Applying TSSL in database schema modeling: visualizing the syntax of gradual transitions

Since database conceptual modeling is a complex cognitive activity, finding an appropriate pedagogy to deliver the topic to novice database designers is a challenge for Information Systems (IS) educators. The four-level TSSL model that is known in the area of human-computer interactions (HCI) is used to explain and demonstrate how instructional design can minimize extraneous cognitive load in the conceptual modeling task of designing a database schema. The instructional design approach puts focus on the syntactic level of TSSL, to explain how visualizing gradual transitions between hierarchic levels of the schema is effective in database modeling. The current work demonstrates the approach, and at the next phase we plan to experimentally test the effectiveness of the approach by comparing performance and attitudes of students who are exposed to emphasizing the syntax of the gradual transitions in schema structure to those who are not exposed to itPeer Reviewe

Improved Teaching of Database Schema Modeling by Visualizing Changes in Levels of Abstraction

Conceptual modeling of databases is a complex cognitive activity, particularly for novice database designers. The current research empirically tests a new pedagogy for this activity. It examines an instructional approach that stresses visualizing gradual transitions between levels of abstraction in different hierarchic levels of a relational database schema. The new approach builds on a four-level TSSL model from the field of human-computer interaction. TSSL, an acronym for the Task, Semantics, Syntax, and Lexical levels, is applied here to describe the levels of conceptual database modeling and to explain how improved instructional design can help minimize extraneous cognitive load during the design of database schemas. We tested the effectiveness of the proposed instructional approach via a controlled experiment carried out on IS students. We divided students into two groups, those exposed to a visual emphasis on the syntax of gradual transitions in a schema structure and those not exposed to it. We then measured performance in terms of errors in students’ solutions while also recording their perceptions and attitudes toward the instructional approach and the activity of database modeling. Our results show that the new approach is an effective tool for teaching database modeling

DYNAMIC CONTEXTUALIZATION IN COMPUTER SUPPORTED COOPERATIVE WORK

Computer-mediated collaboration, a dominant mode of organizational communication particularly in dispersed and multinational organizations, introduces unique opportunities but also new problems. One of these problems is the higher risk of misunderstandings, which is more likely to occur in computer-mediated teamwork than in face-to-face teams (Cramton, 2001). Moreover, it may be particularly acute when distributed workers come from different functional backgrounds, holding different perspectives (Dougherty, 1992; Powell et al., 2004). Dispersed collaborations are also likely to suffer problems of culture since collaborators are embedded in a different local work setting with its own rules, language, histories, and myths (Armstrong & Cole, 2002). Current theories of communication suggest that misunderstanding may be reduced by contextualization, i.e., providing contextual information to explain a core message. However, a central hypothesize in the current research, is that contextualization is beneficial in some situations but not in others. Treating contextualization as a form of adaptive behavior, the research model aims to understand its contingent impact on performance in collaborative tasks. The motivation for contextualization is explained, arguing that it can be predicted by the extent to which the perspectives of the collaborators are different or shared: a difference of perspectives between collaborators motivates them to contextualize in order to increase mutual understanding and thereby increase performance. Computer support should also motivate communicators to contextualize by making it easier for them to do so. A controlled experiment tested these relationships in a collaborative machine-assembly task performed by dyads. The collaborators\u27 perspectives and the level of computer support were manipulated, and contextualization behavior, mutual understanding and performance were measured. Results show that contextualization is effective only for dyads with different perspectives and may be detrimental when perspectives are similar. When computer support is available, users may contextualize even if it is counterproductive. Therefore, computer-mediated collaboration should be designed to ensure only effective contextualization. Some potential practical implications for collaborative systems are offered

Designing an E-Mail Prototype to Enhance Effective Communication and Task Management: A Case Study

This paper deals with communicational breakdowns and misunderstandings in computer mediated communication (CMC) and ways to recover from them or to prevent them. The paper describes a case study of CMC conducted in a company named Artigiani. We observed communication and conducted content analysis of e-mail messages, focusing on message exchanges between customer service representatives (CSRs) and their contacts. In addition to task management difficulties, we identified communication breakdowns that result from differences between perspectives, and from the lack of contextual information, mainly technical background and professional jargon at the customers’ side. We examined possible ways to enhance CMC and accordingly designed a prototype for an e-mail user interface that emphasizes a communicational strategy called contextualization as a central component for obtaining effective communication and for supporting effective management and control of organizational activities, especially handling orders, price quoting, and monitoring the supply and installation of products

