There has been an increasing necessity for scalable optimization methods, especially due to the explosion in the size of datasets and model complexity in modern machine learning applications. Scalable solvers often distribute the computation over a network of processing units. For simple algorithms such as gradient descent the dependency of the convergence time with the topology of this network is well-known. However, for more involved algorithms such as the Alternating Direction Methods of Multipliers (ADMM) much less is known. At the heart of many distributed optimization algorithms there exists a gossip subroutine which averages local information over the network, and whose efficiency is crucial for the overall performance of the method. In this paper we review recent research in this area and, with the goal of isolating such a communication exchange behaviour, we compare different algorithms when applied to a canonical distributed averaging consensus problem. We also show interesting connections between ADMM and lifted Markov chains besides providing an explicitly characterization of its convergence and optimal parameter tuning in terms of spectral properties of the network. Finally, we empirically study the connection between network topology and convergence rates for different algorithms on a real world problem of sensor localization.

Grammar error handling (GEH) is an important topic in natural language processing (NLP). GEH includes both grammar error detection and grammar error correction. Recent advances in computation systems have promoted the use of deep learning (DL) models for NLP problems such as GEH. In this survey we focus on two main DL approaches for GEH: neural machine translation models and editor models. We describe the three main stages of the pipeline for these models: data preparation, training, and inference. Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline. We compare the performance of different models and conclude with proposed future directions.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples.

Artificial Intelligence is emerging as a strong force for enterprises to innovate and transform. The report is based on survey of 500+ CXOs across India to study the maturity of AI adoption along with key challenges faced in their enterprise AI journeys. This report is part of a joint endeavor by NASSCOM and EY to determine the pulse of Indian enterprises in evaluating and deploying AI technologies, and to assess in-depth, the implications of AI technology advancements on key focus sectors, including BFSI, Retail, Healthcare and Agriculture. It also delves into the key enablers required to fast track AI adoption and proposes a roadmap to guide enterprises in their adoption journey. Key Highlights 1. AI reality check ~60% of CXOs believe that AI will disrupt their businesses within 3 years, yet only 25% have deployed AI solutions Operational efficiency, customer experience and revenue growth are the top three reasons for implementing AI From amongst the four key focus sectors, BFSI takes the lead on AI adoption followed by Retail, Healthcare and Agriculture Enabling core operations and enhancing customer service/experience are the top functional beneficiaries of AI 2. Impediments to AI adoption Key hurdles comprise technology and data, ability to prove ROI, talent and culture and trust, regulation and ethics Business leaders report differences in their experiences with, and perceptions regarding AI adoption ~55% enterprises, that have deployed AI, believe cultural impediments and low maturity of external ecosystem are biggest challenges ~60% enterprises that have not implemented AI believe that low level of enterprise digitization is holding them back 3. Enhancing AI maturity AI maturity model aims to provide enterprises a frame of reference for their current state maturity Eight dimensions allow organizations to map themselves against three stages of maturity Roadmap for moving up the AI maturity ladder from Beginner to Advanced Understanding the “art of possible” is a crucial step for enterprises to start their AI journey 4. Making it happen Strategic planning and integrated governance act as key AI enablers, effectively leveraging data, technology and talent Over 55% of CXOs stated that they trust AI to make strategic and/or operational decisions 74% enterprises have either formal strategy or C-suite sponsorship to initiate or scale-up AI programs 88% enterprises state that their risk management frameworks require improvement

Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user's final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time that reveal the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user's conceptualization process while also helping guide users to their own discoveries.

The $k$-segmentation of a video stream is used to partition it into $k$ piecewise-linear segments, so that each linear segment has a meaningful interpretation. Such segmentation may be used to summarize large videos using a small set of images, to identify anomalies within segments and change points between segments, and to select critical subsets for training machine learning models. Exact and approximate segmentation methods for $k$-segmentation exist in the literature. Each of these algorithms occupies a different spot in the trade-off between computational complexity and accuracy. A novel heuristic algorithm is proposed in this paper to improve upon existing methods. It is empirically found to provide accuracies competitive with exact methods at a fraction of the computational expense. The new algorithm is inspired by Lloyd's algorithm for K-Means and Lloyd-Max algorithm for scalar quantization, and is called the LM algorithm for convenience. It works by iteratively minimizing a cost function from any given initialisation; the commonly used $L_2$ cost is chosen in this paper. While the greedy minimization makes the algorithm sensitive to initialisation, the ability to converge from any initial guess to a local optimum allows the algorithm to be integrated into other existing algorithms. Three variants of the algorithm are tested over a large number of synthetic datasets, one being a standalone LM implementation, and two others that combine with existing algorithms. One of the latter two -- LM-enhanced-Bottom-Up segmentation -- is found to have the best accuracy and the lowest computational complexity among all algorithms. This variant of LM can provide $k$-segmentations over data sets with up to a million image frames within several seconds.

Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs. We extend this framework by proposing to minimize a sum of discrepancies between segments. In particular, we propose to select the change points so as to maximize the cross-entropy between successive segments, balanced by a penalty for introducing new change points. We propose a dynamic programming algorithm to solve this problem and analyze its complexity. Experiments on two challenging datasets demonstrate the advantages of our method compared to three state-of-the-art approaches.

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On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis Authors Content type: OriginalPaper Published: 27 August 2020 Nature inspired optimization algorithms or simply variations of metaheuristics? Nature inspired optimization algorithms or simply variations of metaheuristics? Nature inspired optimization algorithms or simply variations of metaheuristics? Electric Charged Particles Optimization and its application to the optimal design of a circular antenna array Authors H. R. E. H. Bouchekara Content type: OriginalPaper Published: 20 August 2020 CHIRPS: Explaining random forest classification Authors Mohamed Medhat Gaber R. Muhammad Atif Azad Content type: OriginalPaper Published: 04 June 2020 Image classifiers and image deep learning classifiers evolved in detection of Oryza sativa diseases: survey Authors N. V. Raja Reddy Goluguri Content type: EditorialNotes Published: 28 May 2020 Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making Authors (first, second and last of 4) José Carlos R. Alcantud Content type: OriginalPaper Published: 19 May 2020

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.