HUSBANDS AND WIVES A film review by Frank Maloney Copyright 1992 Frank Maloney
HUSBANDS AND WIVES is a film written and directed by Woody Allen. It stars Allen, Mia Farrow, Judy Davis, Sydney Pollack, Juliette Lewis, Liam Neeson, and Lysette Anderson. Cinematography by Carlo Di Palma. Rated R for subject matter, language, and scenes of simulated sex.
HUSBANDS AND WIVES is going to be the most widely seen and discussed film made to date by Woody Allen. Despite the aroma of scandal and lurid headlines that clings to the film, it is not only possible, but important, to view HUSBANDS AND WIVES as a film, not some weird and vaguely shocking extension of tabloid scandal-mongering. You and I have no real knowledge of the situation in the Allen's and Farrow's lives, the only thing we can know, the only thing we can judge, is the impressive achievement this film represents.
The writing and acting in this film are distinguished, inspired, and very moving. The camerawork is problematical. The writing is entirely accessible, unlike some of Allen's recent work (SHADOWS AND FOG springs to mind here) which seemed calculated to appeal to some specialized readers of "New Yorker" magazine and the smarmier film students among us. The writing is about the chilling and disorienting experience of growing old with someone else, about our dependence on others, about the importance of compromise in real life. It is also creative and inventive, moving us between a "conventional" dramatic movie and a talking-head documentary (a technique used in elsewhere). The documentary portions represent some of the best moments in the film, with the characters speaking into the camera words that always have two meanings to the audience. Of course, Allen has always been a leading purveyor of irony, but here in this film he rises to a new level of both irony and insight.
As for the acting, every one of the principals has gotten into the heart of the meaning of his or her character. Especially impressive is Judy Davis, who largely dominates the cast, although Mia Farrow rises to the occasion with the best work I've ever seen from her. Sydney Pollack, the director of OUT OF AFRICA, TOOTSIE, THEY SHOOT HORSES, DON'T THEY, and others, is comfortable and natural as Davis's estranged husband; Pollack is an occasional actor, and it will be a pleasure to see him again. Juliette Lewis, who was the best thing about POINT FEAR, is amazing and hilarious as the brilliant writing student in Allen's class. They share a scene in the back of a taxi (in which we never see, only hear, Allen) that is one of the high points of the film. Allen himself is quieter, more serious than is often the case. His own characterization as Gabe is complex and uncompromising in his willingness to stand up to the x-ray vision of his own filmmaking.
(By the way, Allen is still casting cameos, a la SHADOWS AND FOG. Look especially for Nora Ephron in a very funny party scene.)
Carlo Di Palma's hand-held camerawork will either drive to you to distraction or to delight. Fortunately, the jiggliest, most disorienting technique is confined to the opening scene, after which the camera largely settles down. Slightly upsetting, too, are the jump-cuts Allen throws in at random; jump-cuts look like a snippet of film is missing. The effect is interesting once or twice, but mostly I was not entirely sure that we were not looking at a damaged print. If it hadn't been opening night, I would have entertained the idea even more seriously. Some of you will see in these camera and editing techniques an homage, perhaps, to Cassavetes and Godard; others may bemoan the abandonment of Allen's former standard of visual elegance. As I say, sometimes it works, sometimes it's distracting. In either case, it is not the reason why this is one of Woody Allen's greatest movies ever.
And it is a great movie, brilliant, sparkling, insightful, moving and touching, thoughtful, challenging, and a whole lot more. You simply must go to HUSBANDS AND WIVES. Pay what you must. You will be amply rewarded. Fortunately, because of events entirely outside this film, it has opened much more widely than is usual for a Woody Allen. It will be very interesting to see if TriStar's calculated and possibly cynical marketing gamble pays off.
[Note on r.a.m.r. #1526: Due to my error, the movie I referred to as THE PANAMA CONNECTION should have been referred to as THE PANAMA DECEPTION. My apologies for the inconvenience. -Frank Maloney]
-- Frank Richard Aloysius Jude Maloney
.
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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For some, being professional might mean dressing smartly at work, or doing a good job. For others, being professional means having advanced degrees or other certifications, framed and hung on the office wall. Professionalism encompasses all of these definitions. But, it also covers much more.
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Chicago Fire trade McCarty to expansion Nashville
The Chicago Fire traded veteran midfielder Dax McCarty to Nashville SC on Tuesday, receiving $100,000 and a draft pick from the expansion side in return.
The Fire will receive $50,000 in general allocation money and $50,000 in targeted allocation money plus a second-round pick in the 2021 draft.
McCarty gives Nashville an experienced leader to build around heading into its inaugural MLS campaign in 2020.
“Dax is a proven leader and winner and has done so in every club he’s been in throughout his career. He embodies the character and values of what our club will be on and off the field,” Nashville general manager Mike Jacobs said in a statement.
The 32-year-old is a two-time All-Star with 20 goals and 58 assists in 361 MLS contests with FC Dallas (2006-10), D.C. United (2011), New York Red Bulls (2011-16) and Chicago (2017-19).
“I couldn’t be more excited to be joining Nashville SC for the club’s inaugural MLS season and beyond,” McCarty said. “The buzz around the club is easy to feel and see. The opportunity and challenge of building something special from the beginning will be a unique experience that I feel privileged to be a part of. I can’t wait to get to Nashville and start preparing for the 2020 season.”
McCarty made the MLS Best XI in 2015 and helped the Red Bulls win the Supporters’ Shield in 2013 and 2015.
—Field Level Media


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Dear, Thank you for participating in MindTrails. We hope that the skills you learned during the study have helped you in your everyday life. We are reminding you to complete the two month follow-up, which will be available in just two weeks. Your responses will help us improve our intervention for future participants and are greatly appreciated. We will send you detailed instructions in two weeks on how to proceed. If you have any questions, let us know by contacting us at<EMAIL_ADDRESS>Best, The MindTrails Team |


VDIR
Section: User Commands (1)Updated: June 2012
Index Return to Main Contents
NAME
vdir - list directory contentsSYNOPSIS
vdir [OPTION]... [FILE]...DESCRIPTION
List information about the FILEs (the current directory by default). Sort entries alphabetically if none of -cftuvSUX nor --sort.
