The novel coronavirus (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) after it was first discovered in Wuhan, China [1]. Over one year, the virus has infected more than 68 million people worldwide [2]. The virus can be fatal for elderly people or ones with chronic diseases [3]. Different countries across the globe have imposed several social practices and strategies to reduce the spread of the infection and to ensure the well-being of the residents. These practices and strategies include but are not limited to social distancing, restricted and authorized travels, remote work and education, reduced working staff in organizations, and frequent COVID-19 tests. These measures have been proved potential in reducing the disease spread and death in the previous pandemics [3], [4]. Several studies have focused on machine learning time series models to forecast the number of COVID-19 infections in different countries [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. This is to aid the government in designing and regulating efficient virus spread-mitigating strategies and to enable healthcare organizations for effective planning of health personnel and facilities resources. Based on the forecasted infections, the government can either make the confinement laws stricter or can ease them.

Every novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in automated testing tools. Therefore, this systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods.

Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties as they arise in real-world scenarios. The resulting Risk Spot Detector (RSD) is applied and tested on naturalistic driving data of a multi-lane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, while existing methods directly encourage the diversity of learned agent networks with various strategies. However, we argue that these dedicated designs for agent networks are still limited by the indistinguishable VD network, leading to homogeneous agent behaviors and thus downgrading the cooperation capability. In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. Specifically, our approach leverages contrastive learning to maximize the mutual information between the temporal credits and identity representations of different agents, encouraging the full expressiveness of credit assignment and further the emergence of individualities. The algorithm implementation of the proposed CIA module is simple yet effective that can be readily incorporated into various VD architectures. Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/CIA.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide, usually associated with smoking and environmental occupational exposures. Prior studies have shown that current COPD diagnosis (i.e., spirometry test) can be unreliable because the test can be difficult to do and depends on an adequate effort from the testee and supervision of the testor. Moreover, the extensive early detection and diagnosis of COPD is challenging. We address the COPD detection problem by constructing two novel COPD physiological signals datasets (4432 medical records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset), demonstrating their complex coupled fractal dynamical characteristics, and performing a rigorous fractional-order dynamics deep learning analysis to diagnose COPD with high accuracy. We find that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages--from stage 0 (healthy) to stage 4 (very severe). We exploit these fractional signatures to develop and train a deep neural network that predicts the suspected patients' COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation levels). We show that our COPD diagnostics method (fractional dynamic deep learning model) achieves a high prediction accuracy (98.66% 0.45%) on WestRo COPD dataset and can serve as an excellent and robust alternative to traditional spirometry-based medical diagnosis. Our fractional dynamic deep learning model (FDDLM) for COPD diagnosis also presents high prediction accuracy when validated by a dataset with different physiological signals recorded (i.e., 94.01%

Spacecraft faces various situations when carrying out exploration missions in complex space, thus monitoring the anomaly status of spacecraft is crucial to the development of \textcolor{blue}{the} aerospace industry. The time series telemetry data generated by on-orbit spacecraft \textcolor{blue}{contains} important information about the status of spacecraft. However, traditional domain knowledge-based spacecraft anomaly detection methods are not effective due to high dimensionality and complex correlation among variables. In this work, we propose an anomaly detection framework for spacecraft multivariate time-series data based on temporal convolution networks (TCNs). First, we employ dynamic graph attention to model the complex correlation among variables and time series. Second, temporal convolution networks with parallel processing ability are used to extract multidimensional \textcolor{blue}{features} for \textcolor{blue}{the} downstream prediction task. Finally, many potential anomalies are detected by the best threshold. Experiments on real NASA SMAP/MSL spacecraft datasets show the superiority of our proposed model with respect to state-of-the-art methods.

Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguation benchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark named GLADIS with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences; (3) three datasets that cover the general, scientific, and biomedical domains. We then pre-train a language model, \emph{AcroBERT}, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.

Nowadays, numerous applications incorporate machine learning (ML) algorithms due to their prominent achievements. However, many studies in the field of computer vision have shown that ML can be fooled by intentionally crafted instances, called adversarial examples. These adversarial examples take advantage of the intrinsic vulnerability of ML models. Recent research raises many concerns in the cybersecurity field. An increasing number of researchers are studying the feasibility of such attacks on security systems based on ML algorithms, such as Intrusion Detection Systems (IDS). The feasibility of such adversarial attacks would be influenced by various domain-specific constraints. This can potentially increase the difficulty of crafting adversarial examples. Despite the considerable amount of research that has been done in this area, much of it focuses on showing that it is possible to fool a model using features extracted from the raw data but does not address the practical side, i.e., the reverse transformation from theory to practice. For this reason, we propose a review browsing through various important papers to provide a comprehensive analysis. Our analysis highlights some challenges that have not been addressed in the reviewed papers.

The global battle for AI dominance is underway, according to author Paul Scharre, a former Army Ranger and current VP and director of studies at the Center for New American Security -- a think tank specializing in national security issues. Scharre previously served as a strategic planner at the Office of the Secretary of Defense, working to establish policies on unmanned and autonomous systems and emerging weapons technologies, and established DOD policies on intelligence, surveillance, and reconnaissance programs. In his latest book, "Four Battlegrounds: Power in the Age of Artificial Intelligence," Scharre explores how the international battle for the most powerful AI technology is changing global power dynamics. That battle, he says, is a global competition to seek the best and most efficient data, computing hardware, human talent, and institutions adopting AI technology -- which will determine the next global superpower. In your new book, you argue there's a battle for global power going on in the form of a revolution brought about by artificial intelligence.

"What's different is that everybody can do it now," said Britt Paris, an assistant professor of library and information science at Rutgers University who helped coin the term "cheapfakes." Reams of manipulated content have circulated on TikTok and elsewhere for years, typically using more homespun tricks like careful editing or the swapping of one audio clip for another. In one video on TikTok, Vice President Kamala Harris appeared to say everyone hospitalized for Covid-19 was vaccinated. In fact, she said the patients were unvaccinated. Graphika, a research firm that studies disinformation, spotted deepfakes of fictional news anchors that pro-China bot accounts distributed late last year, in the first known example of the technology's being used for state-aligned influence campaigns.