Reviewerly/exHarmony huggingface dataset:
Detection and parameter estimation of frequency hopping signal based on the deep neural network

In order to solve the problem of low detection rate of frequency hopping (FH) signal under low SNR, an FH signal detection technology based on deep neural network (vgg16 network) is proposed. Firstly, this paper introduces the principle of FH signal. Then, this paper presents the SSD deep neural network detection framework, and uses this framework to detect FH signal and estimate its parameters. Time-frequency distribution maps of FH signals are used as the input of deep neural network model to carry out tag learning and training, and then the trained model is used to realise FH signal detection. At the same time, aiming at the noise problem of time-frequency distribution map, the time-frequency graph correction method of K-means clustering algorithm is proposed. After modification, it is more tolerant to noise. The simulation results show that when the SNR is –4 dB, the detection rate of FH signal can reach more than 88%. Finally, the performance of parameter estimation of FH signal is analysed.
The Recent Trends in Biometric Traits Authentication Based on Internet of Things (IoT)

Biometric authentication is a system that provides a trusted independent authentication and confirmation of the identity of parties individually. It is also a technical term that refers to metrics related to human's body characteristics and authentication implement used in computer science. Individual authentication biometric systems are expert and intelligent systems in which concerns and requirements are rapidly developed in various areas, such as security and communication systems, healthcare applications, and several areas. This research work presents an overview of the biometric authentication based on the internet of things. That interconnects several computing devices embedded in the daily appliances to the internet, and it can enable the devices to communicate with each other, develops the end user's quality of life, and improves competence and sustainability from day-to-day activities. Thus, some expectations are proposed for the next few years to increase the number of users and devices of the internet of things. The integration of IoT with biometric traits will enhance security systems in different fields and make them more robust and authentic.
Gesture Recognition Method Based on Attention Mechanism for Complex Background

Abstract Human-computer interaction technology is still an unaccomplished goal due to complex background and changeable lighting conditions. A two-stage gesture recognition method is proposed to improve the accuracy of gesture recognition. The first stage combines attention mechanism and atrous spatial pyramid pooling (ASPP) which aims to segment gesture from complex background. The second stage uses a two stream convolutional neural network, combines features from the original picture and the output of the first stage, aims to realize the classification of gestures. It can recognize gestures more accurately compared with other gesture recognition methods.
A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining

As a result of changes in approach from traditional to virtual banking system, security in data exchange has become more important; thus, it seems essentially necessary to present a pattern based on smart models in order to reduce fraud in this field. A new algorithm has been provided in this article to improve security and to specify the limits of giving special services to Internet banking users in order to pave appropriate ground for virtual banking. In addition to identifying behavioral models of customers, this algorithm compares the behaviors of any customer with this model and finally computes the rate of trust in customer’s behavior. The hybrid data-mining and knowledge based structure has been adapted in this algorithm according to fuzzy systems. In this research, qualitative data was gathered from interviews with banking experts, analyzed by Expert Choice to identify the most important variables of customer behavior analysis, and to analyze customer behavior and customer bank Internet transaction data for a period of one year by MATLAB and Clementine. The results of this survey indicate that the potential of the given structure to recognize the rate of trust in Internet bank user’s behavior might be at reasonable level for experts in this area.
Rain to Rain: Learning Real Rain Removal Without Ground Truth

Image deraining is a low-level restoration task that has become quite popular during the past decades.Although recent data-driven deraining models exhibit promising results, most of these models are trained on synthetic rain data sets which do not generalize well when applied to real rain images.While recent real-rain data sets have achieved favorable generalization performance, generating rain-free ground-truths can be tedious and time-consuming.To address this problem, in this work, we present rain to rain training, an unsupervised training method for single image deraining.Our experiments show that it is possible to train single image deraining models by using only rain images.This can be achieved by simply training models to map pairs of rain images.We also introduce the idea of using the least overlapping training pairs, a method of selecting adequate training pairs that enables rain to rain training to achieve equivalent deraining performance compared to supervised training.
Street View Text Detection Methods: Review Paper

