Reviewerly/exHarmony huggingface dataset:
Fast and accurate superpixel algorithms for 360<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mo>∘</mml:mo></mml:msup></mml:math> images

Superpixels are fundamental in many visual computing applications, and most existing algorithms are designed to work with pinhole-based images. However, immersive applications are gaining visibility with the growing number of devices for capturing and visualizing 360∘ media. This paper introduces two fast and accurate superpixel algorithms tailored to the spherical domain. The methods are assessed under common figures of merit, and a benchmark annotated dataset. Additionally, an annotation-free evaluation metric for exploiting larger datasets is introduced. The methods introduced in this paper perform quantitatively close to or better than state-of-the-art approaches and are faster.
Feature Detection Based on Significancy of Local Features for Image Matching

Feature detection and matching procedure require most of processing time in image matching where the time dramatically increases according to the number of feature points. The number of features is needed to be controlled for specific applications because of their processing time. This paper proposes a feature detection method based on significancy of local features. The feature significancy is computed for all pixels and higher significant features are chosen considering spatial distribution. The method contributes to reduce the number of features in order to match two images with maintaining high matching accuracy. It was shown that this approach was faster about two times in average processing time than FAST detector for natural scene images in the experiments.
Strikethrough Removal from Handwritten Words Using CycleGANs

Obtaining the original, clean forms of struck-through handwritten words can be of interest to literary scholars, focusing on tasks such as genetic criticism. In addition to this, replacing struck-through words can also have a positive impact on text recognition tasks. This work presents a novel unsupervised approach for strikethrough removal from handwritten words, employing cycle-consistent generative adversarial networks (CycleGANs). The removal performance is improved upon by extending the network with an attribute-guided approach. Furthermore, two new datasets, a synthetic multi-writer set, based on the IAM database, and a genuine single-writer dataset, are introduced for the training and evaluation of the models. The experimental results demonstrate the efficacy of the proposed method, where the examined attribute-guided models achieve \(F_1\) scores above 0.8 on the synthetic test set, improving upon the performance of the regular CycleGAN. Despite being trained exclusively on the synthetic dataset, the examined models even produce convincing cleaned images for genuine struck-through words.
Normalization of Transliterated Mongolian Words Using Seq2Seq Model with Limited Data

The huge increase in social media use in recent years has resulted in new forms of social interaction, changing our daily lives. Due to increasing contact between people from different cultures as a result of globalization, there has also been an increase in the use of the Latin alphabet, and as a result a large amount of transliterated text is being used on social media. In this study, we propose a variety of character level sequence-to-sequence (seq2seq) models for normalizing noisy, transliterated text written in Latin script into Mongolian Cyrillic script, for scenarios in which there is a limited amount of training data available. We applied performance enhancement methods, which included various beam search strategies, N-gram-based context adoption, edit distance-based correction and dictionary-based checking, in novel ways to two basic seq2seq models. We experimentally evaluated these two basic models as well as fourteen enhanced seq2seq models, and compared their noisy text normalization performance with that of a transliteration model and a conventional statistical machine translation (SMT) model. The proposed seq2seq models improved the robustness of the basic seq2seq models for normalizing out-of-vocabulary (OOV) words, and most of our models achieved higher normalization performance than the conventional method. When using test data during our text normalization experiment, our proposed method which included checking each hypothesis during the inference period achieved the lowest word error rate (WER = 13.41%), which was 4.51% fewer errors than when using the conventional SMT method.
AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model's generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for extracting useful information from mammograms with weak annotations for efficient and accurate breast mass segmentation. Specifically, we adopt a weakly-supervised learning strategy in the Teacher to extract anatomy structure from mammograms with weak annotations by reconstructing the original image. Besides, knowledge distillation is used to suggest morphological differences between benign and malignant masses. Moreover, the prior knowledge learned from the Teacher is introduced to the Student in an end-to-end way, which improves the ability of the student network to locate and segment masses. Experiments on CBIS-DDSM have shown that our method yields promising performance compared with state-of-the-art alternative models for breast mass segmentation in terms of segmentation accuracy and IoU.
A Comparison of Learnheuristics Using Different Reward Functions to Solve the Set Covering Problem

The high computational capacity that we have thanks to the new technologies allows us to communicate two great worlds such as optimization methods and machine learning. The concept behind the hybridization of both worlds is called Learnheuristics which allows to improve optimization methods through machine learning techniques where the input data for learning is the data produced by the optimization methods during the search process. Among the most outstanding machine learning techniques is Q-Learning whose learning process is based on rewarding or punishing the agents according to the consequences of their actions and this reward or punishment is carried out by means of a reward function. This work seeks to compare different Learnheuristics instances composed by Sine Cosine Algorithm and Q-Learning whose different lies in the reward function applied. Preliminary results indicate that there is an influence on the quality of the solutions based on the reward function applied.
Research on Recognition of Pointer Meter Based on Improved East Algorithm

