Anomaly detection is an important research problem because anomalies often contain critical insights for understanding the unusual behavior in data. One type of anomaly detection approach is dependency-based, which identifies anomalies by examining the violations of the normal dependency among variables. These methods can discover subtle and meaningful anomalies with better interpretation. Existing dependency-based methods adopt different implementations and show different strengths and weaknesses. However, the theoretical fundamentals and the general process behind them have not been well studied. This paper proposes a general framework, DepAD, to provide a unified process for dependency-based anomaly detection. DepAD decomposes unsupervised anomaly detection tasks into feature selection and prediction problems. Utilizing off-the-shelf techniques, the DepAD framework can have various instantiations to suit different application domains. Comprehensive experiments have been conducted over one hundred instantiated DepAD methods with 32 real-world datasets to evaluate the performance of representative techniques in DepAD. To show the effectiveness of DepAD, we compare two DepAD methods with nine state-of-the-art anomaly detection methods, and the results show that DepAD methods outperform comparison methods in most cases. Through the DepAD framework, this paper gives guidance and inspiration for future research of dependency-based anomaly detection and provides a benchmark for its evaluation.

Object detection is an elementary Computer Vision task which deals with the classification of objects in a digital image to a particular class (such as airplanes, cars, humans, etc). This can further be used in the implementation of real-world systems like face detection, pedestrian detection, automated driving systems, video surveillance, among other applications. It has gathered a lot of attention in the last few years since it is closely related to video analysis. They also help provide keen insights on the image contents. In recent years, deep learning methods have gained momentum and are now able to learn large amount of features, at comparatively deeper levels, and are thus able to address the problems faced earlier in traditional network architectures, such as artificial neural networks.

Different theories have been proposed model's ability to rank a well-organized document to describe the properties that contribute to higher than its noisy counterparts created by discourse coherence and some have been integrated corrupting sentence order in the original document with computational models for empirical (binary discrimination task), and neural evaluation. A popular approach is the entitybased models have achieved remarkable accuracy on model which hypothesizes that coherence this task. Recent efforts have targeted additional can be assessed in terms of the distribution of tasks such as recovering the correct sentence and transitions between entities in a text - by order (Logeswaran et al., 2018; Cui et al., 2018), constructing an entity-grid (Egrid) representation evaluating on realistic data (Lai and Tetreault, (Barzilay and Lapata, 2005, 2008), building 2018; Farag and Yannakoudakis, 2019) and on Centering Theory (Grosz et al., 1995). Subsequent focusing on open-domain models of coherence work has adapted and further extended (Li and Jurafsky, 2017; Xu et al., 2019). Egrid representations (Filippova and Strube, However, less attention has been directed to 2007; Burstein et al., 2010; Elsner and Charniak, investigating and analyzing the properties of coherence 2011; Guinaudeau and Strube, 2013). Other that current models can capture, nor what research has focused on syntactic patterns knowledge is encoded in their representations and that cooccur in text (Louis and Nenkova, how it might relate to aspects of coherence.

Mathematicians formulate complex mathematical models based on user requirements to solve a diverse range of problems in different domains. These models are, in most cases, represented through several mathematical equations and constraints. This modelling task comprises several time-intensive processes that require both mathematical expertise and (problem) domain knowledge. In an attempt to automate these processes, we have developed an ontology for Mixed Integer Linear Programming (MILP) problems to formulate expert mathematician knowledge and in this paper, we show how this new ontology can be utilized for modelling a relatively straightforward MILP problem, a Machine Scheduling example. We also show that more complex MILP problems, such as the Asymmetric Travelling Salesman Problem (ATSP), however, are not readily amenable to simple elicitation of user requirements and the utilization of the proposed mathematical model ontology. Therefore, an automatic mathematical modelling framework is proposed for such complex MILP problems, which includes a problem (requirement) elicitation module connected to a model extraction module through a translation engine that bridges between the non-expert problem domain and the expert mathematical model domain. This framework is argued to have the necessary components to effectively tackle the automation of modelling task of the more intricate MILP problems such as the ATSP.

Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc.This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.

It's been called'the future of warfare'. Off-the-shelf unmanned aerial systems (UAS), carrying a'payload' of explosives or biological material, flown by terrorists or enemy armed forces into a crowded building or military base. Now the University of Technology Sydney (UTS) and Sydney ASX-listed defence tech company DroneShield have produced next-generation drone technology to better identify threats from these aggressive UAS. In a partnership funded by the NSW and Australian Governments, UTS and DroneShield – an Australian developer of counter-UAS solutions – have produced an optical system for detection, identification and tracking of fast-moving threats such as nefarious UAS, comprised of a camera and Convolutional Neural Network (CNN). UTS and DroneShield began working together in October 2019 – just a month after one of the most recent examples of aggressive use of drones when the oil facilities at Abqaiq–Khurais in Saudi Arabia were attacked by a swarm of UAS.

A polar bear on melting ice: It's a favorite image of nature documentaries and charity ads alike, never failing to put you in the emotional dumps for a simple reason--it forces you to grapple with a changing world, a darker future. But that emotion is often temporary, replaced quickly by others, because its effects are not immediately or directly felt, explained Peter Schlosser, the vice president and vice provost of global futures at Arizona State University. Footage of houses on fire in California, Oregon, and Australia alarms us, but falls short of making us understand that our own home may be next. These "delusions of escape," in the words of science fiction author Kim Stanley Robinson, or "failures of imagination," in the words of Future Tense academic director Ed Finn, placate us into reactive, piecemeal, short-sighted decision-making. But storytelling lights the path forward, agreed Robinson, Finn, Schlosser, Future Tense fellow Alexandra Zapata Hojel, and Malka Older, also a sci-fi author.

For this week's ML practitioner's series, Analytics India Magazine got in touch with Hiroki Yamamoto (tereka), a Kaggle Master. Hiroki is currently working as a data scientist and is ranked in the top 100 of the world's largest platforms for data science competitions– Kaggle. In this interview, Hiroki shares his experience of competing on Kaggle and how it has helped in growing as a data scientist. Hiroki: I got a master's degree in information technology back in 2015. During my graduation, I have worked on image processing research using deep learning -- for example, autoencoders.

AI robots are still not sophisticated enough to understand humans or the complexity of social situations, says UNSW's Dr Masimiliano Cappuccio. "So we need to think about how we interact with social and companion robots to instead help us become more aware of our own behaviour, limitations, vices or bad habits," says Dr Cappuccio, the Deputy Director of Values in Defense and Security Technology at UNSW Canberra. "And this can be in the areas of greater self-discipline and self-control but also in learning virtues such as generosity and empathy." Dr Cappuccio is the lead author of Can Robots Make Us Better Humans? Virtuous Robotics and the Good Life with Artificial Agents which was written in collaboration with UNSW Art & Design's Dr Eduardo Sandoval and Professor Mari Velonaki along with academics from the University of Western Sydney and Chalmers University of Technology in Sweden It is also the first in a collection co-edited by Dr Cappuccio, Dr Sandoval and Prof. Velonaki and published in the International Journal of Robotics as a special issue titled Virtuous Robotics: Artificial Agents and the Good Life.

Australia Post is using machine learning to calculate the arrival time of parcels down to a two-hour window based on data from a new route planner that has just been rolled out to delivery drivers. Executive general manager for transformation and enablement John Cox on Tuesday said the new feature is currently being trialled to give customers a more accurate estimate on delivery times. "What we're trialling – and this is not out in the public yet – is what we call an estimated time of arrival," Cox told the Digital Transformation Agency's 2020 Digital Summit. "So based off when the postie scans the parcel in the morning to put in their van, we'll be able to notify the consumer that it will be delivered within that window of time." The estimate is calculated using the "single scanning platform" that all deliver drivers use to scan their parcels before their run, which also determines the "optimal" route for each delivery run.