2023.03.26 19:21 No-Reindeer6348Help! Traveling from Idaho to Vegas and back!
So! My 2012 Honda civic LX is doing something funky, I’ve had it for 4 years with no problems and put probably 50,000 miles on it for a total 208,000. Before our trip we swapped our oil and filter, and air intake filter, a headlight, rotated tires. On our way down from Idaho to Vegas we stopped in southern utah for 20 minutes and when we got back in the shifter’s trigger wouldn’t unlock, we couldn’t shift. So we popped the little cap next to it and shoved a knife in there and bypassed it; shifted no problem. But we drive for maybe half a mile and the light that’s indicates traction control is off, ( I hope this makes sense) that light turns on, and the power steering light comes on, now like I said we checked all fluids and made sure the car was ready to roll 800 miles and besides the power steering is an electric one, not hydraulic, we checked, but it’s working fine! And we don’t need traction in Nevada so it hasn’t been giving us any problems so far besides the shifter. We took it to orielly amd had them throw a code reader on it and no codes. So we took it back to where we were staying and reset that battery, ya know like unhooked the battery and hooked positive and negative together for like 40 minutes thinking it must just be an electric problem because car is driving fine. Any adivce or ideas is welcome, we are on our way back right now. Thanks!
When I try to connect a set of Roland TD-07’s to GarageBand via iPhone. First my issue was that I was able to connect and record a track. But then the recording was behind the guitar track. So I reassessed and shut off my local control. Now I’m having an issue with latency on the pads all together. Though a shorter delay. I don’t hear my Roland’s until I hear the delayed strokes. I’m connected Bluetooth module to iPhone then printer connector to a camera connector to lightning usb. Is there an option in garage band to somehow reduce the latency of the strokes or is it unfixable due to the Bluetooth-GarageBand thing?
2023.03.26 18:30 Mystery9819Calling all motorbike users
Hello everyone I had my CBT (compulsory basic training) for a motorbike I passed everything but one module slow control. I have an xiaomi pro 2 how could I practice slow control on my xiaomi so I pass my CBT anybody with technical know how please help thank you
2023.03.26 18:19 Dysheki[FS][US-WA] RSV-L4500U White box server, Networking cards, Misc GPUs and more.
Hello, looking to offload some stuff I've accumulated over the last year or so. Prices include shipping to CONUS only *except the Rosewill*, sorry! Timestamps: https://imgur.com/a/FkDjhvu
Whitebox Server
4U Rosewill RSV-L4500U - X10SRA-F motherboard - Xeon E5-2650Lv3 Includes all hard drive brackets and keys. - $350
Networking Items
Ruckus R610 - Running Unleashed firmware. Includes mount and 3d printed bracket. - $140 shipped.
2023.03.26 18:11 Eph289Miner Explorations 1: Of Mines and Mods
Hey, we’re the STOBETTER team here with another exploration into STO’s mechanics and this time we’re starting on a new series of exploring the depths of mines. This is an especially difficult one for me to write because Must. Resist. Urge. To. Make. Mine. Gags. There’s just so many of them that come to mined. See what I mean? Anyway, if you’d told me even three years ago that I would care about mine mechanics, I would have buried any such suggestions. Yet, here we are and since I’ve done Leap of Faith on a toon with cold-based kit modules I guess you can say hell has frozen over enough to where we can mine it. Let’s dive in.
Why care about mines?
For the average STO shipbuilder, mines are not going to be something to build around or even include. If I had to pull numbers out of thin air, my guess is that there’s probably 50% of the playerbase running some Frankenbuild that defies classification. The stuff that Jay’s nightmares are made of. Of the remaining 50%, probably 50-75% of them are running what’s essentially an energy build. The rest are running exotic builds, and to a lesser extent projectile builds and to an even lesser extent other focuses that are optimized around specific ideas like carriers or mines. The point is that mines have three use cases and none of them are common. It’s hard to effectively mix mines into an energy build because:
They don’t trigger Super Charged Weapons
They lack really strong global set bonuses like the Lorca’s Ambition 2-piece
They’re even less reliable than torps on normal/advanced for actually hitting things due to their slow travel time, long cooldown, and difficulty in getting them to hit targets reliably.
They're poorly understood.
