text:
CLEAR-COM EXHIBITS LATEST IN BROADCAST INTERCOMS AT BVE 2013
By Vanessa Bishop published 6 February 13
Clear-Com®, a global leader in critical voice communications systems, is displaying its latest range of broadcast intercomsolutions at BVE 2013 (Stand J16). Included among the intercom offerings on display are the recently unveiled Eclipse HX matrices and HelixNet Partyline system. The company is also presenting its BroaMan interface options and the latest upgrades to the Tempest®2400 wireless intercom.
"Since last year's show, Clear-Com has unveiled several exciting new products for the Broadcast market and we're thrilled to have the chance to demo them at BVE 2013," says Simon Browne, Director of Product Management, Clear-Com. "Clear-Com has built a strong portfolio of advanced wired and wireless intercoms to meet the critical demands of those in broadcast studios and mobile productions. Our latest developments will enable us to become an even more dominant force as an intercom solution provider."
The Eclipse HX Digital Matrix Systems, along with the Eclipse HX (EHX) Configuration Software, are powerful matrix intercoms that offer fast performance, high system capacity, and simplified operations and administration. Eclipse HX comprises the Eclipse HX-Omega, Eclipse HX-Median and Eclipse HX-PiCo matrices, as well as the EHX software. In broadcast facilities where resources are often shared across multiple studios or production vehicles, fast and highly intuitive systems are critical to execute system-wide changes in real time, with minimal impact on users. Users can make changes on the Eclipse HX systems quickly and without advanced notice. This capability is especially important during live broadcasts.
Clear-Com has architected the EHX software for rapid processing of configuration and routing. This optimises system performance and speed. The EHX user interface is designed to be intuitive and easy to set up. The 6RU Eclipse HX-Omega frame offers 460 ports, making it the highest-density intercom matrix on the market. PiCo-Link provides intelligent audio linking between co-located Eclipse HX-PiCos, allowing users to expand resources with just two rack-unit spaces. Clear-Com's V-Series and i-Series panels, frame cards and interfaces are all compatible with the new Eclipse HX range, providing a seamless logical upgrade path for most existing Eclipse customers.
The Tempest®2400with Seamless Roaming and Tempest®2400 MasterBelt add advanced wireless capabilities to the Tempest family of digital wireless intercom systems. The latest additions afford users even more wireless coverage options. The Tempest2400 with Seamless Roaming is ideal for engineers and users that need to move freely throughout large venues separated by sizeable distances. These environments include multi-studio complexes, multilevel facilities and sports arenas. Users can effortlessly and seamlessly roam across 16 different coverage zones without interference or dropouts.
For small-scale productions, such as reality TV programmes, where a mobile, un-tethered system is needed, the Tempest2400 MasterBelt is an ideal wireless solution. It offers most of the features, reliability and robustness of the Tempest2400 BaseStation within the compact, portable packaging of a BeltStation. The MasterBelt can be paired with any standard Tempest2400 two-channel BeltStation to create a full-featured, full-duplex two-channel system.
The HelixNet™ Partyline Intercom System is the industry's first digital network partyline intercom with the ability to provide efficiency, cost-savings and flexibility from setup to operation. The revolutionary HelixNet platform has proven itself by providing communication at high-profile events such as the 2012 London Olympics and Paralympics.
Included in the HelixNet portfolio of products are the HMS-4X Main Station, HBP-2X HelixNet Beltpacks, the HLI-2W2 two-wire Interface Module and the HLI-4W2 four-wire Interface Module. Each HMS-4X Main Station offers four intercom channels, program audio and power for up to 20 beltpacks over a shielded twisted-pair cable (e.g. microphone cable, CAT-5 or CAT-6). Users can work over existing shielded cable infrastructure to centrally manage the entire system from the Main Station with a single cable. The HelixNet beltpack also functions as a source assignment panel, allowing users to select any two of four channels to get the job done. Furthermore, the high-fidelity system delivers superb audio clarity, and is rugged enough to withstand the demanding work environments.
Clear-Com will also feature the BroaMan DiViNe V3R-FX-ICOM-SDI video and intercom signal transport interfaces, the latest offerings from its expanded partnership with Optocore/BroaMan.The DiViNe V3R-FX-ICOM-SDI interfaces provide scalable, protocol-independent routing, repeating, transport and distribution of multiple signal types over optical fibre. They are equipped with (four) four-wire RJ45 matrix ports or panel ports, with four SD or HD video BNC signals in or out, and one SD or HD video BNC in or out. The units are therefore not only capable of connecting matrix ports to panels, but also routing up to five SD/HD/3G-SDI coaxial video signals that are converted to and from single-mode DiViNe optical fibre links. They also allow for distribution of audio and control data through an optical network.
About Clear-Com®
Clear-Com, an HME company, is a global provider in professional voice communications systems since 1968. We develop and market proven intercom technologies such as Analog & Digital Partyline, Digital Matrix, Wireless and Intercom-over-IP systems for critical communication applications in broadcast, performance venues, military, government and enterprise markets. Recognized for our legacy of intercom innovations, production teams around the world have come to depend on Clear-Com for clear, reliable and scalable communications solutions. For more information, please visit www.clearcom.com.


Fast trains from Barcelona to Logroño take around 3 hours and 40 minutes, covering a distance of approximately 398 kilometres. There are frequent services on the rail route between Barcelona and Logroño.
1. Are there direct trains from Barcelona to Logroño?
Direct trains from Barcelona to Logroño have been found.
We usually find around 4 direct trains on the route from Barcelona to Logroño every weekday.
2. How long is the train journey from Barcelona to Logroño?
The Barcelona to Logroño train travel time is normally about 3 hours and 40 minutes, whatever time you make the journey.
3. What are the Barcelona to Logroño train times and schedule?
On weekdays the earliest direct train to Logroño is usually scheduled to depart Barcelona around 07:30 and the last train is around 20:20. This is the same at the weekend.
What's the Barcelona to Logroño train price?
The earlier you book the better, and the cheapest tickets we've found for trains from Barcelona to Logroño are $47.86.
Trains departing 30 days in advance will cost around $31 while booking 7 days in advance the price is around $47. Booking on the day of travel is likely to be more expensive, so it is worth booking in advance if you can to save money.
When to book Barcelona to Logroño train tickets?
Trains in Spain usually open for booking around 4 months in advance. If booking is not yet open for your dates then you can set a booking alert to receive an email on the day that the cheapest tickets are released for your journey from Barcelona to Logroño by train.
Which train companies operate between Barcelona and Logroño?


> in other ways, because of the inconsistency of the implementations.
> of variables or macros? havent you ever wanted to define "Oxford Blue"
> as a colour and use it throughout?
don't have a problem with that; it means CSS renderers can be mighty fast.
completely different kind of thing -- a programming language.