Mandatory arguments to long options are mandatory for short options too.
- -a, --all
- do not ignore entries starting with .
- -A, --almost-all
- do not list implied . and ..
- --author
- with -l, print the author of each file
- -b, --escape
- print octal escapes for nongraphic characters
- --block-size=SIZE
- use SIZE-byte blocks. See SIZE format below
- -B, --ignore-backups
- do not list implied entries ending with ~
- -c
- with -lt: sort by, and show, ctime (time of last modification of file status information) with -l: show ctime and sort by name otherwise: sort by ctime
- -C
- list entries by columns
- --color[=WHEN]
- colorize the output. WHEN defaults to `always' or can be `never' or `auto'. More info below
- -d, --directory
- list directory entries instead of contents, and do not dereference symbolic links
- -D, --dired
- generate output designed for Emacs' dired mode
- -f
- do not sort, enable -aU, disable -ls --color
- -F, --classify
- append indicator (one of */=>@|) to entries
- --file-type
- likewise, except do not append `*'
- --format=WORD
- across -x, commas -m, horizontal -x, long -l, single-column -1, verbose -l, vertical -C
- --full-time
- like -l --time-style=full-iso
- -g
- like -l, but do not list owner
- --group-directories-first
- group directories before files.
- augment with a --sort option, but any use of --sort=none (-U) disables grouping
- -G, --no-group
- in a long listing, don't print group names
- -h, --human-readable
- with -l, print sizes in human readable format (e.g., 1K 234M 2G)
- --si
- likewise, but use powers of 1000 not 1024
- -H, --dereference-command-line
- follow symbolic links listed on the command line
- --dereference-command-line-symlink-to-dir
- follow each command line symbolic link that points to a directory
- --hide=PATTERN
- do not list implied entries matching shell PATTERN (overridden by -a or -A)
- --indicator-style=WORD
- append indicator with style WORD to entry names: none (default), slash (-p), file-type (--file-type), classify (-F)
- -i, --inode
- print the index number of each file
- -I, --ignore=PATTERN
- do not list implied entries matching shell PATTERN
- -k
- like --block-size=1K
- -l
- use a long listing format
- -L, --dereference
- when showing file information for a symbolic link, show information for the file the link references rather than for the link itself
- -m
- fill width with a comma separated list of entries
- -n, --numeric-uid-gid
- like -l, but list numeric user and group IDs
- -N, --literal
- print raw entry names (don't treat e.g. control characters specially)
- -o
- like -l, but do not list group information
- -p, --indicator-style=slash
- append / indicator to directories
- -q, --hide-control-chars
- print ? instead of non graphic characters
- --show-control-chars
- show non graphic characters as-is (default unless program is `ls' and output is a terminal)
- -Q, --quote-name
- enclose entry names in double quotes
- --quoting-style=WORD
- use quoting style WORD for entry names: literal, locale, shell, shell-always, c, escape
- -r, --reverse
- reverse order while sorting
- -R, --recursive
- list subdirectories recursively
- -s, --size
- print the allocated size of each file, in blocks
- -S
- sort by file size
- --sort=WORD
- sort by WORD instead of name: none -U, extension -X, size -S, time -t, version -v
- --time=WORD
- with -l, show time as WORD instead of modification time: atime -u, access -u, use -u, ctime -c, or status -c; use specified time as sort key if --sort=time
- --time-style=STYLE
- with -l, show times using style STYLE: full-iso, long-iso, iso, locale, +FORMAT. FORMAT is interpreted like `date'; if FORMAT is FORMAT1<newline>FORMAT2, FORMAT1 applies to non-recent files and FORMAT2 to recent files; if STYLE is prefixed with `posix-', STYLE takes effect only outside the POSIX locale
- -t
- sort by modification time
- -T, --tabsize=COLS
- assume tab stops at each COLS instead of 8
- -u
- with -lt: sort by, and show, access time with -l: show access time and sort by name otherwise: sort by access time
- -U
- do not sort; list entries in directory order
- -v
- natural sort of (version) numbers within text
- -w, --width=COLS
- assume screen width instead of current value
- -x
- list entries by lines instead of by columns
- -X
- sort alphabetically by entry extension
- -1
- list one file per line
SELinux options:
- --lcontext
- Display security context. Enable -l. Lines will probably be too wide for most displays.
- -Z, --context
- Display security context so it fits on most displays. Displays only mode, user, group, security context and file name.
- --scontext
- Display only security context and file name.
- --help
- display this help and exit
- --version
- output version information and exit
SIZE may be (or may be an integer optionally followed by) one of following: KB 1000, K 1024, MB 1000*1000, M 1024*1024, and so on for G, T, P, E, Z, Y.
Using color to distinguish file types is disabled both by default and with --color=never. With --color=auto, ls emits color codes only when standard output is connected to a terminal. The LS_COLORS environment variable can change the settings. Use the dircolors command to set it.
Exit status:
- 0
- if OK,
- 1
- if minor problems (e.g., cannot access subdirectory),
- 2
- if serious trouble (e.g., cannot access command-line argument).
AUTHOR
Written by Richard M. Stallman and David MacKenzie.REPORTING BUGS
Report vdir bugs to<EMAIL_ADDRESS>coreutils home page: <http://www.gnu.org/software/coreutils/>
General help using GNU software: <http://www.gnu.org/gethelp/>
Report vdir translation bugs to <http://translationproject.org/team/>
COPYRIGHT
Copyright © 2010 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>.This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law.
