Reviewerly/exHarmony huggingface dataset:
Context-Aware 3D Object Detection From a Single Image in Autonomous Driving

Camera sensors have been widely used in Driver-Assistance and Autonomous Driving Systems due to their rich texture information. Recently, with the development of deep learning techniques, many approaches have been proposed to detect objects in 3D from a single frame, however, there is still much room for improvement. In this paper, we generally review the recently proposed state-of-the-art monocular-based 3D object detection approaches first. Based on the analysis of the disadvantage of previous center-based frameworks, a novel feature aggregation strategy has been proposed to boost the 3D object detection by exploring the context information. Specifically, an Instance-Guided Spatial Attention (IGSA) module is proposed to collect the local instance information and the Channel-Wise Feature Attention (CWFA) module is employed for aggregating the global context information. In addition, an instance-guided object regression strategy is also proposed to alleviate the influence of center location prediction uncertainty in the inference process. Finally, the proposed approach has been verified on the public 3D object detection benchmark. The experimental results show that the proposed approach can significantly boost the performance of the baseline method on both 3D detection and 2D Bird’s-Eye View among all three categories. Furthermore, our method outperforms all the monocular-based methods (even these trained with depth as auxiliary inputs) and achieves state-of-the-art performance on the KITTI benchmark.
Depixelation and image restoration with meta-learning in fiber-bundle-based endomicroscopy

In order to efficiently remove honeycomb artifacts and restore images in fiber-bundle-based endomicroscopy, we develop a meta-learning algorithm in this work. Two sub-networks are used to extract different levels of features. Meta-training is employed to train the network with small amount of simulated training data, enabling the optimal model to generalize to new tasks not seen in the training set. Numerical results on both USAF target and endomicroscopy images of living mice tissues demonstrate that the algorithm can restore high contrast image without pixilated noise using shorter time. Additionally, no prior information on the shape of the underlying tissues and the distribution of fiber bundles is required, making the method applicable to a variety of fiber-bundle-based endomicroscopy imaging conditions.
Particle Swarm Optimization with Modified Initialization Scheme for Numerical Optimization

Particle swarm optimization (PSO) is a popular optimization algorithm due to its desirable characteristics of fast convergence and easy implementation. Conventional PSO with poor quality of initial population tends to suffer with rapid diversity loss and converge to local optima regions prematurely. In this paper, a PSO with modified initialization scheme (PSOMIS) is proposed to generate the initial population with better quality in terms of fitness and diversity of solutions by leveraging the ergodicity nature of chaotic system and the ability of oppositional based learning to cover wider regions of solution space. Extensive evaluations of the proposed PSOMIS equipped with different chaotic maps are performed by using the test functions of CEC 2014. Simulation studies reported that each chaotic map tends to perform well in solving certain types of optimization problem, where PSOMIS with circle map has shown the best performance.
Immersive Technologies in the Healthcare Space

The field of life sciences and healthcare sector have always benefited from various digital innovations. One of the most beneficial technologies for healthcare is immersive technology. Immersive technologies such as VR, AR, and MR provide a full immersive experience by blending real and virtual worlds. Extended Reality (XR) is the new term to represent all immersive technologies. Currently, these technologies are used in almost all fields with the convergence of other relevant technologies. The role of these immersive technologies is inevitable in the healthcare sector as well. It provides many use cases with the support of other digital technologies and software techniques such as AI-based software methods, smart devices, sensors, robots, high performance computers (quantum computers), 6G networks and other compatible technologies. This chapter aimed to relate the state of the art in Immersive technologies related to healthcare by conducting a systematic review of the recent literature. With the support of digital technologies and artificial intelligence (AI), immersive technologies can offer significant assistance to medical professionals, surgeons, and even medical students in various fields. For instance, it can help a surgeon plan a complex surgery procedure by allowing the doctor to analyze layer by layer of a 3D holographic image of the patient. Without any physical limitation, medical students can study anatomy by examining a holographic body. Importantly, immersive technologies challenge age-related declines, especially by increasing morale. In conclusion, immersive technologies have already developed a strong identity in the healthcare sector and will be key components of healthcare in the future.
Research on Network Security Situation Prediction Based on Improved and Optimized BP Neural Network

