Causal discovery for quantitative data has been extensively studied but less is known for categorical data. We propose a novel causal model for categorical data based on a new classification model, termed classification with optimal label permutation (COLP). By design, COLP is a parsimonious classifier, which gives rise to a provably identifiable causal model. A simple learning algorithm via comparing likelihood functions of causal and anti-causal models suffices to learn the causal direction. Through experiments with synthetic and real data, we demonstrate the favorable performance of the proposed COLP-based causal model compared to state-of-the-art methods. We also make available an accompanying R package COLP, which contains the proposed causal discovery algorithm and a benchmark dataset of categorical cause-effect pairs.

To minimize collision risks in the multi-agent path planning problem with stochastic transition dynamics, we formulate a Markov decision process congestion game with a multi-linear congestion cost. Players within the game complete individual tasks while minimizing their own collision risks. We show that the set of Nash equilibria coincides with the first-order KKT points of a non-convex optimization problem. Our game is applied to a historical flight plan over France to reduce collision risks between commercial aircraft.

WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly-captured CSI samples can be easily collected. {\color{black}In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient WiFi sensing model based on a novel geometric self-supervised learning algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement.

In this paper, we focus on the problem of feature learning in the presence of scale imbalance for 6-DoF grasp detection and propose a novel approach to especially address the difficulty in dealing with small-scale samples. A Multi-scale Cylinder Grouping (MsCG) module is presented to enhance local geometry representation by combining multi-scale cylinder features and global context. Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the samples whose scales are in low frequency by apriori weights while OBS captures more points on small-scale objects with the help of an auxiliary segmentation network. They alleviate the influence of the uneven distribution of grasp scales in training and inference respectively. In addition, Noisy-clean Mix (NcM) data augmentation is introduced to facilitate training, aiming to bridge the domain gap between synthetic and raw scenes in an efficient way by generating more data which mix them into single ones at instance-level. Extensive experiments are conducted on the GraspNet-1Billion benchmark and competitive results are reached with significant gains on small-scale cases. Besides, the performance of real-world grasping highlights its generalization ability. Our code is available at https://github.com/mahaoxiang822/Scale-Balanced-Grasp.

Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert users` cyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in non-expert users` tweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert users` perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.

Alex Svinov is the CEO and Co-founder of Insquad, the platform to build remote development teams. He believes that the future of work is in remote teams – and this notion will radically change the world as it will bring opportunity and talent closer to each other. Alex launched Insquad after facing challenges in hiring senior tech talent for his previous startup. He tried staffing services, but they were expensive and did not give a lot of value to him as a startup. So he decided to solve this problem and help the startup community as well as offer great opportunities to the talent in underprivileged countries. Alex is a serial entrepreneur and angel investor -- 10 investments in IT companies all over the world -- Forbes council member and Alchemist mentor. In the past 10 years, he has created several successful startups in industry areas that he had no experience in before -- FinTech, outsourcing, HRTech, and food service. He is passionate about making new tech products and services and helping distributed teams achieve their goals. Alex met with Bill Clinton and Queen Elizabeth in Moscow's high school. Outside of work, he's a father of 3, plays tennis and regularly participates in amateur tournaments. Today I have with me, Alex Svinov. Now, Alex is the CEO and Co-founder of Insquad, which is a platform to build remote development teams and as the world basically circulates around technology these days, it's very very important to get a great development team and remote now as we know with the pandemic has produced change the way we work. He believes that the future work is in remote teams and this notion will radically change the world as well bring opportunity and talent closer to each other. Alex launched Insquad one year ago because he faced challenges hiring senior tech talents for his previous startup. He tried staffing services but they were expensive and did not give a lot of value to him as a startup.

The University of Adelaide and MTX Group have entered into a research collaboration to develop new insights in machine learning (ML) and artificial intelligence (AI). Bringing together their academic research and commercial expertise and experience, the two organisations will undertake specific, outcomes-focussed research. They will use AI to model uncertainty with a view to avoiding failure within systems that may be used in defence and business environments. The University and MTX Group have jointly been awarded $100,000 under the Artificial Intelligence for Decision Making Initiative which is a collaborative project between the Australian Government's Office of National Intelligence (ONI) and the Defence Science and Technology Group (DST). Dr Duong Nguyen and Dr George Stamatescu from the University's School of Computer Science will work alongside Dr Ammar Mohemmed from MTX Group.

The graph represents a network of 1,719 Twitter users whose tweets in the requested range contained "iot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 23 November 2022 at 12:43 UTC. The requested start date was Wednesday, 23 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 19-day, 11-hour, 51-minute period from Thursday, 03 November 2022 at 13:08 UTC to Wednesday, 23 November 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

On September 29, 2021, the United States and the European Union's (EU) new Trade and Technology Council (TTC) held their first summit. It took place in the old industrial city of Pittsburgh, Pennsylvania, under the leadership of the European Commission's Vice-President, Margrethe Vestager, and U.S. Secretary of State Antony Blinken. Following the meeting, the U.S. and the EU declared their opposition to artificial intelligence (AI) that does not respect human rights and referenced rights-infringing systems, such as social scoring systems.1 During the meeting, the TTC clarified that "The United States and European Union have significant concerns that authoritarian governments are piloting social scoring systems with an aim to implement social control at scale. These systems pose threats to fundamental freedoms and the rule of law, including through silencing speech, punishing peaceful assembly and other expressive activities, and reinforcing arbitrary or unlawful surveillance systems."2 The implicit target of the criticism was China's "social credit" system, a big data system that uses a wide variety of data inputs to assess a person's social credit score, which determines social permissions in society, such as buying an air or train ticket.3 The critique by the TTC indicates that the U.S. and the EU disagree with China's view of how authorities should manage the use of AI and data in society.4

Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%.