Reviewerly/exHarmony huggingface dataset:
A Video Frame Extrapolation Scheme Using Deep Learning-Based Uni-Directional Flow Estimation and Pixel Warping

This paper investigates video frame extrapolation, which can predict future frames from current and past frames. Although there have been many studies on video frame extrapolation in recent years, most of them suffer from the unsatisfactory image quality of the predicted frames such as severe blurring because it is difficult to predict the movement of future pixels for multi-modal video frames, especially with fast changing frames. An additional process such as frame alignment or recurrent prediction can improve the quality of the predicted frames, but it hinders real-time extrapolation. Motivated by the significant progress in video frame interpolation using deep learning-based flow estimation, a simplified video frame extrapolation scheme using deep learning-based uni-directional flow estimation is proposed to reduce the processing time compared to conventional video frame extrapolation schemes without compromising the image quality of the predicted frames. In the proposed scheme, the uni-directional flow is first estimated from the current and past frames through a flow network consisting of four flow blocks and the current frame is forward-warped through the estimated flow to predict a future frame. The proposed flow network is trained and evaluated using the Vimeo-90K triplet dataset. The performance of the proposed scheme is analyzed using the trained flow network in terms of prediction time as well as the similarity between predicted and ground truth frames such as the structural similarity index measure and mean absolute error of pixels, and compared to that of the state-of-the-art schemes such as Iterative and cycleGAN schemes. Extensive experiments show that the proposed scheme improves prediction quality by 2.1% and reduces prediction time by 99.7% compared to the state-of-the-art scheme.
A Robust Image Watermarking Scheme Based on Image Normalization and Contourlet Transform

It is a challenging work to solve the geometric attack in the field of digital watermarking. In order to solve the synchronization between the host image and the watermark, image normalization is introduced. Firstly, the geometrically invariant space of image is constructed by using image normalization, and a region of interest (ROI) is obtained from the normalized image by utilizing the invariant centroid theory. Then, the contourlet transform is performed on the ROI. Low-pass sub-band coefficients are divided into non-overlapping blocks. Finally, as the energy of the blocks are not the same, an adaptive quantization step is used to embed the binary watermark information on the maximum singular value of the block. The experimental results demonstrate that the watermarked images exhibit high visual quality, and the scheme is effective against common image processing and geometric distortions.
Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Finding relevant research papers is a challenging task due to the enormous number of scientific publications published each year. In recent years, the scientific community has delved into the analysis of citations at a deep level, specifically examining the content of the papers, in order to identify more important documents. Citations serve as potential parameters for determining linkages between research articles. They have been extensively used for various academic purposes, such as calculating journal impact factors, determining researchers’ h-index, allocating research grants, and identifying the latest research trends. However, it has been argued by researchers that not all citations are equally influential. As a result, alternative techniques have been proposed to identify important citations based on content, metadata, and bibliographic information. Nevertheless, the current state-of-the-art approaches still require further improvement. Additionally, the use of deep learning models and word embedding techniques in this context has not been extensively studied. In this research work, we propose an approach based on two primary modules: 1) Section-wise citation count, and 2) metadata-based analysis of citation intent. Our study also involves conducting several experiments using deep learning models combined with FastText, word2vec, and BERT-based word embeddings to perform citation analysis. These experiments were conducted on two benchmark datasets, and the results were compared with a contemporary study that employed a rich set of content-based features for classification. Our findings indicate that the deep learning CNN model coupled with FastText word embedding achieves the best results in terms of accuracy, precision, and recall. It outperforms the existing state-of-the-art model with a precision score of 0. 97.
FlwrBC: Incentive Mechanism Design for Federated Learning by Using Blockchain

The growth of information technology has resulted in a massive escalation of data and the demand for data exploration, particularly in the machine learning sector. However, machine learning raises concerns about data privacy because algorithms require large amounts of data to learn and make accurate predictions. Such data often contain personal information about individuals, and there is a risk that this information could be accessed or misused by unauthorized parties. It is crucial for organizations that use machine learning to prioritize personal data protection and ensure that appropriate safeguards are in place to prevent privacy breaches. Federated learning (FL) and blockchain technology are two increasingly popular approaches to distributed computing. Federated learning is a distributed machine-learning approach that trains machine-learning models on decentralized datasets without centralizing the data. Federated learning offers several benefits, including improved data privacy. Ensuring the benefit of clients in federated learning is vital for the success of this distributed machine learning approach, especially when combined with blockchain technology, as it offers a secure and transparent way to store and verify data. In this study, we propose a combination of federated learning and blockchain as a solution to some of the challenges faced by both approaches. By leveraging the decentralized nature of federated learning and the security and transparency of blockchain, our approach tends to overcome issues such as data privacy and trustworthiness of results. The evaluation results demonstrated that the proposed approach has many potential applications in various domains.
Attributed Network Embedding Using an Improved Weisfeiler-Lehman Schema and a Novel Deep Skip-Gram

