Reviewerly/exHarmony huggingface dataset:
Development of a hardware cryptosystem based on a random number generator with two types of entropy sources

In modern software, crypto-algorithms are widely used for both data encryption tasks, and authentication and integrity checks. There are well-known and proven crypto-algorithms. Their cryptoresistance is either mathematically proven or based on the need to solve a mathematically complex problem (factorization, discrete logarithm, etc.). On the other hand, in the computer world, information constantly appears about errors or «holes» in a particular program (including one that uses crypto-algorithms) or that it was broken (cracked). This creates distrust both in specific programs and in the possibility to protect something in general by cryptographic methods not only from special services, but also from ordinary hackers. A promising direction of research in this field is the implementation of a hybrid random number generator with two types of entropy sources in cryptosystems. The method and means of implementing a hybrid random number generator with two types of entropy sources: external – based on Zener diode noise and internal – based on the uncertainty state of the transistor-transistor logic structure are presented. One of the options for the practical implementation of a random number generator is presented, where two sources are used as a source of entropy: an external source – Zener diode noise and an internal source – the undefined state of the transistor-transistor logic structure. The functional diagram of the proposed random number generator with two types of entropy sources is given. The MATLAB/Simulink model of the proposed random number generator is built, the results of the statistical analysis of the generated random sequences by the NIST SP 800-22 test package are given.
Digital Gap and Academic Performance in the Covid-19 Pandemic: The Case of a Public School in Bogota

This study analyzes the academic performance of students from an official school in Bogot&amp;aacute; (Colombia) who had asymmetric access to Information and Communication Technologies during the first year of the Covid-19 pandemic. The Classical Linear Regression Model is employed using data from a survey of a representative group of students in the year 2020. Results show technological gaps between students that are associated with asymmetric academic performance against students with limited or null access to the Internet and electronic devices. Results also show that, among students with permanent access to the Internet, women have better scores compared to men&amp;rsquo;s scores, which implies that women can achieve better academic results than men in virtual education. It is also noted that the sole access to an electronic device does not guarantee good academic achievements if there is no full access to the Internet, and if ICT-use skills have not been developed.
BIO-INSPIRED ENSEMBLE FEATURE SELECTION (BIEFS) AND ENSEMBLE MULTIPLE DEEP LEARNING (EMDL) CLASSIFIER FOR BREAST CANCER DIAGNOSIS

Breast cancer is a major disease diagnosed in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC). Ensemble Multiple Deep Learning (EMDL) classifier is introduced which combines several individual models such as AdaBoost-Convolutional Neural Network (A-CNN), Long Short-Term Memory Network (LSTM), and Deep Auto-Encoder (DAE) is introduced to obtain better generalization performance. Then, these three classifiers such as A-CNN, LSTM, and DAE are used for ensemble classification using a Weight Majority Voting (WMV) mechanism. Wisconsin Diagnosis Breast Cancer (WOBC), and Wisconsin Diagnosis Breast Cancer (WDBC) are collected from University of California, Irvine (UCI) repository for experimentation. Evaluation metrics like Precision, Recall, F-measure, Accuracy, and Area Under Curve (AUC) are used to compare the results of proposed system and existing classifiers. All experiments are executed within a simulation environment and conducted in MATrix LABoratory R 2014 a (MATLAB 2014a) tool. The proposed EMDL classifier is showing better performance as compared to the traditional classification models.
War Optimization Method for Image Encryption Algorithm Based on A Chaotic Bit-Plane Decomposition

In this paper, develop image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a War Optimization Algorithm (WOA). Initially, utilization of SHA-256 hash algorithm for computing the plaintext images hash parameter as initial parameter of the fractional Lorenz hyperchaotic system after the process. Use the chaotic sequence for permuting plaintext image in a bit plane to achieve the scrambled images. After that, block the scrambles image into four sub images of similar size and count the hash parameter of every row of every block through the SHA-256 hash algorithm as the initial parameter of the Sine-Tent Logistic chaotic system. Utilize the achieved chaotic sequence to substitute the images. After that, the four sub-block images to get the last encrypted image and the population can be achieved. At last, utilization information entropy of ciphertext images as the fitness function of WOA. Choose the ciphertext image with the optimal information entropy of ciphertext images as the fitness function of the WOA. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best war source meanwhile. The proposed method is implemented in MATLAB and performance is analyzed with performance metrices. The proposed method is compared with the conventional techniques.
Determinants of Egypt’s Food Imports Based on Vector Error-Correction Model (VECM)

The issue of food and the provision of society's needs have a priority in the Egyptian economic policy. Therefore, the state tends to import large quantities of these commodities annually from the global market to bridge the gap between consumption and domestic production. This study focuses on determining the main factors that affect Egyptian food imports during the period 1990–2019, using a Vector Error-Correction Model (VECM) that will help policymakers use the appropriate monetary, fiscal, and trade tools to manage Egypt’s food imports and decrease the import cost. The empirical results revealed that the most important factors' effects on Egypt's food imports are population growth and inflation rate, but imported food prices and GDP per capita have an effect only in the long run. On the other hand, there are no significant effects of the foreign reserves, exchange rate, and agricultural production on food imports.
There are no minimal effectively inseparable theories

