nils-herrmann/clean_scirepeval huggingface dataset:
Service-Oriented Computing

The Internet of Things (IoT) paradigm refers to the network of physical objects or “things” embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. IoT will usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g. SmartGrid), to healthcare management and urban life (e.g. SmartCity). However, because of its fine-grained, continuous and pervasive data acquisition and control capabilities, IoT raises concerns about the security and privacy of data. Deploying existing data security solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in data security and privacy, we present initial approaches to techniques and services for securing IoT data, including efficient and scalable encryption protocols, software protection techniques for small devices, and fine-grained data packet loss analysis for sensor networks.  Elisa Bertino is professor of computer science at Purdue University, and serves as Director of Purdue Cyber Center and Research Director of the Center for Information and Research in Information Assurance and Security (CERIAS). She is also an adjunct professor of Computer Science & Info Tech at RMIT. Prior to joining Purdue in 2004, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, at Telcordia Technologies. Her recent research focuses on data security and privacy, digital identity management, policy systems, and security for drones and embedded systems. She is a Fellow of ACM and of IEEE. She received the IEEE Computer Society 2002 Technical Achievement Award, the IEEE Computer Society 2005 Kanai Award and the 2014 ACM SIGSAC outstanding contributions award. She is currently serving as EiC of
Research on nursing handoffs for medical and surgical settings: an integrative review.

To synthesize outcomes from research on handoffs to guide future computerization of the process on medical and surgical units.



Handoffs can create important information gaps, omissions and errors in patient care. Authors call for the computerization of handoffs; however, a synthesis of the literature is not yet available that might guide computerization.



PubMed, CINAHL, Cochrane, PsycINFO, Scopus and a handoff database from Cohen and Hilligoss.



Integrative literature review.



This integrative review included studies from 1980-March 2011 in peer-reviewed journals. Exclusions were studies outside medical and surgical units, handoff education and nurses' perceptions.



The search strategy yielded a total of 247 references; 81 were retrieved, read and rated for relevance and research quality. A set of 30 articles met relevance criteria.



Studies about handoff functions and rituals are saturated topics. Verbal handoffs serve important functions beyond information transfer and should be retained. Greater consideration is needed on analysing handoffs from a patient-centred perspective. Handoff methods should be highly tailored to nurses and their contextual needs. The current preference for bedside handoffs is not supported by available evidence. The specific handoff structure for all units may be less important than having a structure for contextually based handoffs. Research on pertinent information content for contextually based handoffs is an urgent need. Without it, handoff computerization is not likely to be successful. Researchers need to use more sophisticated experimental research designs, control for individual and unit differences and improve sampling frames.
Online inference for time-varying temporal dependency discovery from time series

Large-scale time series data are prevalent across diverse application domains including system management, biomedical informatics, social networks, finance, etc. Temporal dependency discovery performs an essential part to identify the hidden interactions among the observed time series and helps to gain more insight into the behavior of the applications. However, the time-varying sparsity of the interactions among time series often poses a big challenge to temporal dependency discovery in practice. This paper formulates the temporal dependency problem with a novel Bayesian model allowing for both the sparsity and evolution of the hidden interactions among the observed time series. Taking advantage of the Bayesian modeling, an online inference method is proposed for time-varying temporal dependency discovery. Extensive empirical studies on both the synthetic and real application time series data are conducted to demonstrate the effectiveness and the efficiency of the proposed method.
Relationship Banking : What Do We Know ?

This paper briefly reviews the contemporary literature on relationship banking. We start out with a discussion of the raison d’ˆ etre of banks in the context of the financial intermediation literature. From there we discuss how relationship banking fits into the core economic services provided by banks and point at its costs and benefits. This leads to an examination of the interrelationship between the competitive environment and relationship banking as well as a discussion of the empirical evidence. Journal of Economic LiteratureClassification Numbers: G20, G21, L10.
Data Preparation for Mining World Wide Web Browsing Patterns

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system .
Data Security and Privacy in Cloud Computing

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user’s concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becomingmore important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture.This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.
Towards Summarization of Written Text Conversations

The immense growth of social media and web technologies have enabled users to create, share and exchange information in virtual communities and networks. The ease of usability has attracted considerable amount of users who collaborate on such social platforms. Content generated from such interactions and conversations contains a lot of information that could be of very good commercial and educational value. This necessitates the need to extract information from such conversations. Therefore, the task of information extraction has received lot of attention from industry as well as research community. However, the proportion of content generated is so huge that it leads to the problem of information overload and redundancy. These problems can be dealt appropriately by presenting crux of the content rather than the whole user-generated text. Automatic extraction of important topics, followed by summarization of the conversation may help in reducing considerable human effort to understand the content. Automatic text summarization is a well-known solution to the problem of information overload [3, 5]. Summarization of news articles has been explored from theory to practical models, but summarization of user-generated content has not been paid much attention to. This can be attributed to the noisy and unstructured text from social media which makes traditional Natural Language Processing (NLP) techniques difficult to apply. For our purpose, we have chosen real-time conversations that are composed of emails and chats from technical domain. Such conversations suffer from a bigger problem of data sparseness as the relations between various entities or objects of discussion and their attributes is not explicitly present. In our research, we try to address these problems by designing two different approaches, a supervised machine learning approach and a topic-focused approach. The machine learning approach models the sentence as a set of features and builds a trainable summarizer using these feature vectors. Thereafter, in topic-focused approach we find hidden topics of the conversation using external resources and bind our summary to these topics using a semantic sentence scoring model. In this thesis, we have mainly concentrated on extractive methods of summarization with sentence as a basic unit of the summary. This transforms our problem of summarization to “the selection of most representative and informative sentences”. We design a supervised machine learning framework where each sentence is represented as a set of features. Along with the frequency and heuristics based methods, we devise new features in the form of Discourse Marker and Sentiment Score to capture the relevancy of a sentence more accurately. We have used Rhetorical Structure Theory (RST) to describe the organization of text. Our feature set aims to capture the statistical, linguistic and sentimental aspects along with the dialogue structure of the conversation. Conversations are modeled using a set of features which
Distinguishing the cause and consequence of face inversion: the perceptual field hypothesis.

