Reviewerly/exHarmony huggingface dataset:
Challenges in Image Matching for Cultural Heritage: An Overview and Perspective

Image matching, as the task of finding correspondences in images, is the upstream component of vision and photogrammetric applications aiming at the reconstruction of 3D scenes, their understanding and comparison. Such applications are of special importance in the context of cultural heritage, as they can support archaeologists to digitally preserve, restore and analyze antiquities, but also to compare their changes over time. The success of deep learning, now firmly established, paired with the evolution of computer hardware, has led to many advances in image processing, including image matching. Despite this progress, image matching still offers challenges, in terms of the matching process itself but also on other practical and technical aspects. This paper gives an overview of the current status of the research in image matching with a particular focus on cultural heritage, presenting both strengths and weaknesses of the most recent approaches by means of visual comparisons on exemplar challenging image pairs. Besides assisting researchers and practitioners in the choice of the most suitable solution for a given task, this analysis also suggests lines of research worth to be investigated by the community in the near future.
Session-based recommendation with time-aware neural attention network

In situations where user information and detailed knowledge of user behaviors are challenging to obtain, the session-based recommendation is essential. The session-based recommendation (SBR) relies on the current anonymous session sequence to predict the next action. Recently, sequence modeling methods such as recurrent neural networks have been widely used to model user preferences in session-based recommendations. However, the existing SBR methods usually ignore the click time of interacted items, resulting in inaccurate user preference models and poor recommendations. In this article, we propose a time-aware neural attention network to model dynamic user preferences in SBR tasks. First, a global session graph is constructed based on all sessions, and a graph neural network is used to learn the embedding of items. Then, a gated recurrent unit is used to refine the item embedding and capture the general interests of the user based on the current session. Next, a novel time-aware neural attention network is proposed and used to model the main purpose of the user in the session. Finally, the user's general interests and main purposes are combined to obtain dynamic user preferences for generating recommendations. Extensive experiments show that our method outperforms state-of-the-art session-based recommendation methods significantly and consistently.
LGLNN: Label Guided Graph Learning-Neural Network for few-shot learning

Graph Neural Networks (GNNs) have been employed for few-shot learning (FSL) tasks. The aim of GNN based FSL is to transform the few-shot learning problem into a graph node classification or edge labeling tasks, which can thus fully explore the relationships among samples in support and query sets. However, existing works generally consider the graph learned by node features which ignore the initial pairwise label constraints and thus are generally not guaranteed to be optimal for FSL tasks. Also, existing works generally learn graph edges independently based on node's own features which lack of considering the consistent relationships among different edges. To address these issues, we propose a novel Label Guided Graph Learning-Neural network (LGLNN) model for FSL tasks. The aim of LGLNN is to incorporate the label information to learn an optimal metric graph for GNN by employing the pairwise constraint propagation. The main advantage of LGLNN is that it can learn the metrics (both similarity and dissimilarity) for each graph edge by aggregating the metric information from its neighboring edges and thus can conduct metric learning of all edges cooperatively and consistently. Experimental results demonstrate the effectiveness and better performance of the proposed LGLNN method.
RL-AFEC

Real-time video communication is profoundly changing people's lives, especially in today's pandemic situation. However, packet loss during video transmission degrades reconstructed video quality, thus impairing users' Quality of Experience (QoE). Forward Error Correction (FEC) techniques are commonly employed in today's audio and video conferencing applications, such as Skype and Zoom, to mitigate the impact of packet loss. FEC helps recover the lost packets during transmissions at the receiver side, but the additional bandwidth consumption is also a concern. Since network conditions are highly dynamic, it is not trivial for FEC to maintain video quality with a fixed bandwidth overhead. In this paper, we propose RL-AFEC, an adaptive FEC scheme based on Reinforcement Learning (RL) to improve reconstructed video quality with an aim to mitigate bandwidth consumption for different network conditions. RL-AFEC learns to select a proper redundancy rate for each video frame, and then adds redundant packets based on the frame-level Reed-Solomon (RS) code. We also implement a novel packet-level Video Quality Assessment (VQA) method based on Video Multimethod Assessment Fusion (VMAF), which leverages Supervised Learning (SL) to generate video quality scores in real time by only extracting information from the packet stream without the need of visual contents. Extensive evaluations demonstrate the superiority of our scheme over other baseline FEC methods.
HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning

There have been few efforts made to automate the cytomorphological categorization of bone marrow cells. For bone marrow cell categorization, deep-learning algorithms have been limited to a small number of samples or disease classifications. In this paper, we proposed a pipeline to classify the bone marrow cells despite these limitations. Data augmentation was used throughout the data to resolve any class imbalances. Then, random transformations such as rotating between 0∘ to 90∘, zooming in/out, flipping horizontally and/or vertically, and translating were performed. The model used in the pipeline was a CoAtNet and that was compared with two baseline models, EfficientNetV2 and ResNext50. We then analyzed the CoAtNet model using SmoothGrad and Grad-CAM, two recently developed algorithms that have been shown to meet the fundamental requirements for explainability methods. After evaluating all three models' performance for each of the distinct morphological classes, the proposed CoAtNet model was able to outperform the EfficientNetV2 and ResNext50 models due to its attention network property that increased the learning curve for the algorithm which was represented using a precision-recall curve.
Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on manifold learning for the forecasting of high-dimensional time series. At the first step, we embed the time series into a reduced low-dimensional space using a nonlinear manifold learning algorithm such as Locally Linear Embedding and Diffusion Maps. At the second step, we construct reduced-order regression models on the manifold, in particular Multivariate Autoregressive (MVAR) and Gaussian Process Regression (GPR) models, to forecast the embedded dynamics. At the final step, we lift the embedded time series back to the original high-dimensional space using Radial Basis Functions interpolation and Geometric Harmonics. For our illustrations, we test the forecasting performance of the proposed numerical scheme with four sets of time series: three synthetic stochastic ones resembling EEG signals produced from linear and nonlinear stochastic models with different model orders, and one real-world data set containing daily time series of 10 key foreign exchange rates (FOREX) spanning the time period 03/09/2001-29/10/2020. The forecasting performance of the proposed numerical scheme is assessed using the combinations of manifold learning, modelling and lifting approaches. We also provide a comparison with the Principal Component Analysis algorithm as well as with the naive random walk model and the MVAR and GPR models trained and implemented directly in the high-dimensional space.
Cross-Site Scripting Attacks and Defensive Techniques: A Comprehensive Survey

The advancement of technology and the digitization of organizational functions and services have propelled the world into a new era of computing capability and sophistication.The proliferation and usability of such complex technological services raise several security concerns.One of the most critical concerns is cross-site scripting (XSS) attacks.This paper has concentrated on revealing and comprehensively analyzing XSS injection attacks, detection, and prevention concisely and accurately.I have done a thorough study and reviewed several research papers and publications with a specific focus on the researchers' defensive techniques for preventing XSS attacks and subdivided them into five categories: machine learning techniques, server-side techniques, client-side techniques, proxy-based techniques, and combined approaches.The majority of existing cutting-edge XSS defensive approaches carefully analyzed in this paper offer protection against the traditional XSS attacks, such as stored and reflected XSS.There is currently no reliable solution to provide adequate protection against the newly discovered XSS attack known as DOM-based and mutation-based XSS attacks.After reading all of the proposed models and identifying their drawbacks, I recommend a combination of static, dynamic, and code auditing in conjunction with secure coding and continuous user awareness campaigns about XSS emerging attacks.
Feature engineering to detect fraud using healthcare claims data