A replicated naming service is created and maintained to provide configuration and topology information for a network, such as an undersea cable system, to a network management system (NMS). Designated hosts are chosen in each element management system (EMS) subnetwork (or cable station) to run a replicated naming server and a replication mechanism is set up in all of the hosts. The replicated naming servers are identified throughout the network and each host publishes configuration information to each of the replicated naming servers. Thus, each replicated naming server provides configuration and topology information for substantially an entire network. In one embodiment, the naming service is implemented using the Common Object Request Broker Architecture (CORBA) and the configuration information is published periodically using a naming service builder application.
This application claims the benefit of U.S. Provisional Application Serial No. 60/325,936 filed on Sep. 28, 2001, which is fully incorporated herein by reference.
One type of telecommunications network is an undersea transmission network (also referred to as a cable system). A transmission network or cable system  10 is often comprised of several cable stations 12 interconnected by cable segments 14, as shown in FIG. 1. The cable stations 12 include telecommunications equipment (not shown), for example, as a connecting point for land and undersea networks. The set of cable stations 12 and cable segments 14 form a graph with an arbitrary topology. Although the graph formed in the example shown is a simple ring, more complicated topologies are possible (e.g., with branching units).
In an exemplary transmission system, as shown in FIG. 2, each EMS is located at an EMS site  20, which is typically co-located with the cable station 12 that it manages. The NMS 24 is typically somewhere else (possibly in one of the cable stations). Each EMS, in turn, is physically comprised of one or more hosts 26, forming an EMS sub-network 28. All of these EMS sub-networks 28 are combined together to form a cable system wide management network 30 to which the NMS 24 has access.
Although CORBA makes physical location transparent to applications, it is important to know the actual physical architecture of the system because it has performance implications. At startup, the NMS  24 needs to determine what there is to be managed in the network (i.e., the configuration information) and what the network looks like (i.e., topology information). Previously, this information was made available to the NMS by manual provisioning or by complete auto-discovery. According to manual provisioning, the user manually enters the topological information in the NMS by typing through a user interface. Manual provisioning is a labor intensive and more error-prone solution due to the potentially large amount and complexity of information to be entered. The user provisioning the system typically must have a view of the whole system. According to complete auto-discovery, the NMS connects to a Local or Global Area Network and uses a network management protocol to detect the equipment to be managed. Although previous auto-discovery solutions were automatic and desirable to some extent, this solution is relatively expensive to implement and is unable to detect nodes or hosts missing in the configuration if the hosts cannot be contacted by the NMS.
According to yet a further aspect of the present invention, a naming service builder computer program product is provided for use in a naming service including one or more naming servers. The naming service builder computer program product comprises code for reading a local configuration file for a host in a communications network, code for creating an interoperable object reference (IOR) for each entry in the configuration file, and code for binding the IOR to a name in each of the naming servers.
FIG. 6 is a flow chart illustrating a method of publishing configuration information to a naming server, according to one embodiment of the present invention.
One challenge when using the replicated solution is keeping the replicas synchronized with each other. In one example, a simplistic replicated naming service can have a replica per host. In some networks (e.g., a cable system) where there are typically dozens of hosts, keeping the replicas synchronized can generate a considerable amount of traffic when there is a replica per host. According to this example, the total number of updates to synchronize the replicas increases at a rate of order n 2, where n is the total number of hosts in the network.
According to one embodiment, FIG. 3 shows two of the hypothetical y EMS sites  20 in a network (e.g., cable stations in a cable system). Each EMS site 20 represents an instance of a distributed EMS. As shown, each EMS site 20 has one or more (N) hosts 26 in its EMS configuration and different EMS sites 20 can have different numbers (N) of hosts 26. In each EMS site 20, one of the hosts 26 is a designated host 26 d running the replicated naming server (or replica). All of the hosts 26 in the network, whether designated or not, publish their local configuration information to each of the replicated naming servers only, as will be described in greater detail below. Given a cable system with y EMS sites, each host 26 performs a total of y updates. As the total number of hosts 26 in the network increases, there is a linear increase in the total number of necessary updates. This reduces the number of updates necessary to keep the replicated naming servers synchronized, when compared with the approach having one replica per host.
Each replicated naming server running on the designated hosts  26 d preferably contains substantially complete configuration information for the communications network being managed. The configuration and topology information is thus highly available because many replicated naming servers can be reached and the NMS only needs to access one of the replicated naming servers to discover the network topology as a whole.
Referring to FIG. 4, one method of creating and maintaining the replicated naming service is described in greater detail. The designated hosts  26 d are chosen from the other hosts 26 in the network, step 112. In general, any host 26 that is part of the EMS may be chosen as the designated host 26 d. In one preferred embodiment, the designated host 26 d is a host 26 that will always be present in any configuration of the distributed EMS and that has a hostname with a specific name pattern. In a cable system, for example, each host performs some specific task in the network management and the hosts are named after the function performed in the network. One host in an exemplary cable system is present in all possible EMS configurations and has a hostname with a specific suffix "_osps". By choosing hosts 26 with the specific suffix "_osps" to be the designated hosts 26 d, each EMS in the exemplary cable system has a designated host 26 d that can easily be identified. In other types of networks, the designated hosts can be chosen differently.
Once the designated hosts  26 d are chosen, a replication mechanism is set up in all of the hosts 26, step 114. In general, setting up the replication mechanism includes installing and configuring the software and files used to create and maintain the replicated naming service, as will be described in greater detail below.
After the replication mechanism is set up, each of the hosts  26 throughout the network identifies the replicated naming servers running on the designated hosts 26 d, step 116. Given the design of the distributed EMS, any other host 26 in any other EMS that belongs to the same network or cable system has access to the designated hosts 26 d and can access them by name. In one preferred embodiment, each host 26 includes a list of all hosts 26 in the system and the set of designated hosts 26 d can be determined at any time from the list of hosts by selecting the specific name patterns from the list. In the exemplary cable system discussed above, the designated hosts having the suffix "_osps" (e.g., Site1_osps, Site2_osps, . . . ) can be selected from the list of hosts.
In one embodiment based on the Unix operating system, an entry is created for each host  26 in the system in the /etc/hosts configuration file in all hosts 26. The replicated naming servers can then be identified using a grep command to search the /etc/hosts file for the specific name pattern of the designated hosts 26 d and to create a list of designated hosts 26 d. The grep command can be executed in each of the hosts 26 by calling a Unix shell script, as is known to those of ordinary skill in the art. The shell script can be installed and configured in each of the hosts 26 as part of the replication mechanism set up process.
In other systems where other naming conventions are used, the designated hosts may be identified differently, for example, by selecting hosts with a specific IP address pattern. In systems without a hosts file listing all hosts, a special configuration file can be created to provide a list of designated hosts. Other search commands or techniques known to those of ordinary skill in the art can also be used to identify the designated hosts  26 b running the replicated naming servers.
When the replicated naming servers have been identified, each of the hosts  26 publishes its local configuration information to the replicated naming servers, step 118. In one preferred embodiment, a naming service builder application 50, as shown in FIG. 5, is used to publish the local configuration information contained in one or more configuration files 52 on each of the hosts 26 to a replicated naming server 54 on the designated host 26 d in each EMS site 20. In the exemplary embodiment of a cable system, configuration information can include information pertaining cable segments, cable terminations, fiber pair terminations, and network elements. In one embodiment, a Unix shell script is used to invoke the naming service builder application 50. For example, a shell script can be configured during the replication mechanism set up to identify the designated hosts (e.g., using the grep command) and then invoke the naming service builder application 50 for publishing the configuration information to the replicated naming server 54 on each of the identified designated hosts 26 d.
In the preferred embodiment using the CORBA naming service, the naming service builder application  50 is a CORBA application, called naming service builder (nsb). Referring to FIG. 6, one method of publishing the configuration information using the CORBA-based nsb application is described in greater detail. The nsb application 50 reads a configuration file 52, step 120. For each entry in the configuration file 52, the nsb application 50 manufactures an interoperable object reference (IOR), step 122, and binds it to a name in each naming server 54, step 124. This invokes operations of the Naming Context CORBA interface known to those of ordinary skill in the art.
The nsb application  50 creates a hierarchy of naming contexts some of which are pre-defined according to the naming structure established. In a cable system, for example, "CableSegment" and "FiberPairTermination" may be pre-defined as naming contexts. Other naming context names and the names associated with the IORs are obtained from the configuration file(s) 52.
According to the preferred method of maintaining the replicated naming service, the naming servers are updated periodically. To update the replicated naming servers, the steps of identifying the replicated naming servers, step  116, and publishing the local configuration information, step 118, are preferably repeated. The periodic updates detect if an EMS site has been added or removed from the network configuration, for example, by searching the /etc/hosts file that is updated as the network configuration changes. The periodicity depends on how often the system configuration is modified. While some systems have volatile configurations, an undersea cable system is a domain with rather static configuration, once installation and commissioning phases are complete.
In one preferred embodiment based on the Unix operating system, the scheduling of periodic updates is achieved by creating Unix crontab entries, which are automatically managed by the cron application. The Unix shell script created to detect the designated hosts  26 d and to invoke the naming service builder application 50 can be placed in the crontab of each host 26 and executed periodically. The determination of the periodicity for the updates and the creation of crontab entries in the hosts are preferably part of the replication mechanism set up process.
publishing local configuration information for each of said hosts to each of said replicated naming servers, wherein each of said replicated naming servers provides substantially complete configuration information pertaining to said communications network, and wherein said NMS is capable of obtaining said substantially complete configuration information to discover a topology of said communications network by accessing any one of said replicated naming servers.
2. The method of claim 1 wherein each said EMS subnetwork includes only one replicated naming server.
3. The method of claim 1 wherein identifying said replicated naming server comprises searching a hosts file for a specific name pattern.
4. The method of claim 1 wherein publishing said local configuration information is performed by executing a naming service builder application in each of said hosts.
5. The method of claim 4 wherein said naming service builder application is a Common Object Request Broker Architecture (CORBA) application.
binding said IOR to a name in each said replicated naming server.
7. The method of claim 1 wherein publishing said local configuration information is performed periodically for each of said hosts.
8. The method of claim 7 further comprising scheduling a time for publishing said local configuration information for each of said hosts.
9. The method of claim 1 wherein publishing said local configuration information includes publishing said local configuration information at different times for each of said hosts.
10. The method of claim 1 wherein said communications network is a cable system.
binding said IOR to a name in at least one naming server, wherein said NMS is capable of obtaining configuration information for said at least one EMS subnetwork by accessing said at least one naming server.
12. The method of claim 11 wherein each of said replicated naming servers is located in a respective said EMS subnetwork.
13. The method of claim 12 wherein each said EMS subnetwork includes only one naming server.
a naming service builder application executed in each said host for periodically publishing said local configuration information to each of said replicated naming servers.
code for binding said IOR to a name in each said replicated naming server located in each said designated host.
16. The system of claim 15 wherein said naming service builder application is a Common Object Request Broker Architecture (CORBA) application.
17. The system of claim 15 wherein each said naming service builder application publishes said local configuration information at a scheduled time.
18. The system of claim 15 wherein each said naming service builder application publishes said local configuration information at a different time.
means for scheduling a time for each of said hosts to publish said local configuration information, wherein each of said hosts publishes said local configuration information at different times.
20. The system of claim 19 wherein said means for identifying a replicated naming server uses a search command to search a hosts file for a specific name pattern.
21. The system of claim 19 wherein said means for periodically publishing includes a naming service builder application.
means for binding said IOR to a name in each said replicated naming server located in each said designated host.
23. The system of claim 19 wherein said means for scheduling a time uses crontab entries.
code for binding said IOR to a name in said at least one naming server, wherein said at least one replicated naming server provides configuration information for said communications network.


Atrocious business ethics. PayPal reached into my business account in 11/20113 to the tune of $1200 without authorization. chase bank got the money back for me – so now they want me to pay them back. PayPal are the ones that made a bad business decision – why should I pay for their mistake? also eBay steering all sellers to use PayPal and of course, as we all know eBay owns paypal. monopolizing the electronic funds transfer market? mysterious "software integration" issues arising between authorize.net and eBay 11/2012 (took over 1 month to resolve). was this pre-orchestrated and intentionally implemented by eBay/PayPal??????once again to force all to funnel their dollars into PayPal (which they keep for as long as they can- playing the float)……would you consider this ethical business practice?


Final Fantasy Tactics War of the Lions prices (PSP) are updated daily for each source listed above. The prices shown are the lowest prices available for Final Fantasy Tactics War of the Lions the last time we updated.
Chart shows the price of Final Fantasy Tactics War of the Lions at the end of each month going back as long as we have tracked the item.


