Reviewerly/exHarmony huggingface dataset:
A Low-illumination Image Enhancement Algorithm Based on Morphological-Retinex(MR) Operator

Low-illumination images were often caused by night environments, which are lack contrast and sharpness. Night image recognition, capture, or autonomous-driving systems require meritorious algorithms to enhance the low-illumination images. The Retinex-based method has been used in image enhancement universally, but previous literature had some enhancement limitations. Since the earlier methods show poor robustness, the night recognition and low-illumination image training programs were prone to fail. For solving those problems, a new algorithm called Morphology-Retinex (MR) is proposed to enhance the details and edges of the low illumination images. We also offer a new automatic color restoration method to restore the authentic color of the object. As a result, the scores of PNSR and SSIM are 15% higher than traditional methods, and the clarity is excellent, which is about 50% higher than similar algorithms in recent years. This algorithm can improve the quality of low-illuminance image data sets and reduce the difficulty of image recognition. When training machine learning models using low-illumination image data sets, this method will increase the success rate.
A New Functional Clustering Method with Combined Dissimilarity Sources and Graphical Interpretation

Clustering is an essential task in functional data analysis. In this study, we propose a framework for a clustering procedure based on functional rankings or depth. Our methods naturally combine various types of between-cluster variation equally, which caters to various discriminative sources of functional data; for example, they combine raw data with transformed data or various components of multivariate functional data with their covariance. Our methods also enhance the clustering results with a visualization tool that allows intrinsic graphical interpretation. Finally, our methods are model-free and nonparametric and hence are robust to heavy-tailed distribution or potential outliers. The implementation and performance of the proposed methods are illustrated with a simulation study and applied to three real-world applications.
Feature Optimization of Digital Image Watermarking Using Machine Learning Algorithms

Machine learning implied an active and productive work in digitalized watermarking. The digital image watermarking always faced security threats during communication over the network—the possibility of threats minimized with the process of feature optimization using machine learning algorithms. The ML \(({\varvec{M}}{\varvec{a}}{\varvec{c}}{\varvec{h}}{\varvec{i}}{\varvec{n}}{\varvec{e}}\boldsymbol{ }{\varvec{L}}{\varvec{e}}{\varvec{a}}{\varvec{r}}{\varvec{n}}{\varvec{i}}{\varvec{n}}{\varvec{g}})\) algorithms provide the facility of pattern generation in digital image watermarking. Now a day’s various ML algorithms are used in digital image watermarking, such as support vector machine, KNN, and PNN. In this work, the feature optimization-based digital watermarking approaches for the protection of watermark in multimedia data. The used algorithms of machine learning in digital watermarking focus on the security strength of data. The strength of security depends on the imperceptibility and robustness of the watermark. The imperceptibility of the image indicates the value of the image similarity index of the watermark image. For the quality improvement of a digital image, watermarking proposed ensemble-based algorithms for the optimization and pattern generation of watermarking. Our defined discussed algorithms implemented in software \(({\varvec{M}}{\varvec{A}}{\varvec{T}}{\varvec{L}}{\varvec{A}}{\varvec{B}})\) and used a reputed image dataset for the watermarking process. For the validation of proposed algorithms measured some standard parameters’ value, the value of parameters signs that the proposed algorithms are compared good than DWT, KNN, and PNN.
Optimal Rate-Distortion-Leakage Tradeoff for Single-Server Information Retrieval

Private information retrieval protocols guarantee that a user can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privately</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">losslessly</i> retrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates when all files are stored uncoded on a single server. Information leakage is measured in terms of the average success probability for the server of correctly guessing the identity of the desired file. The main findings are: i) The derivation of the optimal tradeoff between download rate, distortion, and information leakage when the file size is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infinite</i> . Closed-form expressions of the optimal tradeoff for the special cases of “no-leakage” and “no-privacy” are also given. ii) A novel approach based on linear programming (LP) to construct schemes for a finite file size and an arbitrary number of files. The proposed LP approach can be leveraged to find provably optimal schemes with corresponding closed-form expressions for the rate-distortion-leakage tradeoff when the database contains at most four bits. Finally, for a database that contains 320 bits, we compare two construction methods based on the LP approach with a nonconstructive scheme downloading subsets of files using a finite-length lossy compressor based on random coding.
U-Net based skeletonization and bag of tricks