Insurance fraud is ranked second in the list of expensive crimes in the United States, with healthcare fraud being the second highest amongst all insurance fraud. Contrary to the popular belief, insurance fraud is not a victimless crime. The cost of crime is passed onto law-abiding citizens in the form of increased premiums or serious harm or danger to beneficiaries. To combat this kind of societal threat, there is an intense need for healthcare fraud detection systems to evolve. Some common roadblocks in implementing digital advancements (as seen in other domains) to healthcare are the complexity, heterogeneity of the data systems, and varied health program models across the United States. In other words, data are not stored in a centralized manner due to the sensitive domain nature, thus making it difficult to implement a robust real-world fraud-detection system. At the same time, in addition to the complexity of the varied systems involved, there is also the need to meet certain standards before a fraud actor can be prosecuted in a litigation setting. Thus, there is a human aspect to the fraud detection process flow in the real-world.
Systematic review of the “semantic network” definitions

Knowledge can be represented through semantic networks, which have the advantages of being simple, clear, and easy to visualize. The literature points to the existence of a diversity of “semantic network” definitions, which can be conceived of as structures based on graphs with edges or arcs. According to this assumption, vertices can be assigned meanings or be labeled, and they can represent concepts, objects, or individuals; edges can have direction or be labeled, and they represent relationships or associations. A semantic network model can be designed to capture only one or several aspects of a semantic structure. We performed a systematic review of the literature with the aim of identifying and classifying the family of “semantic network” definitions adopted by the authors of the studies reviewed. This study mapped the “semantic network” definitions present in the literature and analyzed aspects such as the mathematical representation used, vertices and edges representation, the attribution of meanings to each network component and the semantic network representation. We also analyzed the core of words linked to the family of the “semantic network” term in a semantic network based on their definitions.
Self-Supervised Monocular Depth Estimation With Frequency-Based Recurrent Refinement

Self-supervised monocular depth estimation has succeeded in learning scene geometry from only image pairs or sequences. However, it is still highly ill-posed for self-supervised depth estimation to generate high-quality depth maps with both global high accuracy and local fine details. To address this issue, we propose a novel frequency-based recurrent refinement scheme to improve the self-supervised depth estimation. Since the global and local depth representation can be correlated to high/low frequency coefficients in the frequency domain, we propose a frequency-based recurrent depth coefficient refinement (RDCR) scheme, which progressively refines both low frequency and high frequency depth coefficients with an RNN-based architecture in a multi-level manner. During the recurrent process, the depth coefficients generated from the previous time step are used as the input to generate the current depth coefficients, yielding progressively optimized depth estimations. Meanwhile, considering that the depth details often appear in areas with high image frequency, we further improve depth details during the RDCR process by leveraging the image-based high frequency components. Specifically, in each RDCR module, we enhance the high frequency depth representations by selecting and feeding the informative image-based high frequency features with a learned feature weighting mask. Extensive experiments show that the proposed method achieves globally accurate estimation with fine local details, outperforming other self-supervised methods in both quantitative and qualitative comparisons.
Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for computing resources. At the same time, the overall power consumption of data centers has been rising beyond 1% of worldwide electricity consumption. The usage of renewable energy in data centers contributes to decreasing their carbon footprint and overall electricity costs. Several green-energy-aware resource allocation approaches have been studied recently. None of them takes advantage of the joint migration of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jobs</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy</i> in green data centers to increase energy efficiency. This paper presents an optimization approach for energy-efficient resource allocation in mini data centers. The observed momentum around edge computing makes the design of geographically distributed mini data centers highly desirable. Our solution exploits both virtual machines (VMs) and energy migrations between green compute nodes in mini data centers. These nodes have energy harvesting, storage, and transport capabilities. They enable the migration of VMs and energy across different nodes. Compared to VM allocation alone, joint-optimization of VM and energy allocation reduces utility electricity consumption by up to 22%. This reduction can reach up to 28.5% for the same system when integrating less energy-efficient servers. The gains are demonstrated using simulation and a Mixed Integer Linear Programming formulation for the resource allocation problem. Furthermore, we show how our solution contributes to sustaining the energy consumption of old-generation and less efficient servers in mini data centers.
DULBC: A dynamic ultra-lightweight block cipher with high-throughput

