Reviewerly/exHarmony huggingface dataset:
Discussion on the Application Prospect of Airborne Geophysical Prospecting in Site Selection and Construction of Nuclear Waste Disposal Sites

In recent years, the international climate problem has become increasingly prominent, mankind is facing an unprecedented environmental crisis, and the demand for clean energy such as nuclear energy has become increasingly prominent, especially China's serious dependence on energy. The utilization of nuclear energy is a complex systematic project, in which the disposal of nuclear waste is related to the development and utilization of nuclear energy, and the site selection and construction of disposal sites is the key to restricting the development of nuclear energy. In order to realize the rapid site selection and construction of nuclear waste disposal sites, this paper relying on the application of airborne geophysical exploration in key projects in china, analyzes the methods of airborne gamma spectroscopy, airborne magnetism, airborne electromagnetism and other methods to solve the geological problems of nuclear waste disposal sites ability: Aerial gamma spectroscopy can provide potassium, uranium and thorium content, which can quickly determine the distribution range of granite bodies; aeromagnetic measurement can provide magnetic field and three-dimensional magnetic spatial distribution, and can analyze regional fractures and deep rock development characteristics; aerial electromagnetic measurement can provide plane time constant and three-dimensional resistivity parameters, which can be used to analyze the deep development characteristics of rock mass and faults. Comprehensive aerial geophysical exploration can analyze the characteristics of deep geological bodies from the aspects of radioactivity, magnetism and electrical properties, and can provide a three-dimensional geophysical basis for the rapid site selection and construction of nuclear waste disposal sites, application prospects.
Graph-of-Code: Semantic Clone Detection Using Graph Fingerprints

The code clone detection issue has been researched using a number of explicit factors based on the tokens and contents and found effective results. However, exposing code contents may be an impractical option because of privacy and security factors. Moreover, the lack of scalability of past methods is an important challenge. The code flow states can be inferred by code structure and implicitly represented using empirical graphs. The assumption is that modelling of the code clone detection problem can be achieved without the content of the codes being revealed. Here, a Graph-of-Code concept for the code clone detection problem is introduced, which represents codes into graphs. While Graph-of-Code provides structural properties and quantification of its characteristics, it can exclude code contents or tokens to identify the clone type. The aim is to evaluate the impact of graph-of-code structural properties on the performance of code clone detection. This work employs a feature extraction-based approach for unlabelled graphs. The approach generates a ‘Graph Fingerprint’ which represents different topological feature levels. The results of code clone detection indicate that code structure has a significant role in detecting clone types. We found different GoC-models outperform others. The models achieve between 96% to 99% in detecting code clones based on recall, precision, and F1-Score. The GoC approach is capable in detecting code clones with scalable dataset and with preserving codes privacy.
Mitigating Timing-Based NoC Side-Channel Attacks With LLC Remapping

Recent CPU microarchitectural attacks utilize contention over the NoC to mount covert and side-channel attacks on multicore CPUs and leak information from victim applications. We propose NoIR, a dynamic LLC slice selection mechanism using slice remapping to obfuscate interconnect contention patterns. NoIR reduces contention variance by 92.18% and mean IPC degradation due to cache invalidation is limited to 7.38% for SPEC CPU 2017 benchmarks for a 1000-access threshold. While previous defenses focused on redesigning the NoC and routing algorithms, we show that a top-down system-level approach can significantly raise the bar for a NoC security vulnerability with minimal modifications to the NoC hardware.
Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets Tracking With Leader-Follower Model

This paper addresses the multiple resolvable group targets tracking (MRGTT) problem wherein the leader-follower (LF) model is adopted to evolve the group structure. Since there is an interaction between the destination and each leader, such that an attraction force of the destination information on the leader needs to be considered. To cope with the time-varying number of targets/groups and target-measurement association uncertainty, the so-called labeled multi-Bernoulli (LMB) filter is regarded as the filtering tool and combined with the LF model, named LF-LMB. Totally speaking, the implementation of the proposed LF-LMB filter consists of three steps. Firstly, the stochastic differential equation (SDE) of the state is used to, respectively, characterize the motion models of the leader and the follower. Then, the state transition equations (STEs) for the leader and the follower are derived. Lastly, the derived STEs are used to modify the prediction process of the standard LMB filter. Simulation experiments are carried out to demonstrate the superiority (e.g., optimal sub-pattern assignment (OSPA) and OSPA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{(2)}$</tex-math></inline-formula> errors are reduced by at least 15 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> comparing to the standard LMB filter) and robustness of the proposed approach.
Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposal. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with a feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we show that the proposed feature-enhancing scheme can facilitate image- and smear-level classification.
Hybrid-MVS: Robust Multi-View Reconstruction With Hybrid Optimization of Visual and Depth Cues