Nowadays, in this digital word text is one the vital knowledge carrier. Text can be perceived as structure of symbols which is accustomed to convey, transfer, or inherit civilization and way of life. It is among the major influential novelties of man kind; text performs crucial role in existence of human being. Text can be seen in all places in our day-to-day life. Thus, obtaining data from text which is present in scenarios of our day-to-day life is important. Processing the text that is present in natural scenario has two primary tasks namely Text identification and Text acknowledgment. The method of taking out text body from the natural scene is called as Text detection whereas the procedure of categorizing the characters from scene image which form expressive words is called as Text recognition. There are so many applications of scene text detection in different fields, e.g., intelligent inspection, blind navigation, industrial automation, automotive assistance, robot navigation, instant translation, automatic driving and product search, etc. Thus, street view text detection and recognition have allured rising research consideration in past few years. This paper as first step focuses on Text detection methods for natural scene images.
Continuation Multiple Instance Learning for Weakly and Fully Supervised Object Detection

Weakly supervised object detection (WSOD) is a challenging task that requires simultaneously learning object detectors and estimating object locations under the supervision of image category labels. Many WSOD methods that adopt multiple instance learning (MIL) have nonconvex objective functions and, therefore, are prone to get stuck in local minima (falsely localize object parts) while missing full object extent during training. In this article, we introduce classical continuation optimization into MIL, thereby creating continuation MIL (C-MIL) with the aim to alleviate the nonconvexity problem in a systematic way. To fulfill this purpose, we partition instances into class-related and spatially related subsets and approximate MIL's objective function with a series of smoothed objective functions defined within the subsets. We further propose a parametric strategy to implement continuation smooth functions, which enables C-MIL to be applied to instance selection tasks in a uniform manner. Optimizing smoothed loss functions prevents the training procedure from falling prematurely into local minima and facilities learning full object extent. Extensive experiments demonstrate the superiority of CMIL over conventional MIL methods. As a general instance selection method, C-MIL is also applied to supervised object detection to optimize anchors/features, improving the detection performance with a significant margin.
Which Project Management Methodology is better for AI-Transformation and Innovation Projects?

This research aims to explore and investigates which project management methodology is recommended for artificial intelligence (AI) transformation projects. Aligning projects with strategic business goals is necessary to achieve the desired outcomes for any digital transformation program. Project management methodology helps standardize studies of an ongoing project that might have a continuous effect on the organization. It also helps secure the standard practices, language, and processes that ensure the smooth flow of a project and are vital for project management. The current research will evaluate the most widely used project management methodologies and to which extent they support the sudden transformation witnessed due to Covid-19, including AI technologies.
Subject-adaptive Skeleton Tracking with RFID

With the rapid development of computer vision, human pose tracking has attracted increasing attention in recent years. To address the privacy concerns, it is desirable to develop techniques without using a video camera. To this end, RFID tags can be used as a low-cost wearable sensor to provide an effective solution for 3D human pose tracking. User adaptability is another big challenge in RF based pose tracking, i.e., how to use a well-trained model for untrained subjects. In this paper, we propose Cycle-Pose, a subject-adaptive realtime 3D human pose estimation system, which is based on deep learning and assisted by computer vision for model training. In Cycle-Pose, RFID phase data is calibrated to effectively mitigate the severe phase distortion, and High Accuracy LowRank Tensor Completion (HaLRTC) is employed to impute missing RFID data. A cycle kinematic network is proposed to remove the restriction on paired RFID and vision data for model training. The resulting system is subject-adaptive, achieved by learning to transform the RFID data into a human skeleton for different subjects. A prototype system is developed with commodity RFID tags/devices and evaluated with experiments. Compared with a traditional system RFIDPose, higher pose estimation accuracy and subject adaptability are demonstrated by Cycle-Pose in our experiments using Kinect 2.0 data as ground truth.
Image Captioning Methods and Metrics

