Reviewerly/exHarmony huggingface dataset:
The Effect of COVID Pandemic on Cryptocurrency Markets; A Literature Review

The consequences of COVID-19 on the financial sector have gained so much attention. At the same time, another growing part of the financial markets is cryptocurrencies, and it comes to the question: What is the impact of COVID-19 as a shock on crypto markets? This study is the first attempt to survey the growing literature on the relationship between the COVID outbreak and crypto markets. I first review research on the implications of COVID on the crypto market in a general setting. Then I examine research on herding behavior during COVID in the crypto market and the efficiency and volatility of cryptocurrencies during the same period. I conclude by discussing the crypto market's relationship with other financial assets during COVID.
Side-channel Attack Countermeasure Based on Power Supply Modulation

As the number of IoT devices grows exponentially every year, so do the security threats. Due to their mobility and limited size, power and performance, these devices are particu-larly vulnerable to side-channel attacks that are based on device physical leaks. In this paper, we modulate the power supply voltage to secure the devices against two types of side-channel attacks: differential and correlation power analysis attacks (DPA and CPA) that aim to reveal cryptographic secret key and attacks that process the leaked signal to obtain the information on the activity inside the device (e.g. identify the keystrokes when typing a password). We perform both types of attacks on a low-cost microcontroller used in a variety of IoT devices and find the most effective voltage modulation for both of the targeted attacks. The proposed countermeasure is easy to implement and does not require re-designing the microcontroller, thereby avoiding high costs of fabrication and testing. It is extremely effective against cryptographic attacks as it increases the minimum number of traces required to disclose (MTD) by two orders of magnitude. For non-cryptographic attacks the correlation coefficient between the leaked signal and the sensitive information is lowered by 33%.
Focal-Pyramid-Based Vehicle Segmentation in SAR Images

How to effectively identify vehicles in SAR (synthetic aperture radar) images is a challenging problem in the SAR image understanding community. Compared with the bounding-box-based localization by object detection networks, object segmentation could provide the complete description of vehicles, which not only detects vehicles’ positions but also obtains their contours. However, vehicle segmentation in SAR images is a hard task due to speckle noise. To address this problem, this letter proposes a focal-pyramid-based network for vehicle segmentation in SAR images, called FVSNet, consisting of a focal pyramid module and a segmentation module. The focal pyramid module is built for extracting the multi-scale semantic feature maps from inputted SAR images, where the channel weights of the feature maps from backbone are automatically adjusted via a channel attention and the ambiguous features derived from speckle noise are eliminated via a designed mask-based cross attention. The segmentation module is designed to enlarge the feature maps outputted from the focal pyramid module with higher-resolution feature maps to obtain vehicle segmentation results. In addition, considering that there exists no open-source manually annotated dataset for handling the SAR vehicle segmentation task, we build and release a SAR vehicle-segmentation dataset based on the public MSTAR dataset. Experimental results on this dataset demonstrate the effectiveness of the proposed network in comparison with eight existing networks.
Technical Debt in Service-Oriented Software Systems

Service-Oriented Architectures (SOA) have become a standard for developing software applications, including but not limited to cloud-based ones and enterprise systems. When using SOA, the software engineers organize the desired functionality into self-contained and independent services, that are invoked through end-points (API calls). At the maintenance phase, the tickets (bugs, functional updates, new features, etc.) usually correspond to specific services. Therefore, for maintenance-related estimates it makes sense to use as unit of analysis the service-per se, rather than the complete project (too coarse-grained analysis) or a specific class (too fine-grained analysis). Currently, some of the most emergent maintenance estimates are related to Technical Debt (TD), i.e., the additional maintenance cost incurred due to code or design inefficiencies. In the literature, there is no established way on how to quantify TD at the service level. To this end, in this paper, we present a novel methodology to measure the TD of each service considering the underlying code that sup-ports the corresponding endpoint. The proposed methodology relies on the method call graph, initiated by the service end-point, and traverses all methods that provide the service functionality. To evaluate the usefulness of this approach, we have conducted an industrial study, validating the methodology (and the accompanying tool) with respect to usefulness, obtained benefits, and usability.
The Impact of Filtering for Breast Ultrasound Segmentation using A Visual Attention Model