ENHANCING COMPUTER MEDIATED COMMUNICATION BY DESIGNING AN EMAIL PROTOTYPE: A CASE STUDY

In today\u27s modern world, computer supported cooperative work (CSCW) and computer mediated communication (CMC) are central and most crucial in the activities of organizations and in their success in achieving their goals and purposes. Organizations are established to achieve goals that one person cannot accomplish alone, and the knowledge that is collected by individuals should be reserved for the general use of the organizational community. Now, organizational workers need more than ever to share the knowledge they each gather, and they are involved in joint activities that need the support of information systems. Communication between individuals is at large extent in the form of computer mediated cooperation, and computerized applications ascribed as groupware (group support systems) include shared environments, whiteboards, electronic group calendars, chat rooms and more. Groupware systems support groups of people that work together by facilitating communication between them and by improving coordination. The overall success of organizations is certainly dependent on computer mediated communications that need to be designed to achieve a high level of mutual understanding and minimal occurrences of communication breakdowns. Perhaps the most widespread mode of CMC at work is email. Organizational workers are engaged in this asynchronous communication on a daily basis, having multiple contacts and the ability to preserve exchanged messages in an archive for future use. This study examines the possible ways to enhance CMC among users exchanging email messages in a company named Artigiani that specializes in manufacturing handles, hooks, hangers, and bathroom accessories. We closely Artigiani, and conducted content analysis of email messages. We were particularly interested in the exchanging of messages between customer service representatives (CSRs) and their customers which are professional workers such as carpenters, contractors, architects, interior designers and personal customers. CSRs in Artigiani are in great pressure to respond quickly to emails, and they feel stressed by the high volume of incoming messages, and by the fact that they tend to lose important items when they need them (such as previous messages exchanged, and items that they wish to attach to new messages). In addition, we identified communication breakdowns and misunderstanding that mainly result from differences in knowledge and perspectives of communicators. We follow previous work in CMC that stress the need for reinventing the email client, and put our focus on a communicational strategy called contextualization, which is the activity of providing the explicit addition of contextual information to a core message to ensure effective communication. We present a prototype for an email user interface that puts contextualization as a central component for enhancing effective CMC and for effectively managing and controlling organizational activities, especially the ongoing management of product ordering, and related decision making. examined communication i

Standoff Detection via Single-Beam Spectral Notch Filtered Pulses

We demonstrate single-beam coherent anti-Stokes Raman spectroscopy (CARS), for detecting and identifying traces of solids, including minute amounts of explosives, from a standoff distance (>50 m) using intense femtosecond pulses. Until now, single-beam CARS methods relied on pulse-shapers in order to obtain vibrational spectra. Here we present a simple and easy-to-implement detection scheme, using a commercially available notch filter, that does not require the use of a pulse-shaper.Comment: 3 pages, 3 figure

EXPANDER – an integrative program suite for microarray data analysis

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users a

Topology-Hiding Computation for Networks with Unknown Delays

Topology-Hiding Computation (THC) allows a set of parties to securely compute a function over an incomplete network without revealing information on the network topology. Since its introduction in TCC\u2715 by Moran et al., the research on THC has focused on reducing the communication complexity, allowing larger graph classes, and tolerating stronger corruption types. All of these results consider a fully synchronous model with a known upper bound on the maximal delay of all communication channels. Unfortunately, in any realistic setting this bound has to be extremely large, which makes all fully synchronous protocols inefficient. In the literature on multi-party computation, this is solved by considering the fully asynchronous model. However, THC is unachievable in this model (and even hard to define), leaving even the definition of a meaningful model as an open problem. The contributions of this paper are threefold. First, we introduce a meaningful model of unknown and random communication delays for which THC is both definable and achievable. The probability distributions of the delays can be arbitrary for each channel, but one needs to make the (necessary) assumption that the delays are independent. The existing fully-synchronous THC protocols do not work in this setting and would, in particular, leak information about the topology. Second, in the model with trusted stateless hardware boxes introduced at Eurocrypt\u2718 by Ball et al., we present a THC protocol that works for any graph class. Third, we explore what is achievable in the standard model without trusted hardware and present a THC protocol for specific graph types (cycles and trees) secure under the DDH assumption. The speed of all protocols scales with the actual (unknown) delay times, in contrast to all previously known THC protocols whose speed is determined by the assumed upper bound on the network delay

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development