Reviewerly/exHarmony huggingface dataset:
Saliency for free: Saliency prediction as a side-effect of object recognition

Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods.
Determinants of Employee Performance with Work Motivation as an Intervening Variable at the Semarang City Search and Rescue Office

Employees are the main key in increasing productivity and the success of agency activities. Employees have an important role in reducing or minimizing the impact of loss or damage caused by a disaster. This study aims to analyse and empirically test the influence of leadership on employee work motivation, organizational culture on employee work motivation, leadership on employee performance, organizational culture on employee performance, work motivation on employee performance, work motivation in mediating the influence of leadership on employees. performance, and work motivation in mediating the influence of organizational culture on employee performance. The method in this study using the sampling method, the population in this study is the SAR Department of Semarang City. The research sample was 125 samples. The results show that leadership has a positive effect on work motivation, organizational culture has a positive effect on work motivation, leadership has a positive effect on performance, organizational culture has a positive effect on performance, work motivation has a positive effect on performance, leadership has a positive effect on performance through work motivation, and culture organization has a positive effect on performance through work motivation. The conclusion from the results of this study is that the leadership that has been carried out has a positive impact on employees at the Semarang City Search and Rescue Service
BCDnet: Parallel heterogeneous eight-class classification model of breast pathology

Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women's health. With the help of computer vision technology, it has important application value to automatically classify pathological tissue images to assist doctors in rapid and accurate diagnosis. Breast pathological tissue images have complex and diverse characteristics, and the medical data set of breast pathological tissue images is small, which makes it difficult to automatically classify breast pathological tissues. In recent years, most of the researches have focused on the simple binary classification of benign and malignant, which cannot meet the actual needs for classification of pathological tissues. Therefore, based on deep convolutional neural network, model ensembleing, transfer learning, feature fusion technology, this paper designs an eight-class classification breast pathology diagnosis model BCDnet. A user inputs the patient's breast pathological tissue image, and the model can automatically determine what the disease is (Adenosis, Fibroadenoma, Tubular Adenoma, Phyllodes Tumor, Ductal Carcinoma, Lobular Carcinoma, Mucinous Carcinoma or Papillary Carcinoma). The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. After the information output by the fully connected layer of the two convolutional bases is fused, it is classified and output by the SoftMax function. The model experiment uses the publicly available BreaKHis data set. The number of samples of each class in the data set is extremely unevenly distributed. Compared with the binary classification, the number of samples in each class of the eight-class classification is also smaller. Therefore, the image segmentation method is used to expand the data set and the non-repeated random cropping method is used to balance the data set. Based on the balanced data set and the unbalanced data set, the BCDnet model, the pre-trained model Resnet50+ fine-tuning, and the pre-trained model VGG16+ fine-tuning are used for multiple comparison experiments. In the comparison experiment, the BCDnet model performed outstandingly, and the correct recognition rate of the eight-class classification model is higher than 98%. The results show that the model proposed in this paper and the method of improving the data set are reasonable and effective.
Breast Cancer Classification Using Deep Convolutional Neural Networks

Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances with 96% of accuracy in the breast cancer classification task when compared with state-of-the-art modelsKeywords— Breast cancer classification, Convolutional Neural Network (CNN), deep learning, DenseNet, histopathological images
Probing Security through Input-Output Separation and Revisited Quasilinear Masking

