Reviewerly/exHarmony huggingface dataset:
Toward High-quality Face-Mask Occluded Restoration

Face-mask occluded restoration aims at restoring the masked region of a human face, which has attracted increasing attention in the context of the COVID-19 pandemic. One major challenge of this task is the large visual variance of masks in the real world. To solve it we first construct a large-scale Face-mask Occluded Restoration (FMOR) dataset, which contains 5,500 unmasked images and 5,500 face-mask occluded images with various illuminations, and involves 1,100 subjects of different races, face orientations, and mask types. Moreover, we propose a Face-Mask Occluded Detection and Restoration (FMODR) framework, which can detect face-mask regions with large visual variations and restore them to realistic human faces. In particular, our FMODR contains a self-adaptive contextual attention module specifically designed for this task, which is able to exploit the contextual information and correlations of adjacent pixels for achieving high realism of the restored faces, which are however often neglected in existing contextual attention models. Our framework achieves state-of-the-art results of face restoration on three datasets, including CelebA, AR, and our FMOR datasets. Moreover, experimental results on AR and FMOR datasets demonstrate that our framework can significantly improve masked face recognition and verification performance.
Intelligent recognition of radar emitters with agile waveform based on deep reinforcement learning

Abstract In the traditional radar emitter recognition task, the number of emitter category tags is fixed and relatively small. We input the pulse characteristic parameters into the machine learning model to model the radar emitter. The radar emitters with agile waveform involves four different waveform categories. The number of specific emitter categories contained in different waveform categories is extremely unbalanced. Their parameters vary widely, so only using the same model or strategy to model each waveform category cannot effectively extract the core features of the radar emitters with agile waveform. The above is the main reason for the low recognition accuracy of the recognition task of radar emitters with agile waveform. In this paper, a deep reinforcement learning framework is designed to be used in the intelligent recognition task of radar emitters with agile waveform. The task is regarded as a two‐step decision game. We use CNN and Bi‐LSTM to model the radar emitter, and calculate the initial state and transition state of the game. At the same time, we design the penalty function in reinforcement learning and increase the penalty for the wrong decision in the first step to deal with the imbalance of the number of emitters between different waveform categories. Finally, the Q ‐Learning algorithm with approximate values is used to learn the control strategy of the game, that is, the modeling and recognition strategy adopted for emitters of different waveform categories. The simulation experiment results show that the deep reinforcement learning framework constructed in the article can improve the recognition accuracy of 1.2% compared with the state‐of‐the‐art method in the intelligent recognition task of radar emitters with agile waveform.
A novel enhanced cuckoo search algorithm for global optimization

Cuckoo search algorithm (CS) is an excellent nature-inspired algorithm that has been widely introduced to solve complex, multi-dimensional global optimization problems. However, the traditional CS algorithm has a low convergence speed and a poor balance between exploration and exploitation. In other words, the single search strategy of CS may make it easier to trap into local optimum and end in premature convergence. In this paper, we proposed a new variant of CS called Novel Enhanced CS Algorithm (NECSA) to overcome these drawbacks mentioned above inspired by the cuckoos’ behaviors in nature and other excellent search strategies employed in intelligent optimization algorithms. NECSA introduces several enhancement strategies, namely self-evaluation operation and modified greedy selection operation, to improve the searchability of the original CS algorithm. The former is proposed to enhance the exploration ability and ensure population diversity, and the latter is employed to enhance the exploitation ability and increase search efficiency. Besides, we introduced adaptive control parameter settings based on the fitness and iteration number to increase the convergence speed and the accuracy of the search process. The experimental results and analysis on the CEC2014 test have demonstrated the reliable performance of NECSA in comparison with the other five CS algorithm variants.
M3V: Multi-modal Multi-view Context Embedding for Repair Operator Prediction