SEE ALSO
The full documentation for vdir is maintained as a Texinfo manual. If the info and vdir programs are properly installed at your site, the command- info coreutils aqvdir invocationaq
should give you access to the complete manual.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:12 GMT, December 24, 2015


Introducing distributed execution and MapReduce framework
In case you did not pay attention to the area of large scale distributed computing – there is a revolution going on! It is becoming increasingly evident that the software ecosystems built around so called Big Data are at the forefront of cloud computing innovation. Unfortunately, there has been more debate around determining how big Big Data actually is rather than defining common set of requirements for the large scale Big Data computational platforms.
Stephen O’Grady of RedMonk summarized this phenomena succinctly: “Big Data, like NoSQL, has become a liability in most contexts. Setting aside the lack of a consistent definition, the term is of little utility because it is single-dimensional. Larger dataset sizes present unique computational challenges. But the structure, workload, accessibility and even location of the data may prove equally challenging.”
Zack Urlocker, an advisor and board member to several startup companies in the area of SaaS was equally vocal in his criticism regarding complexity of the existing systems : “You pretty much gotta be near genius level to build systems on top of Cassandra, Hadoop and the like today. These are powerful tools, but very low-level, equivalent to programming client server applications in assembly language. When it works its [sic] great, but the effort is significant and it’s probably beyond the scope of mainstream IT organizations.”
This is exactly where we are positioning Infinispan’s roadmap as we are announcing initial steps into the area of distributed execution and MapReduce framework built on top of Infinispan. Infinispan’s distributed data grid is a most natural fit for such a platform. We have already built an infrastructure for essentially unlimited linear in-memory data scaling. However, having such a data grid without an ability to execute large scale computation on it is like having a Ferrari without a drivers licence. Listening to the criticism regarding the lack of direction in Big Data field and complexity of the existing distributed execution frameworks our focus was primarily on simplicity without sacrificing power and a rich feature set such a framework should have.
Simple distributed execution model
The main interfaces for simple distributed task execution are DistributedCallable and DistributedExecutorService. DistributedCallable is essentially a version of the existing Callable from java.util.concurrent package except that DistributedCallable can be executed in remote JVM and receive input from Infinispan cache. Tasks' main algorithm is essentially unchanged, only the input source is changed. Exisiting Callable implementation most likely gets its input in a form of some Java object/primitive while DistributedCallable gets its input from Infinispan cache. Therefore, users who have already implemented Callable interface to describe their task units would simply extend DistributedCallable and use keys from Infinispan execution environment as input for the task. Implentation of DistributedCallable can in fact continue to support implementation of an already existing Callable while simultaneously be ready for distribited execution by extending DistributedCallable.
public interface DistributedCallable extends Callable {
/**
* Invoked by execution environment after DistributedCallable
* has been migrated for execution to
* a specific Infinispan node.
*
* @param cache
* cache whose keys are used as input data for
* this DistributedCallable task
* @param inputKeys
* keys used as input for this DistributedCallable task
*/
public void setEnvironment(Cache cache, Set inputKeys);
}
DistributedExecutorService is an simple extension of a familiar ExecutorService from java.util.concurrent package. However, the advantages of DistributedExecutorService are not to be overlooked. For the existing Callable tasks users would submit to ExecutorService there is an option to submit them for an execution on Infinispan cluster. Infinispan execution environment would migrate this task to an execution node, run the task and return the results to the calling node. Of course, not all Callable task would benefit from this feature. Excellent candidates are long running and computationally intensive tasks.
The second advantage of the DistributedExecutorService is that it allows a quick and simple implementation of tasks that take input from Infinispan cache nodes, execute certain computation and return results to the caller. Users would specify which keys to use as input for specified DistributedCallable and submit that callable for execution on Infinispan cluster. Infinispan runtime would locate the appriate keys, migrate DistributedCallable to target execution node(s) and finally return a list of results for each executed Callable. Of course, users can omit specifying input keys in which case Infinispan would execute DistributedCallable on all keys for a specified cache.
MapReduce model
Infinispan’s own MapReduce model is an adaptation of Google’s original MapReduce. There are four main components in each map reduce task: Mapper, Reducer, Collator and MapReduceTask.
Implementation of a Mapper class is a component of a MapReduceTask invoked once for each input entry K,V. Every Mapper instance migrated to an Infinispan node, given a cache entry K,V input pair transforms that input pair into a result T. Intermediate result T is further reduced using a Reducer.
public interface Mapper {
/**
* Invoked once for each input cache entry
* K,V transforms that input into a result T.
*
* @param key
* the kay
* @param value
* the value
* @return result T
*/
T map(K key, V value);
}
Reducer, as its name implies, reduces a list of results T from map phase of MapReduceTask. Infinispan distributed execution environment creates one instance of Reducer per execution node.
public interface Reducer {
/**
* Reduces a result T from map phase and return R.
* Assume that on Infinispan node N, an instance
* of Mapper was mapped and invoked on k many
* key/value pairs. Each T(i) in the list of all
* T's returned from map phase executed on
* Infinispan node N is passed to reducer along
* with previsouly computed R(i-1). Finally the last
* invocation of reducer on T(k), R is returned to a
* distributed task that originated map/reduce
* request.
*
* @param mapResult
* result T of map phase
* @param previouslyReduced
* previously accumulated reduced result
* @return result R
*
*/
R reduce(T mapResult, R previouslyReduced);
}
Collator coordinates results from Reducers executed on Infinispan cluster and assembles a final result returned to an invoker of MapReduceTask. #[.cm] #
public interface Collator {
/**
* Collates all results added so far and
* returns result R to invoker of distributed task.
*
* @return final result of distributed task computation
*/
R collate();
/**
* Invoked by runtime every time reduced result
* R is received from executed Reducer on remote
* nodes.
