Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Network (QGNN) that implements message passing via Parameterized Quantum Circuits (PQCs). Our Quantum Graph Convolutional Layers (QGCLs) encode features into quantum states, process graphs with NISQ-compatible unitaries, and retrieve embeddings through measurement. Applied to D2D power control for SINR maximization, our QGNN matches classical performance with fewer parameters and inherent parallelism. This end-to-end PQC-based GNN marks a step toward quantum-accelerated wireless optimization.

Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting periods for in-person consultations, which cause suboptimal treatment. Significant efforts have been directed toward developing mental health dialogue systems utilizing the existing open-source mental health-related datasets. However, currently available datasets either discuss general topics or various strategies that may not be viable for direct application due to numerous ethical constraints inherent in this research domain. To address this issue, this paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. This dataset comprises both speech and text data, which can facilitate the development of a reliable pipeline for mental health dialogue systems. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset. The results show the merit of using this dataset.

This study employs Natural Language Processing (NLP) to analyze the intricate linguistic and emotional dimensions within the plays of Bernard-Marie Kolt\`es, a central figure in contemporary French theatre. By integrating advanced computational techniques, we dissect Kolt\`es' narrative style, revealing the subtle interplay between language and emotion across his dramatic oeuvre. Our findings highlight how Kolt\`es crafts his narratives, enriching our understanding of his thematic explorations and contributing to the broader field of digital humanities in literary analysis.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

As Editor-in-Chief, it is my pleasure to introduce a new program: Communications' digital initiative that connects leading-edge research and technology insights and breakthroughs from ACM's conferences to a much larger audience. The idea is to select compelling topics of broad interest and highlight them in a vibrant conversation with key leaders in an interactive digital event--one you can participate with live or view later via the ACM Digital Library. We held our first digital initiative in February. Below are some details about the event and link to watch it. Communications' first Digital Event was an exciting discussion with AI research leaders from academia and industry who explored how science and AI are transforming each other.

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure of PSTNs, i.e. the execution success probability, where the probability distributions of the contingent durations are ordinary, not necessarily parametric, nor symmetric (e.g. histograms, PERT), as long as these can be discretized. In practice, one would obtain ordinary distributions by considering empirical observations (compiled as histograms), or even hand-drawn by field experts. In this new realm of PSTNs, we study and formally define concepts such as degree of weak/strong/dynamic controllability, robustness under a predefined dispatching protocol, and introduce the concept of PSTN expected execution utility. We also discuss the limitation of existing controllability levels, and propose new levels within dynamic controllability, to better characterize dynamic controllable PSTNs based on based practical complexity considerations. We propose a novel fixed-parameter pseudo-polynomial time computation method to obtain both the success probability and expected utility measures. We apply our computation method to various PSTN datasets, including realistic planetary exploration scenarios in the context of the Mars 2020 rover. Moreover, we propose additional original applications of the method.

Aaron Hertzman's Viewpoint "Computers Do Not Make Art, People Do," (May 2020, p. 45) makes excellent points as to why it is very unlikely that computers will ever replace artists. While I don't think he quite stated such, it appears to me that he may be of the opinion that replacement of (natural) intelligence (of human beings) with artificial intelligence is very unlikely. Most, if not all, of the endeavors we are addressing are based on digital technology, and possibly cannot replace analog entities. It is unfortunate, however, that with the hype these days, people are either unaware of reality, or simply ignoring reality, with undesirable consequences. I like to cite a voicemail transcription I received recently.

Writing on the 50th Earth Day brings to mind the origins of U.S. environmental movement. DDT is, of course, Bis(4-chlorophenyl)- 1,1,1-trichloroethane, perhaps the most effective insecticide ever invented. DDT was used widely with remarkable effectiveness in the 1940s and 1950s to combat malaria, typhus, and the other insect-borne human diseases. Its efficacy was unsurpassed in insect control for crop and livestock production, and even villages and homes. In short, it was a wonder chemical.7

With the number of cybersecurity threats increasing daily, the ability of today's cybersecurity tools and human cybersecurity teams to keep pace is being overwhelmed by an avalanche of malware. According to Cap Gemini's 2019 Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security report, 56% of survey respondents said their cybersecurity analysts cannot keep pace with the increasing number and sophistication of attacks; 23% said they cannot properly investigate all the incidents that impact their organization; and 42% said they are seeing an increase in attacks against "time-sensitive" applications like control systems for cars and airplanes. Special report: Cybersecurity: Let's get tactical (free PDF) This ebook, based on the latest ZDNet / TechRepublic special feature, explores how organizations must adapt their security techniques, strengthen end-user training, and embrace new technologies like AI- and ML-powered defenses. "In the Internet Age, with hackers' ability to commit theft or cause harm remotely, shielding assets and operations from those who intend harm has become more difficult than ever," the report states. "The numbers are staggering -- Cisco alone reported that, in 2018, they blocked seven trillion threats on behalf of their customers. With such ever-increasing threats, organizations need help. Some organizations are turning to AI [artificial intelligence], not so much to completely solve their problems (yet), but rather to shore up the defenses."

We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions. We illustrate a problem with current DRL formulations, which rely on an overly broad definition of allowed distributions for the adversary, leading to learned classifiers that are unable to predict with any confidence. We propose a solution that incorporates unlabeled data into the DRL problem to further constrain the adversary. We show that this new formulation is tractable for stochastic gradient-based optimization and yields a computable guarantee on the future performance of the learned classifier, analogous to -- but tighter than -- guarantees from conventional DRL. We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither. Inspired by these results, we extend our DRL formulation to active learning with a novel, distributionally-robust version of the standard model-change heuristic. Our active learning algorithm often achieves superior learning performance to the original heuristic on real datasets.