The probing security model is widely used to formally prove the security of masking schemes. Whenever a masked implementation can be proven secure in this model with a reasonable leakage rate, it is also provably secure in a realistic leakage model known as the noisy leakage model. This paper introduces a new framework for the composition of probing-secure circuits. We introduce the security notion of input-output separation (IOS) for a refresh gadget. From this notion, one can easily compose gadgets satisfying the classical probing security notion –which does not ensure composability on its own– to obtain a region probing secure circuit. Such a circuit is secure against an adversary placing up to t probes in each gadget composing the circuit, which ensures a tight reduction to the more realistic noisy leakage model. After introducing the notion and proving our composition theorem, we compare our approach to the composition approaches obtained with the (Strong) Non-Interference (S/NI) notions as well as the Probe-Isolating Non-Interference (PINI) notion. We further show that any uniform SNI gadget achieves the IOS security notion, while the converse is not true. We further describe a refresh gadget achieving the IOS property for any linear sharing with a quasilinear complexity Θ(n log n) and a O(1/ log n) leakage rate (for an n-size sharing). This refresh gadget is a simplified version of the quasilinear SNI refresh gadget proposed by Battistello, Coron, Prouff, and Zeitoun (ePrint 2016). As an application of our composition framework, we revisit the quasilinear-complexity masking scheme of Goudarzi, Joux and Rivain (Asiacrypt 2018). We improve this scheme by generalizing it to any base field (whereas the original proposal only applies to field with nth powers of unity) and by taking advantage of our composition approach. We further patch a flaw in the original security proof and extend it from the random probing model to the stronger region probing model. Finally, we present some application of this extended quasilinear masking scheme to AES and MiMC and compare the obtained performances.
The Role of Youtube as an Information Source About Coronary Artery Disease During COVID-19 Pandemic

Abs tractAim: There are videos on Coronavirus disease-2019 (COVID-19) and coronary artery disease at various quality levels on Youtube.To investigate the quality of Turkish language videos on Youtube about COVID-19 and coronary artery disease. Methods:The study was conducted between 1 st and 3 rd September 2020.Two doctors investigated keywords including coronary artery disease, COVID-19", "coronavirus, heart disease", "coronavirus, chest pain", "COVID-19, heartache" and "COVID-19, heart disease"."Coronary artery disease", "heartache", "chest pain" and "heart disease" are the Turkish translations for coronary artery disease, heart pain, chest pain and heart disease, respectively.Firstly, for each video, video length, number of days on Youtube, and the number of views and comments were recorded, along with the number of "likes" and "dislikes".Sources of the videos were categorized into three groups: "health care professionals", "new agencies" and "non-professional individuals".Moreover, DISCERN and medical information and content index (MICI) were evaluated.Results: Finally, 92 Youtube videos met the study inclusion criteria.The present study included 36 informative videos, 34 patient experience videos and 22 news update videos, and none were categorized in the misleading group.The shortest video length was found in patient experience videos (p=0.001).The DISCERN scores of videos were 3.5±1.1 for informative videos, 1.8±0.4 for patient experience videos and 0.9±1.3 for news update videos.The statistical analyses revealed that informative videos had significantly higher DISCERN scores when compared to patient experience videos and news update videos (p=0.005 and p=0.001, respectively).The mean MICI score was 4.1±1.5 for informative videos. Conclusion:The present study showed that videos about coronary artery disease and COVID-19 are generally poor quality and low reliability.
MRZ code extraction from visa and passport documents using convolutional neural networks

Detecting and extracting information from the machine-readable zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity. However, computer vision methods for performing similar tasks, such as optical character recognition, fail to extract the MRZ from digital images of passports with reasonable accuracy. We present a specially designed model based on convolutional neural networks that is able to successfully extract MRZ information from digital images of passports of arbitrary orientation and size. Our model achieves 100% MRZ detection rate and 99.25% character recognition macro-f1 score on a passport and visa dataset.
Blockchain Processing Technique Based on Multiple Hash Chains for Minimizing Integrity Errors of IoT Data in Cloud Environments