We’re here to help sort out that last one but first back to those use cases. Per above, if it’s not useful for the most popular build, i.e. energy builds, why would you slot a mine launcher? If you’re running a projectile build, mine launchers will deal more damage than most turrets/omnis in the aft weapon slots due to scaling with things like Projectile Weapon Training, Ordnance Accelerator, Ceaseless Momentum, etc. It’s somewhat debatable for certain aft energy weapons depending on your overall build, but in general, mines are going to do well, especially on DPS-parsing maps. If you’re running an exotic build and have extra aft weapon slots after you’ve chosen from the pool of Dyson Proton weapon (2-piece with the torp), Dark Matter Torpedo (if it’s not in the front and using the WAHDBB for the 2-piece), Morphogenic (on a Tac-focused ship), Advanced Inhibiting/Altamid Omnis for the -DRR procs or -DRR procs+crit. Any remaining slots would probably benefit from slotting a mine launcher, specifically if you’re using any of the following: Resonating Payload Modification personal trait, Ceaseless Momentum starship trait, Deconstructive Resonance Emitter console, and the more the better. The Fek’Ihri Torment Engine, which is basically must-slot, also has certain interactions with specific mines in that mines with DOT effects (Thoron-Infused Quantum) can trigger it, and its +Physical bonus benefits Web mines. If you’re building specifically around mines, we recognize this is the most niche of niche setups, but it can work. I’ve stomped around a variety of Random Advanced TFOs in my minelayer and have parsed over 650K with it in a team environment. even before I really knew what I was doing with it. Okay, now that most people have closed this tab or hit "back" since this doesn't pertain to their build, let's go deeper.
Miner Movements
One of the biggest issues with mines is getting them to arm and fire on targets since they spawn where you launch them, take a few seconds to arm, and then need a target within their radius to attack. There are basically three ways to do this:
You can use the Relocate Mines captain ability, which pulls ALL your mines to within 2-5 km of the target. We’ve tested this on Hive and even pre-spawning a ton of mines, it will pull them all from spawn to 30 km away at the initial engagement. At minimum cooldown (likely achieved through the use of the Intelligence Agent Attache captain trait), this is up every 30 seconds.
You can wait for them to move off and attack on their own. Most mines have a chase distance of 3-4 km on their own, doubled by the Hot Pursuit trait.
You can chuck them at targets with Kinetic Magnet, which pulls all mines (and targetable torpedoes) within 10 km to attack the marked target. With good cooldown support, this can be used every 30 seconds.
There’s also the Covert Mine Layer Suite, which is 1) expensive, and 2) causes your mines to creep slowly towards enemies, creeping from 10km down to within 6 km. Honestly, even with the -.5s shared mine recharge time (more on that later) and Cat1 mine damage, I can’t recommend this. I prefer a combination of 1 and 3.
One last note on Kinetic Magnet that we’ve said before but is worth repeating. Kinetic Magnet confuses ALL mines/torpedoes, turning them into some weird un-aligned state of both friendly and enemy meaning you can not only kill yourself but your teammates too. You can’t kill your allies with Tricobalt Torpedoes, regular Tricobalt Mines, Heavy Gravimetric Torpedoes, even though you can blow yourself up with them. However, you can absolutely nuke somebody 100% to 0 with a well-timed Kinetic Magnet if they’re hugging their target and all the mines hit. If there was a (NSFW) Skippy’s List for TFOs, using Kinetic Magnet to blow up your allies, especially PUGs, in TFOs would be on it. In other words, this is not recommended. Still, between Kinetic Magnet and Relocate Mines you can chuck mines at your desired foe fairly often and effectively. Certain enemies with damage immunities (Voth), lots of speed (Hur’q when not controlled/tractored/Gravity Well’d), or both (Undine) are not going to be great targets for mines. Ditto for those with lots of AOE (Sphere Builders, Krenim) since all mines are targetable and fly rather slowly. Hapless, slow/immobile enemies with lots of HP (Borg, arguably Tzenkethi) are great targets for mines as are enemies that spawn in rather predictably (Hur’q on Swarm, Binary Stars, Starbase One, etc.) Also, there’s no experience quite like playing Counterpoint and chucking the maximum number of mines at Terok Nor in the opening part of the third segment.
Miner Limits
You’re going to see some repetition from the wiki here. As a reMINEDer (Editor’s note: boo), you are limited on how many mines you can have of a given type active at a time. The general rule is 4x the base number of mines when dispersal patterns aren’t involved, so for mines that launch 4 mines per pattern, the 4x rule means 16 total of each base type (e.g. 16 max Photon mines of any type). Tricos get 4 total since they launch 1. Nukara Web Mines are limited to 4 total and launch 2 per salvo. This means that to maximize mine output on those projectile builds slotting multiple mine launchers, you’re slotting more than one type. When using a mine pattern, you can have a single additional set from each rank of each Dispersal Pattern. Since they share a cooldown, this generally means you’re only slotting 1 pattern at 1 rank so this is largely useless. The general rule is 4x standard mine launches + 1 pattern. If you’re into micro-mine-aging (Editor’s note: please stop) during the briefing phase, make sure your mine pattern doesn’t apply to the same mine twice. Some other exceptions:
Cluster torpedoes don’t count toward this limit
Deep Space Mine (Task Force Ordnances 3-piece) does not count toward this limit
Thoron Infused Quantum Mines don’t count as quantums.