Consumer-level RGB-D cameras have been widely used for dense 3D reconstruction of scenes. Especially for textureless or non-lambertian surfaces, consumer RGB-D cameras can ensure completeness of the reconstructed models at a low cost. However, the reconstruction quality relies heavily on the accuracy of the depth sensors. Digital cameras are also used popularly for capturing high-resolution pictures to achieve high-quality dense reconstruction of the scenes, but cannot handle textureless or non-lambertian regions well due to the visual ambiguity problem. To ensure both completeness and accuracy of the reconstructed 3D models, we propose a hybrid multi-view reconstruction pipeline named Hybrid-MVS, which combines the high-resolution images taken by a digital camera and the low-resolution RGB-D frames captured by a consumer RGB-D camera for robust reconstruction of complicated scenes with challenging textureless and non-lambertian surfaces. Unlike most existing multi-sensor systems which require explicit hardware calibration and synchronization of various sensors, the calibration and synchronization problems between the digital camera and RGB-D camera are implicitly solved for compositing reliable depth prior of the digital images in our pipeline. Especially, we propose a hybrid MVS framework for robust PatchMatch stereo and Delaunay meshing, which tightly couples both visual cues given by the digital images and depth cues from the RGB-D frames to maximize the complementary advantages. The experiments with quantitative and qualitative evaluations demonstrate the effectiveness of the proposed Hybrid-MVS framework, which can successfully achieve high-quality 3D reconstruction of complicated natural scenes with robustness to weakly textured and non-lambertian areas.
Distributed Clustering for Cooperative Multi-Task Learning Networks

Distributed learning enables collaborative training of machine learning models across multiple agents by exchanging model parameters without sharing local data. Each agent generates data from distinct but related distributions, and multi-task learning can be effectively used to model related tasks. This paper focuses on clustered multi-task learning, where agents are partitioned into clusters with distinct objectives, and agents in the same cluster share the same objective. The structure of such clusters is unknown apriori. Cooperation with the agents in the same cluster is beneficial and improves the overall learning performance. However, indiscriminate cooperation of agents with different objectives leads to undesired outcomes. Accurately capturing the clustering structure benefits the cooperation and offers many practical benefits; for instance, it helps advertising companies better target their ads. This paper proposes an adaptive clustering method that allows distributed agents to learn the most appropriate neighbors to collaborate with and form clusters. We prove the convergence of every agent towards its objective and analyze the network learning performance using the proposed clustering method. Further, we present revision2 a method of computing combination weights that approximately optimizes the network's learning performance to determine how one should aggregate the neighbors' model parameters after the clustering step. The theoretical analysis is well-validated by the evaluation results using target localization and digits classification, showing that the proposed clustering method outperforms existing distributed clustering methods as well as the case where agents do not cooperate.
Thick Cloud Removal for Multitemporal Remote Sensing Images: When Tensor Ring Decomposition Meets Gradient Domain Fidelity

Thick clouds in remote sensing (RS) images deteriorate the visual quality and hinder subsequent applications. The emerging multitemporal RS images with rich temporal information bring the opportunity for cloud removal. How to effectively exploit the rich temporal information of the multitemporal RS images remains challenging. As multitemporal RS images with the same geographic scene, the spatial gradient of RS images at different time nodes has a resemblance, which can guide the reconstruction of the cloudy region. Motivated by this, we suggest a gradient domain fidelity with respect to the guided gradient for thick cloud removal in multitemporal RS images, which faithfully preserves the fine edges and textures compared to the original pixel domain fidelity. Armed with the gradient domain fidelity, we propose a low-rank tensor ring decomposition model (TRGFid) for the thick cloud removal problem. In the proposed model, the guided gradient of the cloudy region is availably estimated by using the regression method from the cloud-free region of different time nodes. Moreover, we develop an efficient proximal alternating minimization-based algorithm for solving the proposed nonconvex model. Extensive simulated and real experiments show that the proposed method outperforms its competitors, and preserves fine edges and textures.
Unsupervised Learning

