Reviewerly/exHarmony huggingface dataset:
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization

Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies. However, the growing scale of signals, both in volumes and dimensions, overpowers traditional statistical state-space or supervised learning tools. Thus, state-of-the-art methods based on unsupervised deep learning are sought in recent research. However, we experienced flaws when implementing those methods, such as requiring partial supervision and weaknesses to high dimensional datasets, among other reasons discussed in this paper. We propose a practical approach for inferring anomalies from large multivariate sets. We observe an abundance of time series in real-world applications, which exhibit asynchronous and consistent repetitive variations, such as IT, weather, utility, and transportation. Our solution is designed to leverage this behavior. The solution utilizes spectral analysis on the latent representation of a pre-trained autoencoder to extract dominant frequencies across the signals, which are then used in a subsequent network that learns the phase shifts across the signals and produces a synchronized representation of the raw multivariate. Random subsets of the synchronous multivariate are then fed into an array of autoencoders learning to minimize the quantile reconstruction losses, which are then used to infer and localize anomalies based on a majority vote. We benchmark this method against state-of-the-art approaches on public datasets and eBay's data using their referenced evaluation methods. Furthermore, we address the limitations of the referenced evaluation methods and propose a more realistic evaluation method.
The 5th AI City Challenge

The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems.
PISE: Person Image Synthesis and Editing with Decoupled GAN

Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose PISE, a novel two-stage generative model for Person Image Synthesis and Editing, which is able to generate realistic person images with desired poses, textures, or semantic layouts. For human pose transfer, we first synthesize a human parsing map aligned with the target pose to represent the shape of clothing by a parsing generator, and then generate the final image by an image generator. To decouple the shape and style of clothing, we propose joint global and local per-region encoding and normalization to predict the reasonable style of clothing for invisible regions. We also propose spatial-aware normalization to retain the spatial context relationship in the source image. The results of qualitative and quantitative experiments demonstrate the superiority of our model on human pose transfer. Besides, the results of texture transfer and region editing show that our model can be applied to person image editing. The code is available for research purposes at https://github.com/Zhangjinso/PISE.
Learning Position and Target Consistency for Memory-based Video Object Segmentation

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.
Artificial intelligence in marketing: A systematic literature review

The digital transformation fostered by the increasing leverage of artificial intelligence (AI) has been a critical influencing factor unleashing the next wave of enterprise business disruption. Marketing is one of the business streams witnessing this transformation on a very intense scale. Contemporary marketing has begun to experiment with modern, cutting-edge technologies, such as AI, deploying them in mainstream operations to ensure accelerated success. This article explores the use of AI in marketing as an emergent stream of research. Based on inferences from earlier studies, the study categorizes marketing into five distinct functional themes—integrated digital marketing, content marketing, experiential marketing, marketing operations, and market research—and 19 sub-functional themes (activity levers). Across the chosen themes and sub-themes, the study further dovetails into and identifies 170 featured use cases of the extant literature, where AI is leveraged by marketing in delivering superior quality outcomes and experiences. By way of a systematic literature review (SLR), the article evaluates 57 qualifying publications in the context of AI-powered marketing and qualitatively and quantitatively ranks them based on their coverage, impact, relevance, and contributed guidance, and elucidates the findings across various sectors, research contexts, and scenarios. The study discusses the practitioner and academic research implications and proposes a future research agenda to study the continuous transformation fostered by accelerated adoption of AI across the marketing landscape.
Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To mitigate the model reliance towards the background, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. We term our method as Background Erasing (BE). It is worth noting that the implementation of our method is so simple and neat and can be added to most of the SOTA methods without much efforts. Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased dataset Diving48.
Goal-Oriented Gaze Estimation for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes shared between different classes, which are highly local, strong prior for localization of object attribute is beneficial for visual-semantic embedding. Interestingly, when recognizing unseen images, human would also automatically gaze at regions with certain semantic clue. Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL. We aim to predict the actual human gaze location to get the visual attention regions for recognizing a novel object guided by attribute description. Specifically, the task-dependent attention is learned with the goal-oriented GEM, and the global image features are simultaneously optimized with the regression of local attribute features. Experiments on three ZSL benchmarks, i.e., CUB, SUN and AWA2, show the superiority or competitiveness of our proposed method against the state-of-the-art ZSL methods. The ablation analysis on real gaze data CUB-VWSW also validates the benefits and accuracy of our gaze estimation module. This work implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks. The code is available at https://github.com/osierboy/GEM-ZSL.
Prototype Completion with Primitive Knowledge for Few-Shot Learning

