Reviewerly/exHarmony huggingface dataset:
An Image Enhancement Method in Coal Mine Underground Based on Deep Retinex Network and Fusion Strategy

Due to the high dust concentration and low illuminance in coal mines, the collected images have shortcomings such as low contrast and overall darkness, which brings severe challenges to subsequent image registration and target detection. This paper proposes a mine image enhancement method based on deep learning and fusion strategies. First, CNN is used to decompose the image into illumination components and reflection components, and then based on the reflectivity consistency constraint and the illumination mapping smoothness constraint, the enhancement network is used to enhance the image And adjust to obtain the initial enhanced image, and then introduce the camera response model to simulate the multiple exposure process, find the best exposure rate, generate a virtual over-exposure image, and finally combine the original low-illuminance image, use image block decomposition for reconstruction and fusion to obtain underground low-illuminance Enhance the image. The experimental results show that compared with mainstream enhancement algorithms, the enhancement strategy in this article is better than other algorithms in subjective and objective evaluation. It can improve the contrast and brightness of the image while avoiding color distortion and over-enhancement, making it a follow-up image The processing work laid the foundation.
GrAR: A novel framework for Graph Alignment based on Relativity concept

Social media will continue growing rapidly and integration of social media information has become important. Information integration and many tasks require graph alignment or finding the same nodes in different networks. There are several methods for graph alignment. Many of these methods introduce a new feature vector to represents node structure. Then compare all node pairs between two graphs without using any graph information. According to our current knowledge, many methods use either local node properties or global node properties, and fewer methods use node's structural role for graph alignment. We propose a relativity based graph alignment framework (GrAR) that takes a feature vector for each node or ID vector as input and extracts structural role properties for each node for graph alignment task. Experimental results on different datasets indicate that if the ID vector is strong enough, the proposed framework improves the efficacy of the ID vector. Furthermore, to deal with the presence of noise, a relative-degree feature based on the concept of relativity is introduced. Our results show that the proposed feature vector is noise-resistant and increases the efficiency of graph alignment tasks alone or within the framework.
Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers

Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active learning, and enable continual learning. Bayesian learning provides a principled approach to address all these limitations, at the cost of an increase in computational complexity. This paper studies distributed Bayesian learning in a wireless data center setting encompassing a central server and multiple distributed workers. Prior work on wireless distributed learning has focused exclusively on frequentist learning, and has introduced the idea of leveraging uncoded transmission to enable &#x201C;over-the-air&#x201D; computing. Unlike frequentist learning, Bayesian learning aims at evaluating approximations or samples from a global posterior distribution in the model parameter space. This work investigates for the first time the design of distributed one-shot, or &#x201C;embarrassingly parallel&#x201D;, Bayesian learning protocols in wireless data centers via consensus Monte Carlo (CMC). Uncoded transmission is introduced not only as a way to implement &#x201C;over-the-air&#x201D; computing, but also as a mechanism to deploy <i>channel-driven MC sampling</i>: Rather than treating channel noise as a nuisance to be mitigated, channel-driven sampling utilizes channel noise as an integral part of the MC sampling process. A simple wireless CMC scheme is first proposed that is asymptotically optimal under Gaussian local posteriors. Then, for arbitrary local posteriors, a variational optimization strategy is introduced. Simulation results demonstrate that, if properly accounted for, channel noise can indeed contribute to MC sampling and does not necessarily decrease the accuracy level.
Entanglement resonance in the asymmetric quantum Rabi model

We investigate the entanglement features in the interacting system of a quantized optical field and a two-level system which is statically driven, known as the asymmetric quantum Rabi model (AsymQRM). Intriguing entanglement resonance valleys with the increase of the photon-atom coupling strength and peaks with the increase of the driving amplitude are found. It is revealed that both of these two kinds of entanglement resonance are caused by the avoided level crossing of the associated eigenenergies. In sharp contrast to the quantum Rabi model, the entanglement of the AsymQRM collapses to zero in the strong coupling regime except when the driving amplitude equals to $m\omega/2$, with $m$ being an integer and $\omega$ being the photon frequency. Our analysis demonstrates that such entanglement reappearance is induced by the hidden symmetry of the AsymQRM. Supplying an insightful understanding on the AsymQRM, our results will be helpful in exploring the hidden symmetry and in preparing photon-atom entanglement in light-matter coupled systems.
Detecting Extremely Small Lesions in Mouse Brain MRI with Point Annotations via Multi-task Learning