Skeletonization is a process focused on providing a compact and simple representation of an object by extracting the skeleton pixels from the given shape in a binary image. This method has been widely applied in various image processing and computer vision applications. In addition to traditional approaches which are not robust and provide low accuracy results, many efforts have been made for creating deep learning based methods to overcome these disadvantages. However, skeletonization is still a new topic in the deep learning world. In this paper, we propose our solution for the Pixel SkelNetOn challenge in the third edition of the "Deep Learning for Geometric Computing" workshop at ICCV 2021, which includes (1) modification of U-Net architecture using the attention mechanism, (2) implementation of auxiliary task learning for a more effective training process and (3) application of several tricks for improving the skeletonization model’s performance. Our method achieved 0.8000 on the Pixel SkelNetOn validation set and second place in the leaderboard. We also release our code to facilitate future research at https://github.com/namdvt/skeletonization.
Tight Security Bounds for Micali’s SNARGs

Succinct non-interactive arguments (SNARGs) in the random oracle model (ROM) have several attractive features: they are plausibly post-quantum; they can be heuristically instantiated via lightweight cryptography; and they have a transparent (public-coin) parameter setup.
Security Analysis of End-to-End Encryption for Zoom Meetings

In the wake of the global COVID-19 pandemic, video conference systems have become essential for not only business purposes, but also private, academic, and educational uses. Among the various systems, Zoom is the most widely deployed video conference system. In October 2020, Zoom Video Communications rolled out their end-to-end encryption (E2EE) to protect conversations in a meeting from even insiders, namely, the service provider Zoom. In this study, we conduct thorough security evaluations of the E2EE of Zoom (version 2.3.1) by analyzing their cryptographic protocols. We discover several attacks more powerful than those expected by Zoom according to their whitepaper. Specifically, if insiders collude with meeting participants, they can impersonate any Zoom user in target meetings, whereas Zoom indicates that they can impersonate only the current meeting participants. Besides, even without relying on malicious participants, insiders can impersonate any Zoom user in target meetings though they cannot decrypt meeting streams. In addition, we demonstrate several impersonation attacks by meeting participants or insiders colluding with meeting participants. Although these attacks may be beyond the scope of the security claims made by Zoom or may be already mentioned in the whitepaper, we reveal the details of the attack procedures and their feasibility in the real-world setting and propose effective countermeasures in this paper. Our findings are not an immediate threat to the E2EE of Zoom; however, we believe that these security evaluations are of value for deeply understanding the security of E2EE of Zoom.
Rotten web citations cited in scholarly journals: use of time travel for retrieval

Purpose The present study compares the use of web citations as references in leading scholarly journals in Library and Information Science (LIS) and Communication and Media Studies (CMS). A total of 20 journals (each 10 from LIS and CMS) were selected based on the publishing history and reputation published between 2008 and 2017. Design/methodology/approach The present study compares the use of web citations as references in leading scholarly journals in LIS and CMS. A PHP script was used to crawl the Uniform Resource Locators (URLs) collected from the reference list. A total of 12,251 articles were downloaded and 555,428 references were extracted. Of the 555,428 references, 102,718 web citations were checked for their accessibility. Findings The research findings indicated that 76.90% URLs from LIS journals and 84.32% URLs from Communication and Media Studies journals were accessible and others were rotten. The majority of errors were due to HTTP 404 error code (not found) in both the disciplines. The study also tried to retrieve the rotten URLs through Time Travel, which revived 61.76% rotten URLs in LIS journal articles and 65.46% in CMS journal articles. Originality/value This is an in-depth and comprehensive comparative study on the availability of web citations in LIS and CMS journals articles spanning a period of 10 years. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web citations will be accessible in the future.
Effectiveness of an Intelligent Question Answering System for Teaching Financial Literacy: A Pilot Study