Black Ops Mines are also their own thing. They launch blackout and blade mines which count together BUT ALSO separately from Photon.
Mine Cooldowns
The main methods of reducing mine cooldowns are the Ordnance Accelerator console from the Gamma Reputation, which reduces it by 20% and conveniently also reduces the global cooldown (time between mine launches) by -1.5 seconds on paper. There’s also the Covert Mine Layer Suite, which on paper reduces the global cooldown by 0.5 seconds. However, we’ve done some extensive testing with frame analysis of video for the global cooldown, and here’s the global cooldown results, which likely due to server performance and some spaghetti code differ from expected results:
Setup
Expected
Tested
OA+CML
1.5
2.3
OA Only
2
2.3
CML Only
3
3.2
Base
3.5
3.4
TableformattingbroughttoyoubyExcelToReddit However, as you can see, there’s one pretty key takeaway: the Covert Mine Layer Suite does not reduce global cooldown. As far as the individual cooldown reduction, let’s go back to that 20% from Ordnance Accelerator. Per standard Cryptic math, the equation is not 20% off the final, such that a 20 second mine cooldown is reduced to 16 seconds, but a 20% recharge haste so the equation is:
Final cooldown = Base cooldown / (1 + recharge haste )
For example a 20 second mine cooldown with Ordnance Accelerator is:
20 / (1.2) = 16.6667
There are also mine cooldown reduction Projectile Weapons Officers, which basically work the same as the torp ones. On use of a mine, they have a chance to reduce cooldown by 2/3/4/5 seconds depending on rarity. They’re also expensive since they come from a C-store doff pack.
Datamining
Mr. Tilor did a bunch of data collection and base damage derivation using Tribble to collect his data. Jay and I added to it by doing controlled PvP testing to check the radius where we had a minelayer launching mines at a target while another player was a certain distance away from the initial target. This was a little persnickety since the mine explosion damage is checked based on edge-to-edge for hitboxes while the distance measurement is center-to-center, but we tested it enough to have good confidence. We did not test all the mines for this part, just the ones we were interested in and had conveniently available. Additional mines can be tested upon request unless it’s a Lobi mine, in which case I’ll need 40 keys because that’s an expensive experiment, or unless it’s the Tractor Mines. Look, it’s possible that Tractor Mines do fancy things like proccing stuff but that has yet to be deter-mined (Editor’s note: We are docking your pay) and my desire to test them is low since they don’t have a listed tooltip damage.
Mine Type
Type
Base Dmg
Chase Range
Mines
Dispersal 1
Dispersal 2
Dispersal 3
Cat1 Preload
Reload
Estimated Base DPS
Notes
Photon
Photon
852.54
4
4
7
10
14
118%
15
227.34
Biomolecular
Photon
852.54
4
4
7
10
14
118%
17
200.60
Black Ops Blackout
Photon
900.00
4
2
4
5
7
123%
15
120.00
Is 3 on Beta
Black Ops Blade
Photon
90.00
4
2
3
5
7
120%
15
12.00
Is 4 on Beta
Black Ops Blade Explosion
Photon
449.97
4
2
3
5
7
123%
15
60.00
Afte 60s
Tetryon
Photon
869.40
4
4
7
10
14
120%
15
231.84
20% ShPen
Quantum
Quantum
1,055.57
3.5
4
7
10
14
118%
20
211.11
20% ShPen
Modulating Competition
Quantum
793.03
3.5
4
7
10
14
177%
20
158.61
Mine damage scales per second
Tethered Quantum
Quantum
1,114.39
3
2
4
6
8
115%
20
111.44
Thoron Infused
Quantum
1,055.57
3.5
4
7
10
14
121%
20
211.11
20% ShPen, extremely weak DoT
Plasma
Plasma
669.83
3.5
4
7
10
14
118%
16
167.46
Burn DoT is per mine, stacks a lot
Corrosive Plasma
Plasma
1,136.82
0
1
2
3
4
115%
15
75.79
They last for 1 minute before despawning
Chroniton
Chroniton
726.28
3.5
4
7
10
14
118%
20
145.26
Inhibiting
Chroniton
726.20
3.5
4
7
10
14
121%
20
145.24
Advanced Inhibiting
Chroniton
726.20
3.5
4
7
10
14
120%
20
145.24
The 2% bonus from the T6 reputation doesn't apply
Transphasic
Transphasic
700.00
3.5
4
7
10
14
118%
20
140.00
80% ShPen
Tricobalt
Tricobalt
4,457.40
3.5
1
2
3
4
118%
30
148.58
Tractor Beam
Other
2.5
3
5
7
9
No tooltip for damage
Concentrated Tachyon
Other
3.5
4
7
10
14
Scales with DrainX, not anything else, 1km AoE is really weird. -2.5% Shield Hardness per mine
Nukara
Other
321.00
3.5
2
4
6
8
112%
30
171.20
50% ShPen, every 0.5s for 4s. Doesn't scale with most +mine or +[Type]. Does scale 2x with Torment Engine
TableformattingbroughttoyoubyExcelToReddit The colony mines, FWIW, are the same as the standard mines except for having the [Proc] mod. They have the same base damage.
