Accurate and timely diagnosis of multi-class skin lesions is hampered by subjective methods, inherent data imbalance in datasets like HAM10000, and the "black box" nature of Deep Learning (DL) models. This study proposes a trustworthy and highly accurate Computer-Aided Diagnosis (CAD) system to overcome these limitations. The approach utilizes Deep Convolutional Generative Adversarial Networks (DCGANs) for per class data augmentation to resolve the critical class imbalance problem. A fine-tuned ResNet-50 classifier is then trained on the augmented dataset to classify seven skin disease categories. Crucially, LIME and SHAP Explainable AI (XAI) techniques are integrated to provide transparency by confirming that predictions are based on clinically relevant features like irregular morphology. The system achieved a high overall Accuracy of 92.50 % and a Macro-AUC of 98.82 %, successfully outperforming various prior benchmarked architectures. This work successfully validates a verifiable framework that combines high performance with the essential clinical interpretability required for safe diagnostic deployment. Future research should prioritize enhancing discrimination for critical categories, such as Melanoma NOS (F1-Score is 0.8602).

AI, especially Large Language Models (LLMs) like ChatGPT, have rapidly developed and gained widespread adoption in the past five years, shifting user preference from traditional search engines. However, the generative nature of LLMs raises concerns about presenting misinformation as fact. To address this, we developed a web-based application that helps K-12 students enhance critical thinking by identifying misleading information in LLM responses about major historical figures. In this paper, we describe the implementation and design details of the DoYouTrustAI tool, which can be used to provide an interactive lesson which teaches students about the dangers of misinformation and how believable generative AI can make it seem. The DoYouTrustAI tool utilizes prompt engineering to present the user with AI generated summaries about the life of a historical figure. These summaries can be either accurate accounts of that persons life, or an intentionally misleading alteration of their history. The user is tasked with determining the validity of the statement without external resources. Our research questions for this work were:(RQ1) How can we design a tool that teaches students about the dangers of misleading information and of how misinformation can present itself in LLM responses? (RQ2) Can we present prompt engineering as a topic that is easily understandable for students? Our findings highlight the need to correct misleading information before users retain it. Our tool lets users select familiar individuals for testing to reduce random guessing and presents misinformation alongside known facts to maintain believability. It also provides pre-configured prompt instructions to show how different prompts affect AI responses. Together, these features create a controlled environment where users learn the importance of verifying AI responses and understanding prompt engineering.

Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and diminishes their effective threat response. We present EXPLICATE: a framework that enhances phishing detection through a three-component architecture: an ML-based classifier using domain-specific features, a dual-explanation layer combining LIME and SHAP for complementary feature-level insights, and an LLM enhancement using DeepSeek v3 to translate technical explanations into accessible natural language. Our experiments show that EXPLICATE attains 98.4 % accuracy on all metrics, which is on par with existing deep learning techniques but has better explainability. High-quality explanations are generated by the framework with an accuracy of 94.2 % as well as a consistency of 96.8\% between the LLM output and model prediction. We create EXPLICATE as a fully usable GUI application and a light Chrome extension, showing its applicability in many deployment situations. The research shows that high detection performance can go hand-in-hand with meaningful explainability in security applications. Most important, it addresses the critical divide between automated AI and user trust in phishing detection systems.

AI Explorations and Their Practical Use in School Environments is an ISTE initiative funded by General Motors. The program provides professional learning opportunities for educators, with the goal of preparing all students for careers with AI. Recently, we spoke with three more participants of the AI Explorations program to learn about its ongoing impact in K-12 classrooms. Here, they share how the program is helping their districts implement AI curriculum with an eye toward equity in the classroom. Monica Rodriguez is a kindergarten teacher with Ector County Independent School District in Odessa, Texas.

Non-convex relaxation methods have been widely used in tensor recovery problems, and compared with convex relaxation methods, can achieve better recovery results. In this paper, a new non-convex function, Minimax Logarithmic Concave Penalty (MLCP) function, is proposed, and some of its intrinsic properties are analyzed, among which it is interesting to find that the Logarithmic function is an upper bound of the MLCP function. The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $L\gamma$-norm. Consider that its explicit solution cannot be obtained when applying it directly to the tensor recovery problem. Therefore, the corresponding equivalence theorems to solve such problem are given, namely, tensor equivalent MLCP theorem and equivalent weighted tensor $L\gamma$-norm theorem. In addition, we propose two EMLCP-based models for classic tensor recovery problems, namely low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA), and design proximal alternate linearization minimization (PALM) algorithms to solve them individually. Furthermore, based on the Kurdyka-{\L}ojasiwicz property, it is proved that the solution sequence of the proposed algorithm has finite length and converges to the critical point globally. Finally, Extensive experiments show that proposed algorithm achieve good results, and it is confirmed that the MLCP function is indeed better than the Logarithmic function in the minimization problem, which is consistent with the analysis of theoretical properties.

Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...

Artificial intelligence, robots, and automation are rising in importance in many areas. As noted in the recent book, "The Future of Work: Robots, AI, and Automation," there are exciting advances in finance, transportation, national defense, smart cities, and health care, among other areas. Businesses are developing solutions that improve the efficiency and effectiveness of their operations and using these tools to improve the way their firms function. Yet there also are concerns about the impact of these developments on jobs and personal privacy. A Pew Research Center national survey revealed considerable unease about emerging trends.

The Lost World: Jurassic Park 1997 AMC Sun. Tomorrow Never Dies 1997 EPIX Wed. 10 p.m., Thur. The X-Files: Fight the Future 1998 IFC Thur. Hard to Kill 1990 Sundance Mon. 8 p.m., Tue. A scientist gives his bodyguard superhuman powers in order to fight racists. A lawyer unwittingly becomes friends with an unstable woman who has a criminal history. A successful businesswoman puts her family, career and life on the line to satisfy her addiction to sex. With his father trapped in the wreckage of their spacecraft, a youth treks across Earth's now-hostile terrain to recover their rescue beacon and signal for help. In the future a cutting-edge android in the form of a boy embarks on a journey to discover his true nature. An 11-year-old boy experiences the worst day of his young life but soon learns that he's not alone when other members of his family encounter their own calamities. A struggling writer falls in love with a stenographer while trying to finish his new novel in 30 days.