*
* @param remoteNode
* address of the node where reduce phase occurred
* @param remoteResult
* the result R of reduce phase
*/
void reducedResultReceived(Address remoteNode, R remoteResult);
}
[.cm]#Finally, MapReduceTask is a distributed task uniting Mapper, Reducer and Collator into a cohesive large scale computation to be transparently parallelized across Infinispan cluster nodes. Users of MapReduceTask need to provide a cache whose data is used as input for this task. Infinispan execution environment will instantiate and migrate instances of provided mappers and reducers seamlessly across Infinispan nodes. Unless otherwise specified using onKeys method input keys filter all available key value pairs of a specified cache will be used as input data for this task. #
[.cm]#MapReduceTask implements a slightly different execution model from the original MapReduce proposed by Google. Here is the pseudocode of the MapReduceTask. #
mapped = list()
for entry in cache.entries:
t = mapper.map(entry.key, entry.value)
mapped.add(t)
r = null
for t in mapped:
r = reducer.reduce(t, r)
return r to Infinispan node that invoked the task
On Infinispan node invoking this task:
reduced_results = invoke map reduce task on all nodes, retrieve map{address:result}
for r in reduced_results.entries:
remote_address = r.key
remote_reduced_result = r.value
collator.add(remote_address, remote_reduced_result)
return collator.collate()
Examples
In order to get a better feel for MapReduce framework lets have a look at the example related to Infinispan’s grid file system. How would we calculate total size of all files in the system using MapReduce framework? Easy! Have a look at GridFileSizeExample.
public class GridFileSizeExample {
public static void main(String arg[]) throws Exception {
Cache cache = null;
MapReduceTask task =
new MapReduceTask(cache);
Long result = task.mappedWith(new Mapper() {
@Override
public Long map(String key, GridFile.Metadata value) {
return (long) value.getLength();
}
}).reducedWith(new Reducer() {
@Override
public Long reduce(Long mapResult, Long previouslyReduced) {
return previouslyReduced == null ? mapResult : mapResult + previouslyReduced;
}
}).collate(new Collator(){
private Long result = 0L;
@Override
public Long collate() {
return result;
}
@Override
public void reducedResultReceived(Address remoteNode, Long remoteResult) {
result += remoteResult;
}});
System.out.println("Total filesystem size is " + result + " bytes");
}
}
In conclusion, this is not a perfect and final distributed execution and MapReduce API that can satisfy requirements of all users but it is a good start. As we push forward and make it more feature rich while keeping it simple we are continuously looking for your feedback. Together we can reach the ambitious goals set out in the beginning of this article.


https://www.exploit-db.com/papers/12975/
http://pentestmonkey.net/cheat-sheet/sql-injection/mssql-sql-injection-cheat-sheet
https://www.acunetix.com/blog/articles/blind-out-of-band-sql-injection-vulnerability-testing-added-acumonitor/
https://dzone.com/articles/sqli-part-3-in-band-and-inferential-sqli
https://www.gracefulsecurity.com/sql-injection-out-of-band-exploitation/


I have a Computer Science background and have been working in the field for a while. I have significant experience in relational databases and concepts, I'm also enjoying learning about cloud and big data. I'm enrolled in NJIT for a Certificate course in Data Mining.
I enjoy reading, travelling and volunteering.


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Buffalo Wings
Story
It's Sunday. Kickoff is in 5 minutes. Time to end the hiatus. We bought a whole chicken for 5 bucks, broke it down and fried it up into a huge pile of buffalo style chicken. Less money, more food, flavor dialed in just to your liking. This one is a no brainer. We added in a link to the deep fryer that we use. One of those kitchen tools that isn't terribly expensive but makes all the difference.
Recipe
Buffalo Wings
September 14, 2014
Turn a whole chicken into a heaping pile of buffalo wings on the cheap
Prep time:
Cook time:
Total time:
Yield: 1 heaping mound of buffalo chicken
Ingredients:
Chicken, whole: 1flour: 3 c
cornstarch: 1/4 c
black pepper: 2 tsp
smoked chili powder: 2 tsp
garlic: 2 tsp
salt: 2 tsp
milk: 1 c
egg: 2
fry oil (canola or vegetable): enough to cover
hot sauce: 1 c
butter: 1 tbl
Directions:
1. Remove wings and legs from chicken. Break down thighs and breasts into bite size chunks. We'll have some boneless wings and some traditional wings with this method.
2. Preheat oil to 350 degrees F in your Deep Fryer or cast iron.
3. Mix flour, cornstarch, and all spices. Dredge chicken pieces in flour mixture, be sure to coat all surfaces.
4. Whisk together eggs and milk. Dunk chicken in egg and milk mixture, be sure to cover all surfaces. At this point, I like to take a few spoonfuls of the milk mixture and drizzle them into flour, this will create crunchy chunks on the exterior of the chicken.
5. Dredge chicken back into flour mixture, again be sure to coat well.
6. Fry in oil until golden and crispy, about 5 minutes on each side.
7. Melt butter into hot sauce, toss wings in sauce and enjoy with blue cheese dressing.
2. Preheat oil to 350 degrees F in your Deep Fryer or cast iron.
3. Mix flour, cornstarch, and all spices. Dredge chicken pieces in flour mixture, be sure to coat all surfaces.
4. Whisk together eggs and milk. Dunk chicken in egg and milk mixture, be sure to cover all surfaces. At this point, I like to take a few spoonfuls of the milk mixture and drizzle them into flour, this will create crunchy chunks on the exterior of the chicken.
5. Dredge chicken back into flour mixture, again be sure to coat well.
6. Fry in oil until golden and crispy, about 5 minutes on each side.
7. Melt butter into hot sauce, toss wings in sauce and enjoy with blue cheese dressing.


DESPERATE MEASURES A film review by Matt Williams
RATING: * * 1/2* out of * * * *
To save a loved one, how great a cost would you be willing to pay? Would you be willing to break the law? If so, which ones, and how many times? Is that one life worth the lives of others? How about a potential threat to those lives? These are several of the questions that float just below the surface of Desperate Measures, which is otherwise a routine thriller. Ultimately, the film could have been a better one with a more thorough exploration of these issues, but, as it stands, it is a mildly entertaining piece of work.
