channel. Many situations exist in which some terminals are "hidden" from each other (either because they are out-of-sight or out-of-range). In this paper we show that the existence of hidden terminals significantly degrades the performance of CSMA. Furthermore, we introduce and analyze the busy-tone multiple-access (BTMA) mode as a natural extension of CSMA to eliminate the hidden-terminal problem. Numerical results giving the bandwidth utilization and packet delays are shown, illustrating that BTMA with hidden terminals performs almost as well as CSMA without hidden terminals.
Privacy Preserving Social Tie Discovery Based on Cloaked Human Trajectories

Discovering social connections of people has become a flourishing research topic considering the rich social information inferable from human trajectories. Existing social tie detection methods often require exact locations of users, which cause serious privacy concerns. Although cloaking is a common technique for location anonymization, it has rarely been applied in social tie detection due to the potential loss of significant location information. In this paper, we propose a semantic tree model for social tie detection, which supports different levels of privacy preserving and allows better understanding of location content of the cloaking regions. We propose a novel algorithm that can infer the social ties between users using only their cloaked trajectories without exposing their exact locations. We model the obscured regions generated by the cloaking algorithms in a semantic region tree and infer the similarity between two users based on their temporal and spatial relations in the tree. We evaluate our proposed approach using real trajectory dataset and show that our algorithm can identify social ties successfully with 15% higher accuracy compared with existing approach.