We address the problem of finding context embeddings for faulty locations to allow a learning-based APR tool to learn and predict the repair operators used at the faulty locations. We introduce M3V, a new multi-modal multi-view context embedding approach, which represents the context of a faulty location in two modalities: (1) texts that capture its signature in a natural language using the tree-LSTM model, and (2) graphs that capture its structure with two views, data and control dependences, using the GNN model. We then fuse these two modalities to learn a probabilistic classifier from correct code that, once given a faulty location, will produce a probabilistic distribution over a set of repair operators. We have evaluated M3V against the state-of-the-art context embedding approaches in repairing two common types of bugs in Java, null pointer exceptions (NPE) and index out of bounds (OOB). Trained and tested with 75673 code samples from 20 real-world projects, a learning-based APR tool can predict repair operators more effectively with our context embeddings in repairing NPE bugs, by achieving higher accuracies (11% – 41%) and higher F1 scores (16% – 143%). For OOB bugs, these improvements are 9% – 30% and 15% – 79%, respectively.
Thermal infrared face recognition with face mask based on the generative method

With the development of artificial intelligence technology, visible light face recognition is becoming more and more popular. But visible light can't solve the problem of facial occlusion. Because the visible light cannot pass through the occlusion and is received by the camera, a lot of information is lost. In reality, when criminals wear masks, they can't recognize faces in visible light. But thermal infrared technology can solve this problem because the heat emitted by the face can be captured by the thermal infrared camera through the mask. In this paper, the generative deep learning method is used to remove the mask in the independent data set, and the recognition result is good.
Does depth estimation help object detection?

Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield improvements. Many factors affect the performance of object detection when estimated depth is used. In this paper, we comprehensively investigate these factors with detailed experiments, such as using ground-truth vs. estimated depth, effects of different state-of-the-art depth estimation networks, effects of using different indoor and outdoor RGB-D datasets as training data for depth estimation, and different architectural choices for integrating depth to the base object detector network. We propose an early concatenation strategy of depth, which yields higher mAP than previous works' while using significantly fewer parameters.
Hardware-friendly User-specific Machine Learning for Edge Devices

Machine learning (ML) on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the privacy of user data. In contrast, the type of data processed at edge devices is user-specific and limited to a few inference classes. In this work, we explore building smaller, user-specific machine learning models, rather than utilizing a generic, compute-intensive machine learning model that caters to a diverse range of users. We first present a hardware-friendly, lightweight pruning technique to create user-specific models directly on mobile platforms, while simultaneously executing inferences. The proposed technique leverages compute sharing between pruning and inference, customizes the backward pass of training, and chooses a pruning granularity for efficient processing on edge. We then propose architectural support to prune user-specific models on a systolic edge ML inference accelerator. We demonstrate that user-specific models provide a speedup of 2.9× and 2.3× on the mobile CPUs for the ResNet-50 and Inception-V3 models.
Male pelvic multi-organ segmentation using V-transformer network

Automatic multi-organ segmentation is a cost-effective tool for generating organ contours using computed tomography (CT) images. This work proposes a deep-learning algorithm for multi-organ (bladder, prostate, rectum, left and right femoral heads) segmentation in pelvic CT images for prostate radiation treatment planning. We propose an encoder-decoder network with a V-net backbone for local feature extraction and contour reconstruction. Novel to our network, we utilize a token-based transformer, which encourages long-range dependency, to forward more informative high-resolution feature maps from the encoder to the decoder. In addition, a knowledge distillation strategy was applied to improve the network’s generalization. We evaluate the network using a dataset collected from 50 patients with prostate cancer. A quantitative evaluation of the proposed network’s performance was performed on each organ based on: 1) volume similarity between the segmented contours and ground truth using Dice score, segmentation sensitivity, precision, and absolute percentage volume difference (AVD), 2) surface similarity evaluated by Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). The performance was then evaluated against other state-of-art methods. The average volume similarities achieved by the network over all organs were: Dice score = 0.83, sensitivity = 0.84, and precision = 0.83; the average surface similarities were HD = 5.77mm, MSD = 0.93mm, RMSD = 2.77mm, and AVD =12.85%. The proposed methods performed significantly better than competing methods in most evaluation metrics. The proposed network may be a promising segmentation approach for use in routine prostate radiation treatment planning.
Arbitrary estimation of uranium ore reserves in Qattar–V, North Eastern Desert, Egypt using geographic information system