Image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of Computer Vision (CV) and Natural Language Processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system.
Mask-ShadowNet: Toward Shadow Removal via Masked Adaptive Instance Normalization

Shadow removal is an important yet challenging task in image processing and computer vision. Existing methods are limited in extracting good global features due to the interference of shadow. And also, most of them ignore a fact that features inside and outside the shaded area should be treated disparately because of different semantics or materials. In this letter, we propose a novel deep neural network Mask-ShadowNet for shadow removal. The core of our approach is a well-designed masked adaptive instance normalization (MAdaIN) mechanism with embedded aligners that serves two goals: 1) producing hidden features that considering an illumination consistency of different regions. 2) treating the feature statistics of shadow and non-shadow areas discriminately based on the shadow mask. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on the ISTD benchmark. Our code is available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/penguinbing/Mask-ShadowNet</uri> .
Ontology Construction from Cross Domain Customer Reviews using Expectation Maximization and Semantic Similarity

Recently, researchers are focusing more attention on cross-domain customer review based ontology construction. It seems very little efforts have been taken to construct cross-domain application ontology. Online reviews help customers to get product information in detail. Bringing the reviews of same product from different e-commerce platform into single platform will enable customers make well informed decision. Customers may purchase online product by comparing reviews as manual comparison of reviews is too difficult. In future very large amount of cross-domain applications would be connected to make smart shopping possible. In this paper, two major e-commerce portals, Amazon and Flipkart's reviews are collected, processed and analyzed using expectation maximization (EM) algorithm and semantic similarity. Next, precision and recall, ontology construction and execution time are calculated using state of art models.
Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution

In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable “junction” unit to handle two major problems that exist in MTL—“How to share” and “How much to share.” Specifically, ACF consists of a sharing phase and a reconstruction phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the sharing phase. Subsequently, in the reconstruction phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable “junction” unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from sharing phase, learning an optimal combination for the following reconstruction phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.
Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification

Due to the sheer scale of the Internet of Things (IoT) and 5G, the wireless spectrum is becoming severely congested. For this reason, wireless devices will need to continuously adapt to current spectrum conditions by changing their communication parameters in real-time. Therefore, wireless signal classification (WSC) will become a compelling necessity to decode fast-changing signals from dynamic transmitters. Thanks to its capability of classifying complex phenomena without explicit mathematical modeling, deep learning (DL) has been demonstrated to be a key enabler of WSC. Although DL can achieve a very high accuracy under certain conditions, recent research has unveiled that the wireless channel can disrupt the features learned by the DL model during training, thus drastically reducing the classification performance in real-world live settings. Since retraining classifiers is cumbersome after deployment, existing work has leveraged the usage of carefully-tailored Finite Impulse Response (FIR) filters that, when applied at the transmitter's side, can restore the features that are lost because of the the channel actions, i.e., waveform synthesis. However, these approaches compute FIRs using offline optimization strategies, which limits their efficacy in highly-dynamic channel settings. In this paper, we improve the state of the art by proposing Chares, a Deep Reinforcement Learning (DRL)-based framework for channel-resilient adaptive waveform synthesis. Chares adapts to new and unseen channel conditions by optimally computing through DRL the FIRs in real time. Chares is a DRL agent whose architecture is based upon the Twin Delayed Deep Deterministic Policy Gradients (TD3), which requires minimal feedback from the receiver and explores a continuous action space for best performance. Chares has been extensively evaluated on two well-known datasets with an extensive number of channels. We have also evaluated the real-time latency of Chares with an implementation on field-programmable gate array (FPGA). Results show that Chares increases the accuracy up to 4.1x when no waveform synthesis is performed, by 1.9x with respect to existing work, and can compute new actions within 41 μs.
A Survey on Deep Learning for Intracranial Hemorrhage Detection