Breast cancer can threaten women's health and become a cause of death. Reducing mortality from breast cancer necessitates early recognition of its signs and symptoms. An essential step in building an early detection system is to segment the breast ultrasound image (BUS). The accuracy of segmentation has a direct bearing on the effectiveness of quantitative analysis and the detection of breast tumor. However, this image segmentation becomes constrained because the BUS image has a shallow quality. Therefore, it is necessary to take preprocessing steps to improve the image. This study aims to compare the efficiency of various filtering techniques for BUS segmentation with the visual attention model. There are 12 filters tested in this study, including Mean, Median, Bilateral, Fast nonlinear, Lee, Lee-enhance, Frost, Kuan, Gamma, Wiener, Speckle Reduction Anisotropic Diffusion Filter (SRAD), and Detail Preserved Anisotropic Diffusion Filter (DPAD). The segmentation process uses a Convolutional Neural Network (CNN) based network architecture, namely Visual Geometry Group architecture with 16 layers (VGG-16). The segmentation results were analyzed using three visual attention models. The results showed that the image before filtering and after filtering showed visually significant results.
PriRecT: Privacy-preserving Job Recommendation Tool for GPU Sharing

Machine Learning (ML) jobs significantly benefit when trained on abundant GPU resources. It leads to resource contention when several ML training jobs are scheduled con-currently on a single GPU in the compute cluster. A job's performance is susceptible to its competitor's task on a single GPU. We, in this paper, propose PriRecT, a novel ML job recommendation tool that preserves user privacy for scheduling ML training jobs in the GPU compute cluster. We perform workload characterization for several ML training scripts, and the Futurewei mini-ML Workload Dataset is released publicly [1]. We build a knowledge base of inter and intra-cluster task interference for GPU sharing through a clustering-based approach. For scheduling purposes, PriRecT blinds the user-sensitive information and assigns the job to an existing cluster. Based on clustering results, PriRecT recommends jobs that should run concurrently on a single GPU to minimize task interference and additionally assigns an uncertainty score to account for job variations in the recommendation.
Research on Computer Aided English Teaching in Higher Vocational Education Under the Background of Information Technology

Science and technology are advancing rapidly, computers are being used by more and more people, and the information age has arrived, bringing unlimited possibilities for higher vocational English teaching. English teaching in the new era not only requires schools to teach students English knowledge and English skills, but also requires improving students’ learning methods while improving students’ English reading ability, improving students’ oral expression ability and improving students’ English writing ability. The article studies from three aspects how to use computer-assisted vocational English for high-quality teaching in today’s society: students, teachers, and society, and puts forward three points for students, teachers, and society. And on this basis, four principles of computer-assisted suggestions for English teaching in higher vocational schools: the principle of moderation, the principle of interaction, the principle of teaching students in accordance with their aptitude, and the principle of supervision.
Edge Animation in Software Visualization

An important aspect in software visualization is the visualization of relations between elements. Examples include function calls across source-code files, software clones, and the like. A popular means of visualizing such relations are edges depicted as lines visually connecting the related elements. To diminish the visual clutter that can occur when drawing a large number of edges, hierarchical edge bundles, based on B-Splines, have proven suitable. In addition to a purely static view of software, animations, both of the elements and the edges, can contribute to the usability of a visualization by making changes easier to track for users. In this paper we discuss how B-Splines can be used as a basis for a variety of edge layouts and how edges represented as B-Splines can be animated using B-Spline morphing. We also discuss the challenges of morphing B-Splines, especially of those whose shape does not result solely from the positions of the connected elements (e.g., straight lines), but whose structure depends on multiple aspects (e.g., hierarchical edge bundles) and provide a solution to make arbitrary B-Splines compatible for morphing. The practicality of edge animation is demonstrated by use cases which we already implemented or plan to implement in the future in our software visualization tool SEE.Supplemental material: https://github.com/tinyspline/vissoft2022/archive/refs/tags/1.0.0.zip (see README.md in the zip archive)
Enabling efficient and general subpopulation analytics in multidimensional data streams

