Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments

Fostering medical students' lifelong learning skills with a dashboard, coaching and learning planning.

IntroductionTo develop lifelong learning skills, students need feedback, access to performance data, and coaching. A new medical curriculum incorporated infrastructural supports based on self-regulated learning theory and the Master Adaptive Learner framework to engage students in reflection and learning planning. This study examines students' experience with a performance dashboard, longitudinal coaching, and structured time for goal-setting.MethodsFocus groups with first-year medical students explored performance dashboard usage, coaching and learning planning. We analyzed findings using thematic analysis. Results informed development of a 29-item survey rated strongly disagree (1) to strongly agree (5) to investigate experience with the dashboard, coaching and learning goals program. The survey was distributed to one first-year medical student class. We performed descriptive statistics and factor analysis.ResultsIn three focus groups with 21 participants, students endorsed using the dashboard to access performance information but had trouble interpreting and integrating information. They valued coaches as sources of advice but varied in their perceptions of the value of discussing learning planning. Of 152 students, 114 (75%) completed the survey. Exploratory factor analysis yielded 5 factors explaining 57% of the variance: learning goals development (α = 0.88; mean 3.25 (standard deviation 0.91)), dashboard usage (α = 0.82; 3.36 (0.64)), coaching (α = 0.71; 3.72 (0.64)), employment of learning strategies (α = 0.81; 3.67 (0.79)), and reflection (α = 0.63; 3.68 (0.64)).DiscussionThe student performance dashboard provides efficient feedback access, yet students' use of this information to guide learning is variable. These results can inform other programs seeking to foster lifelong learning skills

Video Conferencing Tool

Video Conferencing Tool (VCT) is a web-based video chat application that allows users anywhere in the world to join real-time streaming video chat rooms. This product is similar to social networking sites that allow web-based video conferencing. The main advantage of VCT compared to existing tools is that it is easy to use and does not require users to download and set up additional hardware. Since this product is a browser-based solution, it allows users from multiple platforms like Windows, Linux, or Mac to join a chat room. My VCT allows users to create new public or private chat rooms or enter into existing chat rooms with the click of a button. VCT allows users to share their live audio and video to all users in the chat room. It also allows users to see the list of attendees in the chat room. VCT users can invite their friends to join video chat rooms by sending a link to their email. Friends can click the link and directly enter chat room without creating an account in VCT. The users also have the option of sending video messages to other users. Adobe Flash Media Server is used as the back end for developing this web site

Longitudinal performance analysis of machine learning based Android malware detectors

This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples

Great SCO2T! Rapid tool for carbon sequestration science, engineering, and economics

CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration of CO2 Tool, pronounced "Scott") to rapidly characterizing saline storage reservoirs. The tool is designed to rapidly screen hundreds of thousands of reservoirs, perform sensitivity and uncertainty analyses, and link sequestration engineering (injection rates, reservoir capacities, plume dimensions) to sequestration economics (costs constructed from around 70 separate economic inputs). We describe the novel science developments supporting SCO2T including a new approach to estimating CO2 injection rates and CO2 plume dimensions as well as key advances linking sequestration engineering with economics. Next, we perform a sensitivity and uncertainty analysis of geology combinations (including formation depth, thickness, permeability, porosity, and temperature) to understand the impact on carbon sequestration. Through the sensitivity analysis we show that increasing depth and permeability both can lead to increased CO2 injection rates, increased storage potential, and reduced costs, while increasing porosity reduces costs without impacting the injection rate (CO2 is injected at a constant pressure in all cases) by increasing the reservoir capacity.Comment: CO2 capture and storage; carbon sequestration; reduced-order modeling; climate change; economic

Analysis of the optical gain characteristics of semiconductor quantum dash materials including the band structure modifications due to the wetting layer

We present a numerical model for the calculation of the opto-electronic properties of a semiconductor InAs-InAlGaAs quantum dash active material including the presence of the wetting layer (WL), formed at the bottom of the dashes, and the quantum mechanical coupling among dashes caused by the high density of the InAs islands. The model calculates self-consistently the conduction and valence band energy diagram of the confined and unconfined states, the corresponding density of states, the electron and hole wavefunctions and the gain spectra. The results obtained are also compared with a more simple model that consider dashes as isolated and without the WL. The comparison evidences the role of the WL in limiting the gain performance such as the maximum gain, the differential gain and the optical gain bandwidth. The numerical tool is then used to design an improved quantum dash material, which allows to overcome these gain limitations even in presence of the WL and the high dash density

Semantic multimedia remote display for mobile thin clients

Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE

First discovery augmented reality for learning solar systems

The development of Augmented Reality (AR) systems in educational settings should be given more attention and recognition on its contribution to the evolution of education. Although this shift of pedagogical method may disrupt the traditional curriculum model, it also offers great opportunity to complement and improve the modern age education model. This paper presents an AR-based mobile application for exploring Space and Science for primary school students called the First Discovery (FD). This application supplements a traditional book that contains 10 target images for solar system and its planets, which can be scanned by the AR camera in FD application. Evaluation was carried out among primary school children, elementary educators as well as parents, which showed a highly favorable response. It is hoped that the proposed FD application is able to improve the ability of children in retaining knowledge after the AR science learning experience, to enhance information accessibility of the science learning content for children as well as to develop creative learning and the ability of children in exploring and problem solvin