Bibliometric analysis and systematic review of AI applied to wastewater treatment. Wastewater treatment technology, economy, management, and reuse were discussed. Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00. Application of AI technology could reduce operational costs by up to 30 %. Combined AI methods could provide higher accuracy and lower error. Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality.

There's no denying the growing skills gap that currently looms over our workforce. The good news is that awareness is increasing. Business leaders and institutions recognize the fundamental need to invest in skills training programs for their people to stay competitive in today's digital economy. Unfortunately, while the skills gap challenge is well established, few are taking action, and the solutions are not moving quickly enough. In 2020, we can expect 200,000 tech jobs to go unfilled in Canada, according ICTC.

New Delhi: Delhi government's VAT department will soon take the help of a machine learning tool to identify bogus firms and tax evasion to plug leaks, which is estimated to be around Rs 300 crore annually. AAP government has shared thousands of VAT returns registered in Delhi between 2012-17, which are being scrutinised by two research scholars -- Aprajit Mahajan and Shekhar Mittal -- who are associated with University of California, Berkeley. Government officials said the researchers are likely to submit their first set of findings by December on the basis of which the VAT department would plan its action against firms that might be involved in systematic evasion of taxes. "It is going to be a first-ever systematic study of tax evasion in an economy with weak compliance," Delhi Dialogue and Development Commission vice-chairman Jasmine Shah said, referring to the researchers' paper'Who is Bogus? According to officials, the VAT department currently carries out surprise inspections to nab defaulters or traders resorting to unfair means to avoid paying taxes by generating bills of fraudulent transactions with firms that exist only on paper.

The researchers, based at UBC's faculty of applied science, designed three types of stealth attack on robotic vehicles that caused the machines to crash, miss their targets or complete their missions much later than scheduled. The attacks required little to no human intervention to succeed on both real and simulated drones and rovers. "We saw major weaknesses in robotic vehicle software that could allow attackers to easily disrupt the behaviour of many different kinds of these machines," said Karthik Pattabiraman, the electrical and computer engineering professor who supervised the study. "Especially worrisome is the fact that none of these attacks could be detected by the most commonly used detection techniques." Robotic vehicles use special algorithms to stay on track while in motion, as well as to flag unusual behaviour that could signal an attack.

This article originally appeared on Forbes. The concept of artificial intelligence is nothing new, yet the hype around it continues to grow. According to CB Insights (via Forbes), AI startups have raised a record $7.4 billion raised in the second quarter of 2019 alone. The excitement does have merit. Artificial intelligence (AI) has the potential to automate many of the tedious tasks humans perform now.

The impact of artificial intelligence on our daily lives is yet to be fully realized. Leading the movement with proprietary natural language processing technology, WebAble's SAAS platform, ChatLeads will be represented in the global stage in Seoul on April 9 at "2019 Global Digital Marketing S.E.T ", said a statement sent to FS. CEO of ChatLeads, Shadab Mahbub, will be speaking at the event about "Utilizing A.I chatbot to deliver cross-cultural marketing". He will also discuss changes to customer experience made by A.I chatbot & Facebook A.I messenger campaigns across Southeast Asia. Shadab will be sharing the stage with leaders from Hubspot, Mercer, and other global leaders in the field. The event will explore the implications of A.I powered customer experiences in an era of fragmented interfaces.

A universal interatomic potential applicable to arbitrary elements and structures is urgently needed in computational materials science. Graph convolution-based neural network is a promising approach by virtue of its ability to express complex relations. Thus far, it has been thought to represent a completely different approach from physics-based interatomic potentials. In this paper, we show that these two methods can be regarded as different representations of the same tight-binding electronic relaxation framework, where atom-based and overlap integral or "bond"-based Hamiltonian information are propagated in a directional fashion. Based on this unified view, we propose a new model, named the tensor embedded atom network (TeaNet), where the stacked network model is associated with the electronic total energy relaxation calculation. Furthermore, Tersoff-style angular interaction is translated into graph convolution architecture through the incorporation of Euclidean tensor values. Our model can represent and transfer spatial information. TeaNet shows great performance in both the robustness of interatomic potentials and the expressive power of neural networks. We demonstrate that arbitrary chemistry involving the first 18 elements on the periodic table (H to Ar) can be realized by our model, including C-H molecular structures, metals, amorphous SiO${}_2$, and water.

Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this paper, AOI has been extended to the field of Predictive Maintenance. The objective is to demonstrate that combining expert knowledge and data collected from the machine can provide good results in the Predictive Maintenance of industrial assets. To this end we adapted the algorithm and used an LSTM approach to perform both the Anomaly Detection (AD) and the Remaining Useful Life (RUL). The results obtained confirm the validity of the proposal, as the methodology was able to detect anomalies, and calculate the RUL until breakage with considerable degree of accuracy.

This paper builds the connection between graph neural networks and traditional dynamical systems. Existing graph neural networks essentially define a discrete dynamic on node representations with multiple graph convolution layers. We propose continuous graph neural networks (CGNN), which generalise existing graph neural networks into the continuous-time dynamic setting. The key idea is how to characterise the continuous dynamics of node representations, i.e. the derivatives of node representations w.r.t. time. Inspired by existing diffusion-based methods on graphs (e.g. PageRank and epidemic models on social networks), we define the derivatives as a combination of the current node representations, the representations of neighbors, and the initial values of the nodes. We propose and analyse different possible dynamics on graphs---including each dimension of node representations (a.k.a. the feature channel) change independently or interact with each other---both with theoretical justification. The proposed continuous graph neural networks are robust to over-smoothing and hence capture the long-range dependencies between nodes. Experimental results on the task of node classification prove the effectiveness of our proposed approach over competitive baselines.

Recently, Hopfield and Krotov introduced the concept of {\em dense associative memories} [DAM] (close to spin-glasses with $P$-wise interactions in a disordered statistical mechanical jargon): they proved a number of remarkable features these networks share and suggested their use to (partially) explain the success of the new generation of Artificial Intelligence. Thanks to a remarkable ante-litteram analysis by Baldi \& Venkatesh, among these properties, it is known these networks can handle a maximal amount of stored patterns $K$ scaling as $K \sim N^{P-1}$.\\ In this paper, once introduced a {\em minimal dense associative network} as one of the most elementary cost-functions falling in this class of DAM, we sacrifice this high-load regime -namely we force the storage of {\em solely} a linear amount of patterns, i.e. $K = \alpha N$ (with $\alpha>0$)- to prove that, in this regime, these networks can correctly perform pattern recognition even if pattern signal is $O(1)$ and is embedded in a sea of noise $O(\sqrt{N})$, also in the large $N$ limit. To prove this statement, by extremizing the quenched free-energy of the model over its natural order-parameters (the various magnetizations and overlaps), we derived its phase diagram, at the replica symmetric level of description and in the thermodynamic limit: as a sideline, we stress that, to achieve this task, aiming at cross-fertilization among disciplines, we pave two hegemon routes in the statistical mechanics of spin glasses, namely the replica trick and the interpolation technique.\\ Both the approaches reach the same conclusion: there is a not-empty region, in the noise-$T$ vs load-$\alpha$ phase diagram plane, where these networks can actually work in this challenging regime; in particular we obtained a quite high critical (linear) load in the (fast) noiseless case resulting in $\lim_{\beta \to \infty}\alpha_c(\beta)=0.65$.