Question answering systems have shown promise as a learning tool in several fields but are yet to be used in the financial literacy domain. The objective of this research is to build an intelligent question answering (IQA) system using state-of-the-art natural language processing technology and test its effectiveness in helping students learn personal finance. The purpose of the system is twofold—one as an intelligent system for practicing and two as a scaffolding tool for finding answers to questions. The IQA system consists of three modules—the question answering module, the evaluation module, and the feedback module—and is built using Google’s state-of-the-art language model for Natural Language Processing: Bidirectional Encoder Representations from Transformers (BERT). The primary research question we attempt to answer is whether the use of the IQA system results in increased learning in the domain of personal finance and if so how. We find significant learning gains (effect size = 1.89) with the affordances of the IQA system contributing to the gains. Students felt that the IQA system helped them learn better mainly by allowing them to practice at their own pace and by providing them with indications of where they had gone wrong and allowing them to read, think, and re-attempt the answer. These promising results suggest that an IQA system could be effective as a support tool for improving learning of personal finance.
Image Compression Algorithm Based On Variational Autoencoder

Abstract Variational Autoencoder (VAE), as a kind of deep hidden space generation model, has achieved great success in performance in recent years, especially in image generation. This paper aims to study image compression algorithms based on variational autoencoders. This experiment uses the image quality evaluation measurement model, because the image super-resolution algorithm based on interpolation is the most direct and simple method to change the image resolution. In the experiment, the first step of the whole picture is transformed by the variational autoencoder, and then the actual coding is applied to the complete coefficient. Experimental data shows that after encoding using the improved encoding method of the variational autoencoder, the number of bits required for the encoding symbol stream required for transmission or storage in the traditional encoding method is greatly reduced, and symbol redundancy is effectively avoided. The experimental results show that the image research algorithm using variational autoencoder for image 1, image 2, and image 3 reduces the time by 3332, 2637, and 1470 bit respectively compared with the traditional image research algorithm of self-encoding. In the future, people will introduce deep convolutional neural networks to optimize the generative adversarial network, so that the generative adversarial network can obtain better convergence speed and model stability.
Strategic Remote Attestation: Testbed for Internet-of-Things Devices and Stackelberg Security Game for Optimal Strategies

Internet of Things (IoT) devices and applications can have significant vulnerabilities, which may be exploited by adversaries to cause considerable harm. An important approach for mitigating this threat is remote attestation, which enables the defender to remotely verify the integrity of devices and their software. There are a number of approaches for remote attestation, and each has its unique advantages and disadvantages in terms of detection accuracy and computational cost. Further, an attestation method may be applied in multiple ways, such as various levels of software coverage. Therefore, to minimize both security risks and computational overhead, defenders need to decide strategically which attestation methods to apply and how to apply them, depending on the characteristic of the devices and the potential losses.To answer these questions, we first develop a testbed for remote attestation of IoT devices, which enables us to measure the detection accuracy and performance overhead of various attestation methods. Our testbed integrates two example IoT applications, memory-checksum based attestation, and a variety of software vulnerabilities that allow adversaries to inject arbitrary code into running applications. Second, we model the problem of finding an optimal strategy for applying remote attestation as a Stackelberg security game between a defender and an adversary. We characterize the defender’s optimal attestation strategy in a variety of special cases. Finally, building on experimental results from our testbed, we evaluate our model and show that optimal strategic attestation can lead to significantly lower losses than naïve baseline strategies.
Application of Deep Reinforcement Learning to Dynamic Verification of DRAM Designs

This paper presents a deep neural network based test vector generation method for dynamic verification of memory devices. The proposed method is built on reinforcement learning framework, where the action is input stimulus on device pins and the reward is coverage score of target circuitry. The developed agent efficiently explores high-dimensional and large action space by using policy gradient method with Å-nearest neighbor search, transfer learning, and replay buffer. The generated test vectors attained the coverage score of 100% for fifteen representative circuit blocks of modern DRAM design. The output vector length was only 7% of the human-created vector length.
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest.Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL.We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset.Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation.We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised crosslingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task 1 .
Automatic Generation of Attack Strategy for Multiple Vulnerabilities Based on Domain Knowledge Graph

