Reviewerly/exHarmony huggingface dataset:
Deep and Low-Rank Quaternion Priors for Color Image Processing

Due to the physical nature of color images, color image processing such as denoising and inpainting has shown extensive and versatile possibilities over grayscale image processing. The monochromatic and the concatenation model have been widely used to process color images by processing each color channel independently or concatenating three color channels as one unified one and then used existing grayscale image processing methods directly without specific operations. These above schemes, however, have some limitations: (1) they would destroy the inherent correlation among three color channels since they cannot represent color images holistically; (2) they usually focus on one specific handcrafted prior such as smoothness, low-rankness, or even deep prior and thus failing to fuse deep and handcrafted priors of color images flexibly. To conquer these limitations, we propose one unified model to integrate deep prior and low-rank quaternion prior (DLRQP) for color image processing under the plug-and-play (PnP) framework. Specifically, the quaternion representation with low-rank constraint is introduced to denote the color image in a holistic way and one advanced denoiser is adopted to explore the deep prior in an iterative process. To tightly approximate the quaternion rank, one nonconvex penalty function is further utilized. We derive an alternate iterative approach to tackle the proposed model. We empirically demonstrate that our model can achieve superior performance over existing methods on both color image denoising and inpainting tasks.
Robust Label and Feature Space Co-Learning for Multi-Label Classification

Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different from traditional multi-label classification methods which focus on feature space learning through regression directly between data samples and labels, our proposed method can further learn robust low rank label space from this traditional regression method. Therefore, our RLFSCL can learn better low rank feature and label representations simultaneously in original noisy and high dimensional spaces. Experimental comparison on five benchmark datasets, including Rcv1s5, Cal500, and Corel16k4 shows that the proposed RLFSCL algorithm outperforms state-of-the-art multi-label classification methods. The code of RLFSCL is made available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JingChuanTang/RLFSCL</uri> .
Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training

This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments.
The construction of legitimacy: a critical discourse analysis of the rhetoric of educational technology in post-pandemic higher education

Whilst technology may have been the ‘saviour’ of HE from the immediate challenges of the pandemic, the opportunistic dialogue emerging in response is imbued with notions of the pandemic as a catalyst for change. Empowered by the apparent success of technology’s deliverance, the door has been opened to unprecedented investment into a pervasive and data-driven paradigm of technology. Utilising a Critical Discourse Analysis of sector-orientated literature published in response to the pandemic, this paper examines the emergent rhetoric of technology and problematises taken for granted assumptions concerning its adoption in the imagined future of HE. This paper argues that such rhetoric is mediatory of neoliberal and consumerist ideologies, and that the portrayal of technology as a wholly beneficial enterprise obscures other issues and inequalities. By positioning educational technology in a uniquely political light, this paper offers a critical lens through which to view this new era of technological pervasion.
Blockchain innovations and the contribution of the intergovernmental organizations

Purpose The United Nations (UN) is globally acknowledged for its unique role as a convening platform to address humanitarian, peace, security and sustainable development challenges. However, it is not often associated with technological innovation. Blockchain technology, an innovation that emerged in the late 2000s, has generated animated discussions that are led, in the most part, by private sector institutions. A dearth of literature highlights the innovative blockchain projects supported by UN entities. The purpose of this study is to provide an overview of blockchain innovations supported by UN entities and explore opportunities for future studies. Design/methodology/approach This paper used an exploratory case study approach with the purpose of providing a broad perspective of blockchain innovations undertaken by UN entities in the quest to meet sustainable and equitable development across the world. Findings This study found 25 blockchain projects by 13 UN entities in 19 countries. The geographical spread of the case studies revealed that two studies have global jurisdiction, five studies in Africa, three in Europe, four in Latin America and the Caribbean and 11 in Asia, Oceania and the Middle East. Of the 19 countries, three had two projects each. Two countries had three projects and the rest had one project each. Of the 13 UN entities, three dominated with UNICEF (part of six projects), UNDP (part of 10 projects) and WFP (part of four projects). Finally, the 25 projects were divided among three categories of blockchain use cases, as defined by UN guidance documents. Five case studies focused on immutable record keeping, five on transfer of value and 15 on smart contracts. Originality/value This study offers a unique overview of blockchain efforts within UN entities. It provides a platform for future studies to reveal implicit assumptions, contrasting explanations and casual connections.
F-ACCUMUL: A Protocol Fingerprint and Accumulative Payload Length Sample-Based Tor-Snowflake Traffic-Identifying Framework