Today's large-scale services ( e.g. , video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a "sketch of sketches" to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.
Context Preserving Data Augmentation for Sequential Recommendation

Item representation learning is a fundamental task in Sequential Recommendation (SR). Effective representations are crucial for SR because they enable recommender systems to learn relevant relationships between items. SR researchers rely on User Historical Interactions (UHI) for effective item representations. While it is well understood that UHI inherently suffers from data sparsity, which weakens item relation signals, seldom considered is the fact the interaction between users and items is mediated by an underlying candidate generation process susceptible to bias, noise and error. These limitations further distort the item relationships and limit the learning of superior item representations. In this work, we seek to amplify weak item relation signals in UHI by augmenting each input sequence with a set of permutations that preserve both the local and global context. We employ a multi-layer bi-directional transformer encoder to learn superior contextualized item representations from the augmented data. Extensive experiments on benchmark datasets for next-item recommendations demonstrate that our proposed SR model can recover item relational dynamics distorted during the candidate generation process. In addition, our approach leads to learning superior item representations for many next-item state-of-the-art models employing RNNs and self-attention networks.
Experiencing Presence in a Gaming Activity Improves Mood After a Negative Mood Induction

Research suggests that immersion in computer games is beneficial for recovering from stress and improving mood. However, no study linked explicit measures of presence—individually experienced immersion—to mood enhancement. In the present experiment, immersion of a gaming activity was varied, and levels of presence and enjoyment were measured and connected to mood repair after a stress-induction. The participants (N = 77) played a game in virtual reality (VR; high immersion), on the desktop (medium immersion), or watched a recording of the game (low immersion). Positive emotions were enhanced in the high and medium, but not the low immersion condition. Presence was a significant predictor in the VR condition. Furthermore, an explanatory mediation analysis showed that enjoyment mediated the effect of presence on mood repair. These findings demonstrate positive effects of presence experiences in gaming. Strong presence in VR seems especially helpful for enhancing mood and building up positive emotional resources.
A Radical Approach to Curriculum Design

A contemporary challenge for education in STEM is the need to produce work-ready graduates for a fast-changing and technology-driven workplace. This study focuses on first-year undergraduate computing students who failed to engage with a traditional, didactic approach to teaching soft skills. A radical curriculum redesign implemented a new methodological integration of action research, mobile technology, and constructivist pedagogy. Students created interactive artefacts with an augmented reality app, on mobile devices, to promote and enhance teamwork and communication skills. Following an action research methodology, students develop soft skills as an emergent aspect of a blended approach to their professionally-inspired project work. This more authentic approach, using their own mobile devices, captured the imagination of students and directed attention to the significance of broader skills relevant to industry. Over the four-year study period, submissions rose from 66% to 93%, with student satisfaction significantly enhanced.
Understanding the Classes Better with Class-Specific and Rule-Specific Feature Selection, and Redundancy Control in a Fuzzy Rule Based Framework

Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.
Fast and Accurate Pose Estimation in Videos based on Knowledge Distillation and Pose Propagation

Existing video-based human pose estimation methods typically adopt large networks to perform body joints localization on all frames. Despite of impressive accuracy performance, the relatively high memory and computation requirements significantly burden their applicability on resource-constraint systems (e.g., embedded devices). To solve this issue, this paper proposes a novel yet effective lightweight framework, called FVPE, for fast and accurate human pose estimation in videos. Specifically, FVPE adopts the knowledge distillation (KD) strategy to train a small pose estimator network, which is capable of executing rapidly with low computational cost. To increase the overall efficiency, FVPE exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, FVPE introduces an online key-frame selection scheme to decide whether the current pose should be calculated by the pose estimator or be propagated from the previous key-frame, being able to flexibly deal with video sequences with different length, frame rate, pose complexity, etc.. Experiments on Penn Action and Sub-JHMDB datasets demonstrate that the proposed method achieves comparative accuracy, but with substantial speed-up.
USE OF FACE RECOGNITION SOFTWARE BY KARHUNEN LOVE METHOD