I published a critical review of the face inversion effect (Rossion, 2008) that triggered a few reactions and commentaries by colleagues in the field (Riesenhuber & Wolff, 2009; Yovel, in press). Here, I summarize my original paper and attempt to identify the source of both the agreements and disagreements with my colleagues, as well as other authors, regarding the nature of the face inversion effect. My view is that the major cause of the detrimental effect of inversion on an observer's performance at individual face recognition is the disruption of a perceptual process. This perceptual process is makes and observer see the multiple features of a whole individual upright face at once. It also makes the percept of a given facial feature highly dependent on the location and identity of the other features in the whole face. The perceptual process is holistic because it is driven by a holistic face representation, derived from visual experience. Hence, an inverted face cannot be perceived holistically: the perceptual field of the observer is constricted for inverted faces, each facial feature having to be processed sequentially, independently, i.e. over a smaller spatial window than the whole face. Consequently, it is particularly difficult to perceive diagnostic cues that involve several elements over a wide space on an inverted face, such as long-range relative distances between features (e.g., relative distance between eyes and mouth), or diagnostic cues that are located far away from usual gaze fixation (e.g., mouth-nose distance or mouth shape when fixating between the eyes). These difficulties are mere consequences of face inversion--the cause being a loss of holistic perception--, and it does not follow that relative distances between internal features are necessarily particularly important to recognize faces, that they should be labeled "configural", or should be given a specific status at the representational level. I argue that distinguishing the cause and consequence(s) of face inversion this way can provide a parsimonious and yet complete theoretical account of the face inversion effect.
The dynamics of innovation : from National Systems and ‘ ‘ Mode 2 ’ ’ to a Triple Helix of university – industry – government relations

The Triple Helix of university–industry–government relations is compared with alternative models for explaining the current research system in its social contexts. Communications and negotiations between institutional partners generate an overlay that increasingly reorganizes the underlying arrangements. The institutional layer can be considered as the retention mechanism of a developing system. For example, the national organization of the system of innovation has historically been important in determining competition. Reorganizations across industrial sectors and nation states, however, are induced by Ž . Ž . new technologies biotechnology, ICT . The consequent transformations can be analyzed in terms of neoevolutionary mechanisms. University research may function increasingly as a locus in the ‘‘laboratory’’ of such knowledge-intensive network transitions. q 2000 Elsevier Science B.V. All rights reserved.
Differentially Private Histogram Publication for Dynamic Datasets: an Adaptive Sampling Approach

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on "one-time" release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods.
The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.
Student assessment using Bayesian nets

We describe OLAE as an assessment tool that collects data from students solving problems in introductory college physics, analyses that data with probabilistic methods that determine what knowledge the student is using, and flexibly presents the results of analysis. For each problem, OLAE automatically creates a Bayesian net that relates knowledge, represented as first-order rules, to particular actions, such as written equations. Using the resulting Bayesian network, OLAE observes a student's behavior and computes the probabilities that the student knows and uses each of the rules.
Parameterized and Dynamic Generation of an Infinite Virtual Terrain with Various Biomes using Extended Voronoi Diagram

The paper describes and extensively evaluates a new method for the parameterized and intelligent generation of an infinite environment including various biomes in a virtual 3D space defined by the user. The biomes might be generated with different set of textures and by using different formulas to form shape of the landscape. Despite different shapes they still blend smoothly between each other. To achieve this goal Gaussian blur and Voronoi diagram algorithms are used. To enable additional parameterization for biomes placement the standard Voronoi algorithm is extended by including cell change along an axis and dispersion of Voronoi cells. Applying latitude into terrain type produces more realistic results. An entire terrain is generated on the CPU using a separate thread to eliminate stuttering during calculations and then the data is sent to the GPU in order to draw it. It forces reducing amount of data as much as possible, because data must be sent through the graphical bus. The tests have been performed by setting up a sample terrain and performing basic actions on this terrain like moving or rotating to gather frame times. The results showed that although the method demands much memory it is efficient and suitable for the real-time processing.
Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM)

The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM.  A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups.  To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing.  The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience.  The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.