To educate current and future software developers further about accessibility, a com- prehensive collection of accessibility laboratory activities is necessary to improve the cur- rent state of computing accessibility education and expand the workforce to create more chances in developing accessible software. These labs are referred to as the Accessibility Learning Labs (ALL), a project funded by National Science Foundation (NSF) . The primary goal of the educational accessibility labs is to increase awareness of the need to cre- ate accessible software by demonstrating fundamental accessibility concepts and providing activities to help the students sympathize with the problems that people with disabilities face everyday. There are five initial “ALL” labs being created. However, this paper will focus on the first accessibility learning lab.
We used a one-between/two-within participants repeated measures ANOVA design. We evaluated two treatments of two interfaces types, and tested the same two conditions in each treatment, counterbalancing 24 participants in each treatment for a total of 48 participants. The study was gendered balanced in each treatment, and ranged in ages from 18-54. The interface types compared a single column (temporal) view with a multiple column (spatial) view of the domain hierarchy; the audio condition compared when in the hierarchy a cue is available (at each point in the hierarchy; only at the final level of the hierarchy). One treatment used persistent text labels for each attribute in a hierarchy; the other used only mouse over text to reveal label name. This yielded a total of 8 interface conditions. To reduce the possible fatigue of participants, we used the Label/No Label treatment for the between subjects portion, so that participants saw only 4 interfaces. A pilot test with 20 participants revealed that the Label/No Label split preserved task focus better than trails that mixed Label/no Label treatments, which proved particularly disorienting to some users. There was no such disorientation when only label or no label interfaces were used. We describe the rational for each treatment/condition below.
ber of the Image Formation and Processing Group at the Beck- man Institute for Advanced Science and Technology from Au- gust 1996 to March 2001. In 2001, he joined the Pervasive Media Management Group at the IBM T. J. Watson Research Center in Hawthorne, NY, as a research staﬀ member. He has worked with the Center for Development of Advanced Computing (C-DAC) in Pune, India, from July 1994 to July 1996, in the Applications De- velopment Group. He has worked with the Kodak Research Lab- oratories of the Eastman Kodak Company in the summer of 1997 and with the Microcomputer Research Laboratories at Intel Cor- poration in the summer of 1998. He is a member of the IEEE and the honor society of Phi Kappa Phi. He has published over 35 re- search articles, publications, and book chapters in the field of me- dia analysis and learning. His research interests include audiovisual signal processing and analysis for the purpose of multimedia un- derstanding, content-based indexing, retrieval, and mining. He is interested in applying advanced probabilistic pattern recognition and machine learning techniques to model semantics in multime- dia data.
25. Next we must tell Orcad how we want the transient PSPICE simulation to be carried out. This is done by creating a Simulation Profile by clicking on PSPICE – Create New Simulation Profile, or by clicking on the like-named icon at the top of the schematic sheet. Enter a name for this simulation profile (such as FW Rect), and hit the Enter key. A Simulation Settings window will appear. Select the “Time Domain (transient)” simulation type, and check the General Settings option. Change the Run To Time box to indicate how far in time we desire to run the transient analysis. Since in this simulation, our source is 60 Hz, and a 60 Hz sine wave repeats every 1/60 = 16.66 ms, we shall run our transient analysis out beyond 2 cycles = 34 ms. So enter 34MS into the Run To Time box, enter 0 into the “Start Saving Data After” box, and enter 50US into the Maximum Step Size box. (In general, the maximum step size should be about 1/1000 of the transient simulation run time.) Leave the Skip Initial Transient Bias Point Calculation box unchecked. Click OK.
