Modelling communicative behaviour can aid in implementing communication We present a new typology for classifying signals from robots when between humans and robots. We argue that designing communication they communicate with humans. For inspiration, we use ethology, in human-robot interaction (HRI) can benefit from a more the study of animal behaviour and previous efforts from literature structured approach to modelling communicative signals since the as guides in defining the typology. The typology is based on robot's interactive capabilities are human-made. Such a model or communicative signals that consist of five properties: the origin typology can offer consistent and accessible ways to design robot where the signal comes from, the deliberateness of the signal, the interactions and identify and describe communicated content. It signal's reference, the genuineness of the signal, and its clarity (i.e., thereby helps to address the following problems: (a) designing robot how implicit or explicit it is). Using the accompanying worksheet, behaviours that communicate what was intended and (b) examining the typology is straightforward to use to examine communicative (mis-)communication in HRI (and experiments). There have signals from previous human-robot interactions and provides guidance been attempts at creating typologies in the past. Some have borrowed for designers to use the typology when designing new robot ideas from animal communication [19].

This paper develops and analyzes a stochastic derivative-free optimization strategy. A key feature is the state-dependent adaptive variance. We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples. A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing. It can otherwise be compared to annealing with state-dependent temperature.

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, existing B5G ML-security surveys tend to place more emphasis on AI/ML model performance and accuracy than on the models' accountability and trustworthiness. In contrast, this paper explores the potential of Explainable AI (XAI) methods, which would allow B5G stakeholders to inspect intelligent black-box systems used to secure B5G networks. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the ML-based security systems to be transparent and comprehensible to B5G stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.

Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness. This paper advances the self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We propose \textit{Adversarial Self-Attention} mechanism (ASA), which adversarially biases the attentions to effectively suppress the model reliance on features (e.g. specific keywords) and encourage its exploration of broader semantics. We conduct a comprehensive evaluation across a wide range of tasks for both pre-training and fine-tuning stages. For pre-training, ASA unfolds remarkable performance gains compared to naive training for longer steps. For fine-tuning, ASA-empowered models outweigh naive models by a large margin considering both generalization and robustness.

However, many users are familiar with the frustrating experience of repeatedly not being understood by their voice assistant [16], so much so that frustration with ASR has become a culturally-shared source of comedy [4, 32]. Bias auditing of ASR services has quantified these experiences. English language ASR has higher error rates: for Black Americans compared to white Americans [24, 45], for stigmatised British accents compared to favored British accents [28], for Scottish speakers compared to speakers from California and New Zealand [44], for speakers whose first language is a tone language compared to those whose first language is not [2], for speakers with Indian accents compared to speakers who with "American" accents [31], for speakers whose first language is English compared to those for whom it is not [28]. It should go without saying, but everyone has an accent - there is no "unaccented" version of English [26]. Due to colonization and globalization, different Englishes are spoken around the world. While some English accents may be favored by those with class, race, and national origin privilege [28], there is no technical barrier to building an ASR system which works well on any particular accent. So we are left with the question, why does ASR performance vary as it does as a function of the global English accent spoken?

Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives. We validate these results experimentally on a popular high-dimensional classification problem and show that our approach easily scales to very large data sets.

This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition. We give an algorithm with provable approximation guarantees for its reconstruction error via connections to higher-order singular values. Specifically, we introduce a novel Tucker packing problem, which we prove is NP-hard, and give a polynomial-time approximation scheme based on a reduction to the 2-dimensional knapsack problem with a matroid constraint. We also generalize our techniques to tree tensor network decompositions. We implement our algorithm using an integer programming solver, and show that its solution quality is competitive with (and sometimes better than) the greedy algorithm that uses the true Tucker decomposition loss at each step, while also running up to 1000x faster.

To conduct regulatory monitoring within Consumer and High Net Worth on a specific set of regulatory requirements (e.g., PEPS, Sanctions, EDD, FIC Amendment Bill, CTR, Waterfall (KYC), AML Training, Quality Assurance, etc.) as prescribed by the Regulatory Monitoring framework and drives first level of defence remediation of breaches. To provide insights on the state of regulatory adherence within allocated portfolio and prepare appropriate reports as input into overall regulatory reporting.

OUTsurance is a customer-centric financial services company with a global foot print. We are vibrant, successful and values orientated with an awesome dynamic culture encapsulated by the ethos that clients and staff "always get something OUT." Our success can be attributed, amongst other things, to the outstanding people that work for us. An ideal candidate will be able to align their personal work values to the OUTsurance values of Awesome Service, Passionate, Honest, Human, Dynamic and Recognition. In accordance with OUTsurance Insurance Company Ltd Employment Equity goals, preference will be given to individuals who meet the job requirements and are from the various designated groups.

What are the problems that Tradeteq solves for its clients? Tradeteq is a smart technology platform powering global trade investments from end-to-end. Trade finance is arguably the oldest banking product, although also the only one that is not easily accessible for institutional investors. We have the technology to make trade finance investable. For those unfamiliar with trade finance, it may not be immediately obvious why institutional investors should consider putting funds into this asset class, nor indeed why those lending to companies trading goods globally do not simply retain the instruments on their books.