Detection of small lesions in magnetic resonance imaging (MRI) images is one of the most challenging tasks. Compared with detection in natural images, small lesion detection in MRI images faces two major problems: First, small lesions only occupy a small fraction of voxels within an image, yielding insufficient features and information for them to be distinguished from the surrounding tissues. Second, an accurate outline of these small lesions manually is time-consuming and inefficient even for medical experts in pathology. Hence, existing methods cannot accurately detect lesions with such a limited amount of information. To solve these problems, we propose a novel multi-task convolutional neural network (CNN), which simultaneously performs regression of lesion number and detection of lesion location. Both lesion number and location can be obtained through point annotations, which is much easier and efficient than a full segmentation of lesion manually. We use an encoder-decoder structure that outputs a distance map of each pixel to the nearest lesion centers. Additionally, a regression branch is added after the encoder to learn the counting of lesion numbers, thus providing an extra regularization. Note that these two tasks share the same encoder weights. We demonstrate that our model enables the counting and locating of extremely small lesions within 3–5 voxels (300 × 300 voxels per image) with a recall of 72.66% on a large mouse brain MRI image dataset (more than 1000 images), and outperforms other methods.
Enhanced Data Encryption in IOT using ECC Cryptography and LSB Steganography

Internet of Things is a dynamic field wherein transfer of data transpires every single instant. Although in recent times various hybrid mechanisms that combine both steganography and cryptography have been proposed to ensure security in data communication networks since a standalone method does not provide adequate security and reliability. In the case of cryptography, if a perpetrator is to guess the secret key, they can easily decrypt and access desired data. On the flip side, if changes are somehow visible in an image, the perpetrator may apply steganography techniques to recover the plaintext. Therefore, this paper presents a novel mechanism for efficient and safe data communication. The proposal uses both cryptographic and steganographic techniques to provide optimum level of security to the secret data. The cryptographic technique used is Elliptic Curve Cryptography and the steganography technique used is LSB.
A Study on the Generalized Normalization Transformation Activation Function in Deep Learning Based Image Compression

Image compression has a long history, being widely applied in numerous applications. Yet, the commonly applied approach is based on traditional signal processing, such as JPEG. The lack of learning process in the approach limits the performance of those methods. Deep learning, recently, shows great performance in image compression, whose success may be attributed to various factors. Activation function is known as one of these influential factors. The present work is devoted to analyze the different effects of various activation functions, and the experimental results suggest that the generalized divisive normalization (GDN) is probably the best activation function in deep image approach-based image compression.
LASTD: A Manually Annotated and Tested Large Arabic Sentiment Tweets Dataset

With the growing attention towards Arabic Sentiment Analysis (SA), the availability of annotated dataset has raised. Although acquiring dataset from social media platforms, microblogs and so on is an easy task, annotation is the hard part. Dataset annotation requires a lot of manual tedious work which stands as a major problem. In addition to that, some datasets are built in house and aren't available for public access. This paper introduces the LASTD which is a manually annotated dataset for Arabic tweets sentiment analysis along with an insight of its statistics and benchmarks. It consists of more than 15K Arabic tweets annotated as positive, negative and neutral. Using 10-cross validation, three different classifiers were trained and tested for 3-class classification problem and 2-class classification problem. The support vector machine (SVM) classifier tends to have the highest accuracy. LASTD is made public for academic research.
A Certificate Authority Domain-based Cross-domain Authentication Scheme for Virtual Enterprise Using Identity Based Encryption