The burette is held vertically on a retort stand. A capillary tube is attached to the lower end of the burette using a rubber tube as shown in fig. The burette is filled with the given liquid. The capillary tube is made horizontal and the liquid is allowed to flow freely through it. When the liquid comes to a known height (h 1 ), which is the height measured
Transportation planning and policy have traditionally been evaluated with mobility-based indicators. These metrics implicitly treat ease of movement—often interpreted as roadway travel speeds—as definitive indicators of success in transportation policy. This perspective has led to the development of highway-intensive metropolitan areas in which vehicle-miles traveled per capita are high. Apart from its environmental implications, this perspective neglects the insight that the purpose of travel is not movement but access; that is, the demand for travel is derived from people’s desires to reach destinations. Movement is only one means to achieving accessibility; the other two are proximity (when people are near to their destinations they can reach them without much movement) and remote connectivity (e.g., via phone or Internet). The current study seeks to promote a shift in transportation policy from mobility-centered to
In the present study, we sought to explore epitope accessi- bility of the gp41 neutralizing antibodies, 2F5 and 4E10, either on the functional spike or during receptor-mediated entry. We sought to determine if these antibodies bind to the static spike on the surface of the HIV-1 or require target cell/receptor engagement to gain access to their MPER binding sites. We first confirmed that the binding of 2F5 and 4E10 to full-length, cleaved JR-FL spikes was inefficient, as we have previously reported for tail-truncated JR-FL spikes (9). To investigate the kinetics of neutralization mediated by the MPER-directed neutralizing antibodies, we performed a modified version of an antibody-virus washout assay using viruses containing envelope glycoproteins derived from both lab-adapted viruses and pri- mary isolates. Following specificity and validation of the anti- body washout assay in the context of viral entry, we confirmed that neutralizing but not nonneutralizing antibodies directed to gp120 could directly access their epitopes in the context of primary isolates. We found that viruses generated with the Env derived from either lab-adapted viruses or particular primary isolates displayed direct accessibility of their contiguous 2F5 and 4E10 epitopes, whereas more resistant viruses required receptor engagement on target cells to provide access to the 2F5 and 4E10 epitopes. We demonstrate that, for the resistant viruses JRCSF and JR-FL, we were able to render direct access on the static spike by generating selected point mutations ei- ther in Env variable regions (V1/V2 or V3) or in the gp41 region of the viral Env. We confirmed that the mutated viruses were CD4 dependent and the Env spikes were not in the receptor-triggered state. Taking these results together, we con- clude that the inefficient binding of 2F5 and 4E10 to most primary isolates is due to the inaccessibility of their cognate epitopes. Based upon direct accessibility in the more sensitive but CD4-dependent isolates, we propose that inaccessibility is not likely due to the formation of epitope after receptor en- gagement but is likely due to steric occlusion resulting from quaternary Env packing. These data have important implica- tions for the structure or exposure of discrete epitopes in the context of the prereceptor-engaged HIV-1 spike, the genera-
Lighting key The lighting of the scenes in videos has been greatly exploited as an important agent to evoke emotions. The balance, the direction, and the intensity of light are used to create effects, inducing certain emotions to the viewers and establishing the mood of the scene . High-key lighting denotes an abundance of bright light and usually involves low contrast and a small difference between the brightest and dimmest light. In contrary, in low-key lighting, the scene is predominantly dark with a high contrast ratio. High-key lighting usually commu- nicates activation and positive emotions, while low-key lighting is more dramatic and is often used to evoke neg- ative feelings. Figure 3a,b illustrates examples of high-key and low-key lighting shots, with the respective distribu- tions of their brightness. In order to compute the lighting key features, a 25-bin histogram is computed by analyz- ing the normalized to [ 0, 1] value component of the hue, saturation, and value (HSV) color space. The mean and variance scores of the value component are low for low- key lighting shots and high for high-key lighting shots; therefore, the lighting quantity ζ i (μ, σ ) for a frame i can
We want to analyze sound in order to derive the number of speakers. Since a wave plot is too large to be processed in its whole, we want to look at certain features. These features are a compressed representation of an audio sam- ple. These features are a lot smaller than the original data and can focus on specific relevant areas of sound. There is a wealth of options available when looking for audio features. Mitrovi´ c et al. did a survey in order to give an overview of the available options. In total, more than 200 papers were investigated by the authors. These features can be used for a variety of applications ranging from speech recognition and audio segmentation to envi- ronmental sound retrieval. For our application we looked at the MFCC and ZCR.
repeated sprint ability test, and an intermittent treadmill test. In Study 2, 24 players performed Carminatti’s test twice within 72 h to determine test-retest reliability. Carminatti’s test required the participants to complete repeated bouts of 5 \u00d7 12 s shuttle running at progressively faster speeds until volitional exhaustion. The 12 s bouts were separated by 6 s recovery periods, making each stage 90 s in duration. The initial running distance was set at 15 m and was increased by 1 m at each stage (90 s. Furthermore, PV T-CAR was significantly correlated with repeated-sprint
C. Place the cuvette into the spectrophometer and record absorbance; this is your initial or “0” time reading. Remove the tube. Repeat recording absorbance at 1, 2, 3, 4, and 5 minutes. Be sure to rotate (use Parafilm to cover) the tube and also clean its surface with a scientific cleaning wipe before each reading.