[Radius] - There Should Only Be One
The most interesting piece out of this for me personally was learning that [Radius] applies once and only once. All of us out there who’ve been running [Radius]x3 Bio-Molecular Photon mines, we have two dead mods. Maybe this worked differently at one point, but [Radius] appears to be only applying once no matter how many instances you have it. It is adding 0.5 km to the radius compared to a mine of the same type without a [Radius] mod. I’m a little surprised this hasn’t been discovered before so maybe it used to work differently, but I’m quite confident in the fidelity of our testing and methodology. Trico Mines with [Radius] from the colony have the biggest blast AOE of any mine we’ve seen at 1.5 km (compared to 1.0 km for standard Tricobalt mines) but [Radius]x3 is not worth rolling into or buying.
That Resonates With Me
Resonating Payload Modification is a personal trait from a lockbox that per the in-game description does the following: Torpedos cause -5 Physical and Kinetic Resistance Rating for 20 sec per stack (5 Stacks max). It’s also a little incomplete in its description. First off, mines also apply this damage, and they are treated as separate sources for the purposes of the stack limit. That is to say, the standard launch of 4 mines hitting a target will apply 4 stacks of Resonating Payload Modification independent of stacks from your torpedoes and yes they’re applied in an AOE to any targets you hit. Tricobalt Mines only add 1 stack by default since they only launch 1 mine at a time. It gets higher when you use a mine pattern. But wait, there’s more. Pottsey mentioned that Black Ops mines have some weird interactions with the trait. We investigated further and he’s correct. Because the blade mines attack each second, each blade mine generates 5 stacks of RPM over 5 seconds. Thus, a non-mine-patterned Black Ops mine launch will generate 12 stacks of RPM (2 blade mines each contribute 5 stacks, 2 blackout mines grant 2 more). Dispersal Pattern Beta II generates 30 stacks, because there are 5 blackout mines (5 stacks) and 5 blade mines (5 stacks each). This is independent of any other stacks you’ve added.
What ships make the best minelayers?
A lot of torp boats are using 4/4 ships to slot mine launchers in the back, so any ship with LtCmdr Cmd, a decent number of tactical seats/consoles, and 4/4 will do well at this. If you’re feeling less interested in using mines, you can do such a build on a 5/3 ship as well. If you really want to build around mines rather than as secondary weapons, the Vorgon Ytijara Dreadnought Cruiser is very hard to get if you don’t have it already since it comes from an Epic Phoenix box, but is unique in having 5 aft weapon slots. However, it does lack Intel seating if you prioritize Kinetic Magnet. Alternately, you can stick with a 4/4 ship and prioritize Intel seating with Kinetic Magnet, preferably on a full Miracle Worker ship to get an extra tactical console and Mixed Armaments Synergy, and that’s how you end up with a Lexington built around . . . mines. I look forward to applying these conclusions and seeing if I can break 700K with it.
Mines Over Matters
It’s worth reminding people that having too many entities on the map (i.e. hangar pets, mines), can start to visually despawn enemies. This is mostly a problem on DPS-chasing maps if you have more than torpboat. If you’re doing DPS runs, torpboat should be coordinated to only have a max of one in the group, just like you generally want only 1 tank. That’s probably enough fun and games for now. I’m sure we’ll be back with more miner (Editor’s note: somebody unplug his keyboard) updates in the future.
Conclusions
Mines are really only viable for torpboats or exotic ships with an aft weapon slot open, or else dedicated minelayers
The global cooldown for mine launchers is 3.5 seconds. Ordnance Accelerator lowers it to around 2.3. Covert Mine Layer Suite has no effect on the global cooldown.
Mines in general pair really well with Resonating Payload Modification. Specifically, Blackout Mines are nuts with Resonating Payload Modification and if you’re running a single mine on an Exotic boat and have that trait, these are the ones to use.
If using multiple launchers, mix mine types to avoid hitting mine limits, but be aware of server limitations from spawning too many
[Radius] does not stack. Only 1 such mod is useful. Tricobalt Mines with [Radius] have the highest AOE of any mine we tested (1.5 km) followed by Thoron Infused Quantum, regular Tricobalt and Bio-Molecular Photon Mines with [Radius] at 1. Most mines have a radius of 0.5 km AOE
You can kill yourself and any nearby allies by using Kinetic Magnet on a target. Innately, only Tricobalt mines do friendly fire damage to yourself because of course they do, but all mines/targetable torpedoes are dangerous when Kinetic Magnet is in play.