Computed Tomography (CT) images of the patient's cranium are widely used in the diagnosis of hemorrhage. They are reviewed by highly skilled professionals to find the existence, location and type of hemorrhage. This process is complex, labor-intensive and requires lot of time. The massive progress in machine learning techniques and availability of high computational power has made it possible to detect such hemorrhages automatically. The paper provides survey of recent deep learning approaches for automatic detection of such acute intracranial hemorrhage and its subtypes. Further the experimental results using windowing technique inspired by practical radiologist's approach and relevant research papers is provided to verify its efficacy as a pre-processing step before feeding DICOM images to the network. Transfer learning approach is used to detect hemorrhage types. The open source Kaggle platform is utilized for computational resources and for carrying out the experimental study. Some key evaluation metrics and loss functions addressing the data imbalance underlying with the problem statement are also presented. The paper concludes with addressing key insights into open issues in the proposed domain.
FSPRM: A Feature Subsequence Based Probability Representation Model for Chinese Word Embedding

Chinese word embedding models capture Chinese semantics based on the character feature of Chinese words and the internal features of Chinese characters such as radical, component, stroke, structure and pinyin. However, some features are overlapping and most methods do not consider their relevance. Meanwhile, they express words as point vectors that cannot better capture different aspect semantics of Chinese words. In this paper, we propose a Feature Subsequence based Probability Representation Model (FSPRM) for learning Chinese word embeddings, in which we first integrate the morphological and phonetic features (stroke, structure and pinyin) of Chinese characters and learn their relevance by designing a feature subsequence to capture relatively comprehensive semantics of Chinese words, then feature probability distribution is proposed for capturing different aspect meanings of Chinese words based on the three internal features and probability representation by estimating its mean as the sum of feature subsequences. Chinese words with similar features may have similar semantics, then we map Chinese words to feature probability distributions and design a similarity-based objective for predicting the contextual words of the target word to learn their semantics. Extensive experiments on word analogy, word similarity, text classification and named entity recognition tasks demonstrate that the proposed method outperforms most state-of-the-art approaches.
Cross Interaction Network for Natural Language Guided Video Moment Retrieval

Natural language query grounding in videos is a challenging task that requires comprehensive understanding of the query, video and fusion of information across these modalities. Existing methods mostly emphasize on the query-to-video one-way interaction with a late fusion scheme, lacking effective ways to capture the relationship within and between query and video in a fine-grained manner. Moreover, current methods are often overly complicated resulting in long training time. We propose a self-attention together with cross interaction multi-head-attention mechanism in an early fusion scheme to capture video-query intra-dependencies as well as inter-relation from both directions (query-to-video and video-to-query). The cross-attention method can associate query words and video frames at any position and account for long-range dependencies in the video context. In addition, we propose a multi-task training objective that includes start/end prediction and moment segmentation. The moment segmentation task provides additional training signals that remedy the start/end prediction noise caused by annotator disagreement. Our simple yet effective architecture enables speedy training (within 1 hour on an AWS P3.2xlarge GPU instance) and instant inference. We showed that the proposed method achieves superior performance compared to complex state of the art methods, in particular surpassing the SOTA on high IoU metrics ([email protected], IoU=0.7) by 3.52% absolute (11.09% relative) on the Charades-STA dataset.
Countering radiometric signature exploitation using adversarial machine learning based protocol switching

A Radiometric signature refers to transceiver specific features that are caused by variations in the manufacturing process even for the same circuit design. While such a radiometric signature constitutes a fingerprint that can be exploited for device authentication, it is a threat to privacy. Particularly, in the realm of wireless networks, an adversary may exploit radio frequency (RF) fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show how an adversary could employ RF fingerprinting to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. To counter such a threat, we propose a novel methodology that requires no hardware changes to the radio transceiver and the associated host device. Our methodology is based on coordinated switching among preset link-layer and physical-layer communication protocols. For the latter, we particularly exploit distributed beamforming. We employ adversarial machine learning to select the protocol configuration for each transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.