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%.

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across industries. Yet, little attention has been given to compare the effectiveness and efficiency of anomaly detectors for streaming data (i.e., of online algorithms). In this paper, we present a qualitative, synthetic overview of major online detectors from different algorithmic families (i.e., distance, density, tree or projection-based) and highlight their main ideas for constructing, updating and testing detection models. Then, we provide a thorough analysis of the results of a quantitative experimental evaluation of online detection algorithms along with their offline counterparts. The behavior of the detectors is correlated with the characteristics of different datasets (i.e., meta-features), thereby providing a meta-level analysis of their performance. Our study addresses several missing insights from the literature such as (a) how reliable are detectors against a random classifier and what dataset characteristics make them perform randomly; (b) to what extent online detectors approximate the performance of offline counterparts; (c) which sketch strategy and update primitives of detectors are best to detect anomalies visible only within a feature subspace of a dataset; (d) what are the tradeoffs between the effectiveness and the efficiency of detectors belonging to different algorithmic families; (e) which specific characteristics of datasets yield an online algorithm to outperform all others.

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facial area, leading to non-realistic results. In this work, we delve into the formulation of altering only the mouth shapes of the target person. This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames. To this end, we propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality by predicting the masked mouth shapes. Our key insight is to exploit desired contextual information provided in audio and visual modalities thoroughly with delicately designed Transformers. Specifically, we propose a convolution-Transformer hybrid backbone and design an attention-based fusion strategy for filling the masked parts. It uniformly attends to the textural information on the unmasked regions and the reference frame. Then the semantic audio information is involved in enhancing the self-attention computation. Additionally, a refinement network with audio injection improves both image and lip-sync quality. Extensive experiments validate that our model can generate high-fidelity lip-synced results for arbitrary subjects.

Process mining is a family of techniques that support the analysis of operational processes, in terms of key performance indicators, using event data Van Der Aalst (2012). Process mining can be used in number of ways, such as in identifying insights into current processes or in identifying actions or places within workflows where interventions should be made to improve performance. Although processing mining is typically used in the context of commercial business environments, there is crossover to other areas where processes play an important role, such as in water quality management processes administered by local government authorities or citizen science projects that use the Business Process Model and Notation (BPMN) Higgins, Williams, Leibovici, Simonis, Davis, Muldoon, van Genuchten, O'Hare and Wiemann (2016). In the case of water quality management, traditional event log data from information technology systems is often lacking in that many tasks, such as the manual sampling of water and the microbial culturing by biologists and laboratory technicians to identify faecal coliforms, are not performed using computers and are not logged. Nevertheless, it is likely that techniques developed to aid explainability and in the evaluation of machine learning algorithms in such cases will prove using in traditional process mining systems where similar problems must be addressed. This paper focuses on mining suitable features to perform inference for the level of bacteria, and specifically Enterococci and Escherichia coli (E.