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Human activity recognition (HAR) using inertial motion data has gained a lot of momentum in recent years both in research and industrial applications. From the abstract perspective, this has been driven by the rapid dynamics for building intelligent, smart environments, and ubiquitous systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc., which necessitate and subsume activity recognition aiming at recognizing the actions, characteristics, and goals of one or more agent(s) from a temporal series of observations streamed from one or more sensors. From a more concrete and seemingly orthogonal perspective, such momentum has been driven by the ubiquity of inertial motion sensors on-board mobile and wearable devices including smartphones, smartwatches, etc. In this paper we give an introductory and a comprehensive survey to the subject from a given perspective. We focus on a subset of topics, that we think are major, that will have significant and influential impacts on the future research and industrial-scale deployment of HAR systems. These include: (1) a comprehensive and detailed description of the inertial motion benchmark datasets that are publicly available and/or accessible, (2) feature selection and extraction techniques and the corresponding learning methods used to build workable HAR systems; we survey classical handcrafted datasets as well as data-oriented automatic representation learning approach to the subject, (3) transfer learning as a way to overcome many hurdles in actual deployments of HAR systems on a large scale, (4) embedded implementations of HAR systems on mobile and/or wearable devices, and finally (5) we touch on adversarial attacks, a topic that is essentially related to the security and privacy of HAR systems. As the field is very huge and diverse, this article is by no means comprehensive; it is though meant to provide a logically and conceptually rather complete picture to advanced practitioners, as well as to present a readable guided introduction to newcomers. Our logical and conceptual perspectives mimic the typical data science pipeline for state-of-the-art AI-based systems. [ABSTRACT FROM AUTHOR]

With the noteworthy achievements of deep learning models, there are transformative applications that aim at cost reduction and the improvement in human quality of life. Nevertheless, recent work aimed at testing a classifier's ability to withstand targeted and black-box adversarial attacks demonstrated that deep learning models, in particular, are brittle and lack certain robustness that makes them particularly weak, and ultimately leading to a lack of trust. For this specific area, a question arises concerning certain regions' sensitivity in the input space against adversarial perturbations for a classification model. This paper aims to study such a problem by looking into a Sensitivity-inspired Constrained Evaluation Method (SICEM) to deterministically evaluate how much a region of the input space is vulnerable to adversarial perturbations compared to other regions and also the entire input space. Our experiments suggest that SICEM can accurately quantify region vulnerabilities on MNIST and CIFAR-10 datasets. [ABSTRACT FROM AUTHOR]

In recent years, deep neural network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks where risk is critical. Misdiagnosis of these models can lead to serious accidents or even death. This concern has led to an interest among researchers to study possible attacks on these models, discovering a long list of vulnerabilities, from which every model should be defended. The adversarial example attack is a widely known attack among researchers, who have developed several defenses to avoid such a threat. However, these defenses are as opaque as a deep neural network model, how they work is still unknown. This is why visualizing how they change the behavior of the target model is interesting in order to understand more precisely how the performance of the defended model is being modified. For this work, three defense strategies, against adversarial example attacks, have been selected in order to visualize the behavior modification of each of them in the defended model. Adversarial training, dimensionality reduction, and prediction similarity were the selected defenses, which have been developed using a model composed of convolution neural network layers and dense neural network layers. In each defense, the behavior of the original model has been compared with the behavior of the defended model, representing the target model by a graph in a visualization. This visualization allows identifying the vulnerabilities of the model and shows how the defenses try to avoid them. [ABSTRACT FROM AUTHOR]

In recent years, studies have revealed that introducing knowledge graphs (KGs) into recommendation systems as auxiliary information can improve recommendation accuracy. However, KGs are usually based on third-party data that may be manipulated by malicious individuals. In this study, we developed a poisoning attack strategy applied on a KG-based recommendation system to analyze the influence of fake links. The aim of an attacker is to recommend specific products to improve their visibility. Most related studies have focused on adversarial attacks on graph data; KG-based recommendation systems have rarely been discussed. We propose an attack model corresponding to recommendations. In the model, the current recommended status and a specified item are analyzed to estimate the effects of different attack decisions (addition or deletion of facts), thereby generating the optimal attack combination. Finally, the KG is contaminated by the attack combination so that the trained recommendation model recommends a specific item to as many people as possible. We formulated the process into a deep reinforcement learning method. Conducting experiments on the movie and the fund data sets enabled us to systematically analyze our poisoning attack strategy. The experimental results proved that the proposed strategy can effectively improve an item's ranking in a recommendation list. [ABSTRACT FROM AUTHOR]