Thank you for your interest. This workshop had to be cancelled due to low registration.
WEEK ONE (5-day): June 24 – 28, 2019 Classes run from 9 am – 4 pm daily and students are welcome to stay late in their studio.
Nature is the inspiration and source of this mixed media course where you will be encouraged to freely combine natural materials and original prints into exciting mixed media artworks. Students will start with capturing the memory of collected natural materials using low-tech printmaking techniques and create colourful original monoprints. These prints will then be combined and encased on a wooden panel or in a shadow box using acrylic paint and mediums using the various collected natural materials such as bark, leaves, seeds, eggshell and sand.
Natasha Smith holds a BA Hons. in Fine Art Printmaking and has been a practicing visual artist for 20 years. Natasha's personal and physical environment inspire her most recent work, which combine printmaking, collage and painting. Natasha is a teaching artist and has taught at the Kootenay School of the Arts and Oxygen Art Centre (where she was a founding member), Red Deer Summer Series and Metchosin International Summer School of the Arts. Natasha has been an artist in residence in Slocan Valley and Nelson Schools as part of the ArtStarts, Artists in the Classroom Program and gives private tuition and customized workshops and lectures at conferences. Natasha exhibits regularly and works from her studio in Krestova, in the West Kootenays, B.C. Canada.
This workshop had to be cancelled and is no longer available for purchase.


President Donald Trump turned yet again to his infamous Twitter account on Saturday to spin a theory about allegations of Russian meddling in the 2016 elections: Namely, that Barack Obama is to blame.
"Since the Obama Administration was told way before the 2016 Election that the Russians were meddling, why no action?" Trump tweeted, before granting himself a mononym. "Focus on them, not T!"
Trump then added the Obama administration "choked" on dealing with the issue, claiming it would "hurt Hillary."
On Friday, Trump had tweeted, "Just out: The Obama administration knew far in advance of November 8 about election meddling by Russia. Did nothing about it. WHY?"
Trump echoed similar themes in a pre-released section of an upcoming interview with Fox & Friends, asking "The CIA gave [Obama] information on Russia a long time before the election. … If he had the information, why didn't he do something about it?"
The president has long been publicly skeptical of the intelligence agency assessment that Russia was involved in leaking embarrassing emails from Democratic email systems — let alone even more sinister allegations that Russia hacked into local election systems in 21 states. So his switch to blaming his predecessor appears to be a tacit admission the evidence is now overwhelming.
A Washington Post story on Friday detailed how Obama's administration, upon becoming aware of the Russian hacking effort five months before he left office, "secretly debated dozens of options for deterring or punishing Russia, including cyberattacks on Russian infrastructure, the release of CIA-gathered material that might embarrass Putin and sanctions that officials said could 'crater' the Russian economy."
But ultimately, by the turn of the year, Obama settled on expelling 35 Russian diplomatic officials, closing two of its compounds and a package of sanctions even White House staff described as "largely symbolic." His most muscular response plan, having cyberweapons covertly implanted throughout Russian infrastructure for a theoretical future retaliation, was only in the planning stages in January 2017.
The ultimate decision whether to pursue the program was left to Trump, who is now facing FBI, Department of Justice and congressional investigations into whether he or his associates colluded with Russia or attempted to obstruct justice as part of a cover-up.


text:
/*
DS1307.h - Header file for the DS1307 Real-Time Clock

Version: 1.1.0
(c) 2014 Korneliusz Jarzebski
www.jarzebski.pl

This program is free software: you can redistribute it and/or modify
it under the terms of the version 3 GNU General Public License as
published by the Free Software Foundation.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

#ifndef DS1307_h
#define DS1307_h

#if ARDUINO >= 100
#include "Arduino.h"
#else
#include "WProgram.h"
#endif

#define DS1307_ADDRESS              (0x68)

#define DS1307_REG_TIME             (0x00)
#define DS1307_REG_CONTROL          (0x07)
#define DS1307_REG_RAM              (0x08)

#ifndef RTCDATETIME_STRUCT_H
#define RTCDATETIME_STRUCT_H
struct RTCDateTime
{
    uint16_t year;
    uint8_t month;
    uint8_t day;
    uint8_t hour;
    uint8_t minute;
    uint8_t second;
    uint8_t dayOfWeek;
    uint32_t unixtime;
};
#endif

typedef enum
{
    DS1307_LOW          = 0x00,
    DS1307_HIGH         = 0x80,
    DS1307_1HZ          = 0x10,
    DS1307_4096HZ       = 0x11,
    DS1307_8192HZ       = 0x12,
    DS1307_32768HZ      = 0x13
} ds1307_sqwOut_t;

class DS1307
{
    public:

	bool begin(void);

	void setDateTime(uint16_t year, uint8_t month, uint8_t day, uint8_t hour, uint8_t minute, uint8_t second);
	void setDateTime(uint32_t t);
	void setDateTime(const char* date, const char* time);
	RTCDateTime getDateTime(void);
	uint8_t isReady(void);

	uint8_t readByte(uint8_t offset);
	uint8_t writeByte(uint8_t offset, uint8_t data);

	void readMemory(uint8_t offset, uint8_t * buff, uint8_t size);
	void writeMemory(uint8_t offset, uint8_t * buff, uint8_t size);

	void clearMemory(void);

	ds1307_sqwOut_t getOutput(void);
	void setOutput(ds1307_sqwOut_t mode);
	void setOutput(bool mode);

	char* dateFormat(const char* dateFormat, RTCDateTime dt);

    private:
	RTCDateTime t;

	char *strDayOfWeek(uint8_t dayOfWeek);
	char *strMonth(uint8_t month);
	char *strAmPm(uint8_t hour, bool uppercase);
	char *strDaySufix(uint8_t day);

	void readPacket(uint8_t offset, uint8_t * buff, uint8_t size);
	void writePacket(uint8_t offset, uint8_t * buff, uint8_t size);

	uint8_t hour12(uint8_t hour24);
	uint8_t bcd2dec(uint8_t bcd);
	uint8_t dec2bcd(uint8_t dec);

	long time2long(uint16_t days, uint8_t hours, uint8_t minutes, uint8_t seconds);
	uint16_t date2days(uint16_t year, uint8_t month, uint8_t day);
	uint8_t daysInMonth(uint16_t year, uint8_t month);
	uint16_t dayInYear(uint16_t year, uint8_t month, uint8_t day);
	bool isLeapYear(uint16_t year);
	uint8_t dow(uint16_t y, uint8_t m, uint8_t d);

	uint32_t unixtime(void);
	uint8_t conv2d(const char* p);

	void writeRegister8(uint8_t reg, uint8_t value);
	uint8_t readRegister8(uint8_t reg);
};

#endif

redpajama_set_name: RedPajamaGithub


text:
Q: Prometheus Alert Manager Wrong Resolved Notification Sent For an alert condition, Prometheus is behaving incorrectly.
The alert gets generated based on the condition and goes to different notification channels correctly. The problem is - after some time the alert resolves (at random time gaps, sometimes within 5 minutes and sometimes after 10 minutes) and then re-generates within a minute. When I check the condition it still matches for the alert to not to resolve.
In fact for a particular scenario, the alert was supposed to be active for 6 hours but during that period I received at least 15 resolved notifications and re-generation of the alert post resolution. I have checked for missing telemetry but telemetry was always present. There is no time offset in the alert condition. This does not make any sense because it is not behaving like this for any other alert and I have at least 80 alerts running in my production environment. Following is the query which is plain:
(sum by(pcName) (cm_summary{regState="p_online"}) == 0 and sum by(pcName) (cm_summary{regState="nb_online"}) == 0 and sum by(pcName) (cm_summary{regState="f40_online"}) == 0 )  and on(pcName) (sum(cm_summary) by (pcName) >= 5)

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
With the increasing interest in human-AI interaction, \emph{human-in-the-loop (HITL)}~\cite{monarch2021hitl} systems have been applied to a wide range of domains, such as material design~\cite{brochu2007activelearn}, animation design~\cite{brochu2010animation_design}, photo color enhancement~\cite{koyam2020seqgallery}, image restoration~\cite{weber2020drawme}, and more~\cite{koyama2014crowd, holzinger2016iml, colella2020interactive_opt, yijun2021melody}.
These systems actively exploit human choices for optimizing machine results. They propose a set of design alternatives and then iteratively adapt their results based on user preference feedback, thereby increasing the quality of the system outcomes and the satisfaction of the human involved while simultaneously speeding up the process.
The ancestor of these approaches is the Design Galleries approach~\cite{marks1997design_galleries}. Its authors state that ``Design Gallery interfaces are a useful tool for many computer graphics applications that require tuning parameters to achieve desired effects''. They proposed a generic user interface (UI) and emphasized techniques for dispersing parameter settings, but they implicitly assumed that the process will always converge and end in a desired solution.

Despite these successful examples, obtaining a desirable result in an interactive process remains a challenging and important issue in general, since it is hard to ``guarantee'', but crucial for such systems to be useful in practice.
We argue that to make progress here, it is particularly insightful for HCI researchers to investigate practical deployments in real workflows of domain experts.
Concretely, our work is guided by the following two research questions, the first of which first relates to our overarching research interest and the second one to the specific case study reported here:
\begin{itemize}
\item[\textbf{RQ1}] \textit{As a domain-specific example, how can the HITL strategy support users in 3D model processing tasks?}
\item[\textbf{RQ2}] \textit{Which challenges arise for human-AI loops in practice and how can we address them?
}
\end{itemize}
To address these questions, we conducted a case study with an industry partner, in which we applied the HITL strategy to the challenging and practically relevant use case of a 3D modeling workflow: Our prototype system helps 3D artists with the problem of \emph{polygon reduction}~\cite{garland1997quadrics, garland1998quadrics} that involves tuning control parameters to remove polygons (mainly triangles or quadrilaterals) from 3D models while preserving their overall appearance (see~\autoref{fig:viscorr}). Although fully algorithmic solutions have improved over the past decades (for instance, using directional fields~\cite{jakob2015instant}), they still suffer from problems~\cite{arvo2015survey} and often produce unusable results for large models or geometric edge cases.
In the meantime, since it often takes 3D artists up to months or years to understand and thus productively work with a new algorithm, this presents a promising target to use an interactive approach in which designers only have to provide their interactive choices to obtain the desired outcome. Thus, we developed a system that optimizes polygon reduction with regard to artists' preferences over the quality of the results based on \emph{Bayesian optimization (BO)}~\cite{shahriari2016boreview, gonzalez2017preferential}. \autoref{fig:diagram} illustrates its general architecture.

After fine-tuning the system using pilot usage feedback, two professional 3D artists used our system for three months in their real-world 3D workflow. Analyzing this deployment and its usage logs, we found a high failure rate when using a human in the loop. Crucially, final ratings from artists diverged from partially inconsistent or inexplicable compared to their previous ratings.
This mismatch motivated us to conduct a follow-up study (N=20) to confirm these failures in a controlled setting. We analyze interaction data and user feedback through the theories of human judgment to explain the observed failures of the human-AI loop with two key insights: 1) optimization using preference lacks mechanisms to deal with inconsistent and contradictory human judgments; 2) machine outcomes, in turn, influence future user choices via heuristic biases and loss aversion. In this light, we conclude the paper by proposing descriptive UI design guidelines for mitigating these practical problems.
\section{Related Work}
We will first briefly summarize recent work in BO on preference learning and polygon reduction methods in geometry processing, which drove our system implementation. Then, we will discuss the fact that recent HCI research emphasized the benefits of using HITL to support user or algorithm efficiency, but that, on the other hand, little is known about what might keep such a system from working properly.

\subsection{Learning Human Preferences using Bayesian Optimization}
As processing human subjective input is challenging, modeling human choices~\cite{fuernkranz2003pref, chu2005preference} is a research topic in preference learning. To exploit preferences, formal models consider a human rater as an unknown utility function that produces ratings, such as on a 5-star scale, concerning evaluation instances.

The optimization of control parameters based on feedback from a human relying on domain knowledge can be described as an optimization problem: Search an optimal \emph{parameter set} $p^*\in \mathcal{P}$ such that $p^* = \operatorname*{argmax}_{p\in \mathcal{P}} h(M(p))$ where $p$ is any parameter set in the \emph{parameter space} $\mathcal{P}$, $M(p)$ is the instance generated using parameter set $p$, and the \emph{preferential function} $h$ returns the human rating of system output $M(p)$.

When querying the function $h$ is costly, for example, because it involves asking a human for a preferential decision, BO is often selected~\cite{brochu2007activelearn, brochu2010animation_design, koyam2020seqgallery, mikkola2020projective} and has provided a wide range of successful applications~\cite{shahriari2016boreview, holzinger2016iml, koyam2020seqgallery, koyama2021ui}. BO actively learns a \emph{posterior} from human preference based on the collected ratings and can then predict an estimated optimal $p^*$ in the next iteration step. Thus, this method captures how artists usually develop their expertise with a new system by exploration and exploitation, resulting in an overall improvement after some iterations.

As a variant, preferential Bayesian optimization (PBO)~\cite{gonzalez2017preferential, koyam2020seqgallery, mikkola2020projective} is an improved version of BO. It evaluates preference on paired samples with an adaptive reference point. The reason for using PBO is that humans are better at differentiating paired samples than they are at determining absolute values~\cite{chu2005preference, brochu2007material_design, brochu2007activelearn} according to Thurstone's law of comparative judgment~\cite{thurstone1927comparelaw}. Therefore, it is considered better for use in HITL systems, and we integrate PBO to learn human preferences.

\subsection{Polygon Reduction in Geometry Processing}

For the specific geometry processing problem, polygon reduction, presented in this paper, we briefly discuss its recent advances and the relevant methods we utilized to build our system. The geometry processing ``No-Free-Lunch'' theorem~\cite{wardetzky2007no_free_lunch} states that not all geometric properties can be well preserved simultaneously in discrete instantiations of a smooth geometry. Therefore, different processing tasks have specialized algorithms and corresponding configurations, such as soft or hard geometries.
In general, polygon reduction methods can be categorized as \emph{local decimation}, \emph{global remeshing}, or a weighted combination of both.

Local decimation means that neighbor vertices and edges are greedily removed. These methods date back to the last century~\cite{garland1997quadrics, garland1998quadrics} and have also been used for levels of detail (LOD) generation~\cite{hoppe1996progress}, even baked into hardware rendering pipelines~\cite{olano2003lodshader}. They are efficient but contain ill-posed cases with results depending on the implementation.
Instead, the general idea of global remeshing~\cite{bommes2013quadsurvey, knoppel2013globalfields, ebke2014lodquad, pietroni2021featureline} is to define a \emph{directional field} as constraint boundary conditions on a \emph{Poisson equation}, and then minimize an artificial energy function. After minimization, a new target mesh can be reconstructed from scratch using the solved solution. Computationally, this is much more costly and cumbersome, but the resulting mesh quality is much better than that from local decimation.
State-of-the-art practical solutions, such as \citet{karis2021nanite}, use a weighted combination of both that balances the processing speed against quality. A large mesh can be split into smaller ones, then processed using mixed global~\cite{jakob2015instant, huang2018quadricflow} and local~\cite{hoppe1996progress, garland1998quadrics} methods, but this also introduces the new problem of cutting a mesh. We refer to Metis~\cite{karypis1997metis, ponchio2009graphcut} as a mature solution for graph partitioning.

\subsection{Decision Error in Human Judgments}
\label{sec:decision-error}
Economics widely studied decision-making when choosing a preferred item from several alternatives. The expected choice utility maximization~\cite{marschak1960choiceutility} forms the theoretical basis. It describes a standard economic model on a finite number of decisions but assumes that individual beings behave rationally. In psychology, \citet{simon1955rational} proposes the concept of \emph{bounded rationality} and proposes to replace this assumption, as rationality is only limited, and  decisions are made by \emph{satisficing}. Later, \emph{prospect theory}~\cite{kahneman1979prospect, tversky1992advprospect} empirically demonstrated human judgments in reality when trying to maximize a certain utility function (wealth) in risky situations (e.g., under time pressure) and explained the behavior with bounded rationality.
The \emph{heuristic biases} constitute a key source of general decision error.
\citet{kahneman1974juedgment} showed that in any decision under uncertainty, System~1 (fast and instinctive thinking) tends to override System~2 (slow and rational reasoning)~\cite{kahneman2011thinking}, hence creating a statistical bias on the decision. More specifically, 1)~\emph{representativeness} substitutes the most readily accessible examples to form a decision, 2)~\emph{availability} uses mental shortcuts, and 3)~\emph{anchoring} as a conclusion bias describes that initial information has a consequence on a later decision.

In addition to heuristics, other effects can also influence judgments: 1)~in a utility maximization context, \emph{diminishing returns}~\cite{shephard1974diminishing} may occur as wealth increases and marginal utility decreases; 2)~\emph{loss aversion}~\cite{kahneman1972subjectiveprob}, as part of the \emph{endowment effect}~\cite{kahneman1991endowment}, describes that people prefer to retain an owned property rather than to acquire an alternative, potentially better one. People hence tend to stick to seemingly safe decisions when a potential gain would require more risk.
3)~In the present understanding, combined with a statistical view~\cite{brandstaetter2006tradeoff,gigerenzer2009homo}, systematic noise descriptively shapes another form of decision error that contributes equally to judgment error as individual bias~\cite{kahneman2021noise}. The decision noise components~\cite{kahneman2021noise} break down into \emph{level noise} (decision variability between groups), \emph{stable pattern noise} (contextual bias within groups), and \emph{transient noise} (purely occasional).

\subsection{Human-in-the-Loop Systems}
The HITL strategy may be applied in different contexts, which connect to different fields, including personalization, co-creation, and decision-making support.

From the human computation~\cite{vonahn2008human, quinn2011human} perspective, a HITL system may be designed to use crowds~\cite{ipeirotis2010quality} as human processors to solve system tasks that neither machine nor human can solve independently. Although using collective intelligence has been largely verified to be beneficial for crowdsourcing tasks~\cite{kamar2012combining}, there are several identified challenges~\cite{reeves2010five} to integrating human computation, which highlighted challenges such as user motivation, sustainability, and input bias. To motivate users to contribute, researchers have used a Game-With-A-Purpose (GWAP) approach~\cite{krause2011human}, but turning a task into a game could be another challenging design problem. Dealing with diverging opinions within small crowds may be difficult because tasks might require a certain level of expertise. Moreover, HITL systems using crowds may suffer from malicious inputs~\cite{ou2019gwap} and lead the entire system toward using biased inputs when the initial samples lack trust.
Particularly for design-related tasks, crowd opinions may not fit individual interests and needs regardless of data bias. Hence using crowd-powered design systems~\cite{koyama2014crowd} is considered limited when individual customization has a higher priority.

In a personalized context, \citet{buschek2021hai_pitfalls} examined the potential pitfalls for achieving user interests in the co-creation context. The limitations on the machine side, identified as lack of machine \emph{creativity}~\cite{lamb2018creativity}, and \emph{usability}~\cite{kocielnik2019imperfectai}, and thus, highlight a biased AI with trained system bias but lacks discussions on the source of bias and the mismatch between individual expectations and system abilities.
In terms of mismatched expectations, \citet{eiband2019meet} reported that users might intentionally provide flawed inputs when a system fails to achieve their satisfaction in everyday intelligent applications. As a follow-up, however, \citet{voelke2020trickai} showed that a user must exhaustively provide noisy feedback to confuse an intelligent system. Still, they lack verification and interpretation as to whether the repeated unsatisfactory results come from system limitations or human behavior change.
For AI-assisted decision-making scenarios, \emph{trustworthiness} becomes a primary social concern regarding \emph{reliability} in areas where a decision is vital, such as clinical decisions~\cite{cai2019medical, knapic2021medical}. Still, it is \emph{implicitly assumed} that the human involved eventually makes a rational decision over \emph{subjectively} untrusted AI outcomes. Factors such as algorithm aversion~\cite{dietvorst2015algorithm_aversion} were confirmed to indicate that users are more biased~\cite{ragot2020perception_bias} towards human results and produce considerable noise even in the judicial area~\cite{dressel2018recidivism}.

Although prior research~\cite{marks1997design_galleries, koyama2014crowd, holzinger2016iml, weber2020drawme, yuksel2020hitlml, colella2020interactive_opt} that involves HITL strategies have shown human knowledge to be helpful for a machine to learn, previous literature rarely discusses the circumstances under which HITL could shine. Especially when users intentionally or unintentionally provide defective or uncertain inputs, it is unclear whether the system can continue to process it effectively and whether other cascading effects will be triggered.
Our paper addresses this research gap and shows challenges that can arise when exploiting individual preferences in a HITL system in practice.
\section{System Design and User Workflow}
\label{sec:system}
\begin{figure*}
\centering
\includegraphics[width=\textwidth]{./assets/3interface-compress.pdf}
\caption{The UI for users to rate variant models. An artist can move each mesh variant to the bigger, central view and activate a wireframe for detailed quality inspection, which was considered necessary in previous work~\cite{botsch2010polygon} because of the alignment of mesh edges is considered a quality metric. Artists must make a professional choice between visual rendering quality and wireframe quality, especially in cases that may sacrifice a bit of wireframe quality for substantial gains in polygon reduction (lower number of triangles).}\label{fig:interface}
\end{figure*}

We approached the polygon reduction problem in a joint project with an industrial collaboration partner and were motivated by the goal to optimize their artists' daily tedious manual tuning workflow. One of the benefits of having simplified 3D mesh models is to reduce rendering complexity due to fewer primitives required in a render pass. 3D mesh-based models in the project ranged from a single water-tight mesh layer to models that were either manually sculpted or automatically converted from solid geometry data formats, containing hundreds of sub-meshes with millions of polygons in total. Domain experts processing these mesh models usually require days or weeks of extensive manual work.

We expose the core functionalities of our system as Web APIs that run the polygon reduction on a GPU server. The developed frontend UI by our industrial partner uses Unity\footnote{\url{https://docs.unity3d.com/2020.3/Documentation/Manual/UIToolkits.html}} as shown in \autoref{fig:interface}.
We engineered a hybrid local/global algorithm based on state-of-the-art research~\cite{garland1998quadrics, jakob2015instant} controlled by nine different parameters for the polygon reduction itself. We determined the initial parameters by domain heuristics in a few pilot tests with our industrial partner.

In our designed workflow, an artist can first upload an original model. The server then simplifies the uploaded model under different parameter settings in the background. When it has computed all alternatives, taking between seconds and minutes, they are downloaded back into the UI. When the artist indicates their ratings of model quality, the system learns from these judgments and continues the process again to generate more optimized models. The rating scale for judgments is 0~(\emph{skip}, meaning not considered due to faulty geometry), 1~(\emph{terrible}) to 5~(\emph{excellent}). Without loss of generality, if the human decided ratings for the four variant models $M_{i} (i = 1, 2, 3, 4)$ are 3, 4, 5, and 1, then this represents six preferential choice relations: $M_1 \preccurlyeq M_2$, $M_1 \preccurlyeq M_3$, $M_2 \preccurlyeq M_3$, $M_4 \preccurlyeq M_1$, $M_4 \preccurlyeq M_2$, and $M_4 \preccurlyeq M_3$ where $\preccurlyeq$ means ``is less preferred.'' 3D models in the next iteration are optimized based on these relations using PBO which made us expect~\cite{gonzalez2017preferential, mikkola2020projective} the system to converge to the desired outcome quickly.
Note that the model quality is \emph{not} equivalent to just visual rendering quality but also involves edge flow and other geometric properties~\cite{botsch2010polygon} that require a subjective decision. Hence human judgments in this task involve visual rendering quality, mesh wireframe alignment quality, and other technical metrics such as volume-preserving, which is also why this task is more preferred to query human opinions than done by full automation.

\section{User Studies}
To evaluate the effectiveness of our system, we conducted two studies; first, a field study with our industrial partner, and second a lab study to verify the effects of the field study. In the field study, we used our system in a real-world setup with our industrial partner, who aimed to optimize their process in customer projects. Here, we had the opportunity to run a field study where designers used the system in their daily workflow. However, due to the uncontrolled environment of field study, making in-depth assessments and conclusions on specific aspects can be hard. Thus, we additionally ran a lab study to understand the failure cases of our system to verify our findings further under controlled conditions.

\subsection{Field Study}
For the field study, we first conducted a set of small pilot experiments to fine-tune our system's parameters to fit the partner's needs and their customer projects.

\paragraph{Participants}
We recruited two full-time 3D technical artists from our industry partner to gain insights into the newly developed workflow involving our interface. Both are male, aged 25 and 35, one has more than three years of experience, and the other has more than eight years of experience in the 3D industry.

\paragraph{Procedure}
The two experts used our system almost daily to evaluate model quality during polygon reduction.
However, they were not restricted to our interface and could also use further software aids (as in their previous workflow) for the model quality inspection, e.g., for accessing more professional curvature visualizations.
When using our tool, the loaded 3D model was computed into four variants. After the experts finished their evaluation (either inside our interface or externally), they rated the models in our interface. These ratings were used for the next optimization iteration to generate new variants (see \autoref{fig:diagram}). The rating process terminated when the experts found the results satisfactory or reset it.

\paragraph{Collected Dataset}
During the three months of the study, we collected 549 evaluation sequences as a field study dataset. This corresponds to 4.5 evaluations per expert and workday.
Of these, 415 sequences terminated in the first iteration without any preference optimization requested. The remaining 134 sequences (number of iterations: $\mu=4.1, \sigma=4.2$, range 1-23) contain sequential preference ratings.
The collected 3D models included various mesh types, including organic, soft surfaces, hard technical surfaces with sharper angles, and combinations, such as machinery parts (see~\autoref{fig:interface}) with both smooth, flowing lines and hard, mechanical edges.

\subsection{Lab Study}
As a lab study, we conducted a within-subjects user study to further understand our system's use with specific assistance information and behaviors in a larger user group with different backgrounds in 3D.

\paragraph{Participants}
We recruited 20 participants using convenience sampling (7 female and 13 male; age $\mu=27.0, \sigma=8.8$, range 18-62). Among them, four had more than one year of industrial experience in 3D modeling, and all others had no experience.

\paragraph{Procedure}
\begin{figure*
\centering
\includegraphics[width=\textwidth]{assets/models-lab2.png}
\caption{Models that are selected in the lab study, from left to right: \emph{monkey, teapot, rose, cow, pumpkin}. Each model was rendered \emph{with} and \emph{without} wireframes separately.}
\label{fig:lab-models}
\end{figure*}

We first welcomed participants and explained the study, answering all open questions before they signed the consent form. Then, participants were presented with different 3D objects in every evaluation session.
The overall procedure in terms of rating and termination process in each evaluation session was similar to in the field study. In detail, we asked participants to balance the trade-off between polygon reduction and quality loss. Thus, they had to indicate their preference using ratings to optimize models iteratively. Participants could terminate fast in a few iterations if satisfied or were stopped at the 11th iteration to limit the time of participation. After each evaluation session, we asked participants 7 questions (see \autoref{sec:open-source}) to indicate their satisfaction and provide overall subjective feedback.
We selected five different 3D models, as shown in \autoref{fig:lab-models}, and each model was rendered \emph{with} and \emph{without} wireframes (instead of allowing users to activate them freely). We picked these five models with the two wireframe representations to ensure our results generalize beyond this small set of objects. We displayed the order of these 3D models and their wireframe display using a Latin square design to avoid learning and fatigue effects. Therefore, we collected $5\times 2=10$ evaluation sessions in total for each participant. On average, each participant spent 90 minutes in the entire study.

\paragraph{Collected Dataset}
We collected 200 evaluation sequences (number of iterations: $\mu=5.1, \sigma=2.9$, range 1-11) by design, and all sequences involved at least one preference optimization.
The selected model covers a similar spectrum of models as the experts had experienced in our field study. These models were also simpler than complex real-world models to reduce the time of machine optimization and participation waiting time.

\section{Results}
\label{sec:results}

Below, we report our analysis of the participants' rating process using collected data from the two studies, also in comparison to each other, and show that if participants are not rated by pure random, they at least behave highly unstable and inconsistent in the rating process. Then, we show selected example cases from the collected data that were also discussed with experts in hindsight concerning why they made a particular rating choice.