Lightweight block ciphers are important for implementing security services on constrained devices today. In this paper, we propose a novel ultra-lightweight block cipher DULBC based on a dynamic SPN structure. The round function of DULBC adopts an unfixed F-function and a bit-based permutation. The choice of the F-function depends on 2 bits of the round keys. This means the encryption process of the cipher is uncertain and its security is greatly improved. Also, a strong 4 × 4 S-box with fewer logic gates is proposed, which costs low hardware resources and ensures excellent performances. We have conducted FPGA implementation and ASIC implementation on DULBC. The throughput of DULBC-80/128 is 555.03/443.84 Mbps and the area of the 16-bit serial architecture of DULBC-80/128 is 1075/1351 GEs, respectively. DULBC is secure and efficient according to our experiments and comparisons.
Bias or biology? Importance of model interpretation in machine learning studies from electronic health records

The rate of diabetic complication progression varies across individuals and understanding factors that alter the rate of complication progression may uncover new clinical interventions for personalized diabetes management.We explore how various machine learning (ML) models and types of electronic health records (EHRs) can predict fast versus slow onset of neuropathy, nephropathy, ocular disease, or cardiovascular disease using only patient data collected prior to diabetes diagnosis.We find that optimized random forest models performed best to accurately predict the diagnosis of a diabetic complication, with the most effective model distinguishing between fast versus slow nephropathy (AUROC = 0.75). Using all data sets combined allowed for the highest model predictive performance, and social history or laboratory alone were most predictive. SHapley Additive exPlanations (SHAP) model interpretation allowed for exploration of predictors of fast and slow complication diagnosis, including underlying biases present in the EHR. Patients in the fast group had more medical visits, incurring a potential informed decision bias.Our study is unique in the realm of ML studies as it leverages SHAP as a starting point to explore patient markers not routinely used in diabetes monitoring. A mix of both bias and biological processes is likely present in influencing a model's ability to distinguish between groups.Overall, model interpretation is a critical step in evaluating validity of a user-intended endpoint for a model when using EHR data, and predictors affected by bias and those driven by biologic processes should be equally recognized.
“yeet nitro boosted”: A Postdigital Perspective on Young People’s Literacy Engagements With the Discord Platform

While the influence of digital technologies on literacy expression has long been a central concern of literacy studies, the proliferation of digital platforms across contemporary life has prompted increased scholarly attention to their influence on literacy. How, this scholarship asks, do platforms’ interfaces, algorithms, and business models produce particular formations of literacy, and what do those formations mean for ethical and equitable literacies learning and living? To reckon with these questions, this article brings a postdigital literacies perspective to data collected at the Giga-Games Camp, a video game design summer camp, where young people interacted with each other using Discord, a social media platform. The author describes how the Giga-Gamers and the Discord platform engaged in the mutually articulated platform practice of economics and argues that attention to such platform practices can help educators and researchers respond to the economic forces (re)shaping literacy in the platform society.
Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis

The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns.This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States.We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship.In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations.Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.
Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review

Abstract Peer review is a key component of the publishing process in most fields of science. Increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work. While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts—yet general frameworks and datasets to support this scenario are missing. Relationships between texts are the core object of the intertextuality theory—a family of approaches in literary studies not yet operationalized in NLP. Inspired by prior theoretical work, we propose the first intertextual model of text-based collaboration, which encompasses three major phenomena that make up a full iteration of the review–revise–and–resubmit cycle: pragmatic tagging, linking, and long-document version alignment. While peer review is used across the fields of science and publication formats, existing datasets solely focus on conference-style review in computer science. Addressing this, we instantiate our proposed model in the first annotated multidomain corpus in journal-style post-publication open peer review, and provide detailed insights into the practical aspects of intertextual annotation. Our resource is a major step toward multidomain, fine-grained applications of NLP in editorial support for peer review, and our intertextual framework paves the path for general-purpose modeling of text-based collaboration. We make our corpus, detailed annotation guidelines, and accompanying code publicly available.1