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful; 2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes; (ii) out-performs state-of-the-art techniques by 2%~9% in terms of classification accuracy. Our code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Blockchain-SDN-Based Energy-Aware and Distributed Secure Architecture for IoT in Smart Cities

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing and collecting of IoT data, and defend against cyber threats. To address these issues, the authors present a distributed and decentralized blockchain-software-defined networking (SDN)-based energy-aware architecture for IoT in smart cities. Thus, SDN is continuously observing, controlling, and managing IoT devices activities and detects possible attacks in the network; blockchain provides adequate security and privacy against cyber attacks, and reduces the central point of failure issues; network function virtualization (NFV) is used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a cluster head selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, and communication overhead) and demonstrate the result that provides higher throughput, lower response time, and lower gas consumption than existing works for smart cities.
Echo state network applied for classification of medium voltage insulators

Insulators are components of electrical power grid that have the function of mechanically supporting cables and isolating electrical potential. The proper functioning of the insulators is essential for the continuity in the supply of electrical energy. When an insulator has its properties damaged, disruptive discharges may shut down the system and impairs the network’s reliability. For this reason, classifying adverse conditions is a critical task to keep the system running. In this paper, the echo state network is used for the classification of the insulators based on the ultrasound signal. Hypertuning is applied to automate the evaluation of the parameters and optimize the network to have a general application. The insulators are evaluated in the laboratory under controlled conditions from the application of 7.95 kV (phase-to-ground), under the same conditions that are found in the field. The assessment is made on perforated and contaminated insulators, which were removed from service due to defects. The echo state network achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling. For comparative analysis, the multilayer perceptron and support-vector machines are evaluated based on the fast fourier transform. The results show that echo state network is promising to classify the evaluated conditions, being more accurate than the multilayer perceptron and support-vector machines based on fast fourier transform.
Manifold Regularized Dynamic Network Pruning

Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective subnetworks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP). We first investigate the recognition complexity and feature similarity between images in the training set. Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods. For example, our method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy degradation on ImageNet. The code will be available at https://github.com/huawei-noah/Pruning/tree/master/ManiDP.
Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulations.
An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.
Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations

The number of casualties in the maritime sector is increasing consistently. To reduce the scope of search area extensions in the event of a maritime accident, maritime authorities and operation centers are currently trying to develop a quick search for survivors at sea using unmanned aerial vehicles (UAVs) in maritime search and rescue (SAR) operations. Here, we propose a two-phase method for solving the coverage path planning (CPP) problem of multiple-UAV areas in maritime SAR. In phase 1, we propose a grid-based area decomposition method that minimizes the decomposed search area to transform the search area into a graph made up of vertices and edges. In phase 2, we formulate a mixed-integer linear programming (MILP) model to derive an optimal coverage path that minimizes the completion time. To solve the model for large-scale instances, a randomized search heuristic (RSH) algorithm is developed. We conducted extensive numerical experiments to validate the performance of the algorithm. Experimental results show that the RSH yields a better solution with an approximately 0.7% optimality gap within a much shorter computation time than that of a commercial solver. In addition, our grid-based CPP algorithm outperforms those used in previous research with respect to the solution quality. Furthermore, we showed the results of real flight experiments in the marine field using the proposed algorithm.
BCHealth: A Novel Blockchain-based Privacy-Preserving Architecture for IoT Healthcare Applications

The advancements in networking technologies have introduced the Internet of Everything (IoE) and smart living concepts. The main idea behind making everything smarter is to improve individuals’ quality of life. An excellent example of such a technology is smart healthcare which provides efficient, sustainable, and real-time human services. However, data security and privacy are among the most important challenges of smart healthcare applications. Blockchain (BC) has been considered as a promising solution for the secure management of healthcare data due to its immutability and transparency features. However, there is a trade-off between transparency and user data privacy which is a prominent challenge in adopting BC for healthcare applications. Some researchers have considered user data privacy and proposed a few solutions; however, data owner’s access control desire has not been considered in the state-of-the-art models. In this paper, to overcome the trade-off challenge between transparency and access control, we propose an architecture (so-called BCHealth) that enables data owners to define their desired access policies over their privacy-sensitive healthcare data. BCHealth is composed of two separate chains for storing access policies and data transactions. We address the real-world development challenges of BC, i.e., scalability, delay, and overhead, by adopting a clustering approach. Our extensive experimental analysis proves the efficiency of BCHealth (in terms of computation and processing time) and its resilience against several security attacks.