As IoT (Internet of Things) devices are diversified in the fields of use (manufacturing, health, medical, energy, home, automobile, transportation, etc.), it is becoming important to analyze and process data sent and received from IoT devices connected to the Internet. Data collected from IoT devices is highly dependent on secure storage in databases located in cloud environments. However, storing directly in a database located in a cloud environment makes it not only difficult to directly control IoT data, but also does not guarantee the integrity of IoT data due to a number of hazards (error and error handling, security attacks, etc.) that can arise from natural disasters and management neglect. In this paper, we propose an optimized hash processing technique that enables hierarchical distributed processing with an n-bit-size blockchain to minimize the loss of data generated from IoT devices deployed in distributed cloud environments. The proposed technique minimizes IoT data integrity errors as well as strengthening the role of intermediate media acting as gateways by interactively authenticating blockchains of n bits into n + 1 and n − 1 layers to normally validate IoT data sent and received from IoT data integrity errors. In particular, the proposed technique ensures the reliability of IoT information by validating hash values of IoT data in the process of storing index information of IoT data distributed in different locations in a blockchain in order to maintain the integrity of the data. Furthermore, the proposed technique ensures the linkage of IoT data by allowing minimal errors in the collected IoT data while simultaneously grouping their linkage information, thus optimizing the load balance after hash processing. In performance evaluation, the proposed technique reduced IoT data processing time by an average of 2.54 times. Blockchain generation time improved on average by 17.3% when linking IoT data. The asymmetric storage efficiency of IoT data according to hash code length is improved by 6.9% on average over existing techniques. Asymmetric storage speed according to the hash code length of the IoT data block was shown to be 10.3% faster on average than existing techniques. Integrity accuracy of IoT data is improved by 18.3% on average over existing techniques.
Evidential Reasoning and Learning: a Survey

When collaborating with an artificial intelligence (AI) system, we need to assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning to determine the confidence (or epistemic uncertainty) in the probabilities of the queried outcome. Pure Bayesian methods, however, suffer from high computational costs. To overcome them, we revert to efficient and effective approximations. In this paper, we focus on techniques that take the name of evidential reasoning and learning from the process of Bayesian update of given hypotheses based on additional evidence. This paper provides the reader with a gentle introduction to the area of investigation, the up-to-date research outcomes, and the open questions still left unanswered.
IS BITCOIN A SAFE HAVEN? A STUDY ON THE FACTORS THAT AFFECT BITCOIN PRICES

Bitcoin and other digital currencies are financial assets with high volatility, which calls for an investigation of the factors that influence their prices and thus has led to a debate on whether they are reliable investment instruments or diversification tools. The present study aims to explore the impact upon Bitcoin prices of commodities such as gold and oil, the S&amp;P 500 index, and the volatility index and financial stress index, which represent the financial risk environment. To this purpose, we analyze this relationship using the Autoregressive Distributed Lag (ARDL) approach based on the monthly data from the 2010-2021 period. The results suggest that both in the long and short run, gold price per ounce does not have a statistically significant effect on Bitcoin price. On the other hand, an increase in crude oil prices has a negative impact on Bitcoin price in the short run, with no significant effect in the long run. The S&amp;P 500 stock market index positively affects the Bitcoin price both in the short and long run. In addition, our analysis results also demonstrate that developments indicating increased risk in the long run tend to reduce Bitcoin returns.Keywords: Bitcoin, gold, oil, volatility, ARDL.JEL Classifications: G11; B23DOI: https://doi.org/10.32479/ijefi.11602
Capitalization and punctuation restoration: a survey

Ensuring proper punctuation and letter casing is a key pre-processing step towards applying complex natural language processing algorithms. This is especially significant for textual sources where punctuation and casing are missing, such as the raw output of automatic speech recognition systems. Additionally, short text messages and micro-blogging platforms offer unreliable and often wrong punctuation and casing. This survey offers an overview of both historical and state-of-the-art techniques for restoring punctuation and correcting word casing. Furthermore, current challenges and research directions are highlighted.
ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning

Reducing the amount of labels required to train convolutional neural networks without performance degradation is key to effectively reduce human annotation efforts. We propose Reliable Label Bootstrapping (ReLaB), an unsupervised preprossessing algorithm which improves the performance of semi-supervised algorithms in extremely low supervision settings. Given a dataset with few labeled samples, we first learn meaningful self-supervised, latent features for the data. Second, a label propagation algorithm propagates the known labels on the unsupervised features, effectively labeling the full dataset in an automatic fashion. Third, we select a subset of correctly labeled (reliable) samples using a label noise detection algorithm. Finally, we train a semi-supervised algorithm on the extended subset. We show that the selection of the network architecture and the self-supervised algorithm are important factors to achieve successful label propagation and demonstrate that ReLaB substantially improves semi-supervised learning in scenarios of very limited supervision on image classification benchmarks such as CIFAR-10, CIFAR-100 and mini-ImageNet. We reach average error rates of 22.34 with 1 random labeled sample per class on CIFAR-10 and lower this error to 8.46 when the labeled sample in each class is highly representative. Our work is fully reproducible: https://github.com/PaulAlbert31/ReLaB.
Integration of DE Algorithm with PDC-APF for Enhancement of Contour Path Planning of a Universal Robot

In the robotic engineering field, the main target, especially in industry, manufacturing, and surgical operations, is reaching the optimal performance of manipulators. The purpose of this paper is to quantify the contour tracking performance of collaborative universal manipulator robot (UR5) by setting the gain of position domain controller. In order to improve and enhance the track of manipulator in experimental applications we utilize differential evolution (DE) optimization, using MATLAB toolbox with an applied robot operating system (ROS). The adopted current approach does not only optimize the gain of position domain controller but also prevent collisions by detecting a “border crossing” without turning off the manipulator and allowing the automation agent to be on the scene, coexisting in harmonic mode and avoiding collisions. This requires the implementation of an algorithm that detects an obstacle to avoid anticipated collisions. For this purpose, the adopted algorithm uses the DE algorithm to modify the artificial potential field (APF). The results of this paper present that on one hand, meta-heuristic optimization algorithm features give the best performance indices for linear and non-linear contours, and on the other hand, DE algorithm features give good modification to APF to generate collision free contour path planning.
NLP for Consumer Protection: Battling Illegal Clauses in German Terms and Conditions in Online Shopping

Online shopping is an ever more important part of the global consumer economy, not just in times of a pandemic.When we place an order online as consumers, we regularly agree to the so-called "Terms and Conditions" (T&C), a contract unilaterally drafted by the seller.Often, consumers do not read these contracts and unwittingly agree to unfavourable and often void terms.Government and non-government organisations (NGOs) for consumer protection battle such terms on behalf of consumers, who often hesitate to take on legal actions themselves.However, the growing number of online shops and a lack of funding makes it increasingly difficult for such organisations to monitor the market effectively.This paper describes how Natural Language Processing (NLP) can be applied to support consumer advocates in their efforts to protect consumers.Together with two NGOs from Germany, we developed an NLP-based application that legally assesses clauses in T&C from German online shops under the European Union's (EU) jurisdiction.We report that we could achieve an accuracy of 0.9 in the detection of void clauses by fine-tuning a pre-trained German BERT model.The approach is currently used by two NGOs and has already helped to challenge void clauses in T&C.
Influence of Blockchain Adoption on Technology Transfer, Performance and Supply Chain Integration, Flexibility and Responsiveness. A Case Study from IT&amp;C Medium-Sized Enterprises

The aim of this paper is to analyze how blockchain technology can improve supply chain variables and technology transfer in medium-sized enterprises from IT&C sector.Blockchain technology is expected to enhance the velocity and reliability of business and managerial processes and increase competitiveness of IT&C companies.The purpose was to see if these combined elements could lead to the generation of competitive advantage, which in turn would influence the firm performance.Based on the study of relevant literature, a conceptual and methodological framework was developed and tested on 383 medium-sized companies from the IT&C sector, using the Confirmatory Factor Analysis.The results of this study show that blockchain technology positively influences supply chain variables and technology transfer, with direct effects on the firm performance.
Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach

Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech. Hate speech presents a challenge as it is directed at different target groups using a completely different vocabulary. Further the authors of the hate speech are incentivized to disguise their behavior to avoid being removed from a platform. This makes it difficult to develop a comprehensive data set for training and evaluating hate speech detection models because the examples that represent one hate speech domain do not typically represent others, even within the same language or culture. We propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision.