You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Our JRS'2012 Data Mining Contest has finished! You can find the final results at the leaderboard and a summary of the competition in the Summary section.

Once again, thank you for participation!

To those of you how are waiting for publication of labels for the test data, as well as for PMIDs of documents and names of the MeSH headings/subheadings: please be patient. Although the JRS'2012 competition ended, there is a mini-contest ongoing for our students at University of Warsaw who attend a Machine Learning course. We do not want to disclose any additional data before it finishes, which is going to be on April 30.

We have revealed all the data related to our competition, including classification of the test cases, PMIDs of documents, as well as the names of columns and target labels. The files can be accessed from the Summary page.

namp wrote:Is it possible to make available the script that you used in order to calculate the score appearing on the leaderboard?

Hello,

I have placed eval.jar file in the 'Public files' folder. It contains a java script which was used for evaluation of results during the competition. You can access it through the Summary section.

Though, you have to remember that the preliminary and final evaluation scores were computed on disjoint subsets of the test data. We can not reveal this partitioning just yet.

Best regards,

<PRESIDIO_ANONYMIZED_PERSON>

Hello,

I was wondering if you could guide me in order to use eval1prelim.jar. I try to run it in command line,but it delivers errors. should we set the arguments for it in command line or it will provide a GUI?

To set up a contest we had to define datasets and an evaluation procedure.The shared file eval.jar constist of one java class FMeasureEvaluationProcedure which extends EvaluationProcedure abstract class from TunedIT framework. You need to inherit from this class if you want to implement an evaluation procedure suitablefor TunedIT based contest.

If you want to use the eval.jar you have a couple of options. If you do not knowthe TunedIT research possibilities, you will find the second one easier for start.1. You can use TunedTester to set up a test and use FMeasureEvaluationProcedure as yourevaluation procedure. Read http://wiki.tunedit.org/ - especially TunedIT Research section.2. The evaluation procedure was not designed to run by hand, but if you makea little effort you can achieve what you want. Of course you may freely extend this example, e.g. to pass paths as args from command line.Please remember to add eval.jar and core.jar (you can read about and download core.jar from http://wiki.tunedit.org/doc:research-architecture)An example may be more or less as follows.

To set up a contest we had to define datasets and an evaluation procedure.The shared file eval.jar constist of one java class FMeasureEvaluationProcedure which extends EvaluationProcedure abstract class from TunedIT framework. You need to inherit from this class if you want to implement an evaluation procedure suitablefor TunedIT based contest.

If you want to use the eval.jar you have a couple of options. If you do not knowthe TunedIT research possibilities, you will find the second one easier for start.1. You can use TunedTester to set up a test and use FMeasureEvaluationProcedure as yourevaluation procedure. Read http://wiki.tunedit.org/ - especially TunedIT Research section.2. The evaluation procedure was not designed to run by hand, but if you makea little effort you can achieve what you want. Of course you may freely extend this example, e.g. to pass paths as args from command line.Please remember to add eval.jar and core.jar (you can read about and download core.jar from http://wiki.tunedit.org/doc:research-architecture)An example may be more or less as follows.

Thank you very much for your comprehensive response.Finally, I coud use tunedtester to evaluate my algorithm's answer,but there is still one question.

As you know, there are two distinct evaluations for traffic prediction contest,preliminary and final. Preliminary samples comprise 35% of test data and final samples - the remaining 65%.

The question is that; are these subsets selected randomly from the whole answer file OR the first 35% of data is evaluated for preliminary and the remaining for final?if the subsets are fixed part of answer file, why we get different answers through different runs of TunedTester?

More generally, why there is an option to run Tunedtester for several times?