Tor is widely used to protect users’ privacy, which is the most popular anonymous tool. Tor introduces multiple pluggable transports (PT) to help users avoid censorship. A number of traffic analysis methods have been devoted to de-anonymize these PT. Snowflake is the latest PT based on the WebRTC protocol and DTLS encryption protocol for peer-to-peer communication, differing from other PT, which defeat these traffic analysis methods. In this paper, we propose a Snowflake traffic identification framework, which can identify whether the user is accessing Tor and which hidden service he is visiting. Rule matching and DTLS handshake fingerprint features are utilized to classify Snowflake traffic. The linear interpolation of the accumulative payload length of the first n messages in the DTLS data transmission phase as additional features are extracted to identify the hidden service. The experimental results show that our identification framework F-ACCUMUL can effectively identify Tor-Snowflake traffic and Tor-Snowflake hidden service traffic.
A Three-Dimensional Structured Light Vision System by Using a Combination of Single-Line and Three-Line Lasers

A multi-line structured light measurement method that combines a single-line and a three-line laser, in which precision sliding rails and displacement measurement equipment are not required, is proposed in this paper. During the measurement, the single-line structured light projects onto the surface of an object and the three-line structured light remains fixed. The single-line laser is moved and intersects with the three-line laser to form three intersection points. The single-line light plane can be solved using the camera coordinates of three intersection points, thus completing the real-time calibration of the scanned light plane. The single-line laser can be scanned at any angle to determine the overall complete three-dimensional (3D) shape of the object during the process. Experimental results show that this method overcomes the difficulty of obtaining information about certain angles and locations and can effectively recover the 3D shape of the object. The measurement system's repetition error is under 0.16 mm, which is sufficient to measure the 3D shapes of complicated workpieces.
A Complete and High-Resolution Estimate of Sardinia’s Rooftop Photovoltaic Potential

The implementation of the energy transition and the building of energy communities are driving forward the exploitation of the potential for rooftop photovoltaic power generation. Estimating rooftop PV generation potential requires the processing of different types of data, such as the cadastral information of buildings, a detailed description of available rooftop areas, and solar irradiance data. High-resolution estimation based on GIS data is normally limited to small survey areas. Instead, by using an algorithm for the efficient calculation of shadows over rooftops, and the integration of solar irradiance over time, we developed a procedure that allows for the rapid full census assessment of rooftop photovoltaic potential with a spatial resolution of 1 m, applicable at the regional scale and requiring minimal computational resources. We applied this approach to the rooftops of buildings in Sardinia, an island and region of Italy of particular interest for the energy transition. In addition to estimating the geographic potential, we carried out a preliminary assessment of the technical and economic potential, yielding a maximal photovoltaic rooftop generation potential of 22 TWh for the entire region.
Influence of chatbots on purchase intention in social commerce

