Reviewerly/exHarmony huggingface dataset:
Locality-based transfer learning on compression autoencoder for efficient scientific data lossy compression

Scientific simulation can generate petabyte-level data per run nowadays. To significantly reduce the data size while simultaneously maintaining the compression quality based on certain user requirements, error-bounded lossy compression techniques such as SZ and ZFP are now becoming popular. However, these techniques still cannot achieve a reduction ratio of more than two orders of magnitude with a low compression error. On the other hand, in deep learning, the autoencoder techniques have been widely used in data compression, especially images. As an alternative, the compression autoencoder (CAE) has recently been investigated to compress the scientific data. Although CAE provides a higher compression ratio than SZ and ZFP, it suffers from a high training overhead, which makes it almost impractical in real compression scenarios. In this paper, we propose a new locality-based transfer learning method in order to significantly increase the training speed of CAE while achieving a high compression ratio. We also adopt incremental learning to maintain a high prediction accuracy and use KL-divergence as an indicator to quickly identify whether a target domain has a low testing error. Our evaluation results show that, after using the locality-based transfer learning, the training time can be reduced by up to 1200 times, and still has a 2 to 4X compression ratio gain over the state-of-the-art scientific data lossy compressor SZ.
35‐1: Halo Mura of OLED and FALD LCD: Measurements and Perception for Automotive Displays

Halo mura affects visual quality of automotive displays. We report on two quantitative methods to measure halo of OLEDs and (FALD) LCDs. The relative halo “luminance” for an OLED is in the range of 0.01 %, which is about 10x larger than that of a state‐of‐the‐art FALD LCD. The decline of halo is exponential and spreads for more than 10 mm. By using the measurements of lines with a length of about 32 pixels as input for simulations, we can extract the halo of a single pixel. A user study resulted in moderate perception of halo at night for negative mode GUIs and relatively high insensitivity for positive mode.
How to do a forensic analysis of Android 11 artifacts

This paper compares the results obtained using several forensic tools on various Android 11 operating system devices. New and old functionalities of Andriod 11 store a vast volume of raw data and important forensic artifacts on mobile devices. The amount and type of data found on mobile devices demand further development of mobile forensics procedures. This development should be based on the streamlined procedures for the existing forensic to obtain more useful forensic artifacts. This means not using only one forensic tool, but combining results from multiple forensic tools in the analysis phase, after the data acquisition from Android 11 devices. The used forensic tools are Belkasoft Evidence Center X, Autopsy, and ALEAPP, with results presented and compared. The collected digital evidence shows the combined results are more substantial than the sum of particular ones
Automatic Generation and Evaluation of Chinese Classical Poetry with Attention-Based Deep Neural Network

The computer generation of poetry has been studied for more than a decade. Generating poetry on a human level is still a great challenge for the computer-generation process. We present a novel Transformer-XL based on a classical Chinese poetry model that employs a multi-head self-attention mechanism to capture the deeper multiple relationships among Chinese characters. Furthermore, we utilized the segment-level recurrence mechanism to learn longer-term dependency and overcome the context fragmentation problem. To automatically assess the quality of the generated poems, we also built a novel automatic evaluation model that contains a BERT-based module for checking the fluency of sentences and a tone-checker module to evaluate the tone pattern of poems. The poems generated using our model obtained an average score of 9.7 for fluency and 10.0 for tone pattern. Moreover, we visualized the attention mechanism, and it showed that our model learned the tone-pattern rules. All experiment results demonstrate that our poetry generation model can generate high-quality poems.
Real World Feedback: How Well Did the Virtual Training Academy Prepare California's COVID-19 Contact Tracing Workforce?

To effectively respond to the COVID-19 pandemic, California had to quickly mobilize a substantial number of case investigators (CIs) and contact tracers (CTs). This workforce was comprised primarily of redirected civil servants with diverse educational and professional backgrounds. The purpose of this evaluation was to understand whether the weeklong, remote course developed to train California's CI/CT workforce (i.e., Virtual Training Academy) adequately prepared trainees for deployment. From May 2020 to February 2021, 8,141 individuals completed the training. A survey administered ~3 weeks post-course assessed two measures of overall preparedness: self-perceived interviewing proficiency and self-perceived job preparedness. Bivariate analyses were used to examine differences in preparedness scores by education level, career background, and whether trainees volunteered to join the COVID-19 workforce or were assigned by their employers. There were no significant differences in preparedness by education level. Compared to trainees from non-public health backgrounds, those from public health fields had higher self-perceived interviewing proficiency (25.1 vs. 23.3, p < 0.001) and job preparedness (25.7 vs. 24.0, p < 0.01). Compared to those who were assigned, those who volunteered to join the workforce had lower self-perceived job preparedness (23.8 vs. 24.9, p = 0.02). While there were some statistically significant differences by trainee characteristics, the practical significance was small (<2-point differences on 30-point composite scores), and it was notable that there were no differences by education level. Overall, this evaluation suggests that individuals without bachelor's degrees or health backgrounds can be rapidly trained and deployed to provide critical disease investigation capacity during public health emergencies.
Current Trends and Challenges towards the Digital Health Era