\subsection{Human-AI Mutual Interventions}

\paragraph{Overview}
In the \emph{field study}, from the 134 sequences with preferential ratings, only 16 sequences ($11.9\%=16/134$) produced a satisfactory outcome. In the \emph{lab study}, among the collected 200 sequences, only 97 sequences ($48.5\%$) were terminated with a satisfactory outcome. Both studies suggest a high failure rate in optimizing HITL outcomes.

\begin{figure*}[t]
\centering
\begin{subfigure}[b]{0.49\textwidth}
    \centering
    \includegraphics[width=0.925\textwidth]{assets/correlation.pdf}
    \caption{The relation between reduction ratio of models in lab study and human perceived visual quality (SSIM, normalized), and the red solid smooth curve shows a second-order polynomial regression.}
    \label{fig:viscorr}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.49\textwidth}
    \centering
    \includegraphics[width=0.925\textwidth]{assets/4-1-stats-rating-stats-compress2.pdf}
    \caption{Field (left) and lab (right) studies' preferential ratings against overall reduction ratio (higher means stronger reduction), *~($p<.05$), ** ($p<.01$), *** ($p<.001$).}
    \label{fig:ratings}
\end{subfigure}
\caption{Overview of the visual influences of polygon reduction and collected rating data.}
\end{figure*}

\paragraph{Effectiveness of Human Judgments}
\autoref{fig:viscorr} shows a second-order polynomial regression between a human perceived quality of 3D models used in our lab study and the overall reduced amount of polygons. We assess perceived visual quality using an average of multiple \emph{structure simularity} (SSIM)~\cite{wang2004image} that compares the rendered visual quality between the reduced and original 3D model in five different camera views. The \emph{reduction ratio} represents the removed polygon count of a resulting model divided by the total polygon count in the original model.
\autoref{fig:ratings} shows the rating distributions in the two studies regarding reduction ratio.

We used Kendall's $\tau$ coefficient to measure the ordinal association between the reduction ratio and rating scale. The result shows a significant correlation ($\tau=0.07, p<.001$) in the field study whereas no significance ($\tau=0.004, p=0.71$) in the lab study. This suggests that field study experts tend to give higher ratings to highly reduced models, but lab study is more diverse.
For a more fine-grained measure between rating scales, we also used Mann–Whitney U tests to check for dependencies between different rating scales and the reduction ratio:
1)~We found an significant difference between \emph{fair} ($Mdn=74.88$) and \emph{excellent} ($Mdn=80.95$) ratings~($U=8756.0$, $p<.001$) and a significant difference between \emph{terrible} ($Mdn=74.65$) and \emph{excellent} ($Mdn=80.95$) ratings~($U=10219.0$, $p=.003$), i.e. in cases where reduction ratio was positively correlated with rating.
On the other hand, we found no significant differences in reduction ratio between \emph{terrible} ($Mdn=74.65$) and \emph{poor} ($Mdn=72.93$) ratings~($U=19705.0$, $p=.62$) or \emph{good} ($Mdn=80.03$) and \emph{excellent} ($Mdn=80.95$) ratings~($U=3602.0$, $p=.37$), i.e., where there would have been a negative correlation. \emph{In sum, this suggests that the collected ratings are effective to the highly reduced models, and the reduction ratio is one of the effectively relevant factors in human judgments}.
2)~We found no significant differences in highly reduced models between good and excellent in the field study, but a significant difference between \emph{good} ($Mdn=60.03$) and \emph{excellent} ($Mdn=66.99$) ratings ($U=131031.0, p=.002$) in the lab study. Although the field study had fewer users, this could also be interpreted such that \emph{experts in the wild use other quality metrics, which lab participants with less expertise overlook}.
3)~Models rated as \emph{good} and \emph{excellent} have higher mean reduction ratio in the field study ($M_{\text{good}}=81.68, M_{\text{excellent}}=75.52$) than in the lab ($M_{\text{good}}=61.10, M_{\text{excellent}}=64.84$, also see~\autoref{fig:ratings}), which suggests that \emph{lab study participants are easier to satisfy by the system outcomes than expert artists}.

\paragraph{Stationarity and Trends of Data}
\autoref{fig:rating-dist} compares how an ideal (far left) and three actual rating distributions (the rest) drift over time: In our context, since the objective of using PBO is to search a polygon reduction configuration to maximize the human ratings~\cite{gonzalez2017preferential}, ideally, in a successful exploring and exploiting sequence of preferences, and the mean rating score should \emph{increase} and drift from low values with high variance towards higher values with lower variance (\emph{non-stationary} and with an increasing \emph{linear trend component}).
However, the actual sequence shown (as most others) stagnates and fluctuates back and forth. From the 200 sequences collected in the lab, 79 continued to at least four iterations (required for the subsequent trend test), and we tested them using an Augmented Dickey-Fuller test. Results show that 36 rating sequences are stationary ($p<.05$). In the remaining 43 non-stationary sequences, a Mann-Kendall test found only four significant increasing trends in the mean rating score ($p<.001$) and only one significant decreasing trend of rating variance ($p<.05$). Another Mann-Kendall test found that only three sequences had increasing and six sequences decreasing trends regarding the machine-estimated optimal reduction ratio ($p<.05$).

In summary, all these results imply for the optimized process, that
1)~on the human side, the rating behavior does not improve over iterations;
2)~on the machine side, the optimized reduction ratio using preferential choices does not improve over iterations.
This suggests that \emph{the human-machine loop as a whole is kept from terminating and fails}.

\begin{figure*}[t!]
\centering
\includegraphics[width=\textwidth]{assets/dist.pdf}
\caption{Comparing an ideal (far left) and actual (the others) rating distributions over time (from top to bottom), the bottom axis 0 to 5 represents the rating scale. An expected rating distribution should move to the right side over time if users are more satisfied with the results, but the actual preferences drift back and forth between 0~(\emph{skip}) and 5~(\emph{excellent}).
}
\label{fig:rating-dist}
\end{figure*}

\begin{figure*}[t]
\centering
\begin{subfigure}[b]{0.49\textwidth}
\centering
\includegraphics[width=\textwidth]{assets/4-3-head2.jpg}
\includegraphics[width=\textwidth]{assets/4-3-cylinder2.jpg}
\caption{
In the same iteration, original (far left) and 4 processed models: 1) Head model: the last 2 models are identical, ratings (left to right): 5, 3, 4, 1; 2) Cylinder model: the middle two are almost identical. Ratings: 5, 4, 3, 2.
}
\label{fig:head-cylinder}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.49\textwidth}
\centering
\includegraphics[width=\textwidth]{assets/4-4-wheel2.jpg}
\includegraphics[width=\textwidth]{assets/4-4-wheel-back2.jpg}
\caption{
Original (far left) and 4 reduction variants, all rated as 5 in the same iteration. The bottom row shows the backside of the models. Objectively, the middle left wheel contains faulty geometry and should receive a 0 (skip) rather than a 5 (excellent).
}
\label{fig:wheels}
\end{subfigure}
\caption{Selected examples that were discussed in the semi-structured interviews.}
\label{fig:models}
\end{figure*}

\subsection{Semi-structured Interviews with Experts}\label{sec:semi_structured_interviews}
In the semi-structured interviews, we discussed with the two expert artists, case by case, inconsistently judged models and why they made a certain (contradicting) choice. \autoref{fig:models} shows three of the discussed models.
\autoref{fig:head-cylinder} contains a head model and a more straightforward example of a CAD-converted cylinder that is cut by a sphere.
Below, we discuss three example cases in more detail:
Ironically, the two head models on the right are identical but received an entirely different rating scores of 4 and 1 from the same rater in the same round (\emph{\setword{case 1}{case:1}}). The middle right cylinder (rated 3) compared to the middle (rated 4) contains fewer polygons and better symmetries but received a lower rating (\emph{\setword{case 2}{case:2}}).
As a slightly more complex case (\autoref{fig:wheels}), a wheel model with reduced variants that all were rated a 5 in the same iteration, but the middle left one did reduce to faulty geometry on the backside (\emph{\setword{case 3}{case:3}}), which the artist had missed.

In \ref{case:1}, \emph{artist A} confirmed that his ratings strongly depended on what he had seen before and admitted that he tended to give one model a terrible rating in each iteration due to previous rating experience. He also mentioned that ``\emph{...I sometimes stopped giving a higher score because I had decided on a different objective}'' in processing the model (e.g., to go for more visual quality but less reduction).
In \ref{case:2}, \emph{artist B} argued that he had scored the middle cylinder mesh higher than the middle right one because ``..\emph{the usually difficult inner hard edges were handled better}..'' in that case. However, the middle right model has an objectively higher reduction ratio, and both contained similar defects on the inner hard circular edge. Their differences are only at a technical level.
Furthermore, he explained that in \ref{case:3}, he did not notice the flaw at first sight and rated the wheel a 5 simply because it was shown from the front. He had made a quick decision based on the visible mesh quality of the tire and based on a similar experience, which is an example of a reasonably simple oversight with potentially harmful consequences. The artist also explained that after many iterations, ``\emph{...it gets frustrating to see the more flawed output after I already had seen a partially good result}.''
\section{Discussion}
Although our evaluation does not examine any entangled causality but only statistical correlation, it is likely that the observed system failure initially starts from human error as the system was initialized with the same prior in each of the sequential evaluations (using a Matérn kernel ($\rho=2, \nu=2.5$) with the statistical properties of being isotropic and stationary~\cite{rasmussen2004gaussian, shahriari2016boreview}). Since errors are further propagated and amplified to system outcomes, we combine theories regarding human decision errors to reflect on and explain our findings.