ABSTRACTABSTRACTThe research investigates the effects of chatbot-delivered verbal (inspirational appeals) and non-verbal (ingratiation) cues on customers’ purchase intentions on social commerce pages (study one). The research using a multi-method approach also investigates the role of social presence and ulterior motives in explaining the effects of the chatbot-delivered human-like cues on customers’ purchase intentions (study two). Study one employs an experiment, followed by a structural equation model in study two. The results of study two enable the researchers to triangulate the results of study one and to understand the underlying causes of those results. The data was collected through an online experiment and an online survey for the two studies. The research reports that: (1) inspirational appeals delivered by chatbots positively influence purchase intentions; (2) ingratiation delivered using double-tap by chatbots does not significantly affect purchase intentions. The current research represents one of the first attempts to explore the influence of inspirational appeal and ingratiation delivered through chatbots on s-commerce purchase intentions. The findings of the study provide managerial insights to social commerce pages that could help engage customers and circumvent the attribution of ulterior motives while making a sale.KEYWORDS: Chatbotingratiationinspirational appealsocial commercesocial presenceulterior motives Disclosure statementNo potential conflict of interest was reported by the author(s).
Chemometrics of the Environment: Hydrochemical Characterization of Groundwater in Lioua Plain (North Africa) Using Time Series and Multivariate Statistical Analysis

This study aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the composition and origin of its chemical elements. Therefore, chemometrics techniques, such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while autocorrelation analysis (AA), supplemented by a simple spectral density analysis (SDA), is used for the TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, total dissolved solids (TDS) and electrical conductivity (EC) controlled three groups. The elements SO42−, K+, and Ca2+ are closely related to TDS, the elements Na+, Cl−, and Mg2+ are closely related to CE, while HCO3− and NO3− indicate the dissociation of other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. Nonetheless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several distinct cyclic mechanisms. Using these techniques, the authors were able to draw the following conclusion: the geochemical processes impacting the chemical composition are (i) dissolution of evaporated mineral deposits, (ii) water–rock interaction, and (iii) evaporation process. In addition, the groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42−, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. Chloride presence mainly increases the electrical conductivity of water. The lithological factor is dominant in the overall mineralization of the Plio Quaternary surface aquifer waters. The origins of HCO3− and NO3− are as follows: HCO3− has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42−, Cl−, Na+, K+, and EC. Ca2+, HCO3−, and NO3− result from human activity such as the usage of fertilizers, the carbonate facies outcrops, and domestic sewage.
Digital cytology: Current status and future prospects

In this paper, we reviewed the basic principle and the currentstatus of digital cytopathology.We reviewed the published papers on digitalcytology and analysed its future prospects.Virtualcytology using digital platform is being increasingly used to render diagnosisrather than conventional glass slide microscopy. Whole slide imaging (WSI)offers the prospect of true virtual microscopy and in the near future, may evenreplace glass slides in routine practice. It may be pivotal in diagnosing andtraining pathology graduates faster and more accurately.The digital cytopathology is a promising field and may have great impact indiagnosis, research and teaching.
High-resolution satellite video single object tracking based on thicksiam framework

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Anomaly detection with dual-stream memory network

Anomaly detection is an essential but challenging task. Existing DNN-based approaches tend to ignore the impact of network history state on extracting spatio-temporal correlations between video events. To address this problem, a Dual-Stream Memory Network (DSM-Net) has been proposed. It leverages historical information from the network to create a dual-stream memory module serving as complementary knowledge for the anomaly detection network. The memory module performs writing and reading in the form of a queue of data features. The writing records the historic information of video events through a moving average encoder, and the reading uses optical flow to uncover behavioral patterns in RGB images. Using a memory sharing strategy, the semantic information of the appearance branch and the motion branch can be integrated to reinforce the network. Results demonstrate that the proposed method on various standard datasets performs favorably when compared to existing methods.
Automatic No-Reference kidney tissue whole slide image quality assessment based on composite fusion models

The visual quality of digitized whole slide images (WSIs) for kidney tissue not only affects the diagnosis and subsequent treatment, but also has a decisive impact on the accuracy of multiclass segmentation, classification and object detection during computer intelligent analysis. Currently, pathologists usually assess image quality through eye screening, which greatly relies on the pathologist experience and brings about subjectivity and non-repeatability issues. In this paper, we develop a no-reference image quality assessment framework including a fused CNN classification module, a quality score conversion module and a comprehensive quality prediction module, which automatically classifies WSIs of kidney tissue into four quality levels: excellent, good, average, and poor, and calculates a rough quality score. The original image and the regions of interest are combined and fused to comprehensively evaluate the quality of a WSI through multiple factors instead of a simple deep learning network. Extensive experiments conducted on our in-house dataset confirm that our proposed framework obtains satisfactory results with an accuracy of 90.05%, surpassing the performance of the typical image quality assessment models, and achieves the level of junior pathologist. Therefore, our proposed method can be embedded into a computer assisted diagnosis system to help pathologists in analysis of histopathological images and judgment of reliability of the images. The source code and trained models will be available at https://github.com/kidneyPathology/WSIQA.
Feature generation and multi-sequence fusion based deep convolutional network for breast tumor diagnosis with missing MR sequences