Thank you for reading and hope you didn't mined all the puns! We'll work these (mines, not puns) into TRINITY...eventually.
2023.03.26 18:04 JustGurangA Conceptual Model of Natural Language Generation
A Conceptual Model of Natural Language Generation with Generator and Analyzer Abstract Natural language generation (NLG) is the task of producing natural language content from non-linguistic input, such as text or voice. NLG has many applications in various domains, such as dialogue systems, summarization, storytelling, and content creation. However, NLG also faces many challenges, such as ensuring the quality, diversity, and relevance of the generated content. In this paper, we propose a conceptual model of NLG that has the concept of a generator and an analyzer. The generator can produce content from a text or voice of a human from a large dataset of information, and the analyzer can check the quality of the content by comparing it to another large dataset of human-generated content. The analyzer can then give feedback to the generator on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The generator can learn from the feedback and generate better content in the next iteration. We describe the components and methods of the model, and discuss its potential benefits and challenges for natural language generation. Introduction Natural language generation (NLG) is the task of producing natural language content from non-linguistic input, such as text or voice. NLG has many applications in various domains, such as dialogue systems, summarization, storytelling, and content creation. For example, a dialogue system can generate natural language responses to user queries or requests; a summarizer can generate concise summaries of long documents or articles; a storyteller can generate engaging stories from keywords or images; and a content creator can generate informative or entertaining content for social media or blogs. However, NLG also faces many challenges, such as ensuring the quality, diversity, and relevance of the generated content. Quality refers to how well the generated content conforms to the linguistic and pragmatic norms of natural language, such as grammar, spelling, punctuation, fluency, coherence, logic, and politeness. Diversity refers to how well the generated content covers different aspects or perspectives of the input or topic, such as facts, opinions, emotions, humor, and creativity. Relevance refers to how well the generated content matches the input or topic, such as context, intention, purpose, and audience. One way to address these challenges is to have a human-in-the-loop approach, where a human can provide feedback or evaluation to the NLG system on the quality, diversity, and relevance of the generated content. However, this approach has some limitations, such as scalability, cost, availability, and subjectivity. Scalability refers to how well the approach can handle large-scale or real-time NLG tasks with high demand or frequency. Cost refers to how much resources or time are required for the human feedback or evaluation. Availability refers to how easily or quickly a human can provide feedback or evaluation. Subjectivity refers to how consistent or reliable the human feedback or evaluation is across different humans or situations. Another way to address these challenges is to have an automated approach, where an NLG system can self-evaluate or self-improve its own generated content without human intervention. However, this approach also has some limitations, such as data quality, model complexity, and evaluation metrics. Data quality refers to how accurate, complete, and representative the data used for training or testing the NLG system is. Model complexity refers to how sophisticated, robust, and generalizable the model used for generating or evaluating the content is. Evaluation metrics refer to how valid, reliable, and interpretable the metrics used for measuring the quality, diversity, and relevance of the content are. In this paper, we propose a conceptual model of NLG that has the concept of a generator and an analyzer. The generator can produce content from a text or voice of a human from a large dataset of information, and the analyzer can check the quality of the content by comparing it to another large dataset of human-generated content. The analyzer can then give feedback to the generator on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The generator can learn from the feedback and generate better content in the next iteration. We describe the components and methods of the model, and discuss its potential benefits and challenges for natural language generation. Model Description The proposed model consists of two main components: a generator and an analyzer. The generator is responsible for producing natural language content from a text or voice of a human from a large dataset of information. The analyzer is responsible for checking the quality of the generated content by comparing it to another large dataset of human-generated content. The analyzer can then give feedback to the generator on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The generator can learn from the feedback and generate better content in the next iteration. Generator The generator is a neural network model that can produce natural language content from a text or voice of a human from a large dataset of information. The dataset of information can be any source of structured or unstructured data, such as databases, knowledge graphs, documents, articles, images, videos, or audio files. The text or voice of a human can be any query, request, command, or instruction that specifies the topic, purpose, or style of the desired content. The generator can use different methods to generate the content, such as sequence-to-sequence models, transformer models, generative adversarial networks (GANs), or variational autoencoders (VAEs). The generator can also use different techniques to enhance the quality, diversity, and relevance of the content, such as attention mechanisms, copy mechanisms, reinforcement learning, or controllable generation. The output of the generator is natural language content that can be in any form or genre, such as text or speech, sentence or paragraph, question or answer, fact or opinion, story or summary, or joke or poem. Analyzer The analyzer is another neural network model that can check the quality of the generated content by comparing it to another large dataset of human-generated content. The dataset of human-generated content can be any source of natural language data that is relevant to the topic, purpose, or style of the desired content. The dataset can be collected from various domains or platforms, such as news websites, social media platforms, blogs, forums, reviews, comments, ratings, or feedback. The analyzer can use