The qattar - V (G-V) uranium occurrence occurs at the contact areas between Gabal Qattar (G - Qattar) granite and Hammamat sedimentary rocks. The aim of this study is to estimate uranium ore reserves using a geographic information system by analyzing all the exploration data sets at G-V. Several geological investigations, including field and mineralogical observations, geochemical and radiometric surveys, have been performed for this uranium occurrence. Integrate these investigations with new radiometric and geochemical measurements to cover all the investigated areas has been done in this study. Using GIS it is possible to visualize the results and to extract the spatial features indicative of uranium mineralization's over G-V uranium occurrence. Our methodology involves the generation of prediction maps for the favorable host rock, structure control of uranium mineralization, alterations in intimate association with uranium, radiometrical and geochemical measurements. Combining all these spatial features leads to the differentiation between the areas with economical and for grades of uranium and the generation of a potential uranium map. From this map, the estimation of uranium ore quantity and quality at G-V uranium occurrences can be calculated using cuboids of constant widths, different lengths, and heights. This method gave arbitrary but reasonable estimates of uranium in selected blocks.
Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection

Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection.However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features.In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE).The proposed method improves the accuracy of video anomaly detection by obtaining more vital information.In the data sorting phase, non-uniform chunking is proposed to divide the video frame into several chunks of different sizes to avoid obtaining unnecessary information and omitting crucial information.In order to well reflect the abnormal motion of objects in the video, a new feature, the inter frame flow feature, which is obtained by merging inter frame difference and optical flow features, is proposed to extract motion feature.Moreover, in this paper, skip connection in the auto encoder is utilized during the training phase to reduce the reconstruction error between the original frames and the reconstruction frames, so that the reconstruction error can be used to detect abnormal events during testing.Experience on three public datasets verifies the effectiveness and accuracy of our proposed method.Experimental results show that the proposed method can detect and locate abnormal events outperforms other recent methods significantly.
Applications of Radial Basis Functional Link Networks in the Exploration for Lala Copper Deposits in Sichuan Province, China

The Lala copper area in Huili County, Sichuan Province, China, is favored by superior regional metallogenic geological conditions due to its location in an extremely important copper–iron metallogenic belt in southwest China, and it has witnessed the formation of a series of unique iron–copper deposits following the superposition of multiple tectonic events. In recent years, major mineral exploration breakthroughs have been achieved in the deep and peripheral zones of this area. Using the Lala copper mining area in Sichuan as an example, this paper describes metallogenic prediction research carried out based on multivariate geoscience information (geological information, geophysics, geochemistry, and remote sensing data) and the application of geographic information system (GIS) technology and the radial basis function neural network (RBFLN) model. The five specific aspects covered in this paper are as follows: (1) we collected geology–geophysics–geochemistry remote sensing data and other information, adopted GIS technology to extract multivariate geoscience ore-forming anomaly information, and established a geoscience prospecting information database; (2) we applied the RBFLN algorithm for information on integrated analysis of ore-forming anomalies in the study area; (3) we applied a statistical method to divide the threshold value to delineate favorable ore-prospecting target areas; (4) we applied three-dimensional (3D) visualization technology, through which sample assistance was verified, to evaluate the performance of the RBFLN model; and (5) the results revealed that the RBFLN model can integrate multivariate and multi-type geoscience information and effectively predict metallogenic prospective areas and delineate favorable target areas. The metallogenic prediction method based on RBFLN technology provides a scientific basis for the exploration and deployment of minerals in the study area. It is obvious that the methods to predict and evaluate mineral resources are developing towards model integration and information intelligent analysis.
Gadgets Splicing: Dynamic Binary Transformation for Precise Rewriting

Many systems and applications depend on binary rewriting technology to analyze and retrofit software binaries when source code is not available, including binary instrumentation, profiling and security policy reinforcement. However, the investigations have found that many static binary rewriters still fail to accurately transform all legal instructions in binaries. Dynamic binary rewriters allow for accuracy, but coverage and rewriting efficiency are limited. Therefore, the existing binary rewriting technology cannot meet all the needs of binary rewriting. In this paper, we present GRIN, a novel binary rewriting tool that allows for high-precision instruction identification. In GRIN, we propose a gadget-based entry address analysis technique. It identifies the entry addresses of the basic blocks in the binary by gathering and executing the basic blocks related to the computation of the entry addresses of the basic blocks. By traversing from these entries as the new entries of the program, we guarantee the correctness of the identified instructions. We have implemented the prototype of GRIN and evaluated on the SPEC2006 and the whole set of GNU Coreutils. We demonstrate that the precision of GRIN is improved to 99.92% compared to current state-of the-art techniques.
Traceability