Attributed Network Embedding (ANE) and the representation of its nodes in a low-dimensional space is a pivotal step in the analysis of real-world networks. One of the biggest challenges in the embedding process of nodes in complex networks is to capture any dynamic changes in both the node itself and in its adjacent. To address the above challenge, in this paper, we propose a novel ANE model that combines an improved Weisfeiler-Lehman Information Aggregation (WLIA) schema with a novel Deep Skip-Gram (DSG) approach. First, an information aggregation of network data is performed using an improved Weisfeiler-Lehman, which captures each node’s attributes and combines them with the attributes of its adjacent nodes in a mathematically proven balanced and fair manner. Next, a novel deep autoencoder model that adopts the Skip-Gram approach to capture the high non-linearity among the nodes and between nodes with their attributes is proposed. In the DSG approach, a deep encoder is paired with a set of deep decoders; the main decoder is for the node itself and the secondary deep decoders act as attention decoders to extract common features from its neighbors. Extensive experimental evaluations have demonstrated that the proposed method is superior in performance compared to recent network embedding models.
Reward Learning With Intractable Normalizing Functions

Robots can learn to imitate humans by inferring what the human is optimizing for. One common framework for this is Bayesian reward learning, where the robot treats the human's demonstrations and corrections as observations of their underlying reward function. Unfortunately, this inference is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">doubly-intractable:</i> the robot must reason over all the trajectories the person could have provided and all the rewards the person could have in mind. Prior work uses existing robotic tools to approximate this normalizer. In this letter, we group previous approaches into three fundamental classes and analyze the theoretical pros and cons of their approach. We then leverage recent research from the statistics community to introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Double MH reward learning</i> , a Monte Carlo method for asymptotically learning the human's reward in continuous spaces. We extend Double MH to conditionally independent settings (where each human correction is viewed as completely separate) and conditionally dependent environments (where the human's current correction may build on previous inputs). Across simulations and user studies, our proposed approach infers the human's reward parameters more accurately than the alternate approximations when learning from either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">demonstrations</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corrections</i> .
INTERBEING: On the Symbiosis between INTERnet and Human BEING

With the advent of the metaverse, the future 3D spatial Internet will be about being inside the Internet rather than simply looking at it from a 2D computer or smartphone screen. This paper aims at exploring the sociality dimension, AI integration in eXtended meta-uni-omni-Verse (XV), and stigmergy principles. It puts a particular focus on the unifying design of virtual, embodied, intelligent cross-reality environments, ranging from our proposed stigmergic Society 5.0 to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interbeing</i> based on the symbiosis between Inter(net) and (human) being—a word that is not in the dictionary yet. In addition to tokenized digital twins, we leverage on hyperintelligent life-like digital organisms that symbiomimic biological superorganisms. In doing so, they lay the foundation for creating a future stigmergic virtual society that benefits from the convergence of digital evolution and biology as well as advanced XR wearables for tapping into the entire reality-virtuality continuum of the metaverse's emerging virtual society
Skeleton-Based Action Recognition With Select-Assemble-Normalize Graph Convolutional Networks

Skeleton-based action recognition has been substantially driven by the development of artificial intelligence technology and deep sensors. Recently, graph convolutional networks (GCNs) have achieved excellent performances in skeleton-based action recognition. However, the performances of GCN-based methods are impaired by inappropriate node partitioning strategy and obstructed long-range information flow. To solve these issues, a novel Select-Assemble-Normalize Graph Convolution Network (SAN-GCN) is proposed to model the spatio-temporal features of skeleton. First, all skeleton joints are selected as root nodes, and the neighborhoods of the root joints are assembled and normalized according to the body structure, which explicitly and interpretably expresses the spatial geometric relation of the skeleton joints. Second, we propose an attention-based assembly and normalization strategy to adaptively capture non-local joints. The adaptive assembly and normalization can avoid the dilution of key long-range features. Moreover, a bi-level aggregation strategy is introduced to learn spatio-temporal dependencies of joints, where the low-level aggregation aligns the normalized neighborhood graphs, and the high-level aggregation aggregates the features of neighbor nodes by a standard convolution kernel. In high-level aggregation, it is convenient to realize factorized spatio-temporal aggregation or unified spatio-temporal aggregation. Extensive experiments on four datasets with different numbers of action patterns demonstrate that our model achieves comparable performance with the state-of-the-art works.
How Software Practitioners Perceive Work-Related Barriers and Benefits Based on Their Educational Backgrounds: Insights From a Survey Study

