The dogfight hardly seemed fair. Two F-16s engaged with an opposing F-16 at an altitude of 16,000 feet above rocky desert terrain. As the aircraft converged from opposite directions, the paired F-16s suddenly spun away from one another, forcing their foe to choose one to pursue. The F-16 that had been left alone then quickly changed course, maneuvering behind the enemy with textbook precision. A few seconds later, it launched a missile that destroyed the opposing jet before it could react.

ShareThis week, we will briefly touch upon the topic of artificial intelligence, or AI, discuss what it is, why it is so important to the American empire and national security, and how the ancient Chamorro people of the Marianas can prepare themselves to be ready for future possible job and entrepreneurial opportunities at the intersection of warfare and technology.  What is AI? Star Wars gone wild?  AI is a constellation or universe of technologies, computer hardware and software, designed to solve specific tasks that reflect and are intended to resemble human cognitive processes to include decision making, reasoning, learning, and perceiving.  AI has applications that reprogram itself to complete specific tasks called machine learning. Within the machine learning space, there are applications that take information to produce outcomes with increasing accuracy. This technology can be interpreted as an evolving manufactured ecosystem that attempts to some degree to self-develop and monitor without human intervention.  This is both potentially dangerous and beneficial stuff.  Why AI is important to the United States and our ancient Chamorro people Part of the answer is found in a report released by the congressionally established National Security Commission on Artificial Intelligence, which outlined the national importance of AI and its application to all facets of American—and by implication, American colonial—society. The commission was established to assess how AI affects imperial competitiveness and technological advantage.  AI is important because it has civilian and military applications that are used every day by every citizen, whether one resides on Saipan, Rota, in New York, Birmingham or elsewhere. If you are looking for a movie to watch or check out what time a restaurant or bar opens, or get vehicle maintenance service, or asking your iPhone a question, AI will be a part of the enabling infrastructure that will get you the answers to your questions.  AI is now found everywhere and is used knowingly or unknowingly, again posing benefits and concerns to society.  If Guam moves toward building a new hospital, AI will be an integral and ubiquitous part of how healthcare decisions are informed, how biotechnology is implemented, and how personal medical information and data are managed, distributed, protected, and delivered. AI will become more important as Guam continues to move toward technological solutions for future energy, food technology and security opportunities. Folks in the NMI will see a greater role of AI as the Commonwealth moves toward electric and eventually unmanned cars. Cyber-attacks against the United States government and other facets of society occur every day. AI was the underbelly used to enable computer networks to find vulnerabilities recently when the government of Guam experienced a cyber-attack. These attacks may come in the form of data harvesting, targeted attacks on individual citizens, or AI enabled attacks on social media, intended to influence or harm the targets.   The American national government is contemplating ways to aggressively deal with data protection, privacy, and security. Congress remains behind the curve on how to craft legislation to address ongoing technological change. Others worry that domestic data surveillance is out of control and has already compromised our privacy and protection.  Why AI is important to the governments of Guam and the CNMI Now is the time for the governments of Guam and the CNMI to consider creating a Marianas Artificial Intelligence, Security and Emerging Technologies Understanding advisory board to learn and more completely seek to comprehend the nature of AI, how it is currently used and how it presents opportunities and vulnerabilities to every aspect of Pacific island life. There is no reason why Guam and CNMI legislative committees overseeing technology cannot hold initial hearings to discuss AI.  Future educational and career opportunities: Pay attention Guam Community College is doing some work with its resources available to students in math and science tutoring and management information systems programming. The University of Guam has resources that can or will eventually be able to contribute toward providing AI jobs for young islanders who have math, engineering, nursing, education, biology, and Chamorro studies backgrounds.  Guam institutions have opportunities to further partner with the Guam Department of Education, and the CNMI Public School System on the implications of AI. Marianas educational institutions would be well served to contemplate the establishment of emerging technology certificate programs to prepare Pacific Islanders for much needed and well-paid technology jobs. All Marianas institutions can also create opportunities to seek partnerships with major American companies and entities in Silicon Valley and elsewhere, intended to create AI related jobs and training.  Lawmakers from Guam and the CNMI as well as the governors can contemplate creating national level digital annex risk management programs for all school age kids interested in future technical challenges that will provide good paying jobs while helping to secure America and its colonies. Science and technology partnerships with Taiwan and South Korea may also be something that can be operationalized for mutual benefit.  The AI race is on, security threats increase A most dangerous aspect of rapidly emerging AI enabled technology and networks is that nation state adversaries such as China and Russia may outpace the United States on this front over the next 10 to 15 years. This presents and will present fundamental risks and threats to the existing militarized resource base currently embedded and/or connected to Guam and the CNMI that may include risks tied to autonomous unmanned weapon system warfare and real future risks associated with crisis stability and human authorized employment of very dangerous nuclear weapons and networks through unmanned platforms.  AI threatens to pose real challenges to traditional process identification and validation issues related to military targeting matters, creating risks to real-time battle assessment decisions that will need to be made in the future.   Now is the time for our Chamorro Pacific Islander civilization to continue the broad conversation on issues of the military and national and colonial security to demystify and assess the importance of AI and what it means to all our families and friends in the 21st century. 