This paper belongs to the research on the limit of the first incompleteness theorem. Effectively inseparable theories (EI) can be viewed as an effective version of essentially undecidable theories (EU), and EI is stronger than EU. We examine the question: are there minimal effectively inseparable theories with respect to interpretability. We propose tEI, the theory version of EI. We first prove that there are no minimal tEI theories with respect to interpretability (i.e., for any tEI theory $T$, we can effectively find a theory which is tEI and strictly weaker than $T$ with respect to interpretability). By a theorem due to Marian B. Pour-EI, we have tEI is equivalent with EI. Thus, there are no minimal EI theories with respect to interpretability. Also we prove that there are no minimal finitely axiomatizable EI theories with respect to interpretability.
The Time-traveling Lawyer: Using Time Travel Stories and Science Fiction in Legal Education

Abstract Science fiction and time travel can be used to inform and enhance the education of law students in profound ways. Within the broader field of law and literature, the relationship between law and science fiction, especially time travel stories, is rich and useful. Themes and concepts in time travel can be applied in the exploration of existing legal philosophies as well as a more expansive and engaging study of power, authority, freedom, and a number of global issues. As governments and people worldwide wrestle with climate change, armed conflict, pandemics, and the increasing significance of artificial intelligence and other advances in technology, time travel stories give students unique contexts in which to consider what law is and the degree to which it defines human experience. For generations, brilliant science fiction writers have offered thought-provoking stories and worlds that law professors and their students can use to reimagine legal thought and practice. Like its close relatives, mythology and fantasy, the science fiction genre is untethered to current social or political experience or projections necessarily corrupted by narrowly conceived historical perspectives. Science fiction writers are interested in illuminating possibilities by considering identifiable problems in unidentifiable environments. It is no accident that gender identity, racism, reproductive rights, extremist ideologies, global health crises, and various recognizable forms of labor exploitation are addressed in provocative and insightful ways by a number of the best science fiction writers. Law has a strong presence in their work. Judges, law givers, ruling groups, and other less familiar forms of power and control appear in these stories and help to move and shape the experience of the time traveler. Law students can draw on the work of these writers to consider old questions in new and refreshingly broad ways. The importance of communication and access to information are also strong themes common to law and science fiction. How are concepts of truth and propaganda significant to power? Is truth necessary for legitimacy? Information technologies introduced in the science fiction world now exist in real time in forms and with speed and volume unimagined even a few decades ago. As artificial intelligence becomes dominant in many aspects of our daily lives, law students must consider how it may change law making, court procedures, entire legal systems, and perhaps even concepts of justice. As a project, law students might develop a case and conduct a trial using an AI judge or try a case to an AI jury. How human is the law? The role of emotional intelligence and concepts like mercy, restorative justice, forgiveness, or retribution are also things they might explore in seminars or other classes using science fiction literature and other time travel media as a framework.
Connecting the building blocks of Open Science: an ecological approach

Open Science covers a large panel of activities aimed at making research more transparent and collaborative. Several definitions provide lists of different components of open science, which creates the need to imagine how to connect these components together. However, the necessary work to interconnect open science services and tools must be completed by a holistic approach that considers open science as an ecosystem producing a “milieu” of knowledge and supported by a vibrant community gathered around shared values. It leads to an ecological approach of open science where the web of interactions within the ecosystem defines its elements rather than the opposite.
Sentiment Analysis of Presidential Candidates Anies Baswedan and Ganjar Pranowo Using Naïve Bayes Method

Presidential elections in Indonesia are carried out in a democratic manner in which the people choose the figures who will nominate themselves for president. With the presidential nomination, there will be many surveys of several figures who have electability to become presidential candidates. Based on a survey that has been issued by several figures who are running for president, namely Anies Baswedan and Ganjar Pranowo, who are the benchmarks for the community to be able to express their opinions from existing social media, one of which is Facebook. This study takes data through a scraping process which is then cleaned or cleaned, then five labels are given, namely: 1 (very negative), 2 (negative), 3 (neutral), 4 (positive), and 5 (very positive). aims to see which sentiment is the highest given by warganet to the upcoming presidential election. This study concludes that netizens have negative sentiments towards figures in the upcoming presidential election. seen from the data randomly generated 49% negative comments, 35% positive comments and 16% neutral. In addition, from 510 data taken by classification using the Naïve Bayes method, as well as testing using the 10-fold cross validation method with Quadgram tokenization resulted in an accuracy of 42.75%, precision 42.10%, and recall 42.70%.
Distributed optimization with inexact oracle

In this paper, we study the distributed optimization problem using approximate first-order information.We suppose the agent can repeatedly call an inexact first-order oracle of each individual objective function and exchange information with its time-varying neighbors.We revisit the distributed subgradient method in this circumstance and show its suboptimality under square summable but not summable step sizes.We also present several conditions on the inexactness of the local oracles to ensure an exact convergence of the iterative sequences towards the global optimal solution.A numerical example is given to verify the efficiency of our algorithm.
Multivariate Analysis with XRD Data as a Fingerprinting Technique to Study Burned Soils