For the sake of better mastering the dynamic changes of network security, in view of its nonlinear time series characteristics, this paper proposes a combination of genetic algorithm and simulated annealing algorithm to improve and optimize the BP neural network. After trial and error, the model was constructed and applied to actual predictions. The results reflect BP neural network on the basis of genetic simulated annealing(GSA-BP) algorithm is effective in predicting the situation of network security.
Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction

Single-view 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the input is unobstructed faces which makes their method not suitable for in-the-wild conditions. We present a method for performing a 3D face that removes eyeglasses from a single image. Existing facial reconstruction methods fail to remove eyeglasses automatically for generating a photo-realistic 3D face “in-the-wild”. The innovation of our method lies in a process for identifying the eyeglasses area robustly and remove it intelligently. In this work, we estimate the 2D face structure of the reasonable position of the eyeglasses area, which is used for the construction of 3D texture. An excellent anti-eyeglasses face reconstruction method should ensure the authenticity of the output, including the topological structure between the eyes, nose, and mouth. We achieve this via a deep learning architecture that performs direct regression of a 3DMM representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related face parsing task can be incorporated into the proposed framework and help improve reconstruction quality. We conduct extensive experiments on existing 3D face reconstruction tasks as concrete examples to demonstrate the method’s superior regulation ability over existing methods often break down.
A Supervised Learning Model for Detecting Ponzi Contracts in Ethereum Blockchain

Blockchain-based currencies, i.e., Ethereum, have increased in popularity among followers since 2009. However, scammers have customized offline frauds to this new ecosystem depending on blockchain’s anonymity. As a result, smart Ponzi contracts are circulating on Ethereum, which appear to be secure investment schemes. We employ data mining techniques to present an effective detection model for smart Ponzi contracts over the Ethereum blockchain. First, we extended the dataset of smart Ponzi contracts and eliminated the imbalanced dataset by performing adaptive synthetic sampling. Next, we defined four kinds of feature sets based on the operation codes (opcodes) of smart contracts such as opcode frequency, count vector, n-gram Term Frequency-Inverse Document Frequency (TF-IDF), and opcode sequence features. It is noteworthy that the feature sets are based on the opcodes of smart contracts, which makes our model more reliable once the smart contract is uploaded to the Ethereum Blockchain. Finally, we designed an ensemble classification model combining Bagging-Tree and XGBoost classifiers, compared to other methods, to increase the detection accuracy of smart Ponzi contracts. The empirical and comparative results show that the ensemble model with only n-gram based features presents the best performance and achieves high precision and recall.
Diagnostic accuracy of mammography in characterizing breast masses using the 5<sup>th</sup> edition of BI-RADS: A retrospective study

Background: Breast imaging-reporting and data system (BI-RADS) is intended for standardizing mammography reporting.Objectives: We aimed to evaluate the diagnostic precision of the BI-RADS assessment scoring system using histopathological findings as the reference standard. We also aimed to assess the positive predictive value (PPV) of different morphological descriptors for malignancy.Materials and Methods: This retrospective record-based analytical study was conducted in the Department of Radiodiagnosis of Acharya Harihar Post Graduate Institute of Cancer, Cuttack, Odisha, a tertiary cancer center in eastern India. We included patients attending the breast cancer unit with various breast complaints who were subjected to mammographic imaging and histopathological examination. The primary outcomes were the sensitivity, PPV, negative predictive value (NPV), and diagnostic accuracy (DA) of the BI-RADS scores for the pathological reports; secondary objective was the evaluation of the mammographic morphological characteristics. Mammography was interpreted using the BI-RADS 5th edition guidelines, without prior knowledge of the biopsy report. A BI-RADS final assessment score between 1 and 5 was assigned, where 1 indicated a normal study, 2 benign, 3 possibly benign requiring follow up, 4 suspicious requiring biopsy, and 5 indicating likely malignant requiring biopsy and further actions.Results: Between February 2020 and December 2020, we included 247 patients. All the category 5 lesions were malignant, while 76.5% of category 4 lesions were malignant. PPVs of BI-RADS categories 4a, 4b, and 4c were 38%, 90%, and 94%, respectively. Mammography had a sensitivity, specificity, PPV, NPV, and DA of 98.7%, 47.6%, 87.5%, 90.9%, and 87.9%, respectively. Morphological features that were significantly associated with malignancy were spiculated margins (P = 0.003, PPV = 100%), microlobulated margins (P = 0.005, PPV = 96.5%), irregular shape (P = 0.002, PPV = 89.6%), microcalcification (P = 0.005, PPV = 92.8%), skin thickening (P < 0.0001, PPV = 100%), and architectural distortion (P = 0.003, PPV = 96.7%).Conclusion: Digital mammography is a sensitive tool for the evaluation of breast lumps, but BI-RADS final assessment score is subjective as it depends on the interpreter's expertise.
Network Security by Merging two Robust Tools from the Mathematical Firmament

