Reviewerly/exHarmony huggingface dataset:
Bi-calibration Networks for Weakly-Supervised Video Representation Learning

The leverage of large volumes of web videos paired with the query (short phrase for searching the video) or surrounding text (long textual description, e.g., video title) offers an economic and extensible alternative to supervised video representation learning. Nevertheless, modeling such weakly visual-textual connection is not trivial due to query polysemy (i.e., many possible meanings for a query) and text isomorphism (i.e., same syntactic structure of different text). In this paper, we introduce a new design of mutual calibration between query and text to achieve more reliable visual-textual supervision for video representation learning. Specifically, we present Bi-Calibration Networks (BCN) that novelly couples two calibrations to learn the correction from text to query and vice versa. Technically, BCN executes clustering on all the titles of the videos searched by an identical query and takes the centroid of each cluster as a text prototype. All the queries constitute the query set. The representation learning of BCN is then formulated as video classification over text prototypes and queries, with text-to-query and query-to-text calibrations. A selection scheme is also devised to balance the two calibrations. Two large-scale web video datasets paired with query and title, named YOVO-3M and YOVO-10M, are newly collected for weakly-supervised video feature learning. The video features of BCN with ResNet backbone learnt on YOVO-3M (3M YouTube videos) obtain superior results under linear protocol on action recognition. More remarkably, BCN trained on the larger set of YOVO-10M (10M YouTube videos) with further fine-tuning leads to 1.3% gain in top-1 accuracy on Kinetics-400 dataset over the state-of-the-art TAda2D method with ImageNet pre-training. Source code and datasets are available at https://github.com/FuchenUSTC/BCN .
Content-Based remote sensing image retrieval method using adaptive tetrolet transform based GLCM features

This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods.
An Ensemble Learning Approach with Gradient Resampling for Class-Imbalance Problems

Imbalanced classification is widely referred in many real-world applications and has been extensively studied. Most existing algorithms consider alleviating the imbalance by sampling or guiding ensemble learners with punishments. The combination of ensemble learning and sampling strategy at class level has achieved great progress. Actually, specific hard examples have little benefit for model learning and even degrade the performance. From the view of identifying classification difficulty of samples, one important motivation is to design algorithms to finely equip different samples with progressive learning. Unfortunately, how to perfectly configure the sampling and learning strategies under ensemble principles at the sample level remains a research gap. In this paper, we propose a new view from the sample level rather than class level in existing studies. We design an ensemble approach in pipe with sample-level gradient resampling, that is, balanced cascade with filters (BCWF). Before that, as a preliminary exploration, we first design a hard examples mining algorithm to explore the gradient distribution of classification difficulty of samples and identify the hard examples. Specifically, BCWF uses an under-sampling strategy and a boosting manner to train T predictive classifiers and reidentify hard examples. In BCWF, moreover, we design two types of filters: the first is assembled with a hard filter (BCWF_h), whereas the second is assembled with a soft filter (BCWF_s). In each round of boosting, BCWF_h strictly removes a gradient/set of the hardest examples from both classes, whereas BCWF_s removes a larger number of harder and easy examples simultaneously for final balanced-class retention. Consequently, the well-trained T predictive classifiers can be used with two ensemble voting strategies: average probability and majority vote. To evaluate the proposed approach, we conduct intensive experiments on 10 benchmark data sets and apply our algorithms to perform default user detection on a real-world peer to peer lending data set. The experimental results fully demonstrate the effectiveness and the managerial implications of our approach when compared with 11 competitive algorithms. History: Accepted by Ram Ramesh, Area Editor for Data Science &amp; Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101176, 71722005, and 72241432], the National Key R&amp;D program of China [Grant 2020YFA0908600] and the Natural Science Foundation of Tianjin City [Grant 18JCJQJC45900]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1274 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0104 ) at ( http://dx.doi.org/10.5281/zenodo.6360996 ).
The state of art and review on video streaming

Nowadays, video streaming is very popular around the world, users use video streaming to watch online movies, education, and do office work. Video streaming is referred to as the transmission of video content, live or recorded from server/cloud to end-users. Video and music files are prearranged and transmitted in sequential packets of data so they can be streamed instantaneously, only the User required a high-speed network for access, and a subscription to streaming via an application. In this paper, we survey and analyze the previous development in video streaming such as 2D, and 3D video streaming, compression technologies, protocols for streaming, cloud video processing, 4K/8K, and challenges, and limitations and offer aspects of future development, which will help to provide quality of service of video streaming and increase the revenue for service providers.
Sources, transport, and deposition of metal(loid)s recorded by sulfide and rock geochemistry: constraints from a vertical profile through the epithermal Profitis Ilias Au prospect, Milos Island, Greece