In order to solve the problems of low recognition accuracy and large memory occupied in the algorithm of pointer meter identification in substation, this paper proposes an improved algorithm of pointer meter automatic identification based on EAST algorithm, Tiny East. Firstly, the algorithm uses the lightweight neural network MOGA to replace the backbone network of East algorithm, thus reducing the memory occupied by the algorithm; Then, the feature pyramid is added to backbone network to strengthen the feature extraction. Besides, we use NMS local sensing algorithm to screen the reading box to get the scale number and position of the instrument. Thus, positioning the pointer line and the center of the instrument; Finally, the identification of the instrument is completed by combining the identified digital and pointer angles. Experimental results show that compared with the unimproved EAST algorithm, the number of parameters of the proposed TINY-EAST algorithm is greatly reduced, and the average recognition accuracy is higher than 98.5&#x0025;. Moreover, the proposed TINY-EAST algorithm has good accuracy and stability in the complex background of pointer instrument and can meet the application requirements of practical substation.
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020

This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importance of evaluating derivative-free optimizers for tuning the hyperparameters of machine learning models. This was the first black-box optimization challenge with a machine learning emphasis. It was based on tuning (validation set) performance of standard machine learning models on real datasets. This competition has widespread impact as black-box optimization (e.g., Bayesian optimization) is relevant for hyperparameter tuning in almost every machine learning project as well as many applications outside of machine learning. The final leaderboard was determined using the optimization performance on held-out (hidden) objective functions, where the optimizers ran without human intervention. Baselines were set using the default settings of several open-source black-box optimization packages as well as random search.
PLCrypto: A Symmetric Cryptographic Library for Programmable Logic Controllers

Programmable Logic Controllers (PLCs) are control devices widely used in industrial automation. They can be found in critical infrastructures like power grids, water systems, nuclear plants, manufacturing systems, etc. This paper introduces PLCrypto, a software cryptographic library that implements lightweight symmetric cryptographic algorithms for PLCs using a standard PLC programming language called structured text (ST). To the best of our knowledge, PLCrypto is the first ST-based cryptographic library that is executable on commercial off-the-shelf PLCs. PLCrypto includes a wide range of commonly used algorithms, totaling ten algorithms, including one-way functions, message authentication codes, hash functions, block ciphers, and pseudo-random functions/generators. PLCrypto can be used to protect the confidentiality and integrity of data on PLCs without additional hardware or firmware modification. This paper also presents general optimization methodologies and techniques used in PLCrypto for implementing primitive operations like bit-shifting/rotation, substitution, and permutation. The optimization tricks we distilled from our practice can also guide future implementation of other computationheavy programs on PLCs. To demonstrate a use case of PLCrypto in practice, we further realize a cryptographic protocol called proof of aliveness as a case study. We benchmarked the algorithms and protocols in PLCrypto on a commercial PLC, Allen Bradley ControlLogix 5571, which is widely used in the real world. Also, we make our source codes publicly available, so plant operators can freely deploy our library in practice.
Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.
Blockchain Technology: A Boon at the Pandemic Times – A Solution for Global Economy Upliftment with AI and IoT

Blockchain technology is a potential sector at the foreground, though it is currently in its embryonic form, but will retain itself as an emerging technology at an early stage. Besides blockchain being a trusted, decentralized database, it has its success in many applications across various fields like education, medicine, insurance, finance, energy, environment, supply chain management, and various other associated operations. The work provides a systematic survey of blockchain, how it works, and its characteristics, applications, and challenges. The aim is to investigate how blockchain technology, besides artificial intelligence and the Internet of Things, would be an effective factor to fight the current pandemic scenario. To the end, a brief survey is carried out on how blockchain technology would be fruitful in uplifting the global economy post-COVID, with a summary of the blockchain toolkit by the World Economic Forum.
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration

Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We first propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields to handle large deformations in images whilst guaranteeing diffeomorphisms in the resultant deformation. We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields and solve this model with a fast algorithm that combines Nesterov gradient descent and the alternating direction method of multipliers (ADMM). Finally, we leverage the computational power of GPU to implement this accelerated ADMM solver on a 3D cardiac MRI dataset, further reducing runtime to less than 2 s. In addition to producing strictly diffeomorphic deformations, our methods outperform both state-of-the-art deep learning-based and iterative DiffIR approaches in terms of dice and Hausdorff scores, with speed approaching the inference time of deep learning-based methods.
Purple dawn: Dead disk forensics on Google's Fuchsia operating system

Fuchsia is the project name for a “modular, capability-based” operating system currently being developed by Google. There is speculation that Fuchsia may be a successor to the Android OS or a replacement for several other operating systems currently supported by the organisation. This paper examines the file systems found in this operating system and provides a breakdown of the content and structure of the unique volume manager and other partitions found on system. The findings outlined in this paper should allow digital investigators to expedite their understanding of the underlying data found on the platform. This paper also highlights how the zxcrypt encryption subsystem may inhibit the ability of practitioners to carry out an investigation of the MinFS partition. As Fuchsia is still in development, these findings are reliant on there not being significant changes made to structure of partitions examined. There remain unanswered questions regarding the content of the BootFS disk image found in the ZIRCON partition and the structure of entries within the Slice Allocation Table in the FVM.
Certification of Model Robustness in Active Class Selection

Active class selection provides machine learning practitioners with the freedom to actively choose the class proportions of their training data. While this freedom can improve the model performance and decrease the data acquisition cost, it also puts the practical value of the trained model into question: is this model really appropriate for the class proportions that are handled during deployment? What if the deployment class proportions are uncertain or change over time? We address these questions by certifying supervised models that are trained through active class selection. Specifically, our certificate declares a set of class proportions for which the certified model induces a training-to-deployment gap that is small with a high probability. This declaration is theoretically justified by PAC bounds. We apply our proposed certification method in astro-particle physics, where a simulation generates telescope recordings from actively chosen particle classes.
A Scalable Blockchain Implementation Model for Nation-Wide Electronic Voting System

Blockchain technology adoption rate is fast growing as seen in cryptocurrency and distributed finance (DiFi) domains. It is also getting lots of attention in many other application areas including electronic voting(e-voting) systems. The electronic voting system is an interesting application use case for blockchain because it helps to solve critical problems within that space- the integrity of voting data, the secrecy of the ballot, and single point of failure. This is because of the characteristics that blockchain technology embodies. One of the challenges, however, is with the scalability of the blockchain network, how the blockchain technology can power the scalability of systems built on it. The aim of this paper, therefore, is to present a Blockchain Implementation Model that tackles scalability concerns for E-Voting System. This model can be adaptable in any national election, specifically, Nigeria’s national elections. The resulting model would present a scalable electronic voting framework by leveraging the security and integrity infrastructures that blockchain technology brings to bear.
Video Popularity Prediction Through Fusing Early Viewership with Video Content

In this paper, an approach for video popularity prediction is proposed, by combining two different methods, video content analysis, and early viewership. Firstly, a multi-modal framework is used, which analyses video content features such as the number of presented people, poses, emotions of presenters and attendants, and an audio events detection algorithm to detect claps, pauses, and speech. Afterwards, all these features feed a linear regression model that predicts each video’s views. In parallel is utilized, a second method that uses interaction metrics such as early viewership to predict popularity. Viewership is crawled through an online tool developed based on Google Analytics. In order to go one step further, a fusion methodology of the views is proposed coming from the video content, with early viewership, while comparisons between models are investigated. The experimental results were based on real-life data, demonstrating that the proposed system is a promising tool for the prediction of the video popularity, having a Mean Absolute Error of less than 7.5.
An efficient general black-box adversarial attack approach based on multi-objective optimization for high dimensional images

In black-box scenarios, the adversarial attack algorithms based on iterative optimization perform best. But it may give a false sense of model robustness due to the design of inefficient queries. The adversarial attack algorithm based on multi-objective evolution optimization has been proven to be very effective for the low-dimensional images. However, when the attack space dramatically increases for the high-dimensional color images, the evolutionary efficiency is limited, and it needs more inefficient queries to generate adversarial examples. In this paper, we propose an efficient black-box adversarial attack approach for high dimensional images based on multi-objective optimization (MOO-HD), which includes some novel strategies to solve the above problems. We also propose the strategy of “The transformation of the pixel block with a random step size” to reduce the attack space. The experimental results on three image datasets with different dimensions show that our algorithm can achieve a higher success rate with fewer queries.
Contrastive Learning for improving End-to-end Speaker Verification

Speaker verification involves examining the speech signal to authenticate the claim of a speaker as true or false. Deep neural networks are one of the successful implementations of complex non-linear models to learn unique and invariant features of data. They have been employed in speech recognition tasks and have shown their potential to be used for speaker recognition also. However, the overfitting problem is remained to prevent the model's performance. In this study, we apply contrastive learning on speaker verification tasks to solve the robustness problem. Besides, we introduce domain adaptive loss on the tasks. Experimental results and ablation study that indicate that our proposed model outperforms various baseline end-to-end methods significantly by at least relative 10%, including d-vector approaches, deep-speaker, and generalized end-to-end model, for text-dependent speaker verification on a company's internal text-dependent voice command DataSet.
Dhempster-Shafer Implementation in Overcoming Uncertainty in the Inference Engine for Diagnosing Oral Cavity Cancer

&lt;em&gt;The word uncertainty in an expert system is related to working with wrong data, wrong information, handling identical situations, the reliability of results, etc. Sources of uncertainty can come from unreliable information. This is usually caused by unclear domain concepts or for inaccurate data. One method for overcoming uncertainty is Dhempster-Shafer's theory. Dempster-shafers come up with approaches to calculate probabilities to look for evidence based on trust functions. In general the Dempster-Shafer theory is written at an interval [Confidence, Reasonable]. Belief (Bel) is a measure of the strength of evidence in support of a series of propositions. In this study an expert system will be developed to diagnose oral cancer that can recognize oral cancer based on the symptoms felt by the user. The results showed the Dempster-shafer was able to overcome the uncertainties in the construction of the inference engine, this is because the accuracy of the test results showed an accuracy of 86.6% Dempster-shafer&lt;/em&gt;.