Fire is a natural process with recognized recurrence. However, ongoing climate change and human activities are causing some disturbances in their natural regimes in most ecosystems. It is important to improve the methodologies used to evaluate the fire-induced changes in soils. This study aims at investigating the potential of the X-ray diffraction (XRD) technique to be used as a fingerprinting technique for burned soils. Multivariate analysis was employed to analyze the XRD data. Hierarchical Cluster Analysis (HCA) and local Partial Least Squares (PLS-2) models were performed. The soil samples are classified as Ferralsols and were collected from an Amazon region, Brazil, from forests, pastures and a slash-and-burn area. The studied temperatures ranged between 25 and 800 °C. Major differences were found for gibbsite, goethite and kaolinite contents due to dehydration. PLS-2 analysis presented better results than HCA as it provided information concerning the two features of the investigated soils, the collection site and the temperature. Therefore, it was possible to characterize soils from different sites and soils heated at different temperatures by using XRD data with multivariate analysis. Such methodology provided important information that may be used in areas with these environmental and soil conditions.
Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction

Japan has many entertaining and unique artworks, especially its signature animation, called anime. Anime is an animation art that is unique in that the characterizations, characters, and storylines are made to resemble human life. The characters have 2 genders called male and female with unique visuals and are the characteristics of each anime character to entertain the audience. Training large-scale data and complex textures because not all of the anime images owned are of high quality, making classification by Machine Learning Algorithms low in accuracy. This study will describe an experiment using an anime face image dataset to classify the gender, namely male or female. From this problem, this research implements feature extraction to produce unique features of anime images with Gray-Level Cooccurrence Matrix (GLCM) and uses the Random Forest Classifier which is a classification algorithm in Machine Learning to classify gender. The results of this study get a good accuracy value of 95%, using 3,612 images where the test data used is 723 images and Homogeneity5 feature being the most relevant feature in increasing the accuracy value with a value of 0.06378389.
Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations.
Domain Adaptive Hand Pose Estimation Based on Self-Looping Adversarial Training Strategy

In recent years, with the development of deep learning methods, hand pose estimation based on monocular RGB images has made great progress. However, insufficient labeled training datasets remain an important bottleneck for hand pose estimation. Because synthetic datasets can acquire a large number of images with precise annotations, existing methods address this problem by using data from easily accessible synthetic datasets. Domain adaptation is a method for transferring knowledge from a labeled source domain to an unlabeled target domain. However, many domain adaptation methods fail to achieve good results in realistic datasets due to the domain gap. In this paper, we design a self-looping adversarial training strategy to reduce the domain gap between synthetic and realistic domains. Specifically, we use a multi-branch structure. Then, a new adversarial training strategy we designed for the regression task is introduced to reduce the size of the output space. As such, our model can reduce the domain gap and thus improve the prediction performance of the model. The experiments using H3D and STB datasets show that our method significantly outperforms state-of-the-art domain adaptive methods.
AWGN Removal Using Modified Steering Kernel and Image Matching

Image noise occurs during acquisition and transmission and adversely affects processes, such as image segmentation and object recognition and classification. Various techniques are being studied for noise removal, and with the recent development of hardware and image processing algorithms, noise removal techniques that combine non-local techniques are attracting attention. However, one disadvantage of this method is that blurring occurs in the edges and boundary line of the resulting image. In this study, we proposed a modified local steering kernel based on image matching to improve these shortcomings. The proposed technique uses image matching to differentiate the weight obtained by the steering kernel according to the local characteristics of the image and calculates the weight of the filter based on the similarity between the center window and the matching window. The resulting images were quantitatively evaluation and enlargement of images were used and compared with the existing noise removal algorithms. The proposed algorithm showed clearer contrast in the enlarged images and better results than the conventional image restoration techniques in the quantitative evaluation using peak signal-to-noise ratio and structural similarity index.
Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classification

In digital pathology, Whole Slide Image (WSI) analysis is usually formulated as a Multiple Instance Learning (MIL) problem. Although transformer-based architectures have been used for WSI classification, these methods require modifications to adapt them to specific challenges of this type of image data. Among these challenges is the amount of memory and compute required by deep transformer models to process long inputs, such as the thousands of image patches that can compose a WSI at $\times 10$ or $\times 20$ magnification. We introduce \textit{SeqShort}, a multi-head attention-based sequence shortening layer to summarize each WSI in a fixed- and short-sized sequence of instances, that allows us to reduce the computational costs of self-attention on long sequences, and to include positional information that is unavailable in other MIL approaches. Furthermore, we show that WSI classification performance can be improved when the downstream transformer architecture has been pre-trained on a large corpus of text data, and only fine-tuning less than 0.1\% of its parameters. We demonstrate the effectiveness of our method in lymph node metastases classification and cancer subtype classification tasks, without the need of designing a WSI-specific transformer nor doing in-domain pre-training, keeping a reduced compute budget and low number of trainable parameters.