Numerical simulations and checks of face recognition software on given image databases represent a type of empirical research. Face recognition software works on the principle of comparing a photo of the person’s face with the photos in the database. The operation of face recognition software can be divided into three stages. The first stage is face detection, the second stage is face tracking and the third stage is face recognition. For this purpose, software solutions have been developed, with different work techniques. However, it is characteristic that regardless of the different techniques, each expresses its effect with a probability expressed in percentages. Simply put, for now, no software solution can be said to be 100% effective. For now, no computer solution can be compared to the human ability to recognize and identify a person.
Almost Rerere: Learning to Resolve Conflicts in Distributed Projects

The concurrent development of applications requires reconciling conflicting code updates by different developers. Recent research on the nature of merge conflicts in open source projects shows that a significant fraction of merge conflicts have limited size (one or two lines of code) and are resolved with simple strategies that use code present in the merged versions. Thus the opportunity arises of supporting the resolution of merge conflicts automatically by learning the way in which developers fix them. In this paper we propose a framework for automating the resolution of merge conflicts which learns from the resolutions made by developers and encodes such knowledge into conflict resolution rules applicable to conflicts not seen before. The proposed approach is text-based, does not depend on the programming languages of the merged files and exploits a well-known and general language (search and replacement regular expressions) to encode the conflict resolution rules. Evaluation results on 14,872 conflicts from 25 projects show that the system can synthesize a resolution for <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 49% of the conflicts occurred during the merge process ( <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 89% if one considers conflicts that have at least one similar conflict in the data set) and can reproduce exactly the same solution that human developers have applied in <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 55% of the cases ( <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 62% for single line conflicts).
Taxonomy Enrichment with Text and Graph Vector Representation

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym (“class-subclass”) relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this talk, she addresses the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy. The author presents a new method which allows achieving high results on this task with little effort, described in [16]. It uses the resources which exist for the majority of languages, making the method universal. The method is extended by incorporating deep representations of graph structures like node2vec, Poincaré embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows them to beat the state of the art.
Dual-Path Geometry-Aware Network for Semantic Segmentation of High-Resolution Aerial Images

Semantic segmentation of high-resolution aerial images is a fundamental research topic for its extensive applications. Different from natural scene datasets, the high-resolution aerial datasets provide additional elevation data such as Digital Surface Model (DSM). However, the current semantic segmentation methods of high-resolution aerial images focus on improving the feature extraction of the spectral images but fail to make full use of DSM images. Besides, the feature fusion of these two disparate data is a challenging problem. Moreover, the tremendous details and the considerable variations in scale of objects limit the representation capacity of existing segmentation networks. To address the above problems, we propose a new dual-path geometry-aware end-to-end DPGANet which consists of Multi-scale Digital Surface Model Awareness(MDSMA) path and Swin Transformer path. The MDSMA path is designed to extract multi-stage 3D geometry features from DSM images. The Res2Net modules in the MDSMA path can enhance the multi-scale representation capability of our network. The Swin Transformer path is designed to extract the multi-stage long-range dependencies from spectral images. Furthermore, for full usage of feature maps produced by corresponding stages of these two paths, we design an Attention Fusion Module(AFM) for memory-saving and computation-effective feature fusion from both spatial and channel dimensions. The segmentation results on the ISPRS Potsdam dataset achieve a competitive performance compared to other state-of-the-art methods.
Enterprise Model for IEEE GRSS Standards Development

IEEE GRSS is the premier engineering society addressing remote sensing for the geosciences. A vast amount of expertise is available through GRSS publications, conferences, and the activities of its committees. The GRSS Standards Committee was established to create open consensus standards based on GRSS expertise. Standards are the pinnacle artifact of the knowledge base that supports technology development. To plan the development of standards, the Standards Committee is developing an enterprise model of the GRSS scope. The Enterprise Model will be a framework for standards development. The model will be used to identify existing GRSS expertise, existing standards, and new standards that need to be developed.