Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions. For example, videos of humans or robots may convey a lot of implicit information about rewarding action sequences, but a DRL machine that wants to profit from watching such videos must first learn by itself to identify and recognize relevant states/actions/rewards. Without relying on ground-truth annotations, our new method called Deep State Identifier learns to predict returns from episodes encoded as videos. Then it uses a kind of mask-based sensitivity analysis to extract/identify important critical states. Extensive experiments showcase our method's potential for understanding and improving agent behavior. The source code and the generated datasets are available at https://github.com/AI-Initiative-KAUST/VideoRLCS.

ChatGPT is becoming a new reality. In this paper, we show how to distinguish ChatGPT-generated publications from counterparts produced by scientists. Using a newly designed supervised Machine Learning algorithm, we demonstrate how to detect machine-generated publications from those produced by scientists. The algorithm was trained using 100 real publication abstracts, followed by a 10-fold calibration approach to establish a lower-upper bound range of acceptance. In the comparison with ChatGPT content, it was evident that ChatGPT contributed merely 23\% of the bigram content, which is less than 50\% of any of the other 10 calibrating folds. This analysis highlights a significant disparity in technical terms where ChatGPT fell short of matching real science. When categorizing the individual articles, the xFakeBibs algorithm accurately identified 98 out of 100 publications as fake, with 2 articles incorrectly classified as real publications. Though this work introduced an algorithmic approach that detected the ChatGPT-generated fake science with a high degree of accuracy, it remains challenging to detect all fake records. This work is indeed a step in the right direction to counter fake science and misinformation.

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper.

The advent of Large Language Models (LLMs), such as OpenAI's ChatGPT, Codex, and GitHub's Copilot, affects the educational landscape at its core, as LLMs offer entirely new possibilities, but also challenges for educators, learners, and institutions. Even though LLMs have only appeared very recently to a broader audience, research has started to address their implications on computing education, particularly programming. The generative potential may be used by educators for the design of new programming tasks [Sa22], or for students to gather formative feedback [Ka23, Zh22]. At the same time, implications for programming pedagogy and assessments are being discussed [Be23, BK23, RTT23], as the lowthreshold availability of LLMs raises new questions with regard to adequate task designs, students' contribution, plagiarism, and ethical conduct. Educators and institutions will soon need to reconsider the design of (formative) assessments. In this context, it is crucial to investigate the capabilities and limitations of LLMs for novice learners of programming, whose challenges have a well-documented history [SS86, Mc01, Lu18].

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.

Building on Papert (1980)'s idea of children talking to computers, we propose ChatLogo, a hybrid natural-programming language interface for agent-based modeling and programming. We build upon previous efforts to scaffold ABM & P learning and recent development in leveraging large language models (LLMs) to support the learning of computational programming. ChatLogo aims to support conversations with computers in a mix of natural and programming languages, provide a more user-friendly interface for novice learners, and keep the technical system from over-reliance on any single LLM. We introduced the main elements of our design: an intelligent command center, and a conversational interface to support creative expression. We discussed the presentation format and future work. Responding to the challenges of supporting open-ended constructionist learning of ABM & P and leveraging LLMs for educational purposes, we contribute to the field by proposing the first constructionist LLM-driven interface to support computational and complex systems thinking.

We consider online learning in the model where a learning algorithm can access the class only via the consistency oracle -- an oracle, that, at any moment, can give a function from the class that agrees with all examples seen so far. This model was recently considered by Assos et al. (COLT'23). It is motivated by the fact that standard methods of online learning rely on computing the Littlestone dimension of subclasses, a problem that is computationally intractable. Assos et al. gave an online learning algorithm in this model that makes at most $C^d$ mistakes on classes of Littlestone dimension $d$, for some absolute unspecified constant $C > 0$. We give a novel algorithm that makes at most $O(256^d)$ mistakes. Our proof is significantly simpler and uses only very basic properties of the Littlestone dimension. We also observe that there exists no algorithm in this model that makes at most $2^{d+1}-2$ mistakes. We also observe that our algorithm (as well as the algorithm of Assos et al.) solves an open problem by Hasrati and Ben-David (ALT'23). Namely, it demonstrates that every class of finite Littlestone dimension with recursively enumerable representation admits a computable online learner (that may be undefined on unrealizable samples).

State-of-the-art automated test generation techniques, such as search-based testing, are usually ignorant about what a developer would create as a test case. Therefore, they typically create tests that are not human-readable and may not necessarily detect all types of complex bugs developer-written tests would do. In this study, we leverage Transformer-based code models to generate unit tests that can complement search-based test generation. Specifically, we use CodeT5, i.e., a state-of-the-art large code model, and fine-tune it on the test generation downstream task. For our analysis, we use the Methods2test dataset for fine-tuning CodeT5 and Defects4j for project-level domain adaptation and evaluation. The main contribution of this study is proposing a fully automated testing framework that leverages developer-written tests and available code models to generate compilable, human-readable unit tests. Results show that our approach can generate new test cases that cover lines that were not covered by developer-written tests. Using domain adaptation, we can also increase line coverage of the model-generated unit tests by 49.9% and 54% in terms of mean and median (compared to the model without domain adaptation). We can also use our framework as a complementary solution alongside common search-based methods to increase the overall coverage with mean and median of 25.3% and 6.3%. It can also increase the mutation score of search-based methods by killing extra mutants (up to 64 new mutants were killed per project in our experiments).

Game-theoretic inverse learning is the problem of inferring the players' objectives from their actions. We formulate an inverse learning problem in a Stackelberg game between a leader and a follower, where each player's action is the trajectory of a dynamical system. We propose an active inverse learning method for the leader to infer which hypothesis among a finite set of candidates describes the follower's objective function. Instead of using passively observed trajectories like existing methods, the proposed method actively maximizes the differences in the follower's trajectories under different hypotheses to accelerate the leader's inference. We demonstrate the proposed method in a receding-horizon repeated trajectory game. Compared with uniformly random inputs, the leader inputs provided by the proposed method accelerate the convergence of the probability of different hypotheses conditioned on the follower's trajectory by orders of magnitude.

Automatic assessment of the quality of scholarly documents is a difficult task with high potential impact. Multimodality, in particular the addition of visual information next to text, has been shown to improve the performance on scholarly document quality prediction (SDQP) tasks. We propose the multimodal predictive model MultiSChuBERT. It combines a textual model based on chunking full paper text and aggregating computed BERT chunk-encodings (SChuBERT), with a visual model based on Inception V3.Our work contributes to the current state-of-the-art in SDQP in three ways. First, we show that the method of combining visual and textual embeddings can substantially influence the results. Second, we demonstrate that gradual-unfreezing of the weights of the visual sub-model, reduces its tendency to ovefit the data, improving results. Third, we show the retained benefit of multimodality when replacing standard BERT$_{\textrm{BASE}}$ embeddings with more recent state-of-the-art text embedding models. Using BERT$_{\textrm{BASE}}$ embeddings, on the (log) number of citations prediction task with the ACL-BiblioMetry dataset, our MultiSChuBERT (text+visual) model obtains an $R^{2}$ score of 0.454 compared to 0.432 for the SChuBERT (text only) model. Similar improvements are obtained on the PeerRead accept/reject prediction task. In our experiments using SciBERT, scincl, SPECTER and SPECTER2.0 embeddings, we show that each of these tailored embeddings adds further improvements over the standard BERT$_{\textrm{BASE}}$ embeddings, with the SPECTER2.0 embeddings performing best.