different methods to evaluate the content, such as classification models, regression models, ranking models, or scoring models. The analyzer can also use different criteria to measure the quality of the content in terms of naturalness, coherence, conciseness, accuracy, and style. Naturalness refers to how fluent and grammatical the content is in terms of syntax, semantics, and pragmatics. Coherence refers to how logical and consistent the content is in terms of structure, organization, and argumentation. Conciseness refers to how brief and clear the content is in terms of word choice, sentence length, and redundancy. Accuracy refers to how correct and factual the content is in terms of information, evidence, and citation. Style refers to how appropriate and expressive the content is in terms of tone, mood, voice, and personality. The output of the analyzer is feedback that can be in any form or level, such as binary or numeric values, labels or categories, scores or ratings, comments or suggestions, or corrections or revisions. Model Discussion The proposed model has some potential benefits and challenges for natural language generation. Benefits One benefit of the model is that it can leverage large-scale datasets of information and human-generated content to produce and evaluate natural language content. This can improve the data quality and model complexity of the NLG system. The model can also use different methods and techniques to generate and evaluate the content according to different criteria and preferences. This can increase the diversity and relevance of the NLG system. Another benefit of the model is that it can have a self-improving mechanism that allows the generator to learn from the feedback of the analyzer and generate better content in the next iteration. This can enhance the quality and performance of the NLG system. Challenges One challenge of the model is that it may require a lot of computational resources and time to train and test the generator and analyzer models on large-scale datasets. This may limit the scalability and efficiency of the NLG system. Another challenge of the model is that it may face some difficulties in finding suitable evaluation metrics that can capture all aspects of quality, diversity, and relevance of natural language content. This may affect the validity and reliability of the NLG system. A third challenge of the model is that it may encounter some ethical or social issues in generating and evaluating natural language content that may have some impact or influence on human perception, behavior, or decision. This may raise some questions about the responsibility and accountability of the NLG system. Conclusion In this paper, we proposed a conceptual model of natural language generation that has the concept of a generator and an analyzer. The generator can produce content from a text or voice of a human from a large dataset of information, and the analyzer can check the quality of the content by comparing it to another large dataset of human-generated content. The analyzer can then give feedback to the generator on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The generator can learn from the feedback and generate better content in the next iteration. We described the components and methods of the model, and discussed its potential benefits and challenges for natural language generation. We hope that this paper can inspire more research and development on natural language generation with generator and analyzer. We believe that this model can offer a new perspective and direction for improving the quality, diversity, and relevance of natural language content. How to Experiment with Natural Language Generation using PyTorch Abstract Natural language generation (NLG) is the task of producing natural language content from non-linguistic input, such as text or voice. NLG has many applications in various domains, such as dialogue systems, summarization, storytelling, and content creation. However, NLG also faces many challenges, such as ensuring the quality, diversity, and relevance of the generated content. In this paper, we propose a method to experiment with NLG using PyTorch, a popular deep learning framework. We describe how to collect or create large-scale datasets of information and human-generated content, how to build and train neural network models for the generator and the analyzer components of the NLG system, how to generate and evaluate natural language content using the models, and how to analyze and report the results of the experiment. We hope that this paper can provide a useful guide for researchers and practitioners who want to explore and improve NLG using PyTorch. Introduction Natural language generation (NLG) is the task of producing natural language content from non-linguistic input, such as text or voice. NLG has many applications in various domains, such as dialogue systems, summarization, storytelling, and content creation. For example, a dialogue system can generate natural language responses to user queries or requests; a summarizer can generate concise summaries of long documents or articles; a storyteller can generate engaging stories from keywords or images; and a content creator can generate informative or entertaining content for social media or blogs. However, NLG also faces many challenges, such as ensuring the quality, diversity, and relevance of the generated content. Quality refers to how well the generated content conforms to the linguistic and pragmatic norms of natural language, such as grammar, spelling, punctuation, fluency, coherence, logic, and politeness. Diversity refers to how well the generated content covers different aspects or perspectives of the input or topic, such as facts, opinions, emotions, humor, and creativity. Relevance refers to how well the generated content matches the input or topic, such as context, intention, purpose, and audience. One way to address these challenges is to have a human-in-the-loop approach, where a human can provide feedback or evaluation to the NLG system on the quality, diversity, and relevance of the generated content. However, this approach has some limitations, such as scalability, cost, availability, and subjectivity. Scalability refers to how well the approach can handle large-scale or real-time NLG tasks with high demand or frequency. Cost refers to how much resources or time are required for the human feedback or evaluation. Availability refers to how easily or quickly a human can provide feedback or evaluation. Subjectivity refers to how consistent or reliable the human feedback or evaluation is across different humans or situations. Another way to address these challenges is to have an automated approach, where an NLG system can self-evaluate or self-improve its own generated content without human intervention. However, this