Ensemble classifiers have been investigated by many in the artificial intelligence and machine learning community. Majority voting and weighted majority voting are two commonly used combination schemes in ensemble learning. However, understanding of them is incomplete at best, with some properties even misunderstood. In this paper, we present a group of properties of these two schemes formally under a dataset-level geometric framework. Two key factors, every component base classifier's performance and dissimilarity between each pair of component classifiers are evaluated by the same metric - the Euclidean distance. Consequently, ensembling becomes a deterministic problem and the performance of an ensemble can be calculated directly by a formula. We prove several theorems of interest and explain their implications for ensembles. In particular, we compare and contrast the effect of the number of component classifiers on these two types of ensemble schemes. Empirical investigation is also conducted to verify the theoretical results when other metrics such as accuracy are used. We believe that the results from this paper are very useful for us to understand the fundamental properties of these two combination schemes and the principles of ensemble classifiers in general. The results are also helpful for us to investigate some issues in ensemble classifiers, such as ensemble performance prediction, selecting a small number of base classifiers to obtain efficient and effective ensembles.

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreferenceaware models are better at tracing the information Figure 1: An example of dialogue summarization: The flow among interlocutors and associating original conversation (in grey) is abbreviated; the summary accurate status/actions with the corresponding generated by a baseline model is in blue; the interlocutors and person mentions.

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.

Random features are a central technique for scalable learning algorithms based on kernel methods. A recent work has shown that an algorithm for machine learning by quantum computer, quantum machine learning (QML), can exponentially speed up sampling of optimized random features, even without imposing restrictive assumptions on sparsity and low-rankness of matrices that had limited applicability of conventional QML algorithms; this QML algorithm makes it possible to significantly reduce and provably minimize the required number of features for regression tasks. However, a major interest in the field of QML is how widely the advantages of quantum computation can be exploited, not only in the regression tasks. We here construct a QML algorithm for a classification task accelerated by the optimized random features. We prove that the QML algorithm for sampling optimized random features, combined with stochastic gradient descent (SGD), can achieve state-of-the-art exponential convergence speed of reducing classification error in a classification task under a low-noise condition; at the same time, our algorithm with optimized random features can take advantage of the significant reduction of the required number of features so as to accelerate each iteration in the SGD and evaluation of the classifier obtained from our algorithm. These results discover a promising application of QML to significant acceleration of the leading classification algorithm based on kernel methods, without ruining its applicability to a practical class of data sets and the exponential error-convergence speed.

A variety of dimensionality reduction techniques have been applied for computations involving large matrices. The underlying matrix is randomly compressed into a smaller one, while approximately retaining many of its original properties. As a result, much of the expensive computation can be performed on the small matrix. The sketching of positive semidefinite (PSD) matrices is well understood, but there are many applications where the related matrices are not PSD, including Hessian matrices in non-convex optimization and covariance matrices in regression applications involving complex numbers. In this paper, we present novel dimensionality reduction methods for non-PSD matrices, as well as their ``square-roots", which involve matrices with complex entries. We show how these techniques can be used for multiple downstream tasks. In particular, we show how to use the proposed matrix sketching techniques for both convex and non-convex optimization, $\ell_p$-regression for every $1 \leq p \leq \infty$, and vector-matrix-vector queries.

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.