Machine learning techniques play a critical role in detecting serious threats in the network. A good dataset helps create robust machine learning systems to address various network security problems, malware attacks, phishing, and host intrusion. For instance, the real-world cybersecurity datasets will help you work in projects like network intrusion detection system, network packet inspection system, etc, using machine learning models. Here is a list of the 8 top cybersecurity datasets you can use for your next machine learning project. About: The ADFA Intrusion Detection Datasets are designed for the evaluation by system call based HIDS.

It is no secret that technology has been transforming the way we work. Every day, new tools and applications appear to automate processes, making them more agile and precise. In the area of finance, especially in the tax sector, the use of Artificial Intelligence has become increasingly fundamental to eliminate manual errors, increase productivity and make the business more strategic – mainly in the UK – where 1,958 hours are spent per year to meet all tax obligations, according to the World Bank. According to the survey conducted by Thomson Reuters in partnership with Live University, 56% of British companies intend to use Artificial Intelligence to optimize tax management. When the debate is about which technology is more functional for the sector, 61% of professionals point to Machine Learning as the innovation most capable of benefiting the segment; 31% bet on Data Science, and 10% prefer chatbots. The fact is that these combined technologies should revolutionize the finance area, as has already been happening with banks, in addition to other sectors such as e-commerce and general service, due to their high precision in collecting and data analysis, problem-solving, and responsiveness.

When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit -- and blew the future of privacy in America wide open. In May 2019, an agent at the Department of Homeland Security received a trove of unsettling images. Found by Yahoo in a Syrian user's account, the photos seemed to document the sexual abuse of a young girl. One showed a man with his head reclined on a pillow, gazing directly at the camera. The man appeared to be white, with brown hair and a goatee, but it was hard to really make him out; the photo was grainy, the angle a bit oblique. The agent sent the man's face to child-crime investigators around the country in the hope that someone might recognize him. When an investigator in New York saw the request, she ran the face through an unusual new facial-recognition app she had just started using, called Clearview AI. The team behind it had scraped the public web -- social media, employment sites, YouTube, Venmo -- to create a database with three billion images of people, along with links to the webpages from which the photos had come. This dwarfed the databases of other such products for law enforcement, which drew only on official photography like mug shots, driver's licenses and passport pictures; with Clearview, it was effortless to go from a face to a Facebook account. The app turned up an odd hit: an Instagram photo of a heavily muscled Asian man and a female fitness model, posing on a red carpet at a bodybuilding expo in Las Vegas. The suspect was neither Asian nor a woman. But upon closer inspection, you could see a white man in the background, at the edge of the photo's frame, standing behind the counter of a booth for a workout-supplements company. On Instagram, his face would appear about half as big as your fingernail. The federal agent was astounded. The agent contacted the supplements company and obtained the booth worker's name: Andres Rafael Viola, who turned out to be an Argentine citizen living in Las Vegas.

Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, $L_2$ regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions -- namely via smooth $L_1$ and Huber regularization penalties. We apply these loss terms to state-of-the-art DDP-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub.

Mapping of spatial hotspots, i.e., regions with significantly higher rates or probability density of generating certain events (e.g., disease or crime cases), is a important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering have been extensively studied by the data mining and statistics communities. In this survey we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy of the clustering process with statistical rigor, covering key steps of data and statistical modeling, region enumeration and maximization, significance testing, and data update. We further discuss different paradigms and methods within each of key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.

Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversarial attacks are threatening the functionality of GNNs. Although numerous works have been proposed to defend adversarial attacks from various perspectives, most of them can be robust against the attacks only on specific scenarios. To address this shortage of robust generalization, we propose to defend the adversarial attacks on GNN through applying the Spatio-Temporal sparsification (called ST-Sparse) on the GNN hidden node representation. ST-Sparse is similar to the Dropout regularization in spirit. Through intensive experiment evaluation with GCN as the target GNN model, we identify the benefits of ST-Sparse as follows: (1) ST-Sparse shows the defense performance improvement in most cases, as it can effectively increase the robust accuracy by up to 6\% improvement; (2) ST-Sparse illustrates its robust generalization capability by integrating with the existing defense methods, similar to the integration of Dropout into various deep learning models as a standard regularization technique; (3) ST-Sparse also shows its ordinary generalization capability on clean datasets, in that ST-SparseGCN (the integration of ST-Sparse and the original GCN) even outperform the original GCN, while the other three representative defense methods are inferior to the original GCN.

We ask the following question: what training information is required to design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting samples that lie far away from the training distribution? Since unlabeled data is easily accessible for many applications, the most compelling approach is to develop detectors based on only unlabeled in-distribution data. However, we observe that most existing detectors based on unlabeled data perform poorly, often equivalent to a random prediction. In contrast, existing state-of-the-art OOD detectors achieve impressive performance but require access to fine-grained data labels for supervised training. We propose SSD, an outlier detector based on only unlabeled in-distribution data. We use self-supervised representation learning followed by a Mahalanobis distance based detection in the feature space. We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin. Additionally, SSD even achieves performance on par, and sometimes even better, with supervised training based detectors. Finally, we expand our detection framework with two key extensions. First, we formulate few-shot OOD detection, in which the detector has access to only one to five samples from each class of the targeted OOD dataset. Second, we extend our framework to incorporate training data labels, if available. We find that our novel detection framework based on SSD displays enhanced performance with these extensions, and achieves state-of-the-art performance. Our code is publicly available at https://github.com/inspire-group/SSD.

Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible ADM.