The adoption of new technologies by large population groups created an optimal background for the promotion of a new digital health era in which patients are self-engaged in their own health monitoring. Quick and effective exchange of medical information between patients, medical doctors and researches could be a powerful tool to detect and react against health issues before they become a social concern. Individuals can also receive personalized information about their health and physical conditions to extend their life quality. This paper reviews current efforts toward the deployment of health care systems based on 21st century technologies and the challenges these systems might face.
Prediction of Parameters in Optimal Operation Point for BPG-based Lossy Compression of Noisy Images

Lossy compression of images corrupted by noise has several peculiarities. First, a specific noise filtering effect is observed. Second, optimal operation point (OOP) can be observed, i.e. such coder parameter (e.g., quantization step) value can exist that quality of compressed image calculated with respect to noise-free image can be better compared to quality of uncompressed (original noisy) image. If OOP exists, it is worth compressing a given image in OOP, if no, other recommendations on coder parameter setting are reasonable. Since noise-free image is not available in practice, it is not possible to determine does OOP exist and what is image quality in it. In this paper, we show that OOP existence for several quality metrics can be predicted quite easily and quickly for grayscale images corrupted by additive white Gaussian noise and compressed by better portable graphics (BPG) encoder. Such a prediction is based on analysis of statistics of discrete cosine transform (DCT) coefficients calculated for a limited number of 8x8 pixel blocks. A scatter-plot of metric improvement (reduction) depending upon these statistics is obtained in advance and prediction curve fitting is performed. Recommendations on encoder parameter setting for cases of OOP absence are given.
Framework for neural network hardware implementation

Artificial neural networks (ANN) are widely used in solving problems like image processing, data mining, or classification. Hardware accelerators are used for increasing the performance and efficiency of neural networks. An option for implementing such an accelerator is the usage of an FPGA-based system, although developing neural networks for FPGAs is very time-consuming and requires hardware design knowledge to do it. This problem tried to be solved by creating a framework that should speed up the design process. At the same time, there is an overall outlook on some efficiency optimization and speed-up options as well. The framework is written in Python and generates a C++ code whit HLS directive. This code can be compiled by Vivado HLS into a hardware descriptive language and packaged as an IP. The Vivado tool can generate a bit file that can be uploaded onto the FPGA device.Among other things, the paper presents a comparison of different approximations of nonlinear transformations (basis functions and activation functions) in terms of accuracy, required resource, and delay needed for evaluating the transformation. The generated neural network module was integrated into a system that was developed by the authors. Using that system, the neural network module was tested and compared to the models implemented in Python.
A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

We propose a simple but effective method to recommend exercises with high quality and diversity for students. Our method is made up of three key components: (1) candidate generation module; (2) diversity-promoting module; and (3) scope restriction module. The proposed method improves the overall recommendation performance in terms of recall, and increases the diversity of the recommended candidates by 0.81% compared to the baselines.
Application of Fairclough's Critical Discourse Analysis Model to Quranic Verses Discussing Peaceful Dealing with Hypocrites

Critical Discourse Analysis (CDA) is an interdisciplinary research approach which declares that discourse is mutually related to social activities and power relations. This approach decodes the structure and hidden ideology of a text by describing and analyzing its language, context and current social and temporal situation. Some of the verses in the Holy Qur’an that deal with hypocrisy discuss dealing with hypocrites peacefully and have a discursive structure. The present study examined these verses as an ideological discourse based on Fairclough’s CDA to elucidate thematic verses’ capacity in the process of meaning creation and open up new horizons for researchers to analyze the thematic verses. The results have revealed that, at the description level, discourse components such as the expressive value of words, active and imperative sentences, and modality in verses are closely tied to dealing with hypocrites peacefully. The situational context analysis at the interpretation level has revealed that the enunciation style of the verses’ textual layer is fully compatible with the social and temporal conditions at the time of their revelation. Analysis at the explanation level demonstrated the depth of the Divine revelation about hypocrisy, suggesting that these verses aim to promote the hegemony of monotheism, trust in God, patience, piety, and forgiveness in the Islamic society in addition to peaceful coexistence.&#x0D; Keywords: The Holy Qur’an, Peaceful coexistence, Discourse analysis, Fairclough’s approach, Ideology
Quantum-resistant University Credentials Verification System on Blockchain