Frank Connor (Andy Garcia) is a San Francisco cop, and a dedicated father. Those two roles are about to conflict when his leukemia-stricken son needs a bone marrow transplant. Unable to find a conventional donor who is compatible, Frank turns to some ethically improper techniques to discover the one man who can save his child's life: Peter McCabe (Michael Keaton).
However, McCabe presents a problem. It turns out that he is a sociopath, a convicted multiple-murderer serving out a life sentence without parole. Frank has to find some way to get McCabe to agree to a bone marrow transfer. And then there's the procedure itself (ably performed by Dr. Samantha Hawkins (Marcia Gay Harden)), which could be McCabe's best hope for escape since he was incarcerated.
The story in Desperate Measures is a straightforward one. You can see the end of the film from the beginning. The ending is never in doubt. Just ask yourself, how many child-in-jeopardy thrillers actually end up with a dead kid?
McCabe's scheme is outlined. We see what he's planning to do, and then he does it (mostly) without flaw. Although there's some interest in seeing how intricately plotted his plan is...it seems mechanical, and lacks a spark that might have made it totally enthralling.
On the dramatic side, there are plenty of conflicts where McCabe uses his medical uniqueness to his own advantage. Frank not only has to recapture McCabe, but he has to keep him alive...or his own son will die. This puts Frank uncomfortably at odds with his own police force, whose goal is to keep McCabe from escaping at any cost.
Realistically, Frank's actions, carrying a tremendous cost of life and property, simply to save his son, would land him in a nice comfy cell (right next to McCabe, perhaps). Of course, the film never follows up the matter quite this far.
Andy Garcia is sympathetic as Frank, but beyond his mindless devotion to his son, there's really very little to his character. He actually covered some of the same ground (the conflict between duty and family) with a much better performance in the neglected Night Falls on Manhattan. Between him and the villain, Keaton is certainly the one to watch. He not only gives the more vibrant performance, but has the better character of the two.
The action scenes in the film are fun to watch, even if, at times, they seem a bit unplausible. Even though the plotting of the film is straightforward, watching Keaton's plan unfold is interesting, and there is suspense...mostly of the "how" variety, rather than "what".
Though there definitely are flaws in the movie, it is entertaining. You might wish the film explored some of its core issues in more depth, but, unlike many thrillers, it does give you something to think about as you leave the theater.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Shreck's Cinema: http://www.shreck.com Home of over 500 reviews, and information on over 600 upcoming releases
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Your offer has been sent to {% if data['defendant'] == "defendant" %}Jan Clark {% elseif data['defendant'] == "claimant" %}Mary Richards {% else %}Jan Clark{% endif %} What happens next {% if data['defendant'] == "defendant" %}Jan Clark {% elseif data['defendant'] == "claimant" %}Mary Richards {% else %}Jan Clark{% endif %}can accept, reject or make a counter offer. We'll let you know when they respond. View your claim status on your money claims account. Help and support Email<EMAIL_ADDRESS>Telephone: 0300 123 7050 Hours: 9am to 5pm, Monday to Friday Find out about call charges Finished {% endblock %}
{% if data['defendant'] == "defendant" %}Jan Clark {% elseif data['defendant'] == "claimant" %}Mary Richards {% else %}Jan Clark{% endif %}can accept, reject or make a counter offer. We'll let you know when they respond.
View your claim status on your money claims account.
Email<EMAIL_ADDRESS>Telephone: 0300 123 7050 Hours: 9am to 5pm, Monday to Friday
Find out about call charges


Hello! It appears that you have not set up your configuration file, it could not be read, the TLS certificate could not be loaded, or a database connection could not be made. A default config file has been generated for you if it did not exist already, but it still requires configuration. You may find the configuration file at <installDir>/configuration/config.json
If this is a transient database connectivity issue, just wait a couple minutes and the server will automatically start once the database is up. Otherwise, review the log output for more information on why the server failed to start. Once the config file has been updated, or the issue resolved, restart this service


Main Page Class Hierarchy Compound List File List Compound Members
TinyXml Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
Generated on Fri Dec 19 18:28:21 2003 for TinyXml by
<IP_ADDRESS> written by Dimitri van Heesch,
© 1997-2001


Bases: object
A response object containing a code (which must be a ResponseCode object) and a message, and optionally some additional data.
also you can add responses, and you will get a useful combination of two responses - see doc string for __add__()
Raise an exception if the response was a failure code
can be changed to return self (for performance) if this object is not modifiable???
Bases: object
We only allow Response codes to be instances of ResponseCode
and can expect at a minimum that status.__nonzero__() will return True for success or False for failure. The implementation can be changed later if a range of failure codes are added
Codes are also callable - they return Response objects with that code and the message (or with ‘Success’ or ‘Failure’ if no message given)
Bases: object
This turns a generic callable which may raise an exception into a callable which will return a Response object depending whether an exception was raised or not.
If it succeeds, then the message is the return value (unless None, in which case the default message).
return a success or failure Response object depending on condition
Wrap a method, and return a response code. The actual return value of the method is returned in the optional extra data; the main success/failure code depends on whether there was an exception.


Tutorial Requests
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The table window presents a spreadsheet-like display of the contents of the given FITS image extension. Row numbers run along the left side of the window and column numbers run along the top (below the menubar). Single click on a data cell to copy the value to the "Edit cell" entry at the bottom of the window, where one can immediately edit the value. Press <Return> in that entry to finalize the changes. If the FITS image is simultaneously displayed in the POW image viewer, then double clicking with the mouse on any pixel in the POW image will cause the corresponding cell in the table display to be highlighted.
Display image data in user's prefered image device (see Preferences).
List of all the available help files.