In breast tumor diagnostic tasks, joint analysis of multiple sequences of MRI can improve the accuracy of diagnosis. More and more studies focus on the correlation between sequences and try applying multiple MRI sequences to computer-aided diagnosis. The deep neural network plays a vital role in this process because of its good feature extraction ability. However, the inevitable presence of missing or unavailable sequences in clinical can lead to degraded performance or even failure of multi-input networks. The key problem solved in this paper is to ensure the diagnostic accuracy of the network despite the presence of missing input sequences. A new breast tumor diagnosis deep learning method, ESF-NET, is proposed. First, ESF-NET combines the attention mechanism for adversarial learning to obtain the potential mapping and relationship between sequences and completes the generation of features for the missing sequences. Then the extended sequence fusion module with different fusion strategies is designed, which makes the final fusion results benefit from the feature enhancement resulting from all fusion strategies. These strategies can be adaptively weighted. We apply EST-NET to a dataset of MRI images of 98 women at high risk of breast cancer, including 33 benign and 65 malignant lesions. Each patient data contains several two-dimensional slices with different axial planes, the dataset consists of 2245 slices. In the presence of sequence missing, ESF-NET achieves a diagnostic accuracy of 85.61% at the slice level and 89.66% at the patient level, an improvement of 8.39% compared to using only a single sequence.
The Social Hashtag Recommendation for Image and Video Using Deep Learning Approach

There has been a lot of interest in the recent year in recommending hashtags for images/videos or posts on social media. Several researchers have researched the impact from numerous perspectives. In this paper, we enhance tag recommendation by recommending suitable hashtags considering both contents of the image/video and users’ history of the hashtag. On the social media image/video-sharing websites (such as Facebook, Instagram, Flickr, and Twitter), users can upload images or videos and tag them with tags. The proposed method generates candidate keywords, i.e., hashtag by combining techniques for textual tags, image and video activity/object recognition content, and acoustic data. To this end, this paper examines different methodologies that associate information that is multi-modal and suggests hashtags for image or video uploader users to generate tags for their images or videos. Although a substantial amount of study has been carried out on item/product recommendations for E-commerce websites, video recommendations for YouTube and Netflix, and friend suggestions on social media websites, research has not been carried out as much on hashtag recommendations for images/video on social media platform/app/websites, which have now turned out to be a vital role of these social media platforms. Here, in this paper, glance at hashtag recommendations for image/video has been carried.
Automatic detection of cancer metastasis in lymph node using deep learning

Lymph node metastases are one of the most indicator of some cancer types such as breast, colon and prostate. Breast cancer mostly spreads to lymph nodes in the armpit and it is one of the most common causes of death in women worldwide. Pathologists need more attention and time to diagnose metastasis in digitized lymph node images and also this process tends to be misinterpreted. In this paper, a promising deep learning based approach was presented to detect cancer metastasis in lymph node images with higher accuracy. The proposed deep learning framework uses ResNet architectures, transfer learning and 1cycle policy which is the method of finding the optimal learning rate. The validity of the proposed approach was evaluated on PCAM dataset consisted of 220,025 lymph node images. The experiments demonstrated that the presented method outperformed most of the current studies, achieving accuracy of 98.60% on the PCAM dataset by using ResNet models and fine-tuning techniques effectively. The burden of diagnosis procedure of metastasis can be lessened with the proposed deep learning framework in the clinical applications.