The production of fraudulent academic certificates has become a global issue. People with unethical thinking try to arrange fraud certificates to authorize themselves to exploit and access various high-end paying jobs and advance their careers. Therefore, this problem leads to identity masquerade. There are different blockchain-based solutions to store and validate these university transcripts and educational credentials, intended to provide decentralized exposure to the system. However, most blockchain-based system designs and platforms use pre-quantum cryptography on either public or private Blockchain and do not cover the whole ecosystem. The current systems cannot survive majority and quantum attacks. This paper uses Post Quantum Cryptography (PQC) powered Lattice-Based Cryptosystems and Hash-Based Signature (HBS) in the proposed system on a consortium-based blockchain solution to shield the distributed network from various attacks making it quantum-proof. The proposed system is indented to bring all the stakeholders under a well-defined process of issuance, recording, sharing, and verification of academic and professional educational credentials under a quantum-proof immutable distributed ledger to promote transparency and tamper-proofing to prevent ongoing frauds. The proposed system provides a better privacy mechanism, incorporates access and consent-based sharing control, secure from quantum and majority attacks and does not required any cryptocurrencies or tokens.
Investigating the Effect of Phishing Believability on Phishing Reporting

Phishing emails are becoming more and more sophisticated, making current detection techniques ineffective. The reporting of phishing emails from users is, thus, crucial for organizations to detect phishing attacks and mitigate their effect. Despite extensive research on how the believability of a phishing email affects detection rates, there is little to no research about the relationship between the believability of a phishing email and the associated reporting rate. In this work, we present a controlled experiment with 446 subjects to evaluate how the reporting rate of a phishing email is linked to its believability and detection rate. Our results show that the reporting rate decreases as the believability of the email increases and that around half of the subjects who detect the mail as phishing, have an intention to report the email. However, the group intending to report an email is not a subset of the group detecting the mail as phishing, suggesting that reporting is still a concept misunderstood by many.
On forcibly <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e48" altimg="si29.svg"><mml:mi>k</mml:mi></mml:math>-connected and forcibly <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e53" altimg="si29.svg"><mml:mi>k</mml:mi></mml:math>-arc-connected digraphic sequences

For nonnegative integer sequences d={(d1+,d2+,…,dn+),(d1−,d2−,…,dn−)} and d′={(d′1+,d′2+,…,d′n+),(d′1−,d′2−,…,d′n−)}, we say that d′ majorizes d, denoted by d′≥d, if dj′+≥dj+ and dj′−≥dj− for 1≤j≤n. A digraphic sequence d={(d1+,d2+,…,dn+),(d1−,d2−,…,dn−)} is forcibly k-connected (resp., k-arc-connected) if each digraph with degree sequence d is k-connected (resp., k-arc-connected). We give a sufficient condition for forcibly k-connected (resp., k-arc-connected) digraphic sequences and observe that if a digraphic sequence d satisfies the condition (which implies d is forcibly k-connected (resp., k-arc-connected)) then every digraphic sequence d′≥d also satisfies the condition. If d violates the condition, then we may take a sequence d′≥d such that there exists a non-k-connected (resp., non-k-arc-connected) d′-realization.
Entity linking systems for literature reviews

Abstract Computer-assisted methods and tools can help researchers automate the coding process of literature reviews and accelerate the literature review process. However, existing approaches for coding textual data do not account for lexical ambiguity; that is, instances in which individual words have multiple meanings. To counter this, we developed a method to conduct rapid and comprehensive analyses of diverse literature types. Our method uses entity linking and keyword analysis and is embedded into a literature review framework. Next, we apply the framework to review the literature on digital disruption and digital transformation. We outline the method’s advantages and its applicability to any research topic.
51‐4: Design, Measurements, and Evaluation of Exterior Displays for Autonomous Cars

Autonomous cars should communicate with other road users such as pedestrians by exterior displays. This raises traffic safety. We build a full‐scale movable battery‐powered mock‐up with RGB LEDs matrix and e‐spaper displays. Measurements and evaluations with subjects were performed. A 35” RGB LED display (128 × 64, 6 mm pixel pitch) and animated icons showed best results.
Temporal Analysis of X.509 Revocations and their Statuses