Unsupervised learning is a class of algorithms; where a machine looks for patterns in a dataset with no labels (or target variables to predict). This is in distinction to supervised learning procedures, such as classification or regression, where a model is given training data of inputs and target variables; and learns to map from the inputs to the target. In unsupervised learning, only the inputs are available, and an algorithm looks for interesting patterns in the data. Unsupervised learning is aka., knowledge discovery.
TinyML Techniques for running Machine Learning models on Edge Devices

Resource-constrained platforms such as micro-controllers are the workhorses in embedded systems, being deployed to capture data from sensors and send the collected data to cloud for processing. Recently, a great interest is seen in the research community and industry to use these devices for performing Artificial Intelligence/Machine Learning (AI/ML) inference tasks in the areas of computer vision, natural language processing, machine monitoring etc. leading to the realization of embedded intelligence at the edge. This task is challenging and needs a significant knowledge of AI/ML applications, algorithms, and computer architecture and their interactions to achieve the desired performance. In this tutorial we cover a few aspects that will help embedded systems designers and AI/ML engineers and scientists to deploy the AI/ML models on the Tiny Edge Devices at an optimum level of performance.
CluSpa: Computation Reduction in CNN Inference by exploiting Clustering and Sparsity

Convolutional Neural Networks (CNNs) have grown in popularity and usage tremendously over the last few years, spanning across different task such as computer vision tasks, natural language processing, video recognition, and recommender systems. Despite the algorithmic advancements that drove the growth of CNN still has considerable computational and memory overhead that poses challenges in achieving real-time performance. Each input image requires millions to even billions of elementary arithmetic operations before the network obtains the result. In CNNs, convolutional and pooling layers are followed by activation layers involving various activation functions. Hence, a lot of work has been done to reduce these costs in the last few years. Numerous optimizations have addressed at both hardware and software levels. In this paper, we propose a software-based solution for improving the performance of inference of networks. We suggest a technique for the approximate computation of the convolution operation based on clustering and sharing of weights. We have utilized Gaussian Mixture Models for clustering. We exploit weight sparsity to further reduce computations on top of the clustering method. We were able to achieve a considerable reduction in the MAC operations and the overall computation speedup on popular CNN architectures
Performance Evaluation of gcForest inferencing on multi-core CPU and FPGA

Decision forests have proved to be useful in machine learning tasks. gcForest is a model that leverages ensembles of decision forests for classification. It combines several decision forests and by adding properties and layered architecture in such a way that it has been proven to give competitive results compared to convolutional neural networks. This paper analyzes the performance of a gcForest model trained on the MNIST digit classification data set on a multi-core CPU based system. Using a performance model-based approach it also presents an analysis of performance on a well-endowed FPGA accelerator card for the same model. It is concluded that the multi-core CPU system can deliver more throughput than the FPGA with batched workload, while the FPGA offers lower latency for a single inference. We also analyze the scalability of the gcForest model on the multi-core server system and with the help of experiments and models, uncover ways to improve the scalability.
Full-Rotation Quantum Convolutional Neural Network for Abnormal Intrusion Detection System

Intrusion detection system (IDS) is a significant mechanism to improve network security. As a promising technique, machine learning (ML) methods has been applied in IDS to obtain high classification accuracy. However, classical ML based IDS methods hit a bottleneck in computing performance in case of huge network traffic and complex high-dimensional data. Due to the parallelism, superposition, entanglement of quantum computing, quantum computing provides a new solution to speed up the classical ML algorithms. This paper proposes a novel IDS scheme based on full-rotation quantum convolutional neural network (FR-QCNN). The key component of the FR-QCNN is the quantum convolution filter, which is composed of coding layer, variational layer and measurement layer. Different from the traditional quantum convolutional neural network, a full-rotation quantum circuit is used in the variational layer of the FR-QCNN, realizing a complete parameter update in the model training. Experiment on dataset from KDD Cup shows that the IDS classification accuracy of FR-QCNN is higher than classical ML models such as convolutional neural network (CNN), decision tree (DT) and support vector machine (SVM), as well as higher than traditional quantum convolutional neural network(QCNN). Meanwhile, FR-QCNN and QCNN have lower space complexity and time complexity than classical ML methods.
Handwritten Text Recognition of Imagery Acquisition Sheet in Tobacco Industry Using an Improved CRNN Network

