BotSpot++: A Hierarchical Deep Ensemble Model for Bots Install Fraud Detection in Mobile Advertising

2022 ◽  
Vol 40 (3) ◽  
pp. 1-28
Author(s):  
Yadong Zhu ◽  
Xiliang Wang ◽  
Qing Li ◽  
Tianjun Yao ◽  
Shangsong Liang
Keyword(s):  
Domain Knowledge ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Fraud Detection ◽  
Ensemble Model ◽  
Graph Structure ◽  
Mobile Advertising ◽  
The World ◽  
Advertising Company ◽  
Noisy Labels

Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the most difficult to detect due to its ability to emulate normal users by implementing sophisticated behavioral patterns to evade from detection rules defined by human experts. Therefore, we proposed BotSpot 1 for bots install fraud detection previously. However, there are some drawbacks in BotSpot, such as the sparsity of the devices’ neighbors, weak interactive information of leaf nodes, and noisy labels. In this work, we propose BotSpot++ to improve these drawbacks: (1) for the sparsity of the devices’ neighbors, we propose to construct a super device node to enrich the graph structure and information flow utilizing domain knowledge and a clustering algorithm; (2) for the weak interactive information, we propose to incorporate a self-attention mechanism to enhance the interaction of various leaf nodes; and (3) for the noisy labels, we apply a label smoothing mechanism to alleviate it. Comprehensive experimental results show that BotSpot++ yields the best performance compared with six state-of-the-art baselines. Furthermore, we deploy our model to the advertising platform of Mobvista, 2 a leading global mobile advertising company. The online experiments also demonstrate the effectiveness of our proposed method.

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

scholarly journals RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection

2021 ◽  
Vol 11 (12) ◽  
pp. 5656
Author(s):  
Yufan Zeng ◽  
Jiashan Tang
Keyword(s):  
Numerical Experiments ◽  
State Of The Art ◽  
Single Layer ◽  
Fraud Detection ◽  
Layer By Layer ◽  
Residual Structure ◽  
Detection Algorithms ◽  
Deep Architecture ◽  
Graph Neural Networks ◽  
Node Embeddings

Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.

scholarly journals D-Penicillamine: The State of the Art in Humans and in Dogs from a Pharmacological and Regulatory Perspective

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Michela Pugliese ◽  
Vito Biondi ◽  
Enrico Gugliandolo ◽  
Patrizia Licata ◽  
Alessio Filippo Peritore ◽  
...  
Keyword(s):  
Veterinary Medicine ◽  
State Of The Art ◽  
European Medicines Agency ◽  
Therapeutic Goals ◽  
First Choice ◽  
Regulatory Perspective ◽  
The World ◽  
United States Food ◽  
States Food ◽  
Food And Drugs Administration

Chelant agents are the mainstay of treatment in copper-associated hepatitis in humans, where D-penicillamine is the chelant agent of first choice. In veterinary medicine, the use of D-penicillamine has increased with the recent recognition of copper-associated hepatopathies that occur in several breeds of dogs. Although the different regulatory authorities in the world (United States Food and Drugs Administration—U.S. FDA, European Medicines Agency—EMEA, etc.) do not approve D-penicillamine for use in dogs, it has been used to treat copper-associated hepatitis in dogs since the 1970s, and is prescribed legally by veterinarians as an extra-label drug to treat this disease and alleviate suffering. The present study aims to: (a) address the pharmacological features; (b) outline the clinical scenario underlying the increased interest in D-penicillamine by overviewing the evolution of its main therapeutic goals in humans and dogs; and finally, (c) provide a discussion on its use and prescription in veterinary medicine from a regulatory perspective.

scholarly journals Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Small Object ◽  
Thick Blood Smear ◽  
Malaria Parasites ◽  
Microscopic Images

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

scholarly journals Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

2019 ◽  
Vol 9 (12) ◽  
pp. 2535
Author(s):  
Di Fan ◽  
Hyunwoo Kim ◽  
Jummo Kim ◽  
Yunhui Liu ◽  
Qiang Huang
Keyword(s):  
Human Computer Interaction ◽  
Video Surveillance ◽  
State Of The Art ◽  
Learning Task ◽  
Face Verification ◽  
Task Learning ◽  
The World ◽  
In The Wild ◽  
And Gender ◽  
Learning Architectures

