When you purchase through links in our articles, we may earn a small commission. Desktop scanners are expensive and pretty limited in what they can do. Even scanning the pages of a book is almost impossible to do cleanly on most scanners. If you want a simpler alternative, SwiftScan is a new scanner app that works for iOS and Android, and it's only $47.97 (reg. SwiftScan is a whole lot easier to use than most desktop scanners.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series AI companies keep destroying old books. There's a backlash against book destruction -- and a better way to scan them. Chris is a veteran tech, entertainment and culture journalist, author of'How Star Wars Conquered the Universe,' and co-host of the Doctor Who podcast'Pull to Open.' Hailing from the U.K., Chris got his start as a sub editor on national newspapers. He moved to the U.S. in 1996, and became senior news writer for Time.com a year later. In 2000, he was named San Francisco bureau chief for Time magazine.

Midjourney announces a full-body ultrasound scanner that uses water and sound waves to create body composition maps in about 60 seconds with no radiation or MRI magnets.

Recently, Model-based Reinforcement Learning (MBRL) methods have demonstrated stunning sample efficiency in various RL domains.

Most scanning apps try to get you to buy a cloud storage subscription or pay for extras. Not FairScan, which is free and open-source, and has some powerful features. If you're interested in going paperless, you probably think you need a scanner. It's true that hardware scanners make turning multipage documents into PDFs very simple. But most of us don't have easy access to a scanner.

The significant heterogeneity poses higher requirements foralgorithm robustness.

AI coding is now everywhere. But not everyone is convinced. Depending who you ask, AI-powered coding is either giving software developers an unprecedented productivity boost or churning out masses of poorly designed code that saps their attention and sets software projects up for serious long term-maintenance problems. The problem is right now, it's not easy to know which is true. As tech giants pour billions into large language models (LLMs), coding has been touted as the technology's killer app. Executives enamored with the potential are pushing engineers to lean into an AI-powered future.

Has Your Pledge to Go Paperless Perished? You promised yourself you'd digitize every last receipt, document, and paper record. But the trick to getting rid of paper is to not worry about being perfect. Wanting to get rid of paper in your life is easy. Following through with that promise to yourself is hard.

Abstract--We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. T o ensure accurate malware classification, we employ the VirusT otal API, integrating multiple detection engines for comprehensive and reliable assessment. Our GitHub, Figshare, and Harvard Dataverse repositories provide open access to the processed dataset and its extensive supplementary metadata, totaling more than 400 GB of data and including the outputs of the feature extraction pipeline as well as the corresponding VirusT otal reports. Our findings underscore the MH-1M dataset's invaluable role in understanding the evolving landscape of malware. The pervasive spread of Android malware poses a significant challenge for cybersecurity research. This challenge stems mainly from the open-source nature and affordability of Android platforms, which grant users access to a large market of free applications. At the same time, malware continually evolves, adapting its tactics to execute more sophisticated and frequent attacks. Such attacks often result in data destruction, information theft, and several other cybercrimes [1], [2], [3]. Machine learning (ML) algorithms have been widely used to uncover malware and have demonstrated remarkable effectiveness in detection systems, leveraging their discriminative capabilities to identify new variants of malicious applications [4], [5], [6]. To mitigate these risks, researchers have developed a variety of methods for detecting Android malware, establishing machine learning as a central focus of contemporary mobile security research [7], [8], [9]. However, the effectiveness of ML models is highly dependent on the quality of the datasets used for training. Many existing datasets suffer from limitations such as outdated data, inadequate representation, and a limited number of samples and features, making them unsuitable for modern malware detection [10], [2], [11], [12]. These issues raise concerns about the reliability of reported performance metrics and can potentially lead to misleading conclusions [2]. A growing body of research in Android malware detection strongly supports the notion that increasing the number of discriminative features can significantly improve classification performance [13], [14], [15]. We present in Table I an overview of widely used Android malware datasets from recent years.