\subsection{Pitfalls}
\paragraph{Error sources on the human side} Based on our observed instability and expert feedback, we argue that the human cognitive errors, which either occur internally or are influenced by the system outcomes, are a crucial part of the overall system uncertainty:
\begin{itemize}[leftmargin=*]
\item[1)] \emph{Heuristic biases}.
a)~The \emph{anchoring bias} explains that earlier experience influences human decisions, including earlier system output and other context factors, such as background knowledge or expertise. In \ref{case:1} (see Section~\ref{sec:semi_structured_interviews}), the artist confirmed that his evaluation depended on meshes he had seen before.
b)~The \emph{availability bias} explains that judgments are based on the quickly accessed memories of relevant examples. The \ref{case:3} matches this bias as the artist decides based on his professional experience.
c)~\emph{Representativeness} shows that decisions made by substitution examples may be occasionally biased. The \ref{case:2} shows this behavior because the actual decision used mental shortcut and was made by judging another similar case.
\item[2)] \emph{Loss aversion and endowment effect}.
a)~Users may become more critical after observing several good results from an intelligent system. Users might stick to what they know and are familiar with and reject newly proposed and objectively good choices, which leads to more negative ratings later in the process. This may explain (\ref{case:3}) why artists stuck to mediocre choices in intermediate stages instead of moving to a broader (but more risky) range of variations.
b)~The software functionality (in our case, this is the software pre-configured camera angle for displaying meshes) as a task context may override information and influence the validity of the human judgment. This also explains the unexpected rating of the wheel model in \ref{case:3}.
c)~Human preferences change over time and may become inconsistent when interacting. A present rating choice also carries long-term influences, in contrast to being just local. In our case, this is explained by the anchoring bias. It was expected to be addressed in PBO, which uses comparative judgments, but as the three discussed cases show, artists still keep previous experience (either accumulated expertise or short-term outcomes) in mind, which changes their preferential choices.
\item[3)] \emph{Diminishing returns}. Judgments may lose precision and contain increasing noise after humans have seen increasingly or partially good results. Hence, preference exploitation may become less effective, and the HITL system can no longer benefit from human knowledge.
In our case, when artists had seen a certain number of increasingly better meshes, they were less sensitive (\ref{case:3}) to further improvements by the algorithm. In contrast, they even gave more critical scores for the occasional poor results.
\end{itemize}
\paragraph{Error sources on the machine side} The other part of overall system uncertainty comes from the underlying algorithm and is emphasized by user errors:
\begin{itemize}[leftmargin=*]
\item[1)] \emph{Stable preference assumption}. The system performance in a HITL system suffers from the model assumption, and the outcomes may be undesired due to an invalid optimization. We observed that human judgments produce strongly local, partially global, time- and context-dependent errors, even with permanent goal changes (\ref{case:1}). This violates the prerequisites of any optimization technique that assumes a unique and stable utility function, including PBO. More importantly, human judgment is a fragile function to optimize for, and the commonly used \emph{independent and identically distributed} (i.i.d.) assumption in these algorithms does not hold in reality for humans. In turn, we need to generally rethink basic assumptions and approaches in the design of HITL systems. We should be more explicit about under what circumstances they can be applied appropriately to detect and exploit changes in latent user preference distributions and systematic errors.
\item[2)] \emph{Complete preference assumption}. The underlying optimization still implicitly assumes a user always has a complete preference, meaning that users are deemed to be able to provide a rating to reflect their preference consistently. In the current design, users rate four models instead of requiring them to choose one of the best. This design can mitigate the completeness assumption violation, as selecting the best might not be possible if comparing objects are not entirely comparable and involves multiple optimizing objectives. However, as the optimization process continues, human raters may lose their preference for rating different models due to bounded rationality.
\end{itemize}

\subsection{Potential Countermeasures}
The heuristics are rather hard to detect by the machine since human ratings may not be entirely judged for consistency (otherwise, the machine could provide ratings on its own, entirely defying the idea of HITL systems). Nevertheless, we propose several design guidelines to at least mitigate different types of decision noise as discussed in~\autoref{sec:decision-error} thereby may be more parametrically guiding users in further optimization steps:

\begin{itemize}[leftmargin=*]
\item[1)] Reduce \emph{level noise}: Provide a timeline to include intermediate results saved by users and allow them to return to those earlier results for comparison. This could help the user to compare new results to known ones and support a more objective comparison across iterations. It could also reduce user frustration and fear of losing the achieved quality, thereby mitigating problems from loss aversion and violated system assumptions;
\item[2)] Reduce \emph{stable pattern noise}: Indicate the optimization intention to the user, such as current system steps regarding exploitation and exploration. This could better frame the current context, therefore, mitigate representativeness and availability bias by keeping users from judging based on earlier examples.
\item[3)] Reduce \emph{transient noise}: One approach could be to occasionally present results from earlier iterations and check for consistency, although this would also assume stable preferences and require more user iterations. Another approach could provide more assistive visualization by highlighting the mesh difference between iterations. This could further reduce user workload and help mitigate simple oversight and obvious mistakes when distracted by unchanged parts or overlooking changed parts.
\end{itemize}

\subsection{Limitations}
Our UI was consciously simplified to a minimum in order not to distract from judgment and in an attempt to avoid usage complexity and improve overall usability. In the field study, due to the limited number of users and to not further confuse users by silently changing system behavior, we did not run any forms of A/B testing.
Although the subjects could explore the quality of the entire mesh by features such as enabling the wireframe, this might still have been too restrictive and lacked information about the changes. Highlighting the crucial changes may be helpful, but it also lacks the ability to customize references to show the difference between different proposals in a sequential optimized workflow. In hindsight, we learned that it might be useful to let users specify which parts of the mesh led to a particular rating.
Next, we wanted to ensure the generalizability of our results and thus, selected five models with two wireframe representations. On the other hand, our results may still suffer from a selection bias in the models we used.
Lastly, conducting a simulated user study~\cite{antti2017abc} and designing further statistically verifying the decision biases might also be helpful to compare simulated human inputs with controlled noise and the actual decision behaviors.
\section{Summary and Future Work}

In this work, we discussed a HITL system where an optimization process in the background exploits sequential user choices to optimize the system's future outcomes iteratively. Our case study provides evidence of challenges to human-AI loops in practice, produced by mutual negative influences.
Based on interaction data in the field and lab and discussions with expert artists, we reflected on concrete influences that can break preference-optimized HITL systems, namely by 1)~human decision biases and noise, 2)~system capabilities to deal with them, and 3)~subsequent impact on future human inputs.

The findings provide some answers to our initial research questions: RQ1) The constraints of cognitive effects and the underlying algorithm, such as \emph{heuristic biases}, \emph{endowment effect}, \emph{diminishing return}, and \emph{violated system assumptions}, can be used to explain our empirical observations. Supporting polygon reduction tasks using the HITL strategy requires resolving these issues. RQ2) The observed constraints also apply in a similar HITL context, and we proposed descriptive UI design directions as promising countermeasures to prevent HITL system outcomes from being highly unstable and eventually non-satisfactory.

For future work, we expect to verify the proposed countermeasures in various scenarios and test for similar or different phenomena in other domains. We will also perform a systematic analysis of the basic building blocks that include other HITL systems and related cognitive factors as a foundation to inform guidelines for more error-tolerant HITL systems.

\section{Open Science}
\label{sec:open-source}

We encourage readers to reproduce and extend our results. Our system tools, technical specifications, anonymized datasets (without proprietary models and associated user ratings), and evaluation scripts are open-sourced that may be found in \url{https://changkun.de/s/infloop}.

\begin{acks}
The authors would like to thank our industrial partners Stefan Sigl, Marco Petrassi, and Marvin Juschus for sharing challenges in their daily 3D workflow; our colleague Kai Holländer for helpful discussions and feedback; to Feng Chen for executing the lab study, and to Prof. Eyke Hüllermeier and Karlson Pfannschmidt for useful discussions in preference learning. 3D mesh artifacts are provided courtesy of WAY Digital Solutions, Jeff H, Jose Olmedo, kenik, yarulesemel, and Stephan Thieme.
This work was supported by the \grantsponsor{1}{Bavarian IuK Program}{https://www.iuk-bayern.de/} (\grantnum{1}{IUK1805-0004//IUK577/002}). Work on this project is also partly funded by the Bavarian State Ministry of Science and the Arts and coordinated by the Bavarian Research Institute for Digital Transformation (bidt).
\end{acks}

\balance

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


Discuss WALS Datapoint Konkow / Prefixing vs. Suffixing in Inflectional Morphology.
Discuss WALS Datapoint Konkow / Position of Tense-Aspect Affixes.
Discuss WALS Datapoint for feature 69 and language wals_code_knw.
Discuss WALS Datapoint for feature 116 and language wals_code_knw.


text:
Coupon Code For Tai Lopez Credit Card
As a child, Tail Lopez started selling cherry tomatoes to earn money for his family. Growing up in a neighborhood full of gang members, it was difficult for him to make friends. Lopez turned to books to make friends. Lopez asked his grandfather for advice about how to live the "Good Life". He replied with a box of books. As a result, Lopez was able to make friends and earn money.
Tai Lopez was born to a poor family but he developed entrepreneurial skills and dropped out of college. However, he eventually went on to become a certified financial planner and owned nightclubs. Since then, he has worked with many renowned entrepreneurs and businessmen. Despite his humble beginnings, he has become a millionaire. But before he became a millionaire, he had to overcome obstacles, including poverty and gang affiliation.
Tai Lopez has since moved on to a new investment firm focused on e-Commerce and brands with offline equity. His social media presence has led him to a great deal of success. He was able to earn $0 an hour working for his mentor in his first two years. Today, he earns hundreds of thousands of dollars every single day. For this reason, he believes that the success of social media can help entrepreneurs to s\\ucceed in business.
Tai Lopez describes his grandfather, a "scientist", and writes about how he started reading books as a child. His grandfather also sent him books to read as a boy. These stories have been mentioned in many biographies, but they are not independently verified. Among the many things he wrote about his grandfather, he mentions working in amish communities and leper colonies. But these are all speculation, so do not believe everything you read.
Tai Lopez is an American investor, entrepreneur, author, and business advisor. His books and podcasts have gained popularity, primarily due to his garage video recordings in 2015. Tai Lopez is currently working with a company called MentorBox. He also owns his own dating website, Elite Global Dating, which has given him an initial sum of money. His podcasts have attracted sponsors and are gaining over 100,000 downloads each month. He has also joined a business mentorship program called the 67 Steps Program.
Tai Lopez has a net worth of $60 million. His YouTube channel has more than 900,000 subscribers and he serves as an advisor to multi-million dollar companies. His entrepreneurial activities have also earned him a reputation as an influential career advisor, lending motivation to students and organizations around the world. As a result, his net worth is predicted to increase by at least $60 million by 2020. It is difficult to predict the future, but Tai Lopez's success has led to a lot of success.
Tai Lopez has been criticized for making himself appear to be a wealthy person on YouTube. However, he has always maintained an excellent level of transparency. Many critics have claimed that Tai Lopez is a fraudster, despite his wealth and lifestyle. His business is still relatively young, but it is worth $5-6 million. The business he founded is a self-improvement company that offers techniques for improving your life. The company's 67-step program can be purchased for $67.
In the near future, Tai Lopez's net worth will be $60 million. YouTube viral videos have made Tai Lopez a popular entrepreneur, motivational speaker, investor, and entrepreneur. He has built an online empire based on his personal brand and marketing strategies through his YouTube videos and podcasts. He has been criticized by some, but he is widely regarded as an influential businessman. He is also an expert in digital marketing and has been a YouTube sensation.
Despite his popularity on the Internet, Tai Lopez's marketing tactics are largely a scam. Although the company claims that their programs are backed up by an experienced team, they are unreliable. Some of his courses cost as much as $7000. The programs are a rip-off. While this is the case with some programs, it is advisable to do your own research before investing in Tai Lopez's products.
Tai Lopez And Jermy In Car
Tai Lopez Tour Dates