Error Reporting
HOL - Error Reporting and Monitoring with DataDog
A dormant bug may exist in the PartsUnlimited site but it has yet to show itself. We want to give customers the best experience possible in terms of getting issues resolved quickly. To do so, we are going to be setting up logging and custom event monitoring for the PartsUnlimited solution using DataDog. This will assist our engineers by providing rapid feedback around critical releases. Proactive monitoring adds to an all around better user experience and gives our engineering team greater confidence when deploying changes.
Datadog may be preferable due to a slight difference in it’s target user base (admins, operators and the like) as opposed to other APM software. It also has some great infrastructure monitoring executables that are easy to install and provide great coverage around your application stack.
Pre-requisites:
-
Visual Studio 2015 Update 3 (NOTE: Visual Studio 2015 preview 5 does not support .net core tooling, ensure the version you are running supports .net core tooling before continuing).
-
An active DataDog account
-
A DataDog API key (after signing up)
Tasks Overview:
Task 1. Create a custom wrapper to log metrics to DataDog - In this task we will be writing some code to perform our logging calls to DataDog. We will go over the steps required to monitor three different areas of the application - the shopping cart, logins and global exceptions thrown by the application that aren’t handled elsewhere.
Task 2. Deploy the PartsUnlimited Solution to Azure - In this task we will be going through the steps required to deploy the PartsUnlimited site to Azure.
Task 3. Trigger some logging to DataDog - In this task we will be using the deployed PartsUnlimited site to trigger some events that will be logged to DataDog.
Task 4. Set up a custom event monitor in DataDog - In this task we will set up a custom monitor to look for particular events logged, then trigger email sendouts to critical or/and on call members of our engineering team.
Task 1: Create a custom wrapper to log metrics to DataDog
Step 1. Clone the repository to a local directory.
Create a parent Working Directory on your local file system. For instance, on a Windows OS you can create the following directory:
C:\Source\Repos
Open a command line (one that supports Git) and change to the directory you created above.
Clone the repository with the following command. You can paste in the URL if you copied it in Step 1. In the example below, the clone will be copied into a directory named HOL. Feel free to use whatever directory name you like, or leave it blank to use the default directory name:
git clone https://github.com/Microsoft/PartsUnlimited.git HOL
After a few seconds of downloading, all of the code should now be on your local machine.
Move into the directory that was just created. In a Windows OS (and assuming you used HOL as the directory name), you can use this command:
cd HOL
Step 2. Create a new setting for our datadog API key and base URL. This is located inside the website project -> ./HOL/src/PartsUnlimitedWebsite/config.json
This will sit under the “Keys” section.
"Keys" {
...
"DataDog":{
"ApiKey": "XXXXXXXXXXXXXXXX",
"BaseUrl": "http://app.datadoghq.com/api/v1/"
}
}
Step 3. Create the configuration settings initializer. These can sit anywhere under the PartsUnlimitedWebsite project.
public interface IDataDogSettings
{
string ApiKey { get; set; }
string BaseUrl { get; set; }
}
public class ConfigurationDataDogSettings : IDataDogSettings
{
public ConfigurationDataDogSettings(IConfiguration config)
{
ApiKey = config[nameof(ApiKey)];
BaseUrl = config[nameof(BaseUrl)];
}
public string ApiKey { get; set; }
public string BaseUrl { get; set; }
}
Step 4. Now we need to set up our dependency injection correctly. Navigate to the Startup class. This will tell our configuration where to look in order to load the DataDog specfic settings (Under Keys -> DataDog)
public void ConfigureServices(IServiceCollection services)
{
...
services.AddScoped<IDataDogSettings>(p => new ConfigurationDataDogSettings(Configuration.GetSection(ConfigurationPath.Combine("Keys", "DataDog"))));
...
}
Step 5. Now we want to create the contract we’re using to communicate with DataDog. This can be found in the DataDog API -> http://docs.datadoghq.com/api/?lang=console#events. Create a new class called DataDogEventRequest.cs under the Telemetry
folder -> ./HOL/src/PartsUnlimitedWebsite/Telemetry/
public class DataDogEventRequest
{
public string Title { get; set; }
public string Text { get; set; }
public string Priority { get; set; }
public List<string> Tags { get; set; }
public string Alert_Type { get; set; }
}
Step 6. Add the Microsoft.AspNet.WebApi.Client nuget package. We need this to perform the PostAsJsonAsync method on our HttpClient.
NOTE: If you’re not using Visual Studio you will need to add the dependency in manually.
- In
./HOL/src/PartsUnlimitedWebsite/project.json
under the ‘dependencies’ section add the following (don’t forget to add a comma on the line above). Check here for the latest version of Microsoft.AspNet.WebApi.Client.
"dependencies": {
...
"Microsoft.AspNet.WebApi.Client": "5.2.3"
}
- After that is done run
dotnet restore
from the command line inside the project folder./HOL/src/PartsUnlimitedWebsite/
Step 7. Create another class called DataDogEventLogger.cs
public interface IEventLogger
{
void Trace(string message);
void TrackException(Exception exception);
}
public class DataDogEventLogger : IEventLogger
{
private readonly IDataDogSettings _config;
public DataDogEventLogger(IDataDogSettings config)
{
if (config == null)
throw new ArgumentNullException(nameof(config));
_config = config;
}
public async void Trace(string message)
{
DataDogEventRequest request = new DataDogEventRequest
{
Title = message,
Priority = "normal",
Text = message,
Alert_Type = "info",
Tags = new List<string> { "trace"}
};
;
await Request(request, "events");
}
public async void TrackException(Exception exception)
{
DataDogEventRequest request = new DataDogEventRequest
{
Title = exception.Message,
Priority = "high",
Text = exception.StackTrace,
Alert_Type = "error",
Tags = new List<string> { "exception" }
};
await Request(request, "events");
}
private async Task<HttpResponseMessage> Request<T>(T payload, string target)
{
using (var client = new HttpClient())
{
client.BaseAddress = new Uri(_config.BaseUrl);
client.DefaultRequestHeaders.Accept.Clear();
client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
string requestTarget = target + $"?api_key={_config.ApiKey}";
return await client.PostAsJsonAsync(requestTarget, payload);
}
}
}
Let’s provide a bit more context for each of these methods.