Despite the X.509 public key infrastructure (PKI) being essential for ensuring the trust we place in our communication with web servers, the revocation of the trust placed in individual X.509 certificates is neither transparent nor well-studied, leaving many unanswered questions. In this paper, we present a temporal analysis of 36 million certificates, whose revocation statuses we followed for 120 days since first being issued. We characterize the revocation rates of different certificate authorities (CAs) and how the rates change over the lifetime of the certificates. We identify and discuss several instances where the status changes from “revoked” to “good”, “unauthorized” or “unknown”, respectively, before the certificate's expiry. This complements prior work that has observed such inconsistencies in some CAs' behavior after expiry but also highlight a potentially more severe problem. Our results highlight heterogeneous revocation practices among the CAs.
A Method for Estimating Losses in a Quantum Channel for Implementing Quantum Key Distribution Technology for Atmospheric Laser Communication Terminals

A wireless optical communication system with a quantum key distribution system is demonstrated. It is implemented on the basis of the coupling of the quantum key distribution system developed for the fiber optic communication system with atmospheric optical communication lines terminals. For an atmospheric communication line with a length of 200 m, loss statistics in an open and quantum communication channel was obtained during several weeks of monitoring, on the basis of which an approach for estimating signal attenuation in a quantum channel was developed. The results of studies of the influence of atmospheric phenomena on the transmission of telecommunication data are presented.
Agent-based multi-tier SLA negotiation for intercloud

Abstract The evolving intercloud enables idle resources to be traded among cloud providers to facilitate utilization optimization and to improve the cost-effectiveness of the service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, in which cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, in this paper a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process is proposed. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can improve the negotiation performance and the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and demonstrates the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
Detecting Correlated Gaussian Databases

This paper considers the problem of detecting whether two databases, each consisting of n users with d Gaussian features, are correlated. Under the null hypothesis, the databases are independent. Under the alternate hypothesis, the features are correlated across databases, under an unknown row permutation. A simple test is developed to show that detection is achievable above ${\rho ^2} \approx \frac{1}{d}$. For the converse, the truncated second moment method is used to establish that detection is impossible below roughly ${\rho ^2} \approx \frac{1}{{d\sqrt n }}$ . These results are compared to the corresponding recovery problem, where the goal is to decode the row permutation, and a converse bound of roughly ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≈1−n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4/d</sup> has been previously shown. For certain choices of parameters, the detection achievability bound outperforms this recovery converse bound, demonstrating that detection can be easier than recovery in this scenario.
Standardization of Humanitarian Project Monitoring and Evaluation using Semantic Web

Around the world, wars and conflicts killed thousands of people, as well as, millions of people are displaced. Millions of people have difficulty accessing even basic services such as clean water, food, a place to live and education. Many humanitarian organizations are trying to alleviate these problems through humanitarian relief by means of works and projects. However, evaluating the success of humanitarian relief through projects can be a difficult task. In this context, Semantic Web technologies can be used to standardize the data obtained from humanitarian project outcomes. In this way, machine-processable data about the humanitarian aid projects cycle can be generated which then can be used for detailed querying and automatic evaluation of project life cycles. This is the aim of our work. We propose a new ontology for humanitarian projects' monitoring and evaluation cycle. We also provide extensive semantic rules to automatically infer project progress and evaluation cycle. In this way, all phases of projects including actors (participants, staff) and project activities can be automatically analyzed and queried. Using extensive semantic rules, we can also infer implicit data about project outcomes.
Segmentation of Different Human Organs on 3D Computer Tomography and Magnetic Resonance Imaging using an Open Source 3D U-Net Framework

Medical segmentation represents a method to delineate the borders of internal human organs on acquired 3D Computer Tomography or MRI images, which are the most currently used imaging modalities. The U-net represents a deep neural network architecture that has the basic structure consisting in two paths and can be used for medical image segmentation. For U-Net Framework implementation we used Monai Toolkit from Kitware Inc, which is a PyTorch-based, open-source framework for deep learning in healthcare imaging used to create advanced training workflows and provides deep learning models.As input data we used two types of internal organs acquired with two image modalities:3D CT and MRI. For visualization we used an in-house VTK based 3D Volume Visualization ApplicationCardioCTNav.For the manual segmentation of the training datasets we used an open source segmentation application ITK-SNAP.Our results shows that the U-Net segmentation method delivers good final results and that is represents an robust method to segment internal organs.