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

Rose Fitzgerald Kennedy bridge over the River Barrow. Design and Construction of a long span extrados bridge

2021 ◽  
Author(s):  
Marcos Sanchez Sanchez ◽  
John Iliff
Keyword(s):  
State Of The Art ◽  
Single Plane ◽  
Robust Approach ◽  
Analysis Techniques ◽  
Long Span ◽  
The World ◽  
Art Analysis ◽  
Concrete Deck ◽  
Design And Construction

<p>This paper describes the key elements from early planning to completion of a new bridge over the River Barrow which is part of the New Ross bypass in the south of Ireland. The structure has a total length of 887m, with a span arrangement of 36-45-95-230-230-95-70-50-36m. The two central twin spans are the longest of its kind in the world (extrados with a full concrete deck). The bridge carries a dual carriageway with a cable arrangement consisting of a single plane of cables located in the central axis of the deck. The design and construction focused in providing a structure with long term durability, resilience, and a robust approach to design scenarios using the Eurocodes and state of the art analysis techniques, including extreme events such as fire and ship impact<i>.</i></p>

Sir Charles Parsons and Electrical Power Generation—a Turbine Designer's Perspective

1994 ◽  
Vol 208 (3) ◽  
pp. 159-176 ◽  
Author(s):  
J R Bolter
Keyword(s):  
Power Generation ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Electrical Power Generation ◽  
Analytical Methods ◽  
State Of The Art ◽  
Electrical Power ◽  
Turbine Generator ◽  
Current State ◽  
The World

Sir Charles Parsons died some three years after the author was born. In this paper the author looks back at the pioneering work of Parsons in the field of power generation. It shows how he was able to increase output of the steam turbine generator from 7.5 kW in 1884 to 50000 kW in 1930 while increasing efficiency from 1.6 to 36 per cent, and relates these achievements to the current state of the art. Blading design, rotor construction and other aspects of turbine engineering are considered. The conclusion is that Parsons and his associates charted the course which manufacturers and utilities throughout the world have continued to follow, although increasingly sophisticated design and analytical methods have succeeded the intuitive approach of Parsons. His constant search for improved efficiency was and is highly relevant to today's concern for the environment. Finally, although it did not become a practical proposition in his lifetime, the paper reviews Parsons' vision of, and continuing interest in, the gas turbine, first mentioned in his 1884 patents.

scholarly journals Turbulence and Flow–Sediment Interactions in Open-Channel Flows

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3169
Author(s):  
Roberto Gaudio
Keyword(s):  
Open Channel ◽  
Field Experiments ◽  
State Of The Art ◽  
Channel Flows ◽  
Special Issue ◽  
Panoramic View ◽  
Open Channel Flows ◽  
River Hydraulics ◽  
The World ◽  
Computing Skills

The main focus of this Special Issue of Water is the state-of-the-art and recent research on turbulence and flow–sediment interactions in open-channel flows. Our knowledge of river hydraulics is becoming deeper and deeper, thanks to both laboratory/field experiments related to the characteristics of turbulence and their link to the erosion, transport, deposition, and local scouring phenomena. Collaboration among engineers, physicists, and other experts is increasing and furnishing new inter/multidisciplinary perspectives to the research in river hydraulics and fluid mechanics. At the same time, the development of both sophisticated laboratory instrumentation and computing skills is giving rise to excellent experimental–numerical comparative studies. Thus, this Special Issue, with ten papers by researchers from many institutions around the world, aims at offering a modern panoramic view on all the above aspects to the vast audience of river researchers.

scholarly journals Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6450
Author(s):  
Taimur Hassan ◽  
Muhammad Shafay ◽  
Samet Akçay ◽  
Salman Khan ◽  
Mohammed Bennamoun ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Aviation Security ◽  
Dual Energy ◽  
Average Precision ◽  
X Ray ◽  
Shot Classification ◽  
Object Proposals ◽  
The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

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