Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. However, previous works often rely on overfitting or robustness features as fingerprints, lacking theoretical guarantees and exhibiting under-performance on generalized models. In this paper, we propose a novel ownership testing method called VeriDIP, which verifies a DNN model's intellectual property. VeriDIP makes two major contributions. (1) It utilizes membership inference attacks to estimate the lower bound of privacy leakage, which reflects the fingerprint of a given model. The privacy leakage fingerprints highlight the unique patterns through which the models memorize sensitive training datasets. (2) We introduce a novel approach using less private samples to enhance the performance of ownership testing. Extensive experimental results confirm that VeriDIP is effective and efficient in validating the ownership of deep learning models trained on both image and tabular datasets. VeriDIP achieves comparable performance to state-of-the-art methods on image datasets while significantly reducing computation and communication costs. Enhanced VeriDIP demonstrates superior verification performance on generalized deep learning models, particularly on table-trained models. Additionally, VeriDIP exhibits similar effectiveness on utility-preserving differentially private models compared to non-differentially private baselines.

Automated detection of defects in Printed Circuit Board (PCB) manufacturing using Solder Paste Inspection (SPI) and Automated Optical Inspection (AOI) machines can help improve operational efficiency and significantly reduce the need for manual intervention. In this paper, using SPI-extracted features of 6 million pins, we demonstrate a data-centric approach to train Machine Learning (ML) models to detect PCB defects at three stages of PCB manufacturing. The 6 million PCB pins correspond to 2 million components that belong to 15,387 PCBs. Using a base extreme gradient boosting (XGBoost) ML model, we iterate on the data pre-processing step to improve detection performance. Combining pin-level SPI features using component and PCB IDs, we developed training instances also at the component and PCB level. This allows the ML model to capture any inter-pin, inter-component, or spatial effects that may not be apparent at the pin level. Models are trained at the pin, component, and PCB levels, and the detection results from the different models are combined to identify defective components.

AI is increasingly being used by lawyers, but chatbots still don't do that well at everyday legal tasks Compared to other AI chatbots, GPT-4 performs best on a test of legal reasoning – but it still falls short of the knowledge required for human lawyers. Early attempts to use AI chatbots in courtrooms have sometimes proven disastrous, and this finding adds to evidence that AI isn't ready to handle the complexities of real-world legal arguments.

The attorneys general from all 50 states have banned together and sent an open letter to Congress, asking for increased protective measures against AI-enhanced child sexual abuse images, as originally reported by AP. The letter calls on lawmakers to "establish an expert commission to study the means and methods of AI that can be used to exploit children specifically." The letter sent to Republican and Democratic leaders of the House and Senate also urges politicians to expand existing restrictions on child sexual abuse materials to specifically cover AI-generated images and videos. This technology is extremely new and, as such, there's nothing on the books yet that explicitly places AI-generated images in the same category as other types of child sexual abuse materials. "We are engaged in a race against time to protect the children of our country from the dangers of AI," the prosecutors wrote in the letter.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The top prosecutors in all 50 states are urging Congress to study how artificial intelligence can be used to exploit children through pornography, and come up with legislation to further guard against it. In a letter sent Tuesday to Republican and Democratic leaders of the House and Senate, the attorneys general from across the country call on federal lawmakers to "establish an expert commission to study the means and methods of AI that can be used to exploit children specifically" and expand existing restrictions on child sexual abuse materials specifically to cover AI-generated images. "We are engaged in a race against time to protect the children of our country from the dangers of AI," the prosecutors wrote in the letter, shared ahead of time with The Associated Press.

Haywood Talcove, CEO of LexisNexis Risk Solutions' Government Group, tells Fox News Digital that criminal groups, mostly in other countries, are advertising on social media to market their AI capabilities for fraud and other crimes. A criminologist recently unearthed a video of a multibillion-dollar, transnational criminal organization that has been stealing from the U.S. government since the pandemic and selling generative artificial intelligence tools to other criminals, an expert says. The 58-second clip, which was meant for the dark web, opens with a person – who goes by "Sanchez" – covered head to toe in black clothing and speaking behind a black skeleton mask with someone else who appears to be digging a grave behind him. "Yes, I sell Chase bank accounts. Yes, I am one of the first people to sell fake bank accounts four years ago," the man who calls himself "Sanchez" said.

Inspired by the vertebrate branch of the animal kingdom, articulated soft robots are robotic systems embedding elastic elements into a classic rigid (skeleton-like) structure. Leveraging on their bodies elasticity, soft robots promise to push their limits far beyond the barriers that affect their rigid counterparts. However, existing control strategies aiming at achieving this goal are either tailored on specific examples, or rely on model cancellations -- thus defeating the purpose of introducing elasticity in the first place. In a series of recent works, we proposed to implement efficient oscillatory motions in robots subject to a potential field, aimed at solving these issues. A main component of this theory are Eigenmanifolds, that we defined as nonlinear continuations of the classic linear eigenspaces. When the soft robot is initialized on one of these manifolds, it evolves autonomously while presenting regular -- and thus practically useful -- evolutions, called normal modes. In addition to that, we proposed a control strategy making modal manifolds attractors for the system, and acting on the total energy of the soft robot to move from a modal evolution to the other. In this way, a large class of autonomous behaviors can be excited, which are direct expression of the embodied intelligence of the soft robot. Despite the fact that the idea behind our work comes from physical intuition and preliminary experimental validations, the formulation that we have provided so far is however rather theoretical, and very much in need of an experimental validation. The aim of this paper is to provide such an experimental validation using as testbed the articulated soft leg. We will introduce a simplified control strategy, and we will test its effectiveness on this system, to implement swing-like oscillations. We plan to extend this validation with a soft quadruped.

Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at the edge while empowering end-users with greater control of their data. As these ML sensors play a crucial role in the development of intelligent devices, clear documentation of their specifications, functionalities, and limitations is pivotal. This paper introduces a standard datasheet template for ML sensors and discusses its essential components including: the system's hardware, ML model and dataset attributes, end-to-end performance metrics, and environmental impact. We provide an example datasheet for our own ML sensor and discuss each section in detail. We highlight how these datasheets can facilitate better understanding and utilization of sensor data in ML applications, and we provide objective measures upon which system performance can be evaluated and compared. Together, ML sensors and their datasheets provide greater privacy, security, transparency, explainability, auditability, and user-friendliness for ML-enabled embedded systems. We conclude by emphasizing the need for standardization of datasheets across the broader ML community to ensure the responsible and effective use of sensor data.

Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce.

Internet of Things (IoT) sensors are nowadays heavily utilized in various real-world applications ranging from wearables to smart buildings passing by agrotechnology and health monitoring. With the huge amounts of data generated by these tiny devices, Deep Learning (DL) models have been extensively used to enhance them with intelligent processing. However, with the urge for smaller and more accurate devices, DL models became too heavy to deploy. It is thus necessary to incorporate the hardware's limited resources in the design process. Therefore, inspired by the human brain known for its efficiency and low power consumption, we propose a shallow bidirectional network based on predictive coding theory and dynamic early exiting for halting further computations when a performance threshold is surpassed. We achieve comparable accuracy to VGG-16 in image classification on CIFAR-10 with fewer parameters and less computational complexity.