Significant advances in wireless detection, networking, and IoT technologies presuppose network security and confidentiality demand. Therefore, we develop a novel text encryption framework that has provable security against attacks on cryptosystems. The framework is based on fundamental mathematics and specifically on the Pell-Lucas sequence in conjunction with elliptic curves. We elaborate the plain text by the implementation of three basic steps. Initially, by applying a cyclic shift on the symbol set, we obtain a meaningless plain text. After that, we conceal the elements of the scattered plain text from the adversaries by using the Pell-Lucas sequence, a weight function, and a binary sequence. The binary sequence encodes each component of the diffused plain text into real numbers. In the final step, the encoded scattered plain text is confused by creating permutations over elliptic curves. We then prove that the proposed encryption framework has provable security against brute-force and known-plaintext attack. It is also extremely secure compared with fundamental spacing analysis.
Multi-domain semantic segmentation with overlapping labels

Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on many datasets becomes a method of choice towards graceful degradation in unusual scenes. Unfortunately, different datasets often use incompatible labels. For instance, the Cityscapes road class subsumes all driving surfaces, while Vistas defines separate classes for road markings, manholes etc. We address this challenge by proposing a principled method for seamless learning on datasets with overlapping classes based on partial labels and probabilistic loss. Our method achieves competitive within-dataset and cross-dataset generalization, as well as ability to learn visual concepts which are not separately labeled in any of the training datasets. Experiments reveal competitive or state-of-the-art performance on two multi-domain dataset collections and on the WildDash 2 benchmark.
Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> distances than state-of-the-art methods.
Frequency-shifting technique for pixelwise absolute phase retrieval

In fringe projection profilometry, phase shifting (PS) is the most used technique for phase retrieval. However, it suffers from periodicity of sine wave in PS; the result is wrapped into [ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> </mml:mrow> <mml:mi>π<!-- π --></mml:mi> <mml:mo>,</mml:mo> <mml:mspace width="thickmathspace" /> <mml:mi>π<!-- π --></mml:mi> </mml:math> ]; and additional phase unwrapping (PU) is necessary to retrieve the absolute phase. In this paper, a more general technique termed frequency shifting is proposed, based on which the behavior of periodicity is eliminated and absolute phase can be retrieved pixelwisely without any phase unwrapping. The effectiveness of the proposed technique was verified by extensive experimental results, and they demonstrate comparable performance with those of the traditional technique combining PS and PU even in only one step and less projection.
Hallucination of Speech Recognition Errors With Sequence to Sequence Learning

Prior work in this domain has focused on modeling errors at the phonetic level, while using a lexicon to convert the phones to words, usually accompanied by an FST Language model. We present novel end-to-end models to directly predict hallucinated ASR word sequence outputs, conditioning on an input word sequence as well as a corresponding phoneme sequence. This improves prior published results for recall of errors from an in-domain ASR system’s transcription of unseen data, as well as an out-of-domain ASR system’s transcriptions of audio from an unrelated task, while additionally exploring an in-between scenario when limited characterization data from the test ASR system is obtainable. To verify the extrinsic validity of the method, we also use our hallucinated ASR errors to augment training for a spoken question classifier, finding that they enable robustness to real ASR errors in a downstream task, when scarce or even zero task-specific audio was available at train-time.
Evolving Data Augmentation Strategies