Virtual enterprise is an effective model of many systems like mobile ad hoc network and smart city. It consists of many independent abstract enterprises which communicate with each other through cross-domain authentication. To realize cross-domain authentication, we propose a scheme which combines threshold secret sharing and identity based encryption to construct certificate authority domain. This scheme minimizes the length of verifying path and realizes decentralization because it does not rely on trusted third party. Because of using disturbance, this scheme allows enterprises to join and quit and makes all enterprises have equal status. Moreover, the threshold and initial quantity of enterprises are equal, which makes the system able to set up on public channel.
Social media and students’ privacy: What schools and districts should know

For many schools and districts in the United States, Facebook has emerged as an important tool for sharing timely information, building a sense of community, highlighting staff and students, and many other purposes. However, neither researchers nor schools and districts have paid enough attention to how their Facebook use may pose a risk to the privacy of individuals — often students who are minors. Joshua Rosenberg, Macy Burchfield, Conrad Borchers, Benjamin Gibbons, Daniel Anderson, and Christian Fischer describe their recent studies showing that 15-20 million photos of students have been shared on publicly accessible Facebook pages of public schools and districts. They estimate that at least 150,000 of these photos — and perhaps as many as a million or more — depict students who are identifiable by name and school or district. They review some of the risks to students that might result from such social media posts and offer practical steps that schools and districts can take to minimize these risks.
An Effective Blockchain Technique to Resist Against Deepfake Videos

Recently, with growth in artificial intelligence as well in deep learning, fake digital data like images, footage, videos and audios has escalated. This fake data is termed as deep fakes. It is a dangerous scenario which is responsible to alter the truth and grind down the trust because of spreading wrong or false message. So it is necessary to secure data. There is one important technique which is used to resist the deep fake videos: blockchain. This paper aimed to identify the origin of video and verify the authenticity of video, to integrate the work with scalable verification mechanism and to implement blockchain based technique for effective deepfake video recognition. In short, it provides the analysis for video signal for identification of the originality of the video.SHA3-256 algorithm which is base for encryption is utilized in blockchain to obtain the hash function.In proposed method the results are compared with the implementation of metadata extraction in which the various properties of the video file are taken for consideration like bitrate, frame rate, etc. for the exact data determination along with the hash value.
Landmark-Guided Rigid Registration for Temporomandibular Joint MRI-CBCT Images with Large Field-of-View Difference

Fused MRI-CBCT images provide desirable complementary information of the articular disc and condyle surface for optimum diagnosis, has been shown to be accurate and reliable in Temporomandibular Disorders (TMD) assessment. But field-of-view difference between multi-modality images brings challenges to conventional registration algorithms. In this paper, we proposed a landmark-guided learning method for Temporomandibular Joint (TMJ) MRI-CBCT images registration. First, end-to-end landmark localization network was used to detect correspondence landmark pairs in the different modality images to generate the landmark guidance information. Then taking image patches centered landmarks as input, an unsupervised learning network regresses the rigid transformation matrix using mutual information as a measure of similarity between image patches. Finally combined landmarks coordinates with the rigid transformation matrix, the whole image registration can be realized. Experiment results demonstrate that our approach achieves better overall performance on registration of images from different patients and modalities with 100x speed-up in execution time.
Artifical Intelligence and CRM

Disruptive technologies such as IoT, big data analytics, blockchain, and AI have changed the ways businesses operate, with AI holding immense marketing transformation potential. AI is influencing marketing strategies, business models, sales processes, customer service options, and customer behaviors. AI-CRM's improving ability to predict customer lifetime value will generate an inevitable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. CSPs are working through the challenging process of digital transformation, driven by the need to compete with fast-moving OTT and consumer tech players. CSPs need to move quickly and can advance digital transformation with solutions that leverage AI which can drive value across the business from network optimization and data analytics through to customer care and marketing engagement. The chapter tries to identify how AI is impacting the CRM in the telecom industry and leveraging the benefits of this technology for better customer management and growth.
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers

Recent work applying deep reinforcement learning (DRL) to solve traveling salesman problems (TSP) has shown that DRL-based solvers can be fast and competitive with TSP heuristics for small instances, but do not generalize well to larger instances. In this work, we propose a novel approach named MAGIC that includes a deep learning architecture and a DRL training method. Our architecture, which integrates a multilayer perceptron, a graph neural network, and an attention model, defines a stochastic policy that sequentially generates a TSP solution. Our training method includes several innovations: (1) we interleave DRL policy gradient updates with local search (using a new local search technique), (2) we use a novel simple baseline, and (3) we apply curriculum learning. Finally, we empirically demonstrate that MAGIC is superior to other DRL-based methods on random TSP instances, both in terms of performance and generalizability. Moreover, our method compares favorably against TSP heuristics and other state-of-the-art approach in terms of performance and computational time.
Automated Unsupervised Graph Representation Learning

Graph data mining has largely benefited from the recent developments of graph representation learning. Most attempts to improve graph representations have thus far focused on designing new network embedding or graph neural network (GNN) architectures. Inspired by the SGC and ProNE models, we instead focus on enhancing any existing or learned graph representations by further smoothing them via graph filters. In this paper, we introduce an automated framework AutoProNE to achieve this. Specifically, AutoProNE automatically searches for a unique optimal set of graph filters for any input dataset, and its existing representations are then smoothed via the selected filters. To make AutoProNE more general, we adopt self-supervised loss functions to guide the optimization of the automated search process. Extensive experiments on eight commonly used datasets demonstrate that the AutoProNE framework can consistently improve the expressive power of graph representations learned by existing network embedding and GNN methods by up to 44%.
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature in most top-performing CNNs is an encoder-decoder architecture inspired by the U-Net. For multi-region brain tumor segmentation, 3D U-Net architecture and its variants provide the most competitive segmentation performances. In this work, we propose an interesting extension of the standard 3D U-Net architecture, specialized for brain tumor segmentation. The proposed network, called E1D3 U-Net, is a one-encoder, three-decoder fully-convolutional neural network architecture where each decoder segments one of the hierarchical regions of interest: whole tumor, tumor core, and enhancing core. On the BraTS 2018 validation (unseen) dataset, E1D3 U-Net demonstrates single-prediction performance comparable with most state-of-the-art networks in brain tumor segmentation, with reasonable computational requirements and without ensembling. As a submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge, we also evaluate our proposal on the BraTS 2021 dataset. E1D3 U-Net showcases the flexibility in the standard 3D U-Net architecture which we exploit for the task of brain tumor segmentation.
I-Quiz: An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Electronic learning (e-learning) has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic, online learning has become the most essential and inevitable medium of learning in primary, secondary and higher education.In recent times, Massive Open Online Courses (MOOCs) have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner's progress and effectiveness of online teaching is the Multiple Choice Question (MCQ) assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses, learners can attempt, as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture, development, evaluation of the I-Quiz system, an intelligent assessment tool, which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner's real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.
On Securing Cloud-Hosted Cyber-Physical Systems Using Trusted Execution Environments

Recently, cloud control systems have gained increasing attention from the research community as a solution to implement networked cyber-physical systems (CPSs). Such an architecture can reduce deployment and maintenance costs albeit at the expense of additional security and privacy concerns. In this paper, first, we discuss state-of-the-art security solutions for cloud control systems and their limitations. Then, we propose a novel control architecture based on Trusted Execution Environments (TEE). We show that such an approach can potentially address major security and privacy issues for cloud-hosted control systems. Finally, we present an implementation setup based on Intel Software Guard Extensions (SGX), and validate its effectiveness on a testbed system.
Demo: Proof-of-Work Network Simulator for Blockchain and Cryptocurrency Research

Blockchain and the proof-of-work (PoW) distributed consensus protocol rely on peer-to-peer (P2P) networking. We build a PoW P2P simulator for the modeling and analyses of permissionless blockchain networking. Our simulator utilizes a built-in randomness generator for the simulations, has an easy-to-use interface and intuitive visualization, supports dynamic/programmable control and modifications, and can generate simulation data for further processing. We publish our simulator in open source to facilitate its use for blockchain and P2P networking research and especially recommend it for scalability research or preliminary testing. To highlight its features and capabilities, we demonstrate the simulator use in this paper to analyze the recent blockchain security research, including 51% attack, eclipse, partitioning, and DoS attack.