Chaganti, R., Vinayakumar, R., Alazab, M., and Pham, T.D. Stegomalware: A Systematic Survey of Malware Hiding and Detection in Images, Machine Learning Models and Research Challenges, Cornell University, October 6, 2021.

Are workers indeed quiet quitting, and if so, where does AI fit into this rising trend? You have almost certainly heard about or seen news reports exclaiming that quiet quitting is here and amongst us all. Yes, indeed, quiet quitting is experiencing its banner headline pronouncements during a seemingly pronounced fifteen minutes of fame. Will the spotlight last longer than a short-lived fad? Will it have endurance and become part of our permanent lexicon? Lots of vital questions abound. I am going to unpack the quiet quitting phenomenon and see what makes the whole matter so notably significant right now. On top of that, I'll introduce a facet that I'm betting most have not realized is getting dragged into the quiet quitting mania. Make sure you are sitting down. The latest dovetailing consideration involves the inclusion of Artificial Intelligence (AI) into the quiet quitting arena. AI is being added to the quiet quitting bandwagon, though not everyone is especially pleased with having AI become inexorably entangled therein. This abundantly raises all sorts of AI Ethics concerns. We will examine how quiet quitting and Ethical AI are going to be at times partners and at other times foes. For my overall ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few.

The EU has identified artificial intelligence (AI) as one of the most relevant technologies of the 21st century and highlighted 1 its importance on the strategy for EU's digital transformation. Having a wide range of applications, AI can contribute in areas as disparate as helping in the treatment of chronic diseases, fighting climate change or anticipating cybersecurity threats.

Arbe Robotics Ltd. (NASDAQ: ARBE) a global leader in Perception Radar Solutions, announced today that HiRain Technologies, the leading Chinese ADAS Tier 1 supplier, was selected by the Port of RiZhao in Shandong Province to provide perception radars based on Arbe's chipset. The deployment has been implemented on FAW Trucks and on automated guided vehicle (AGVs), providing autonomous driving capabilities, advanced perception, and true safety. The first deployment started at the RiZhao port and is expected to expand to additional ports across China. Earlier this year, HiRain announced that it is undertaking major OEM and autonomous driving projects with the Radar Solution it developed using Arbe's Perception Radar Chipset, projected to reach mass production by 2023. According to analyst firm IHS Markit, autonomous trucks will transform the logistics industry, reducing the cost significantly of transporting goods.

Abakar Saidov is co-founder and CEO of Beamery, a leader in talent lifecycle management. In the wake of the "Great Reshuffle," companies continue to reevaluate their approach to recruitment and retention. In order to drive efficiency and remain effective at scale, business leaders are increasingly turning to new technologies for support. One of the most valuable technologies supporting talent management strategies today is artificial intelligence (AI). It has the potential to revolutionize the way in which businesses interact with the wider talent landscape, helping HR teams and recruiters fill much-needed positions and identify the skill sets in most demand.

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force output, small actuation range, and poor compactness. Despite advances in this area, realizing compact soft grippers with high dexterity and force output is still challenging. This paper explores twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand. Tunable stiffness was implemented in the fingers with antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning were experimentally characterized. The gripper achieved a score of 6 on the Kapandji test and recreated 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibited a maximum grasping force of 72 N, which was almost 13 times its own weight. A comparison study revealed that the proposed gripper exhibited equivalent or superior performance compared to other similar soft grippers.

With the rising participation of the common mass in social media, it is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. The caveat here is that only influential people can make such an online polling and reach out at a mass scale. Further, in such cases, the distribution of voters is not controllable and may be, in fact, biased. On the other hand,if we can interpret the publicly available data over social media to probe the political inclination of users, we will be able to have controllable insights about the survey population, keep the cost of survey low and also collect publicly available data without involving the concerned persons. Hence we introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. The advantage of our model is that it neither needs huge training data nor does it need to store social network parameters. Nevertheless, it achieves an accuracy of 93.7\% with no annotated data; further, with only a few annotated examples per class it achieves competitive performance. We found that the model is highly efficient even in resource-constrained settings, and insights drawn from its predictions match the manual survey outcomes when applied to diverse real-life scenarios.

Off-road autonomous unmanned ground vehicles (UGVs) are being developed for military and commercial use to deliver crucial supplies in remote locations, help with mapping and surveillance, and to assist war-fighters in contested environments. Due to complexity of the off-road environments and variability in terrain, lighting conditions, diurnal and seasonal changes, the models used to perceive the environment must handle a lot of input variability. Current datasets used to train perception models for off-road autonomous navigation lack of diversity in seasons, locations, semantic classes, as well as time of day. We test the hypothesis that model trained on a single dataset may not generalize to other off-road navigation datasets and new locations due to the input distribution drift. Additionally, we investigate how to combine multiple datasets to train a semantic segmentation-based environment perception model and we show that training the model to capture uncertainty could improve the model performance by a significant margin. We extend the Masksembles approach for uncertainty quantification to the semantic segmentation task and compare it with Monte Carlo Dropout and standard baselines. Finally, we test the approach against data collected from a UGV platform in a new testing environment. We show that the developed perception model with uncertainty quantification can be feasibly deployed on an UGV to support online perception and navigation tasks.

This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP). With the help of causal language, our framework can place constraints on both the procedure of decision making (procedural fairness) as well as the outcomes resulting from decisions (outcome fairness). Specifically, we show that well-known fairness principles can be encoded either as a utility component, a non-causal component, or a causal component in a SCALES-CMDP. We illustrate SCALES using a set of case studies involving a simulated healthcare scenario and the real-world COMPAS dataset. Experiments demonstrate that our framework produces fair policies that embody alternative fairness principles in single-step and sequential decision-making scenarios.