A Proposed Federal THC Ban Would'Wipe Out' Hemp Products That Get People High The provision, tucked into the spending bill that could end the US government shutdown, would ban intoxicating hemp-derived THC products, including gummies and drinks. A provision in the federal spending bill that could end the US government shutdown would effectively destroy the hemp extracts industry by banning intoxicating hemp-based THC products, including gummies and drinks. The provision, part of the funding bill passed by the US Senate Monday night, would ban the "unregulated sale of intoxicating hemp-based or hemp-derived products, including delta-8, from being sold online, in gas stations, and corner stores," according to a Senate Appropriations Committee summary of the legislation. The bill, accounting for $26.65 billion in funds, is being voted on in the House of Representatives Wednesday. If passed, President Donald Trump is expected to sign it into law.

Existing packer identifiers have significant limitations: signature-based methods lack flexibility and struggle against dynamic evasion, while Machine Learning approaches require extensive training data, limiting scalability and adaptability. Consequently, achieving accurate and adaptable packer identification remains an open problem. This paper presents PackHero, a scalable and efficient methodology for identifying packers using a novel static approach. PackHero employs a Graph Matching Network and clustering to match and group Call Graphs from programs packed with known packers. We evaluate our approach on a public dataset of malware and benign samples packed with various packers, demonstrating its effectiveness and scalability across varying sample sizes. PackHero achieves a macro-average F1-score of 93.7% with just 10 samples per packer, improving to 98.3% with 100 samples. Notably, PackHero requires fewer samples to achieve stable performance compared to other Machine Learning-based tools. Overall, PackHero matches the performance of State-of-the-art signature-based tools, outperforming them in handling Virtualization-based packers such as Themida/Winlicense, with a recall of 100%.

The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The application of packing to the original executable code renders extracting meaningful features and signatures challenging. To deal with the increasing amount of malware in the wild, researchers and anti-malware companies started harnessing machine learning capabilities with very promising results. However, little is known about the effects of packing on static machine learning-based malware detection and classification systems. This work addresses this gap by investigating the impact of packing on the performance of static machine learning-based models used for malware detection and classification, with a particular focus on those using visualisation techniques. To this end, we present a comprehensive analysis of various packing techniques and their effects on the performance of machine learning-based detectors and classifiers. Our findings highlight the limitations of current static detection and classification systems and underscore the need to be proactive to effectively counteract the evolving tactics of malware authors.

Malware developers use combinations of techniques such as compression, encryption, and obfuscation to bypass anti-virus software. Malware with anti-analysis technologies can bypass AI-based anti-virus software and malware analysis tools. Therefore, classifying pack files is one of the big challenges. Problems arise if the malware classifiers learn packers' features, not those of malware. Training the models with unintended erroneous data turn into poisoning attacks, adversarial attacks, and evasion attacks. Therefore, researchers should consider packing to build appropriate malware classifier models. In this paper, we propose a multi-step framework for classifying and identifying packed samples which consists of pseudo-optimal feature selection, machine learning-based classifiers, and packer identification steps. In the first step, we use the CART algorithm and the permutation importance to preselect important 20 features. In the second step, each model learns 20 preselected features for classifying the packed files with the highest performance. As a result, the XGBoost, which learned the features preselected by XGBoost with the permutation importance, showed the highest performance of any other experiment scenarios with an accuracy of 99.67%, an F1-Score of 99.46%, and an area under the curve (AUC) of 99.98%. In the third step, we propose a new approach that can identify packers only for samples classified as Well-Known Packed.

As of 2019, the global warehouse automation market -- that is, programmable machines that pick, sort, and return goods to their shelves, as well as sensor- and AI-based tools that simplify tasks for warehouse workers -- was worth about $15 billion. That number is expected to double within the next four years, with supply chain leaders in an internal Accenture survey citing warehouse automation as one of their top three priorities for digital investment. Clearly, the industry has huge growth potential. But what does this mean for the millions of workers who currently work in warehouses around the world? In the U.S. alone, some 1.5 million workers are employed in the warehouse and storage sector.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Green Bay Packers running back Aaron Jones was seen in a photo over the weekend playing video games in the lobby of the hospital while his girlfriend was in labor with their child. Jones, at first, received scorn on social media. Part of it came from WNBA star Kelsey Plum, who wrote in a since-deleted tweet that the picture was "trash."

Renowned Visual Effects industry veteran Helena Packer, currently marking her 30th anniversary year working within the VFX arena, is currently working to enhance the next era of the visual effects field by developing new tools which will utilize the powerful advancements in digital technologies offered by Artificial Intelligence (AI). Packer explains how her new AI path came into play: In 2018, Raja Koduri, Chief Architect at Intel, approached her, asking if she would like to consult with Intel on the company's research and development of AI. For Packer, it felt like a natural fit, as she has been working to bridge technology with art throughout her entire career. During the past two years, Packer has had the opportunity to collaborate with some of the best minds currently working in AI, including Jason Yang, Co-Founder & CTO at Dgene. Packer, who sits on the Executive Committee of The Academy of Motion Picture Arts and Sciences (AMPAS), and also serves as Chair of the Diversity Committee for AMPAS' VFX Branch, is presently seeking new ways to make content creation easier and more gratifying through the incorporation of AI. "On the professional level, there is, at the moment, a huge surge in content production," Packer says.

A recent Wall Street Journal article by Manhattan Institute writer Oren Cass bears the title quoted above. It echoes futurist Martin Ford's 2015 book title Rise of the Robots, and invites you to consider their overall effects on the future of work. How are you reckoning with the rise of robots in the future of your work? Below are some questions to consider. Over 1,000 participants from 16 countries demonstrated state-of-the-art robotics in competitions such as soccer, rescue and services.

Old school, new school: The Tampa Bay Rays have two pitchers who have started 20 games this year. One is their ace, Blake Snell, whose 2.03 earned-run average ranks second in the American League. The other is Ryne Stanek, a reliever turned “opener” — in his case, a right-hander who works the first inning or so, followed by a left-hander. In a year in which the Rays lost starters Anthony Banda, Jose DeLeon and Brent Honeywell to Tommy John surgery and traded starters Chris Archer and Nathan Eovaldi, the team leads the AL in ERA since May 19, when Sergio Romo debuted as Tampa Bay’s first “opener.” There is no pitching statistic more derided in sabermetrics than wins for a pitcher.

"Attracting Venture Capital for Dummies" is a best seller. The book states on page one, the Venture Capitalists (VCs) goal in life is to find cybersecurity unicorns. Much like a Cyndaquil Pokemon, unicorns have common traits and in order to attract VCs you must exhibit the commonalities of said unicorns. And for bonus points, require lots of data scientists. This is one of the two prerequisites that venture capitalists use to gauge Unicornness.