The demand for software practitioners has increased substantially over the last three decades. As the supply trend has not been able to catch these high demand levels, recruiters have been employing people without traditional software engineering backgrounds. Individuals interested in changing careers, attend courses, take certificates, or involve in graduate programs in software engineering-related programs to acquire necessary skills. These practitioners may have different benefits and may face more challenges compared to their colleagues with traditional software engineering background. Here, we present the results of a survey study performed with 132 software practitioners from 11 countries to explore work-related challenges and the opportunities that participants experience. The result shows that software practitioners from non-software-related backgrounds face more barriers, fewer benefits, and feel less satisfied. However, data shows that these differences reduce with over ten years of experience, involvement in software-related graduate programs, taking software certificates, and mentorship from experienced practitioners.
Semi-Supervised Representation Learning for Segmentation on Medical Volumes and Sequences

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expertise to make large scale annotations. Self/semi-supervised learning methods have been shown to improve the performance by exploiting unlabeled data. However, they are still lack of mining local semantic discrimination and exploitation of volume/sequence structures. In this work, we propose a semi-supervised representation learning method with two novel modules to enhance the features in the encoder and decoder, respectively. For the encoder, based on the continuity between slices/frames and the common spatial layout of organs across subjects, we propose an asymmetric network with an attention-guided predictor to enable prediction between feature maps of different slices of unlabeled data. For the decoder, based on the semantic consistency between labeled data and unlabeled data, we introduce a novel semantic contrastive learning to regularize the feature maps in the decoder. The two parts are trained jointly with both labeled and unlabeled volumes/sequences in a semi-supervised manner. When evaluated on three benchmark datasets of medical volumes and sequences, our model outperforms existing methods with a large margin of 7.3% DSC on ACDC, 6.5% on Prostate, and 3.2% on CAMUS when only a few labeled data is available. Further, results on the M&M dataset show that the proposed method yields improvement without using any domain adaption techniques for data from unknown domain. Intensive evaluations reveal the effectiveness of representation mining, and superiority on performance of our method. The code is available at https://github.com/CcchenzJ/BootstrapRepresentation.
A Novel Anchor-Free Model With Salient Feature Fusion Mechanism for Ship Detection in SAR Images

Ship detection in synthetic aperture radar (SAR) images has gained great attention in civil and military fields. Anchor-based detection algorithms usually rely on preset candidate boxes, and a large amount of anchor boxes with different sizes will result in a large amount of computing resources being consumed. Recently, anchor-free algorithms have found wide applications in ship detection from SAR images. However, there are still some problems which limit the ship detection performance to a certain extent, such as how to effectively fuse salient features and unbalanced distribution of positive samples. In order to tackle the above problems, we propose a novel anchor-free model named SFF-YOLOX with salient feature fusion (SFF) mechanism. Firstly, we redesign the network of YOLOX to obtain the best balance between detection accuracy and running speed. Secondly, a saliency region extraction (SRE) module is introduced to generate the corresponding salient guide map of the input image. Besides, the SFF mechanism is proposed by fusing deep features and salient features to better enhance the discrimination of the multiscale targets. Finally, we improve the SimOTA mechanism by combining the predicted IoUs (intersection over union) and the anchor IoUs to the ground truth bounding boxes to instruct label assignment. We evaluate the detection accuracy and running speed of SFF-YOLOX on the public dataset SSD and test the generalization ability on HRSID and two complex large-scale SAR images, the experimental results prove the model's effectiveness for ship detection task in SAR images.
On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique

In peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction, one of the most appealing features of peer assessment for instructors. Our proposal uses a probabilistic model to estimate a grade for each test, accounting for the degree of precision and bias of grading peers. The grade that the instructor would assign to a test can help enhance the model. Our main hypothesis is that guiding the instructor through supervision of a peer-assessed task by pointing out to them which test to evaluate next can lead to improvement in the validity of the model-estimated grades at an early stage. Moreover, the instructor can decide how many tests to grade based on their own criteria of tolerable uncertainty, as measured by the model. We validate the method using both synthetically generated data and real data collected in an actual class. Models that link the roles of the student as grading peer and as test-taker appear to better exploit available information, although simpler models are more appropriate in specific conditions. The best performing technique for guiding the instructor is that which selects the test with the highest expected entropy reduction. In general, empirical results are in line with the hypothesis of this study.
iTutorGroup: A case study of covert native-speakerism underneath a social justice façade

&lt;p&gt;This article examines the covert native-speakerist strategies iTutorGroup utilizes to discriminate against teachers of nationalities the company appears to deem as undesired. Through content analysis of numerous job application submissions to iTutorGroup’s website, results show iTutorGroup’s automatic hiring process offers teachers of these nationalities a much lower potential wage and only a video-recorded asynchronous interview, if not complete refusal to an interview. In contrast, British, Australasian, and North American nationals are afforded a much higher potential wage as well as a one-on-one live interview. The company conceals these nuanced discriminatory strategies with a façade of equality since they are one of TESOL International Association’s Global Partners. As a Global Partner, iTutorGroup follows suit in pretending to uphold TESOL’s nondiscrimination policies.&lt;/p&gt;
Data Visualization for Collection Analysis

Due to the increasingly digital nature of library resources and collections, it is sometimes difficult to envision a library’s unified holdings and to understand how they have changed over time. Conducting a collection analysis and applying data visualization techniques can be an excellent way to get a top-down view of the collection as a whole. This article outlines the author’s process for a collection analysis of the Weinberg Memorial Library’s entire catalog of print and electronic resources. It explores the rationale behind some key collection analysis decisions and discusses approaches for data extraction and clean-up as well as visualization using the Tableau software.
A Design and Fabrication of an Automated Solar Energy Tracker Integrated Four-Sided Reflector-Based Box-Type Solar Cooker to Increase Efficiency by Absorbing Maximum Solar Energy

As technology continues to advance, it increases the energy demand. As a result, alternative technologies based on renewable energy are conceivable. Another significant contributor to worldwide energy usage has been cooking. Consequently, switching to solar energy for cooking can solve the current energy issue. The box-style solar cooker is the most straightforward tool for gathering incoming solar radiation and transforming it into heat energy. This study discusses the efficiency and power output as examples of thermal performance parameters used to evaluate various cookers. The efficiency of solar cookers has previously ranged from 17% to 25%; however, this project raises it by 33%. This study uses an automated solar tracking system to improve the performance and efficacy of solar cookers. The four-sided reflector-based box-type solar cooker moves following the sun’s rotation to extract the most energy for heating it and getting it ready for cooking. This test model of the device based on a servo motor is intelligently controlled by an Arduino UNO board.
Maximize the Security for Image Processing Using an Improved Watermarking Approach

Creating a dependable and effective digital image watermarking (WM) method necessitates balancing imperceptibility, resilience, capacity, and security. Several studies have combined spatial and transform domains to meet these needs. We investigated the current state of hybrid digital image WM. When developing a hybrid WM strategy, it is critical to consider software compatibility. Following a brief review of the literature, we used a table to analyse and evaluate current hybrid approaches. Furthermore, the limitations and possibilities of these approaches are discussed. We investigated the limitations of current research methodologies and proposed new research directions. The internet’s role in distributing digital material has resulted in more digitalisation and more complex copyright concerns. Copyright breaches are being reduced through the deployment of innovative digital WM techniques. As a result of this research, a WM system capable of dealing with a wide range of threats is being developed. The goal of this study is to maximise security while maintaining visibility and resilience. Discrete wavelets transform (DWT) and singular value decomposition (SVD) were used to investigate covert operations; DWT was used to isolate each level of the host image, which was then processed using SVD. At the time, normalized correlation was the best method for measuring watermarked images. In research, DWT–SVD was more accurate in detecting assaults; it shows structural similarity index and normalized correlation are 98.4 and 98.35, respectively. Watermarked images can withstand a variety of assaults due to their invisibility and resilience; this system repels assaults that alter pixel values better than traditional methods.