approach also has some limitations, such as data quality, model complexity, and evaluation metrics. Data quality refers to how accurate, complete, and representative the data used for training or testing the NLG system is. Model complexity refers to how sophisticated, robust, and generalizable the model used for generating or evaluating the content is. Evaluation metrics refer to how valid, reliable, and interpretable the metrics used for measuring the quality, diversity, and relevance of the content are. In this paper, we propose a method to experiment with NLG using PyTorch, a popular deep learning framework. PyTorch is an open-source library that provides a flexible and easy-to-use platform for building and training neural network models. PyTorch also supports various methods and techniques for natural language processing (NLP) and NLG, such as recurrent neural networks (RNNs), attention mechanisms, transformer models, generative adversarial networks (GANs), and variational autoencoders (VAEs). We describe how to collect or create large-scale datasets of information and human-generated content that are relevant to the objective of the experiment. We also describe how to build and train neural network models for the generator and the analyzer components of the NLG system. The generator model can produce natural language content from a text or voice of a human from a large dataset of information, and the analyzer model can check the quality of the content by comparing it to another large dataset of human-generated content. The analyzer model can then give feedback to the generator model on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The generator model can learn from the feedback and generate better content in the next iteration. We also describe how to generate and evaluate natural language content using the models, and how to analyze and report the results of the experiment. We hope that this paper can provide a useful guide for researchers and practitioners who want to explore and improve NLG using PyTorch. Method In this section, we describe the steps involved in experimenting with NLG using PyTorch. Data Collection The first step is to collect or create a large dataset of information and a large dataset of human-generated content that are relevant to the objective of the experiment. The dataset of information can be any source of structured or unstructured data, such as databases,knowledge graphs, documents, articles, images, videos, or audio files. The dataset of human-generated content can be any source of natural language data that is relevant to the topic, purpose, or style of the desired content. The dataset can be collected from various domains or platforms, such as news websites, social media platforms, blogs, forums, reviews, comments, ratings, or feedback. The size and quality of the datasets may affect the performance and outcome of the experiment. Therefore, it is important to ensure that the datasets are accurate, complete, and representative of the input and output domains. It is also important to preprocess and clean the datasets before using them for training or testing the models. For example, removing noise, duplicates, outliers, missing values, or irrelevant information from the datasets. Model Building The second step is to build or use a neural network model for the generator and another neural network model for the analyzer. The models can be built using PyTorch's modules and functions that provide various layers, activations, optimizers, loss functions, and other utilities for building and training neural network models. The generator model can use different methods to generate natural language content from a text or voice of a human from a large dataset of information, such as sequence-to-sequence models, transformer models, generative adversarial networks (GANs), or variational autoencoders (VAEs). The generator model can also use different techniques to enhance the quality, diversity, and relevance of natural language content, such as attention mechanisms, copy mechanisms, reinforcement learning, or controllable generation. The analyzer model can use different methods to evaluate natural language content by comparing it to another large dataset of human-generated content, such as classification models, regression models, ranking models, or scoring models. The analyzer model can also use different criteria to measure the quality of natural language content in terms of naturalness, coherence, conciseness, accuracy, and style. Naturalness refers to how fluent and grammatical the content is in terms of syntax, semantics, and pragmatics. Coherence refers to how logical and consistent the content is in terms of structure, organization, and argumentation. Conciseness refers to how brief and clear the content is in terms of word choice, sentence length, and redundancy. Accuracy refers to how correct and factual the content is in terms of information, evidence, and citation. Style refers to how appropriate and expressive the content is in terms of tone, mood, voice, and personality. The output of the generator model is natural language content that can be in any form or genre, such as text or speech, sentence or paragraph, question or answer, fact or opinion, story or summary, or joke or poem. The output of the analyzer model is feedback that can be in any form or level, such as binary or numeric values, labels or categories, scores or ratings, comments or suggestions, or corrections or revisions. Model Training The third step is to train the generator and analyzer models on the respective datasets using appropriate methods and techniques. The training process involves feeding the input data to the models, computing the output data and the loss function, updating the model parameters using an optimizer algorithm, and repeating these steps until the models converge to a desired state. The training process may vary depending on the methods and techniques used by the models. For example, sequence-to-sequence models may use teacher forcing or scheduled sampling techniques to train the decoder part of the model. Transformer models may use masked language modeling or next sentence prediction techniques to train the encoder-decoder part of the model. GANs may use adversarial training techniques to train the generator and discriminator parts of the model. VAEs may use variational inference techniques to train the encoder and decoder parts of the model. The training process may also require some hyperparameters tuning and regularization techniques to optimize the performance and generalization of the models. For example, choosing the appropriate learning rate, batch size, number of epochs, number of layers, number of hidden units, dropout rate, weight decay rate, etc. for the models. PyTorch provides various tools and libraries for hyperparameter tuning and regularization, such as PyTorch Lightning, PyTorch Ignite, Ray Tune, Optuna, etc. Content Generation The fourth step is to generate some natural language content using the generator model, given some text or voice of a human as input. The generation process involves feeding the input data to the generator model, computing the output data and the probability distribution over the vocabulary, sampling or selecting the most probable word or token from the distribution, appending the word or token to the output sequence, and repeating these steps until the end-of-sequence token is generated or a predefined length limit is reached. The generation process may vary depending on the methods and techniques used by the generator model. For example, sequence-to-sequence models may use beam search or greedy search techniques to generate the output sequence. Transformer models may use masked language modeling or next sentence prediction techniques to generate the output sequence. GANs may use adversarial sampling or temperature sampling techniques to generate the output sequence. VAEs may use variational sampling or posterior sampling techniques to generate the output sequence. The generation process may also require some decoding strategies and constraints to control the quality, diversity, and relevance of the generated content. For example, using top-k sampling or top-p nucleus sampling strategies to avoid repetition or low-probability words in the output sequence. Using length normalization or coverage penalty constraints to avoid too short or too long output sequences. Using keyword extraction or topic modeling constraints to ensure that the output sequence matches the input or topic. Content Evaluation The fifth step is to evaluate the generated content using the analyzer model, comparing it to the human-generated content dataset and giving feedback to the generator model. The evaluation process involves feeding the generated content and the human-generated content to the analyzer model, computing the output data and the evaluation metrics, comparing the metrics between the generated content and the human-generated content, and giving feedback to the generator model on how to improve the content in terms of naturalness, coherence, conciseness, accuracy, and style. The evaluation process may vary depending on the methods and criteria used by the analyzer model. For example, classification models may use binary or multi-class labels to evaluate naturalness, coherence, conciseness, accuracy, and style of natural language content. Regression models may use numeric values or scores to evaluate naturalness, coherence, conciseness, accuracy, and style of natural language content. Ranking models may use pairwise or listwise comparisons to evaluate naturalness, coherence, conciseness, accuracy, and style of natural language content. Scoring models may use ROUGE, BLEU, METEOR, BERTScore, etc. to evaluate naturalness, coherence, conciseness, accuracy, and style of natural language content. The evaluation process may also require some aggregation and analysis techniques to summarize and interpret the evaluation metrics and feedback. For example, using mean, median, standard deviation, or confidence interval techniques to aggregate the evaluation metrics and feedback across different samples or categories. Using correlation, regression, or significance testing techniques to analyze the relationship between the evaluation metrics and feedback and the input or output variables. The feedback from the analyzer model can be used by the generator model to improve its content in the next iteration. The feedback can be in any form or level, such as binary or numeric values, labels or categories, scores or ratings, comments or suggestions, or corrections or revisions. The feedback can be used by the generator model in different ways, such as updating the model parameters using gradient descent or reinforcement learning algorithms, modifying the input data using data augmentation or editing techniques, or adjusting the output data using post-processing or refinement techniques. Result Analysis The sixth and final step is to analyze and report the results of the experiment, such as how well the generator model improved over time, how well the analyzer model measured the quality of the content, and how well the generated content met the objective of the experiment. The result analysis involves collecting and organizing the data from the experiment, such as the input data, the output data, the evaluation metrics, and the feedback. The result analysis also involves applying some statistical and visual techniques to summarize and interpret the data from the experiment, such as descriptive statistics, inferential statistics, hypothesis testing, charts, graphs, tables, etc. The result analysis should answer some important questions about the experiment, such as:
What is the objective of the experiment?
What are the methods and techniques used by the generator and analyzer models?
What are the datasets of information and human-generated content used for training and testing the models?
How are the models trained and tested on the datasets?
How is natural language content generated and evaluated using the models?
How is feedback given and used by the models to improve natural language content?
How is natural language content improved over time by using feedback? - How is quality measured by using different criteria and metrics?
How is quality compared between generated content and human-generated content?
How is quality related to input or output variables?
How does generated content meet the objective of the experiment? The result analysis should also provide some conclusions and implications of the experiment, such as:
What are the main findings and contributions of the experiment?
What are the strengths and limitations of the experiment?
What are the implications and applications of the experiment for NLG research and practice?
What are the future directions and challenges for NLG research and practice?
Conclusion In this paper, we proposed a method to experiment with NLG using PyTorch, a popular deep learning framework. We described how to collect or create large-scale datasets of information and human-generated content, how to build and train neural network models for the generator and the analyzer components of the NLG system, how to generate and evaluate natural language content using the models, and how to analyze and report the results of the experiment. We hope that this paper can provide a useful guide for researchers and practitioners who want to explore and improve NLG using PyTorch.
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2023.03.26 16:52 MasterBejterHelp with a simple animation in AngularJS
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