We have a private method in this class which will be wrapping up our REST requests to datadog (Dont Repeat Yourself!). This creates a new client with all the required attributes to communicate with the datadog API (API keys and the base URL).
We have the following:
-
The base address “https://app.datadoghq.com/api/v1/”
-
The JSON accept header of “application/json”
-
The API key attachment
We also have the Trace and TrackException methods which, under the hood, are very similar. The only real difference being the information logged to DataDog.
Step 8. Now, in order to catch exceptions make by our application we are going to want some sort of global exception catcher. Let’s create a global exception filter for our application to ensure all unhandled exceptions are logged to DataDog.
public class CustomExceptionFilterAttribute : ExceptionFilterAttribute
{
private readonly IEventLogger _eventLogger;
public CustomExceptionFilterAttribute(
IEventLogger eventLogger)
{
if (eventLogger == null)
throw new ArgumentNullException(nameof(eventLogger));
_eventLogger = eventLogger;
}
public override void OnException(ExceptionContext context)
{
_eventLogger.TrackException(context.Exception);
}
}
Step 9. To ensure this custom exception filter is applied across our application we can add it to the default set of MVC filters. Navigate back to the Startup.cs class. Note we also want to bind our event logger just in case we want this to be used somewhere else in the application.
public void ConfigureServices(IServiceCollection services)
{
...
// get our datadog settings from the config file
IDataDogSettings configurationDataDogSettings =
new ConfigurationDataDogSettings(
Configuration.GetSection(ConfigurationPath.Combine("Keys", "DataDog")));
services.AddScoped(p => configurationDataDogSettings);
services.AddScoped<IEventLogger, DataDogEventLogger>();
// add our custom exception handler to the application filter set
services.AddMvc(
opts =>
{
opts.Filters.Add(new CustomExceptionFilterAttribute(new DataDogEventLogger(configurationDataDogSettings)));
});
...
}
Step 10. Now, let’s add some tracing in our Controllers. Firstly, let’s add some tracing around when people add items to their cart. Navigate to ShoppingCartController.cs
public class ShoppingCartController : Controller
{
...
private readonly IEventLogger _eventLogger;
public ShoppingCartController(
...
, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
public async Task<IActionResult> AddToCart(int id)
{
// Retrieve the product from the database
var addedProduct = _db.Products
.Single(product => product.ProductId == id);
_eventLogger.Trace($"Cart/Server/Add/{addedProduct.Title}");
...
}
}
Now we want to trace when an order has been completed. Navigate to CheckoutController.cs
public class CheckoutController : Controller
{
...
private readonly IEventLogger _eventLogger;
public CheckoutController(
...
, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
public IActionResult Complete(int id)
{
...
if (order != null)
{
_eventLogger.Trace($"Checkout/Server/Complete/OrderId/{id}/Total/{order.Total}");
...
}
...
}
}
Let’s also trace when a category is browsed to - this could provide valuable data for the marketing team by showing them popular categories and not so popular categories. Navigate to StoreController.cs
public class StoreController : Controller
{
...
private readonly IEventLogger _eventLogger;
public StoreController(
...
, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
public IActionResult Browse(int categoryId)
{
...
var categoryModel = _db.Categories.Single(g => g.CategoryId == categoryId);
_eventLogger.Trace($"Store/Server/Browse/{categoryModel.Name}");
...
}
We also want to see what people are searching for, this could potentially assist our UX team to ensure the customer can get to what they want faster and more intuitively. Navigate to SearchController.cs
public class SearchController : Controller
{
...
private readonly IEventLogger _eventLogger;
public SearchController(
...
, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
public async Task<IActionResult> Index(string q)
{
if (string.IsNullOrWhiteSpace(q))
{
return View(null);
}
_eventLogger.Trace($"Search/Server/Index/Query/{q}");
...
}
For our user management, we want to check failures on the change password action. Navigate to ManageController.cs
public class ManageController : Controller
{
private readonly IEventLogger _eventLogger;
public ManageController(UserManager<ApplicationUser> userManager, SignInManager<ApplicationUser> signInManager, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
...
[HttpPost]
[ValidateAntiForgeryToken]
public async Task<IActionResult> ChangePassword(ChangePasswordViewModel model)
{
...
var user = await GetCurrentUserAsync();
if (user != null)
{
...
if (result.Succeeded)
{
...
}
_eventLogger.Trace($"Manage/Server/ChangePassword/Failure/{user.Name}");
...
}
...
}
}
Lets also log when a user has a failed login attempt. Navigate to the AccountController. Just underneath ModelState.AddModelError we want to trace the error and log it to DataDog.
public class AccountController : Controller
{
private readonly IEventLogger _eventLogger;
public AccountController(UserManager<ApplicationUser> userManager, SignInManager<ApplicationUser> signInManager, IEventLogger eventLogger)
{
...
_eventLogger = eventLogger;
}
...
// POST: /Account/Login
[HttpPost]
[AllowAnonymous]
[ValidateAntiForgeryToken]
public async Task<IActionResult> Login(LoginViewModel model, string returnUrl = null)
{
...
ModelState.AddModelError("", "Invalid login attempt.");
_eventLogger.Trace($"Account/Server/Login/Failure/{model.Email}");
return View(model);
}
}
Task 2: Deploy the PartsUnlimited Solution to Azure
Lets get our modified app in to Azure! From the solution view, right click on the PartsUnlimitedWebsite project and then select ‘Publish…’
Now we want to create a publish profile to a Microsoft Azure App Service.