The AcMus Room Acoustic Parameters library for Matlab  was used in order to extract the T30 and C50 reverberation param- eters from the impulse responses using the method described in . Parameters were measured in octave bands from 63 Hz to 8 kHz. Resulting RT60 times for all three sites are displayed in Figure 4(a) and C50 measurements can be seen in Figure 4(b). For comparison IR-1 parameters are shown with a dotted line, and IR-2 parameters with a solid line. The two sets of measurements agree closely for lower frequency bands up to 250 Hz, owing to the inherent omni-directional nature of the loudspeaker at lower frequencies. At higher frequencies the 2 sets of values diverge, as the loudspeaker becomes more directional. For the remainder of the paper, we will concentrate on the IR-2 results.
We propose a cost-sensitive context-aware MI-SC method which can make use of the context among frames in the same video and the context between visual and audiocues for violent and horror video recognition. A video is divided into a series of shots via shot segmentation and a key frame from each shot is selected. The visual feature vector of each key frame is extracted to represent the shot in which the key frame exists. An audio feature vector is extracted for the entire video. A video is represented as a bag of instances which correspond to the visual feature vectors. A graph is constructed using the key frames as nodes to represent their contextual relations. A cost-sensitive sparse coding model is constructed to represent the context between the bag of visual feature vectors and the audio feature vector. We solve the cost-sensitive context-aware MI-SC using the existing feature sign search algorithm via a mathematical transformation.
TCP has many parameters with initial fixed defaults values that can be changed if mentioned explicitly. For example, the default TCP packet size has a size of 1000bytes.This can be changed to another value, say 552bytes, using the command $tcp set packetSize_ 552. When we have several flows, we may wish to distinguish them so that we can identify them with different colors in the visualization part. This is done by the command $tcp set fid_ 1 that assigns to the TCP connection a flow identification of ―1‖.We shall later give the flow identification of ―2‖ to the UDP connection.
Overview: In this lab we will become familiar with the dsPIC30F6015 and review programming a microcontroller to do some simple things. Most of the initial code will be given to you (see the class website), and you will have to modify the code as you go on. The dsPIC30F6015 has been mounted on a carrier board that allows us to communicate with a terminal (your laptop) via a USB cable. In what follows you will need to make reference to the pin out of the dsPIC30F6015 (shown in Figure 1) and the corresponding pins on the carrier (shown in Figure 2)
The most important research direction in this area is likely to be the use of speech recognition. Speech recognition will open much of the video and audio on the Web to be indexed like text documents can be indexed today, since about 55% of the audio we’ve encountered on the Web contains speech. To obtain these numbers, random samples of audio and video URL’s were made, from broad crawls of the World Wide Web starting at http://www.yahoo.com. The 12 thousand files were hand-labeled. The distribution found was 59% speech, 40% percent music, and 1% percent “other”, that did not fall into either of these two categories. About 90% of the speech in audio documents obtained by randomly sampling the 623 thousand document crawl is in English, with almost all of the rest in other European languages.
Situation awareness has been described as the complete understanding of factors that will contribute to the optimal performance of a task under expected and unexpected conditions . Externally paced situations, which are temporally driven, require heightened visual attention to preliminary movements and cues. Selectively attending to relevant advanced cues allows individuals to make antici- pated decisions as to intentions, which increases the speed of reactions. Quick responses become less important when driving a car on a desolate highway; however, vast differ- ences exist when driving in downtown New York City during rush hour. Attempting to simultaneously attend to or become consumed by all the traffic signs, traffic lights, cars, bike messengers, and pedestrians would be ineffective and dangerous, especially when quick decisions must be made. Equally ineffective and dangerous would be narrowing one’s attentional field to the point of becoming consumed by only one singular object, such as the car directly in front of you. For situation awareness to be effective, situational assess- ments must actively access coherent conceptual representa- tions, as each experience expands an individual’s current knowledge base while influencing the acquisition and interpretation of new knowledge.