Traceability is an increasingly prominent research topic in decentralised technosocial systems in fields as diverse as health, sustainability, finance, and supply chain management.At the same time, traceability connotes different meanings and potentialities within each of these fields.This Glossary article homes in on "traceability" as a concept that is deceptively simple but fundamentally crucial in blockchain technologies.First, the entry provides an overview of the historical background of traceability within digital technologies.The entry then outlines the most critical dimensions of the concept by relating the term to questions about accountability, explainability, and speculation.Finally, emergent methodological and theoretical insights concerning traceability as a paradoxical concept in distributed technologies are highlighted.
Implementation of a Smart Contract on a Consortium Blockchain for IoT Applications

Advancements in cryptography and computer science have given birth to blockchain technology. One of the most exciting evolutions of blockchain is the advancements in smart contract technology. Smart contracts can be used for a broad range of use cases, not just financial transactions. Smart contract technology on the public blockchain, represented by Ethereum, due to its public and opaque nature, is not a good choice for many scenarios that do not require full disclosure, such as many IoT applications. On the other hand, the existing blockchain smart contract system still has a strong connection with virtual currency, which also limits its application in non-financial scenarios. In order to solve the above problems and explore more of the potential of smart contracts for the IoT application domain, this paper mainly explores the construction of a smart contract system based on consortium blockchains associated with no virtual currency. Based on the smart contract system designed in this project, blockchain can be more easily applied in payment, product traceability, authority authentication, and other fields. Through a certain centralized way, the system is easier to manage, can reduce the management expenditure, and the power and other resource consumption is less, which is conducive to environmental protection. Results show that our smart contract system has the potential for IoT usage in the future.
Meta-learning Fine-tuned Feature Extractor for Few-shot Image Classification

research-article Share on Meta-learning Fine-tuned Feature Extractor for Few-shot Image Classification: A Case Study on Fine-tuning CNN Backbone with Transformer for Few-shot Learning Authors: Yulin Shen Computer Science and Engineering, University of Electronic Science and Technology of China, China Computer Science and Engineering, University of Electronic Science and Technology of China, ChinaView Profile , Xian Shuai The Chinese University of Hong Kong, China The Chinese University of Hong Kong, ChinaView Profile Authors Info & Claims APIT 2022: 2022 4th Asia Pacific Information Technology ConferenceJanuary 2022 Pages 9–14https://doi.org/10.1145/3512353.3512355Online:14 March 2022Publication History 0citation65DownloadsMetricsTotal Citations0Total Downloads65Last 12 Months65Last 6 weeks23 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Text Mining Based on the Lexicon-Constrained Network in the Context of Big Data

Unstructured textual news data is produced every day; analyzing them using an abstractive summarization algorithm provides advanced analytics to decision-makers. Deep learning network with copy mechanism is finding increasing use in abstractive summarization, because copy mechanism allows sequence-to-sequence models to choose words from the input and put them directly into the output. However, since there is no explicit delimiter in Chinese sentences, most existing models for Chinese abstractive summarization can only perform character copy, resulting in inefficiency. To solve this problem, we propose a lexicon-constrained copying network that models multigranularity in both encoder and decoder. On the source side, words and characters are aggregated into the same input memory using a Transformer-based encoder. On the target side, the decoder can copy either a character or a multicharacter word at each time step, and the decoding process is guided by a word-enhanced search algorithm which facilitates the parallel computation and encourages the model to copy more words. Moreover, we adopt a word selector to integrate keyword information. Experiment results on a Chinese social media dataset show that our model can work standalone or with the word selector. Both forms can outperform previous character-based models and achieve competitive performances.