If you have an app service created in Azure, you can select it from here. Otherwise you can select ‘New…’ to create one.
Create a unique web app name and assign it to a familar resource group. Note: Keep similar items in the same resource group. As your Azure subscription grows this will assist greatly for knowing which things are related.
Now you should see the deployment step allocating your app.
The following form should be filled out. Take note of the server you are deploying to so you can navigate to it in later steps. Select ‘Publish’ if everything looks good!
NOTE: if you are not using Visual Studio you can set this up using the Azure CLI
For example, if you have nodejs installed on a linux machine we can install the Azure CLI via NPM.
npm install -g azure-cli
Or you may need to use sudo to successfully run the npm command.
sudo npm install -g azure-cli
Now run the following commands to create an App Service resource in Azure.
azure config mode arm
azure login
This will prompt you to to login and provide command line access to your Azure subscription.
azure group create -n "ExampleResourceGroup" -l "westus"
azure appserviceplan create --name partsunlimitedserviceplan --location "westus" --resource-group ExampleResourceGroup --sku P1
azure webapp create --name partsunlimitedsite --location "westus" --resource-group ExampleResourceGroup --plan partsunlimitedserviceplan
Now you will need to navigate to the azure portal and turn on local git deployments.
Look for the ExampleResourceGroup we created earlier, then find the partsunlimitedsite web app we created. We then want to alter the deployment options. Select Local Git Repository, create a deployment user then select OK.
Now navigate to the overview page of the web app and note down the git clone url.
Now we want to create the git remote to deploy to (use the clone url we got from the azure portal)
git remote add azure https://theuseryoujustcreate@yoursitenamehere.scm.azurewebsites.net:443/yoursitenamehere.git
Now we need to add a deployment script to ensure our application unpacks correctly on the app service.
Copy these files to the root folder of the application ./HOL
:
deployment
(rename this file to.deployment
)deploy.cmd
Now we’re ready to deploy. Type the following commands to add the deployment files to source. This may take some time to complete.
git add -A
git commit -m "Adding deployment scripts"
git push azure master
Task 2: Trigger some logging to DataDog
Step 1. Navigate to the deployed website.
Step 2. Now, lets trigger some actions.
- Navigate to the login page and try any username with any password a few times.
- Try and add an item to your cart.
- Try using the search bar located at the top of the site.
- Try completing an order.
- Login with the following credentials -> user:
<EMAIL_ADDRESS>password:YouShouldChangeThisPassword1!
. Then try to unsuccessfully change your password under the ‘Profile -> Manage Account’ section.
Step 4. Navigate to the DataDog portal to check and see if everything was logged correctly -> https://app.datadoghq.com/event/stream
Task 3: Set up a custom event monitor in DataDog
Lets say, for example, we want to add a monitor for when failed login attempts occur within a certain time peroid. Log in to the DataDog portal and create a new monitor (Monitors -> New Monitor)
Select the ‘Event’ monitor
Here we want to track items with the #trace tag as that’s what we use for failed login attempts. Note: you can make these more specific if you wish!
Here we will set the criteria for the monitor to notify. Here we have set it to above or equal to 3. You will probably want to set this higher for production.
Here we can specify what we want to send once the monitor has been triggered. We can provide context to ensure the person or people notified have the best information available to fix the issue at hand.
Here you can set which team members you’d like notified if this monitor is triggered.
Now lets test it out! Go back to the application and trigger some failed login attempts.
Give it some time then check your emails. You should have an alert sitting in your inbox.
You will also get a follow up email if the event stops triggering after a set time period. You can leverage this to ensure you don’t have any false positives.
Next steps
Try these labs out for next steps:


Adding Remote Triggers to your Workflows
Remote Triggers allow you to make your workflow actions available through Alfred Remote so that you can control your Mac from your iPad or iPhone.
- Learn how to Add a Remote Trigger to my own workflow
- Take a look at Workflow Trigger Examples
1. Add the Remote Trigger to your workflow
Adding a "Remote" trigger object to your workflow will make its associated workflow action available to add in your Remote. This is done in the Workflows tab of Alfred's preferences.
In the workflow you want to use from Remote, add a Remote object from the + in the top right (Triggers > Remote). Connect it to the appropriate action or actions you want to perform when pressing the button on Remote.
2. Add the action to your Remote page
In the Remote tab, under the "Run Workflow Trigger" action, you'll find the list of workflows containing Remote triggers.
3. Use it in Remote
You can now control that workflow from Remote, and the action will be available on your iPhone or iPad Remote.
Workflow Trigger Examples
To see a few built-in examples of workflow triggers, go to the Workflows tab in Alfred's preferences. Click the + button at the bottom of your workflows sidebar and choose Examples > Remote Trigger Examples.
This workflow contains three Remote trigger examples. Look at them carefully in the Workflow preferences to learn how they're built.
1. The "top" trigger
In this simple example, the trigger is connected to a Terminal Command. This trigger launches the "top" command in Terminal, showing you the top processes on your Mac.
2. The "Dinner Time" trigger
This second example runs a script where the text specified as {query} in the Remote object is passed on to two actions; The Run Script object, which reads out the {query} text using your Mac's Text-to-Speech, and the Large Type object, which shows the same query in large type on your screen.
3. The "Movie Checker" trigger
This third example is more advanced, and makes use of your macOS Selection to perform its action.
If you have text selected in macOS, pressing the Movie Checker object in Remote will launch three browser windows with the selected text as {query}.
For example, if you had highlighted the word "Inception" on your Mac, pressing the Movie Checker object in Remote will launch a YouTube search in Chrome, as well as an IMDB and Rotten Tomatoes search for the keyword in your default browser.
Discover and Create Remote workflows
On the Remote Workflow Triggers page, you can also download the Good Morning workflow as an example.
You'll find more examples on Packal by searching for the "Alfred Remote" tag, and on the Alfred Forum, where you'll also be able to get hands-on help with creating your own Remote workflows.