Abstract Drill core samples from the Profitis Ilias Pb-Zn-Cu-Ag-Au vein mineralization on Milos Island, Greece provide new insights into (i) the metal sources, (ii) the primary vertical metal(loid) distribution, and (iii) the supergene enrichment processes in a transitional shallow-marine to subaerial hydrothermal environment. Metal contents of unaltered and altered host rocks combined with Pb isotope analyses of hydrothermal sulfides suggest that most metal(loid)s were derived by leaching of basement rocks, whereas the distinct enrichment of Te is related to the addition of Te by a magmatic fluid. The trace element contents of base metal sulfides record decreasing Au, Te, Se, and Co, but increasing Ag, Sb, and Tl concentrations with increasing elevation that can be related to progressive cooling and fluid boiling during the hypogene stage. The formation of base metal veins with porous pyrite hosting hessite inclusions at ~ 400 m below the surface was triggered by vigorous fluid boiling. By contrast, the enrichment of native Au associated with oxidized Fe and Cu phases in the shallower part of the hydrothermal system resulted from supergene remobilization of trace Au by oxidizing meteoric water after tectonic exhumation to subaerial levels. Disseminated pyrite with higher Tl/Pb ratios and locally elevated Hg concentrations relative to vein pyrite reflects infiltration of the host rocks by boiled liquids and condensed vapor fluids. The vertical and temporal evolution of the Profitis Ilias mineralization, therefore, provides unique insights into the transport and precipitation of Au, Ag, Te, and related metal(loid)s by multiple fluid processes.
GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search

When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only finding relevant documents but also synthesizing important concepts and the relationships that connect those concepts both within and across documents. This article describes a novel interactive approach designed to help users construct a mental model of an unfamiliar information space during exploratory search. We propose a new semantic search system to organize and visualize important concepts and their relations for a set of search results. A user study ( n =20) was conducted to compare the proposed approach against a baseline faceted search system on exploratory literature search tasks. Experimental results show that the proposed approach is more effective in helping users recognize relationships between key concepts, leading to a more sophisticated understanding of the search topic while maintaining similar functionality and usability as a faceted search system.
An Adaptable Approach to Fault Tolerance in Cloud Computing

Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.
A Clustered Federated Learning Method of User Behavior Analysis Based on Non-IID Data

Federated learning (FL) is a novel distributed machine learning paradigm. It can protect data privacy in distributed machine learning. Hence, FL provides new ideas for user behavior analysis. User behavior analysis can be modeled using multiple data sources. However, differences between different data sources can lead to different data distributions, i.e., non-identically and non-independently distributed (Non-IID). Non-IID data usually introduce bias in the training process of FL models, which will affect the model accuracy and convergence speed. In this paper, a new federated learning algorithm is proposed to mitigate the impact of Non-IID data on the model, named federated learning with a two-tier caching mechanism (FedTCM). First, FedTCM clustered similar clients based on their data distribution. Clustering reduces the extent of Non-IID between clients in a cluster. Second, FedTCM uses asynchronous communication methods to alleviate the problem of inconsistent computation speed across different clients. Finally, FedTCM sets up a two-tier caching mechanism on the server for mitigating the Non-IID data between different clusters. In multiple simulated datasets, compared to the method without the federated framework, the FedTCM is maximum 15.8% higher than it and average 12.6% higher than it. Compared to the typical federated method FedAvg, the accuracy of FedTCM is maximum 2.3% higher than it and average 1.6% higher than it. Additionally, FedTCM achieves more excellent communication performance than FedAvg.
Factors influencing teachers’ satisfaction and performance with online teaching in universities during the COVID-19

The COVID-19 pandemic has significantly changed the teaching model, promoting educational institutions to initiate more explorations in online teaching. This study examines the factors influencing teachers' online teaching performance and satisfaction in universities during the COVID-19. We applied a model of technology acceptance (TAM), expectation confirmation (ECM), and computer self-efficacy (CSE) to develop a questionnaire. The survey was used to collect data from 347 teachers from 6 universities in eastern China to identify factors affecting teachers' performance and satisfaction during the COVID-19. The results indicated that teachers' performance of online teaching is significantly affected by satisfaction, perceived usefulness, and perceived ease of use of online teaching. Meanwhile, confirmation of online teaching expectations and computer self-efficacy significantly impacted teachers' satisfaction with online teaching. This work is an original empirical study guided by multiple theories. It contributes to the online education literature and provides advice regarding how teachers' online teaching satisfaction and performance can be developed in a situation like the one that occurred with COVID-19. This work also broadens the application of TAM and provides an alternative theoretical framework for future research on teachers' online teaching performance.
EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN

In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance.
Design and application of VR-based college English game teaching

With the deepening of international exchanges, colleges and universities have higher and higher requirements for the quality of English teaching. In order to improve the quality of English teaching in colleges and universities, the research proposes to design and construct an English game education and teaching system based on virtual reality technology. First, the modern VR technology is used to build the initial English game teaching system, and then the HMM-DNN algorithm is introduced to improve the virtual system. In the process, the CMUShinx speech recognition platform is used to build a robot control command speech recognition system, in order to obtain a good voice acoustic model and achieve correct speech recognition in the virtual environment. Finally, the performance analysis of the built system is carried out to verify the feasibility of the system model. The results show that the step response coefficient of the model built by the research institute can be kept within 1 M for a long time; When the speech type is White and Cafe1, the correct recognition rate of the constructed model for English speech is as high as 98 % and 95 %, and is always higher than other algorithms; In the comparison of the accuracy of students' oral speech recognition scores by different models, the lowest error of the research model is 2.23 %, which is significantly smaller than the other models. The above results indicate that the overall teaching effect of the system built by the research institute is stable, and it has high feasibility for college students' English teaching, which helps to further improve students' interest and ability in English learning, and is of great significance for college English talent training.
Anchor-based knowledge embedding for image aesthetics assessment

