A preliminary empirical evaluation indicates that this work is seen as very promising, and has the potential to become a foundation for real-world AI tools supporting professionals in the Danish legal sector.

Long traces and large event logs that originate from sensors and prediction models are becoming more common in our data-rich world. In such circumstances, conformance checking, a key task in process mining, can become computationally infeasible due to the exponential complexity of finding an optimal alignment. This paper introduces a novel sliding window approach to address these scalability challenges while preserving the interpretability of alignment-based methods. By breaking down traces into manageable subtraces and iteratively aligning each with the process model, our method significantly reduces the search space. The approach uses global information that captures structural properties of the trace and the process model to make informed alignment decisions, discarding unpromising alignments even if they are optimal for a local subtrace. This improves the overall accuracy of the results. Experimental evaluations demonstrate that the proposed method consistently finds optimal alignments in most cases and highlight its scalability. This is further supported by a theoretical complexity analysis, which shows the reduced growth of the search space compared to other common conformance checking methods. This work provides a valuable contribution towards efficient conformance checking for large-scale process mining applications.

In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students with visual highlights. Unlike traditional systems that provide binary scores, "marking" identifies and categorizes segments of the student response as correct, incorrect, or irrelevant and detects omissions from gold answers. We introduce a new dataset meticulously curated by Subject Matter Experts specifically for this task. We frame "Marking" as an extension of the Natural Language Inference (NLI) task, which is extensively explored in the field of Natural Language Processing. The gold answer and the student response play the roles of premise and hypothesis in NLI, respectively. We subsequently train language models to identify entailment, contradiction, and neutrality from student response, akin to NLI, and with the added dimension of identifying omissions from gold answers. Our experimental setup involves the use of transformer models, specifically BERT and RoBERTa, and an intelligent training step using the e-SNLI dataset. We present extensive baseline results highlighting the complexity of the "Marking" task, which sets a clear trajectory for the upcoming study. Our work not only opens up new avenues for research in AI-powered educational assessment tools, but also provides a valuable benchmark for the AI in education community to engage with and improve upon in the future. The code and dataset can be found at https://github.com/luffycodes/marking.

As part of its annual market performance review, global software giant, Wondershare Technology is releasing a summary of major milestones achieved in 2021, including over 100 million software downloads, strong product adoption, successful social campaigns, and awards from globally renowned brands such as CES 2021, G2 Crowd, and Shorty Awards. Wondershare's recognitions for the year begin with a record-breaking 100 million software downloads and 1200 product upgrades, while the acquisition and integration of Ufoto into its fold deepened its penetration into the fast-growing photo and video editing segment. Underlining Wondershare's commitment to championing creativity and productivity were several social media events such as the Back-to-School campaign to engage students and educators, #WondershareBFF to incentivize creators to showcase their most precious friendship moments, and the explosively viral #WondershareChallenge that garnered 2.8 million impressions and 20,000 engagements. Topping off a very successful year were awards and special recognition from respected consumer brands, such as the "Future Tech Award" for "Best Creative Software Suite" from CES 2021, the Shorty Award for Wondershare Filmora X – Winner and Audience Honoree for Best in Photo and Video, and recognition as a "Leader" and "High Performer" at the G2 Crowd Fall 2021 Awards. Wondershare Filmora – A user-friendly video editing software that is hugely popular with the new generation of social media video creators and professionals alike.

How should an essay be marked? You might think a teacher should simply read it and make a judgment based on the impression it makes: logically coherent, offers evidence to back up its case, reads well, is original – feels like an A. But that, obviously, is risky. What stirs one assessor might not appeal to another. So maybe there needs to be an agreed rubric. The essay must cover certain key points, achieve certain goals.

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.

This article falls within the field of abstract argumentation frameworks. In particular, we focus on the study of frameworks using a proof procedure based on dialectical trees. These trees rely on a marking procedure to determine the warrant status of their root argument. Thus, our objective is to formulate rationality postulates to characterize the marking criterion over dialectical trees. The behavior of the marking procedure is closely tied to the alteration of trees, which is the keystone of any model of change based on dialectical argumentation. Hence, the results achieved in this work will benefit research on dynamics in argumentation.