redpajama_set_name: RedPajamaCommonCrawl


text:
Concert at Sea 2012 was de zevende editie van het jaarlijkse popfestival Concert at Sea. De zevende editie vond plaats op vrijdag 29 en zaterdag 30 juni 2012 met 30.000 bezoekers op vrijdag en 40.000 bezoekers op zaterdag.

Programma
De volgende artiesten stonden op de zevende editie van Concert at Seaː
Cøver at sea
Acda en De Munnik
VanVelzen
Golden Earring
Trijntje Oosterhuis
Waylon
Go Back to the Zoo
De Dijk
Ilse DeLange
The Baseballs
BLØF

Concert at Sea

redpajama_set_name: RedPajamaWikipedia


Three air passengers realize themselves stranded on a secluded island once a plane crash. however before long they discover the island isn't as isolated because it appears. Strange buildings, Associate in Nursing previous portable computer and different findings recommend that the island was used for a few form of research project. however United Nations agency was behind it? And what else is that the island hiding?
Help the survivors get on, avoid starvation, unravel a surprising mystery and build their escape. enjoying as every of the 3 characters, you'll need to make sure of their well being, caring for his or her Comfort, Hunger, Stamina, Fun, and relationship levels so as for them to be ready to explore the island and fulfill quests. realize and use objects to unravel puzzles, collect and cook food, realize your manner through the jungle, and complete collections to earn wonderful bonuses that facilitate the characters keep their energy levels high.
Hurry to the current attractive however unsettling island to rescue the 3 survivors now!
You can unlock further bonuses via in-app purchase from among the game!
Explore beaches, lagoons, jungles and mountains on a fine looking tropical island.
Solve an exciting island mystery before you'll bring the survivors home.
Stay pleased for months due to many quests and collections.


Since defeating Minarets 55-44 on Jan. 14th, the Badgers have now lost 5 straight with the rival Chieftains coming to Oakhurst on Tuesday.
is mountain rival Sierra. The Chieftains will bring a 7-12, 1-4 record to Oakhurst. The Badgers look to even the score with Sierra.
). Since defeating Liberty 51-38 on Jan. 20, the Badger JV has now lost three in a row.


text:
You Are Chosen!
Muslim Community Patrol Cars News Updates
Home Our Top Picks Keep Those Muslim Community Patrol Cars Off American Streets (Part Five)
Keep Those Muslim Community Patrol Cars Off American Streets (Part Five)
Martin Mawyer
Noor Rabah, vice president of Muslim Community Patrol & Services, outside the Police Department's 72nd Precinct, with a car the group intends to use to patrol neighborhoods in Brooklyn. Credit: James Keivom for The New York Times
It's easy to see through the lies and deceptions of Imam Siraj Wahhaj.
He's the radical Muslim cleric who not only approved the deployment of Muslim Community Patrol cars in Brooklyn and Staten Island, but also raised the funds to purchase them.
(Part One. Part Two. Part Three. Part Four.)
What most Americans don't know about Siraj Wahhaj is that this is not the first time he has deployed Muslim patrols units to police the streets of New York City.
It was back in 1988 when Siraj Wahhaj formed his first Muslim Patrol squads to go after 15 drug dens operating near his Brooklyn Al-Taqwa mosque on Fulton Street.
For the next three decades, Wahhaj would tell the story of how the New York City Police Department praised him for his efforts to get drug dealers off the streets.
According to a NY Times article written in 1988, Wahhaj's "Moslem Patrol" carried guns and walkie-talkies and had a "reputation for retribution."
By 1991, Wahhaj's security force was becoming so violent that it raised skeptical eyebrows from both residents and police officers.
"A private police force," the NY Times wrote in 1991, "as heavily armed and sometimes as violent as the drug dealers it is hired to confront, is stalking the dealers in some of Brooklyn's poorest neighborhoods."
Wahhaj would tell the NY Times, "We are willing to fight, willing to die and willing to kill, although I want to stress that we only kill in self-defense."
And kill they did. One guard was charged with second-dgree murder (though later acquitted) and another shot and crippled a man.
The violence and guns seemed acceptable, because Wahhaj was trying to make the streets family friendly.
However, a Muslim I spoke to, Abdul, who lived in the area during those years, tells a very different story on why Wahhaj formed the Moslem Patrol.
"This was just a front," Abdul said. "These Moslem Patrols had their own thing going on, their own drug dealings, and they didn't want these other drug dens operating in their backyard. This is how they got the money to build up that mosque, which used to be made of concrete, but now there's marble everywhere.
"It came from selling drugs," he told me.
But Abdul admitted that Wahhaj did succeed in getting the 15 competing drug dens shut down, though he laughs at the claim the New York Police Department praised his efforts.
"They didn't praise him," he said of the NYPD. "They beat the crap out of him and that's why he eventually shut down the Moslem Patrol."
Siraj Wahhaj help raise funds for the MCP cars and approved their deployment.
According to Abdul, the police didn't like Wahhaj's Moslem Patrol using weapons and clubs to beat up local drug dealers.
"The police didn't like their violent tactics and they went in and beat the sh** out of him while he was in the mosque. They said, 'You ain't that big now, are you'?"
There's no question the thrashing was a setback for Siraj Wahhaj's ego, but he would dust himself off and eventually become known around the world as "America's Imam."
And he has never abandoned his idea that America needs to be sharia compliant.
He once said, "Islam is better than democracy" and spoke of his support for Islamic forms of punishment.
"Where ever you came from, you came to America. And you came for one reason – for one reason only – to establish Allah's deen," he told a Muslim audience in Dallas, TX.
At another Muslim conference in Orlando, FL he said:
"I'm on a mission in New York City and listen, it's our secret. It's our secret. Keep it here in Orlando. I'm trying to get every Muslim in New York City, thousands of Muslim businesses, to encourage them not to put in their store anything that would be detrimental to the people."
Those items would include pornography, alcohol and pork products.
"We're going to start in New York City, then we're coming to Orlando," he said smiling, meaning he was coming to the rest of the United States.
The Muslim Community Patrol cars, which are modeled and decaled to look virtually identical to NYPD police cars, are on New York City streets because Imam Siraj Wahhaj helped purchase them and approved their deployment.
They are there to enforce Sharia on Muslims and non-Muslims alike.
Jews and Christians are no friends to Islam, Imam Wahhaj says in an audio tape.
The liberal media, which couldn't spot a radical Islamist if their necks were under a jihadist's blade, would have Americans believe that these Muslim Patrol cars are simply vehicles to keep Muslims safe from Islamophobes.
But the history of Siraj Wahhaj tells a different story, one filled with lies, deceptions, threats, violence and acts of forced submission.
There is no place in America for Muslim Community Patrol cars and they need to get off our streets.
And I'm not the only one saying it.
In Part Six, we'll examine how four mosques in the Brooklyn community are coming together to demand Siraj Wahhaj keep his Muslim Community Patrol cars out of their area.
Muslim Community Patrol Cars Sparks Backlash. (Part One)
Muslim Patrol Cars Precede the Islamization of Cities (Part Two)
Muslim Community Patrol Cars Set Stage For Sharia Patrol (Part Three)
The Radical Forces Behind the Muslim Community Patrol Cars (Part Four)
Muslim Community patrol cars
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Martin Mawyer is president of Christian Action Network which he founded in 1990. Located in Lynchburg, VA. CAN was formed as a non-profit educational organization to protect America's religious and moral heritage. He is the author of several books, including, "Your Are Chosen: Prepare to Triumph in a Fallen World."
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The Radical Forces Behind the Muslim Community Patrol Cars (Part Four) | Christian Action February 12, 2019 at 5:54 pm
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In this chapter, we describe the use of immunohistochemical methods to detect hypoxia in tumour tissue sections, utilising antibodies specific for endogenous proteins hypoxia inducible factor 1 alpha (Hif1α) and glucose transporter 1 (Glut-1) and the exogenous compound pimonidazole (Pim). Immunohistochemistry is routinely used both diagnostically and in research to label and identify specific cellular proteins of interest. The methods described here enable staining of hypoxic cells and tissue in formalin-fixed paraffin-embedded (FFPE) tumour tissue sections that can then be visualised either using chromogenic or fluorescence detection. All three methods can be used on human, xenograft, or mouse tumour tissue.