Bengaluru: Global professional services company Accenture will acquire umlaut, an engineering consulting and services firm headquartered in Aachen, Germany for an undisclosed amount. The acquisition will scale Accenture's deep engineering capabilities to help companies use digital technologies like cloud, artificial intelligence, and 5G to transform how they design, engineer and manufacture their products as well as embed sustainability. The acquisition of umlaut will add more than 4,200 industry-leading engineers and consultants across 17 countries to Accenture's Industry X services, and expand the company's capabilities across a range of industries, including automotive, aerospace & defense, telecommunications, energy and utilities, Accenture said in a statement. Industry X combines Accenture's powerful data and digital capabilities with deep engineering expertise to offer clients the broadest suite of services for digitizing their engineering functions, factory floors and plant operations, improving productivity, speeding up the transformation of hardware into software-enabled products, and allowing for faster and more flexible product development. "We predicted that digital would ultimately be applied at scale to the core of a company's business - the design, engineering and manufacturing of their products. And, for nearly a decade Accenture has been building the unique capabilities and ecosystem partnerships to combine the power of digital with traditional engineering services," said Julie Sweet, chief executive officer, Accenture.

Last week, Google dropped a set of updates coming to Pixel phones and it's following up that news today with features heading to the broader Android ecosystem. This lot of new features will arrive mostly today or this week, and they span things like Messages, Emoji, Assistant, Android Auto and Voice Access. Of these, the one that will probably be the most useful in our day-to-day interactions with our phones is the new ability to star content in the Messages app. This is like pinning a message in Slack or Telegram -- after you tap and hold a message and star it, you can easily find it later by going to the Starred section in the app. Google said starred messages "will start to roll out more broadly over the coming weeks."

The demand for data science professionals was huge in 2020, and it is only growing in 2021. While industry giants have embraced the capabilities of data science, small and medium-sized businesses have started to leverage the power of data science to improve their business processes. Owing to digital transformation, data science practices help businesses make intelligent and data-backed smart decisions, formulate strategies to make better products and solutions, manage large amounts of data efficiently to get valuable insights and predict outcomes. To utilize most of these features to increase ROI, organizations are rapidly hiring data science professionals. If you are a data scientist or analyst, check out these hottest job openings from companies around the globe and look for your next challenge.

Brussels – NATO leaders warned Monday that China's military ambitions pose "systemic challenges" to their alliance, and agreed to enhance ties with Japan and other Asia-Pacific nations to back the rules-based international order. The tough line against Beijing, taken in a communique released after the NATO summit, came as U.S. President Joe Biden rallies allies to counter what he calls autocracies like China and Russia that are challenging an open international order. "China's stated ambitions and assertive behavior present systemic challenges to the rules-based international order and to areas relevant to alliance security," said the communique from the 30-member organization that brings together North American and European countries. The leaders also expressed concerns over what they called China's coercive policies, while pointing out the country's rapid expansion of its nuclear arsenal and criticizing the opaqueness of its military modernization. The communique, meanwhile, named Australia, Japan, New Zealand and South Korea as countries with which NATO plans to strengthen its "political dialogue and practical cooperation" in a bid to promote cooperative security and support the rules-based international order.

Explainable AI (XAI) methods are used in order to improve the interpretability of these complex "black box" models, thereby increasing transparency and enabling informed decision-making (Guidotti et al, 2018). Despite this, methods to assess the quality of explanations generated by such explainable methods are so far under-explored. In particular, functionallygrounded evaluation methods, which measure the inherent ability of explainable methods in a given situation, are often specific to a particular type of dataset or explainable method. A key measure of functionally-grounded explanation fitness is explanation fidelity, which assesses the correctness and completeness of the explanation with respect to the underlying black box predictive model (Zhou et al, 2021). Evaluations of fidelity in literature can generally be classified as one of the following: external fidelity evaluation, which assesses how well the prediction of the underlying model and the explanation agree, and internal fidelity, which assesses how well the explanation matches the decision-making processes of the underlying model (Messalas et al, 2019). While methods to evaluate external fidelity are relatively common in literature (Guidotti et al, 2019; Lakkaraju et al, 2016; Ming et al, 2019; Shankaranarayana and Runje, 2019), evaluation methods to evaluate internal fidelity using black box models are generally limited to text and image data, rather than tabular (Du et al, 2019; Fong and Vedaldi, 2017; Nguyen, 2018; Samek et al, 2017). In this paper, weproposeanovelevaluation method based onathree phase approach:(1) the creation of a fully transparent, inherently interpretable white box model, and evaluation of explanations against this model; (2) the usage of the white box as a proxy to refine and improve the evaluation of explanations generated by a black box model; and (3) test the fidelity of explanations for a black box model using the refined method from the second phase. The main contributions of this work are as follows: 1.

Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative questionnaires and interviews, we found that both post-stroke survivors and therapists appreciated the potential benefits of AI and robotic coaches to achieve more systematic management and improve their self-efficacy and motivation on rehabilitation therapy. In addition, our evaluation sheds light on several practical concerns (e.g. a possible difficulty with the interaction for people with cognitive impairment, system failures, etc.). We discuss the value of early involvement of stakeholders and interactive techniques that complement system failures, but also support a personalized therapy session for the better deployment of AI and robotic exercise coaches.

Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots of interest from academic and industry researchers. Up to the present, a great variety of Transformer variants (a.k.a. X-formers) have been proposed, however, a systematic and comprehensive literature review on these Transformer variants is still missing. In this survey, we provide a comprehensive review of various X-formers. We first briefly introduce the vanilla Transformer and then propose a new taxonomy of X-formers. Next, we introduce the various X-formers from three perspectives: architectural modification, pre-training, and applications. Finally, we outline some potential directions for future research.

We study the problem of list-decodable mean estimation, where an adversary can corrupt a majority of the dataset. Specifically, we are given a set $T$ of $n$ points in $\mathbb{R}^d$ and a parameter $0< \alpha <\frac 1 2$ such that an $\alpha$-fraction of the points in $T$ are i.i.d. samples from a well-behaved distribution $\mathcal{D}$ and the remaining $(1-\alpha)$-fraction of the points are arbitrary. The goal is to output a small list of vectors at least one of which is close to the mean of $\mathcal{D}$. As our main contribution, we develop new algorithms for list-decodable mean estimation, achieving nearly-optimal statistical guarantees, with running time $n^{1 + o(1)} d$. All prior algorithms for this problem had additional polynomial factors in $\frac 1 \alpha$. As a corollary, we obtain the first almost-linear time algorithms for clustering mixtures of $k$ separated well-behaved distributions, nearly-matching the statistical guarantees of spectral methods. Prior clustering algorithms inherently relied on an application of $k$-PCA, thereby incurring runtimes of $\Omega(n d k)$. This marks the first runtime improvement for this basic statistical problem in nearly two decades. The starting point of our approach is a novel and simpler near-linear time robust mean estimation algorithm in the $\alpha \to 1$ regime, based on a one-shot matrix multiplicative weights-inspired potential decrease. We crucially leverage this new algorithmic framework in the context of the iterative multi-filtering technique of Diakonikolas et. al. '18, '20, providing a method to simultaneously cluster and downsample points using one-dimensional projections -- thus, bypassing the $k$-PCA subroutines required by prior algorithms.

Dropout is attracting intensive research interest in deep learning as an efficient approach to prevent overfitting. Recently incorporating structural information when deciding which units to drop out produced promising results comparing to methods that ignore the structural information. However, a major issue of the existing work is that it failed to differentiate among instances when constructing the dropout architecture. This can be a significant deficiency for many applications. To solve this issue, we propose Constructivism learning for instance-dependent Dropout Architecture (CODA), which is inspired from a philosophical theory, constructivism learning. Specially, based on the theory we have designed a better drop out technique, Uniform Process Mixture Models, using a Bayesian nonparametric method Uniform process. We have evaluated our proposed method on 5 real-world datasets and compared the performance with other state-of-the-art dropout techniques. The experimental results demonstrated the effectiveness of CODA.

Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder architecture, to generate patches. Though existing DL-based APR approaches have proposed different encoder architectures, the decoder remains to be the standard one, which generates a sequence of tokens one by one to replace the faulty statement. This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and 3) not being able to generate project-specific identifiers. In this paper, we propose Recoder, a syntax-guided edit decoder with placeholder generation. Recoder is novel in multiple aspects: 1) Recoder generates edits rather than modified code, allowing efficient representation of small edits; 2) Recoder is syntax-guided, with the novel provider/decider architecture to ensure the syntactic correctness of the patched program and accurate generation; 3) Recoder generates placeholders that could be instantiated as project-specific identifiers later. We conduct experiments to evaluate Recoder on 395 bugs from Defects4J v1.2, 420 additional bugs from Defects4J v2.0, 297 bugs from IntroClassJava and 40 bugs from QuixBugs. Our results show that Recoder repairs 53 bugs on Defects4J v1.2, which achieves 26.2% (11 bugs) improvement over the previous state-of-the-art approach for single-hunk bugs (TBar). Importantly, to our knowledge, Recoder is the first DL-based APR approach that has outperformed the traditional APR approaches on this benchmark.