Security evaluation is an essential activity for understanding the risks of industrial networks. Constant changes in networks and updates of security vulnerabilities lead to increasing costs of security testing. To realize the combined exploitation of multiple vulnerabilities in the industrial control network, Knowledge Graph is proposed to be applied in knowledge-driven attack strategy generation in this paper. A graph paradigm of knowledge about vulnerability exploitation is established by integrating and extracting multi-dimensional domain knowledge. By occupying each device-level node, attack strategies based on domain knowledge improve the performance in comprehensive vulnerability exploitation and flexible response. The feasibility of our method is demonstrated through an industrial network example.
An Approach for Project Management System Based on Blockchain

In the industrial revolution, Project Management Softwares are essential for most projects in industries. They can improve planning and scheduling, make better collaboration, effective task delegation. However, a weakness of current project management software is transparency and anti-repudiation. Although the software design is explicit, the database owner can edit all of the stored data. Hence, we expect to leverage the transparency and immutability of Blockchain into Project Management Software. Blockchain is decentralized, and it is securer than any other system. We have deployed Blockchain into the project management software as a web-based application to make it more trustworthy and fairer. Our project management software module that outsourcing can auction and manage in a transparent and non-repudiation manner. Our Blockchain Project Management Software supports managing work in projects, observing progress, and auctioning tasks in the project. It is done using an encryption key, a data series (such as a password) that can identify the user and access the value of the "account" or "wallet" in the system. When a transaction is agreed between users, it must be approved or authorized before adding it to a block in the chain. For public Blockchain, the decision to add transactions to the chain is made through consensus. It means that most "nodes" or computers on the network must accept that the transaction is valid. The computers on the web are also encouraged to verify transactions through rewards.
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning

Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
The Hidden Curriculum in Online Education Based on Systematized Review

Context: The set of learning and experiences gained by learners is not limited to formal and explicit curriculum, and there are inevitably other factors besides the explicit curriculum. These factors are valuable opportunities that can enrich the learning experience and, on the other hand, are threats that challenge the education system if ignored. Objectives: The purpose of this review was to summarize evidence on the hidden curriculum in online education. Data Sources: This study is a systematized review of scientific-research articles in the field of hidden curriculum in online education. For this purpose, databases of Scopus, Ovid, web of science, PubMed, Google Scholar, ProQuest, ERIC, and Science Direct were searched. No restrictions were placed on the year of publication. Results: Out of 487 articles after excluding irrelevant articles, nine articles were included in this study for full-text review. Of the nine studies reviewed, the hidden curriculum in online education has been improved in one study. Individual characteristics of the learning environment (self-esteem, vulnerability, social acceptance, stress) in two studies, learning environment in five studies, interaction in three studies, teaching and evaluation methods in one study, rules and regulations in two studies, and teaching time in one study have been identified as the main factors behind the formation of a hidden curriculum in online education. Conclusions: The hidden curriculum is a reflection of what students receive in the learning environment; so we need to pay attention to this environment. A review of studies in the hidden e-learning curriculum showed that the hidden curriculum is important because it has a strong influence on the learners in many ways. Online education, like traditional education, has latent learning that can affect education.
Using DWT-DCT-SVD Watermarking For Securing Medical Images

Telemedicine is an advanced digital application in which a huge volume of clinical information must be transmitted safely and productively throughout the business. The security of advanced information, particularly clinical images become critical for few reasons including privacy, authentication, and integrity. Computerized watermarking is a cutting-edge invention that has been developed to increase the security of advanced images. The use of a watermark in clinical images can help to validate and respect the image. Watermark should be covered in general; it does not affect the nature of the clinical image. This article proposes an advanced watermark dependent on discrete wave transform (DWT), discrete cosine transform (DCT), and SVD, contrast the exposure of this strategy and the DWT and SVD based on the watermark. The proposed DWT, DCT and SVD strategy is relatively better than the DWT and SVD technique.