Deep neural networks have shown their advantage in image aesthetics assessment (IAA). However, the current deep IAA models largely work in a data-driven manner, but the ambiguity of aesthetics poses huge challenge. When judging image aesthetics, people usually take advantage of commonsense knowledge. Further, people are good at making relative comparison instead of absolute scoring. Motivated by the above facts, this paper presents a new ANchor-based Knowledge Embedding (ANKE) approach for generic image aesthetics assessment, which makes predictions based on a universal aesthetic knowledge base. First, the knowledge base is built by extracting aesthetic features from anchor images with diversified visual contents and aesthetic levels, which can provide rich reference information for aesthetics assessment. Then, given an image, the model is trained to dynamically pick up the most informative anchors from the knowledge base and adaptively weight the difference features to produce the final aesthetic prediction. Experimental results demonstrate that, with a universally built aesthetic knowledge base, the proposed ANKE model achieves the state-of-the-art performance on three public IAA databases.
Application of ASTER Remote Sensing Data to Porphyry Copper Exploration in the Gondwana Region

Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration information from remote sensing data is crucial for porphyry copper exploration. However, the current method of extracting hydrothermal alteration information from ASTER remote sensing data does not consider the influence of disturbing factors, such as topography, and ignores the weak report of surface minerals, which has significant limitations. Therefore, this paper selects the Gondwana region of the East Tethys–Himalayan tectonic domain as the study area, combines waveform calculation with principal component analysis methods, proposes a spectral feature-enhanced principal component analysis (EPCA) method, and constructs a model to complete the automatic selection of principal components for each scene image. The results show that the etching information extracted by the EPCA method is significantly better than the traditional Crosta method in terms of etching area and spatial aggregation and discovers several prospective mineralization areas that have not yet been explored and exploited, such as Sakya and Xietongmen counties in Rikaze, providing theoretical support for subsequent mineralization exploration and large-scale mineral extraction. Meanwhile, obtaining the alteration information of the whole area can help to understand the distribution of mineralizing elements from a macroscopic perspective in the future, which is of great scientific significance in order to deeply analyze the formation process of metal deposits in mineralizing areas and improve the theory of porphyry mineralization.
Twin-field quantum key distribution with three mutually unbiased bases

Twin-field quantum key distribution (TF-QKD) and its variants provide a promising solution for sharing information-theoretic secure keys between intercity peers since they are able to overcome the fundamental rate-transmittance bound without quantum repeaters. In this paper, we propose to improve the key rate at long distances and the maximum achievable distance for TF-QKD by deriving the error rates under three mutually unbiased bases, i.e., ${\ensuremath{\sigma}}_{X}, {\ensuremath{\sigma}}_{Y}$, and ${\ensuremath{\sigma}}_{Z}$ in two-dimensional Hilbert space. Moreover, learning these error rates, one can add noisy preprocessing to further improve its performance. We also observe that higher bit error rates do not necessarily imply lower key rates when noisy preprocessing is added. Our method does not change the existing physical implementation or experimental operation, but only requires simple postprocessing of the experimental data, which can be directly used to improve the key rate performance of the existing QKD system. The simulation results demonstrate its notable enhancements in terms of key rate at long distances and the maximum achievable distance for the phase-encoded TF-QKD protocol.
A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification

The technology for detecting forged images is good at detecting known forgery methods. It trains neural networks using many original and corresponding forged images created with known methods. However, when encountering unseen forgery methods, the technology performs poorly. Recently, one suggested approach to tackle this problem is to use a hand-crafted generator of forged images to create a range of fake images, which can then be used to train the neural network. However, the aforementioned method has limited detection performance when encountering unseen forging techniques that the hand-craft generator has not accounted for. To overcome the limitations of existing methods, in this paper, we adopt a meta-learning approach to develop a highly adaptive detector for identifying new forging techniques. The proposed method trains a forged image detector using meta-learning techniques, making it possible to fine-tune the detector with only a few new forged samples. The proposed method inputs a small number of the forged images to the detector and enables the detector to adjust its weights based on the statistical features of the input forged images, allowing the detection of forged images with similar characteristics. The proposed method achieves significant improvement in detecting forgery methods, with IoU improvements ranging from 35.4% to 127.2% and AUC improvements ranging from 2.0% to 48.9%, depending on the forgery method. These results show that the proposed method significantly improves detection performance with only a small number of samples and demonstrates better performance compared to current state-of-the-art methods in most scenarios.