Deep Learning Algorithms are widely implemented and have reached state-of-the-art results in several scientific investigations. In medical images domain and Computer-Assisted Detection (CAD) systems, Convolution Neural Networks (CNNs) are the preferred deep network architecture. Despite getting good results, there are still some obstacles to overcome, namely the problem of overfitting. Lately, the Data Augmentation (DA) has been integrating the training pipeline of Deep Neural Networks to mitigate those issues. The effectiveness of classical image transformations in increasing the performance of classification tasks in medical imaging domain has been reported. However, the search for a suitable augmentation strategy is performed manually. This approach, mainly made of trial-and-error tasks can be very time-consuming and complex. Thereupon, a novel data augmentation approach is proposed. The approach is an Evolutionary Machine Learning approach that is able to automatically define an optimised DA strategy for each medical image classification task. Thus, the approach combines two algorithms, an evolutionary algorithm and combined with a deep learning algorithm to find suitable DA strategies. The results obtained demonstrate that the same or better level of performance is achieved when the Transformation Functions (TFs) and their parameters are defined automatically instead of manually.
Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

The scientific world is changing a tarapid pace, with new technology being developed and new trends being set at an increasing frequency. This paper presents a framework for conducting scientific analyses of academic publications, which is crucial to monitor research trends and identify potential innovations. This framework adopts and combines various techniques of Natural Language Processing, such as word embedding and topic modelling. Word embedding is used to capture semantic meanings of domain-specific words. We propose two novel scientific publication embedding, i.e., P UB-G and P UB-W, which are capable of learning semantic meanings of general as well as domain-specific words in various research fields. Thereafter, topic modelling is used to identify clusters of research topics within these larger research fields. We curated apublication dataset consisting of two conferences and two journals from 1995 to 2020 from two research domains. Experimental results show that our PUB-G and PUB-W embeddings are superior in comparison to other baseline embeddings by a margin of ~0.18-1.03 based on topic coherence.
Conversation-Based Adaptive Relational Translation Method for Next POI Recommendation With Uncertain Check-Ins

The uncertain check-ins bring challenges for current static next point-of-interest (POI) recommendation methods. Fortunately, the conversation-based recommendation has been shown the merit of integrating immediate user preference for more accurate recommendations. We, therefore, propose a conversation-based adaptive relational translation (CART) approach for the next POI recommendation over uncertain check-ins. It is equipped with recommender and conversation modules to interactively acquire users' immediate preferences and make dynamic recommendations. Specifically, the recommender built upon the adaptive relational translation method performs location prediction via modeling both users' historical sequential behaviors and the immediate preference received from conversations; the conversation module aims to achieve successful recommendations in fewer conversation turns by learning a conversational strategy, whereby the recommender can be updated via the user response. Extensive experiments on four real-world datasets show the superiority of our proposed CART over the state of the arts.
Security Challenges in the Container Cloud

In recent years, containerization has become a major trend in the cloud due to its high resource utilization efficiency and convenient DevOps support. However, the complexity of container system also introduces attack surfaces. This paper aims to summarize security challenges in the container cloud. In particular, we first divide the whole container system into different layers according to their functionalities, including the kernel layer, the container layer, and the orchestration layer. We then summarize security-related technologies. After that, we discuss the security challenges for each layer. Finally, we present the current protection status for the container system and highlight future research directions. Our study shows that to improve the container cloud security, we need to design and implement more robust kernel isolation mechanisms, conduct systematic and thorough security analysis on existing container techniques, and develop comprehensive configuration checking tools.
Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction

Given that physics-based models can be difficult to derive, data-driven models have been widely used for remaining useful life (RUL) prediction, which is a key element for predictive maintenance. In industrial applications, although the models have to be trained in a short time with limited computational resources, recent research using back propagation neural networks (BPNNs) has focused only on minimizing the RUL prediction error, without considering the time needed for training. Driven by this motivation, here we consider a simple and fast neural network, named extreme learning machine (ELM), and we optimize it for the specific case of RUL prediction. In particular, we propose to apply both single-objective and multi-objective optimization to search for the best ELM architectures in terms of a trade-off between RUL prediction error and training time, the latter being determined by the number of trainable parameters. We perform a comparative analysis on a recent benchmark dataset, the N-CMAPSS, in which we compare the proposed methods with other algorithms based on BPNNs. The results show that while the optimized ELMs perform slightly worse than the BPNNs in terms of RUL prediction error, they require a significantly shorter (up to 2 orders of magnitude) training time.