In the tobacco industry, the acquisition sheet is a kind of document used by the tobacco monopoly administration to record the information about the acquisition of fake and illicit cigarettes when the tobacco crime is seized by the "Internet Plus Express". To automatically identify the information helps to realize the automation of information management of tobacco monopoly administration department, and then build delivering for smoke crime management and prediction system, thus to deliver smoke involved crime effectively, and realize the wisdom of the tobacco industry monopoly, so to handwriting recognition research of the acquisition sheet image is very necessary. In this paper, an improved CRNN network text recognition method is proposed. Firstly, the acquisition sheet image with interference such as shadow and seal is preprocessed based on color channel separation method. Then, in the presence of printed text, the YoloX model is used to detect the handwritten text in the image, and the handwritten text line dataset is obtained. Then, the CNN network is improved by adding Swish activation function and attention mechanism to obtain the depth image features of handwritten text line data. BiLSTM network predicts the input feature sequence and finally realizes the recognition of handwritten text. Through the algorithm in this paper, the detection of handwritten text in the acquisition sheet image reaches an average accuracy of 0.983. The experimental results show that, compared with the CRNN model, the improved CRNN model in this paper has a better effect on the recognition of handwritten text in the acquisition sheet image.
Research on Apparel Trend Prediction Based on CNN-BiLSTM-Attention Model

The existing methods for forecasting clothing trends mostly use traditional time series forecasting methods, and the data sources are mostly sale data from e-commerce websites, which have large errors in forecasting accuracy. This paper proposes a new model CNN-BiLSTM-Attention for predicting clothing trends based on social media data. The Geostyle dataset is pre-processed to get the clothing popularity index. First, One-dimensional CNN is used to extract the important features in the clothing popularity index. Second, the BiLSTM is used to make full use of contextual information. Third, adding an Attention mechanism to the output can highlight relevant information, suppress irrelevant information, and significantly improve prediction accuracy. The experimental results show that our method is significantly better than other traditional time series forecasting methods and existing deep learning methods when applied to apparel trend forecasting.
A Weight Initialization Method for Compressed Video Action Recognition in Compressed Domain

The exponential evolution of big data with its increasing volumes, especially when it comes to videos from smart devices and video sites, has become a real challenge to video analysis tasks algorithms. Processing and storage difficulties are the main problems for these traditional video processing architectures that mostly use RGB frames for video analysis tasks. The process of decoding compressed videos is time-consuming and requires a lot of storage space. Although existing convolutional neural networks (CNNs) based video analysis architectures have realized notable advancements, they still hardly meet the requirements of many real-time scenarios and real-world applications. This is one of the motivations for the computer vision community to move to action recognition with compressed domain compressed videos in order to overcome the aforementioned issues. On the other hand, the performance of prominent methods is very dependent on the correct setting of initialization parameters. The choice of initialization has an impact on the final generalization performance of a neural network. This work proposes a weight initialization technique in compressed domain for compressed videos action recognition tasks. Our approach was tested on UFC-101 and HDBM-51 datasets. The performance evaluation shows the effectiveness of our proposed methodology.
Large-Scale Entity Extraction from Enterprise Data

Adoption of cloud computing by enterprises has exploded in the last decade and most of the applications used by enterprise users have moved to the cloud. These applications include collaboration software(e.g., Word, Excel), instant messaging (e.g., Chat), asynchronous communication (e.g., Email), etc. This has resulted in an exponential increase in the volume of data arising from the interactions of the users with the online applications (such as documents edited, people interacted with, meetings attended, etc.). Activities of a user provide strong insights about her such as meetings attended by the user indicate the set of people the user closely works with and documents edited indicate the topics the user works on, etc. Typically, this data is private and confidential for the enterprise, part of the enterprise, or the individual employee. To provide better experience and assist employees in their activities, it is critical to mine certain entities from this data. In this tutorial, we explain various entities which can be extracted from the enterprise data and assist the employees in their productivity. Specifically, we define and extract various enterprise entities such as tasks, commitments, calendar activity, acronyms, topics, definitions, etc. These entities are extracted using different techniques—tasks and commitments are extracted using intent mining techniques (e.g., sentiment extraction), definitions are extracted using sequence mining techniques, calendars are updated using the user’s flight/hotel booking entities, etc. The entity extraction from enterprise data poses interesting and complex challenge from scalable information extraction point of view: building information extraction models where there is little data to learn from due to privacy and access-control constraints but need highly accurate models to run on a large amount of diverse data from whole of the enterprise. Specifically, we need to overcome the following challenges: