Reviewerly/exHarmony huggingface dataset:
Research on Facial Expression Recognition Based on Improved Inception Network

Abstract Facial expression recognition is one of the hot research fields in computer vision. Deep learning method is widely used due to its excellent performance in feature characterization. However, convolutional neural network, which is commonly used in the field of image classification, is prone to challenges such as too many parameters, difficulty in training and too many network layers. We proposed an improved inception network to solve the above. In this model, small-scale filter was used to replace large-scale one and multi-channel features were extracted. In addition, a smaller stochastic gradient value introduced in the ReLU makes the weights of some neurons be able to update. AM-softmax is adopted as the loss function with respect of the issue that the large intra-class distance about expression features. The recognition rates of the proposed model on CK+ and FER2013 are 98.32% and 71.12%, respectively. The experimental results verify the effectiveness of the proposed algorithm.
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given control parameters, which is then processed by converting the fractional parts of them through a function into a set of non-repeating numbers that leads to a vast number of unpredicted probabilities (the factorial of rows times columns). Double layers of rows and columns permutation are made to the values of numbers for a specified number of stages. Then, XOR is performed between the key matrix and the original image, which represent an active resolve for data encryption for any type of files (text, image, audio, video, … etc). The results proved that the proposed encryption technique is very promising when tested on more than 500 image samples according to security measurements where the histograms of cipher images are very flatten compared with that for original images, while the averages of Mean Square Error is very high (10115.4) and Peak Signal to Noise Ratio is very low (8.17), besides Correlation near zero and Entropy close to 8 (7.9975).
Content Classification Tasks with Data Preprocessing Manifestations

Deep reinforcement learning has a major hurdle in terms of data efficiency. We solve this challenge by pretraining an encoder with unlabeled input, which is subsequently finetuned on a tiny quantity of task-specific input. We use a mixture of latent dynamics modelling and unsupervised goal-conditioned RL to encourage learning representations that capture various elements of the underlying MDP. Our approach significantly outperforms previous work combining offline representation pretraining with task-specific finetuning when limited to 100k steps of interaction on Atari games (equivalent to two hours of human experience) and compares favourably with other pretraining methods that require orders of magnitude more data. When paired with larger models and more diverse, task-aligned observational data, our methodology shows great promise, nearing human-level performance and data efficiency on Atari in the best-case scenario.
Max–min fairness based faucet design for blockchains

In order to have transactions executed and recorded on blockchains such as the Ethereum Mainnet, fees expressed in crypto-currency units of the blockchain must be paid. One can buy crypto-currency called Ether of the Ethereum blockchain from exchanges and pay for the transaction fees. In the case of test networks (such as Rinkeby) or scientific research blockchains (such as Bloxberg), free crypto-currency, Ether, is distributed to users via faucets. Since transaction slots on the blocks, storage and smart contract executions are consuming blockchain resources, Ethers are distributed by fixed small amounts to users. Users may have different amount of Ether requirements; some small amounts and some large amounts during different times. As a result, rather than allowing the user to get a fixed small amount of Ether, a more general distribution mechanism that allows a user to demand and claim arbitrary amounts of Ether, while satisfying fairness among users, is needed. For this end, Max–min Fairness based schemes have been used in centralised settings. Our work contributes a Max–min Fairness based algorithm and its Solidity smart contract implementation that requires low transaction costs independent of the number of users. This is important on the Ethereum blockchain, since a smart contract execution with transaction costs depending on the number of users would mean block gas limit exhaustion problem will eventually be met, making the smart contract ineffective. We report tests which confirm that the low transaction cost aims have been achieved by our algorithm.
Alternative Ruleset Discovery to Support Black-box Model Predictions

The increasing attention to the interpretability of machine learning models has led to the development of methods to explain the behavior of black-box models in a post-hoc manner. However, such post-hoc approaches generate a new explanation for every new input, and these explanations cannot be checked by humans in advance. A method that selects decision rules from a finite ruleset as explanation for neural networks has been proposed, but it cannot be used for other models.In this paper, we propose a model-agnostic explanation method to find a pre-verifiable finite ruleset from which a decision rule is selected to support every prediction made by a given black-box model. First, we define an explanation model that selects the rule, from a ruleset, that gives the closest prediction; this rule works as an alternative explanation or supportive evidence for the prediction of a black-box model. The ruleset should have high coverage to give close predictions for future inputs, but it should also be small enough to be checkable by humans in advance. However, minimizing the ruleset while keeping high coverage leads to a computationally hard combinatorial problem. Hence, we show that this problem can be reduced to a weighted MaxSAT problem composed only of Horn clauses, which can be efficiently solved with modern solvers.Experimental results showed that our method found small rulesets such that the rules selected from them can achieve higher accuracy for structured data as compared to the existing method using rulesets of almost the same size.
DESTUFFATION OF MONEY AND THE EMERGENCE OF DIGITAL CURRENCY

Banking has always been characterized by the use of new communication technologies, so it is not surprising that electronic media have also been actively used by banks, which led to the first forms of electronic money, which emerged from the use of monetary instruments such as bank credit card terminals and systems computer communication of banks with customers. However, from the point of view of the theory of money, electronic money is the same credit, or fiat, money, but not in paper form, but in the form of a record on electronic media. However, after the 1980s, the process of disappearance of the material form of money, or destuffation, became even more intense and, after several stages, it has led to the emergence of the digital currency, which is an adequate form of money in the new digital economy. Modern cryptocurrencies are based on blockchain technology are essentially an electronic combination of a “warehouse receipt” and a “bill of exchange” and serve as a possible basis for new global money. The last stages of this process deserve special attention, namely the development of cryptocurrencies and digital currency of central banks, which reflect the competition between state issuing institutions and new players in the global financial system. As a result of the analysis we can conclude that the process of destuffation of money is nearing its logical conclusion: dereification of money itself as an economic category, because “artificial intelligence” can turn money into a completely intangible, i.e. ideal, form, which will coincide with their form and essence.
Credit Card Fraud Detection Performance Improvement using Advanced Super Gradient Boosting Algorithm

Credit card fraud introduces to the physical loss of a credit card or the destruction of sensitive credit card data. Several text mining procedures can be used for disclosure. This investigation reveals several algorithms that can be used to analyze transactions as a fraud or as a real background. This paper represents the possibility of fraudulent transactions in the prevalence and meaning of credit card usage also, Credit card fraud data collection was used in the investigation. Since the dataset was largely unbalanced, SMOTE (Synthetic Minority oversampling Technique) is applying for an overdose. In addition, jobs selected, and the data set divided into two parts, training data and test data. In this paper, The Advanced Super Gradient Boostingbased Text mining Algorithm (ASGB) suggested to detect the fraud transaction in Credit card transactions. ASGB is a Decision-Tree-Based Ensemble Text mining algorithm that utilizes a gradient boosting framework. In forecast difficulties, including unstructured data (Images, Text, etc.), artificial neural networks tend to exceed all other algorithms or structures. The proposed algorithms used in the experiment were the Hidden Markov Model, Random Forest, Gradient Boosting, and Enhanced Hidden Markov Model. The Experimental Results show that proposed algorithms, a well-tuned ASGB classifier outperforms all of them. And it presents better Precision is 99.1%, and Recall is 99.8%, F-measure is 99.5%.
Pengembangan E-book Interaktif Sebagai Sumber Belajar Mandiri Pada Materi Pemuaian Kelas VII SMP/MTs

The development of interactive e-book as a source of self-learning on the material thermal expansion of the class VII SMP/MTs was carried out based on problems that occurred in one of junior high schools in Songgon, Banyuwangi, East Java, Indonesia. During the online learning process on thermal expansion material, science teachers used printed textbooks and learning videos, but students still had difficulty understanding the material from these two sources. One of the reasons is the lack of interactivity of both sources. The purpose of this study is to describe the results of the validation and student responses to the interactive e-book as a source of self-learning in the VII grade junior high school on thermal expansion material. The research and development design uses Thiagarajan's (1974) 4D model which consists of 4 stages including define, design, develop, and disseminate, but this research is limited to the develop stage. The subjects in this study consisted of 2 lecturers of UIN KHAS Jember as material and media experts, 2 science teachers as user experts and the response test was carried out on 32 students. The results of the validation of experts and users were found to be an average of 92.5% and 92.43%, it shows that interactive e-book as a self-learning resource are declared very valid to be used. The results of student responses have an average score of 90.58%. Descriptions of student responses are explained in the form of wordcloud where most students consider that the interactive e-book developed is "good" because it is equipped with writing, pictures, videos, animations, and practical simulations.
An Ensemble Approach for Classification of Tympanic Membrane Conditions Using Soft Voting Classifier

Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices used by field experts for diagnosis of the disease relevant to otitis media are the most important drawback in the widespread use of these devices. Therefore, there is a need for computer-aided middle ear disease diagnosis systems in order to eliminate the subjectivity and high-cost problems in the diagnosis of the disease. In order to increase accuracy and stability in automatic diagnosis of different eardrum conditions such as normal, earwax plug, myringosclerosis and chronic otitis media, this study proposes a voting ensemble framework including 5 different pre-trained convolutional neural network models. All pre-trained models used in the proposed framework were trained using Public Ear Imagery dataset with 880 otoscopy images. In this framework, performances of both soft and hard voting ensemble frameworks were discussed. The proposed soft voting ensemble framework achieved high performance for different cases of tympanic membrane by 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. The results obtained indicated the highest classification performance ever achieved on the current dataset. As a result, the voting ensemble framework benefiting from the prediction results of the pre-trained deep learning models increased the accuracy in classification of tympanic membrane conditions.
Toward an AI Definition of Antisemitism?

Crafting a definition of antisemitism that can be used for automatic detection of antisemitic messages on social media is challenging. It requires not only a comprehensive understanding of the history of antisemitism and its (re)current forms, but also a comprehensive dataset that is labeled with a clear definition of antisemitism. This labeled dataset, a “Gold Standard,” can then be used by algorithms to identify antisemitic messages. In this chapter, the widely used Working Definition of the International Holocaust Remembrance Alliance (IHRA) is combined with implicit assumptions, such as the images and specific tropes of “classic antisemitic stereotypes” and applied in a rigorous manner to a set of tweets from representative samples to create a labeled dataset of 4,014 tweets. The results show that the percentage of antisemitic messages in conversations about Jews on Twitter is significant. From May to August 2020, counting live tweets only, every seventh tweet in conversations about Jews was found to be antisemitic.
SOCIAL AND PEDAGOGICAL TECHNOLOGIES IN THE IMPLEMENTATION OF YOUTH POLICY AT THE UNIVERSITY: THE EXAMPLE OF GRANT PROJECTS OF THE ROSMOLODEZH AGENCY

The article discusses the social and pedagogical technologies in the implementation of youth policy at the university, aimed at creating conditions for the socialization of students, the formation of over-professional and professional competencies of future specialists. The article also describes successful practices of the Far Eastern State Transport University in the competition of Rosmolodezh among higher educational institutions of Russia, as a positive experience in the use of interactive, modular and design technologies.
Models of Intervention: Helping Agents and Human Users Avoid Undesirable Outcomes

When working in an unfamiliar online environment, it can be helpful to have an observer that can intervene and guide a user toward a desirable outcome while avoiding undesirable outcomes or frustration. The Intervention Problem is deciding when to intervene in order to help a user. The Intervention Problem is similar to, but distinct from, Plan Recognition because the observer must not only recognize the intended goals of a user but also when to intervene to help the user when necessary. We formalize a family of Intervention Problems and show that how these problems can be solved using a combination of Plan Recognition methods and classification algorithms to decide whether to intervene. For our benchmarks, the classification algorithms dominate three recent Plan Recognition approaches. We then generalize these results to Human-Aware Intervention, where the observer must decide in real time whether to intervene human users solving a cognitively engaging puzzle. Using a revised feature set more appropriate to human behavior, we produce a learned model to recognize when a human user is about to trigger an undesirable outcome. We perform a human-subject study to evaluate the Human-Aware Intervention. We find that the revised model also dominates existing Plan Recognition algorithms in predicting Human-Aware Intervention.
A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

The increase of user-generated content (UGC) on the Internet has led previous studies to propose various sentiment analysis approaches to understand public opinion.The primary goal is to enhance engagement through social media by analyzing various feedback.Sentiment analysis is performed based on two approaches i.e. machine learning and lexicon-based.Since approaches based on machine learning require costly preparation of training dataset and the approaches based on lexicon produce unsatisfactory performance, in this paper, both approaches are combined to perform sentiment analysis on Facebook comments.The importance of a lexicon-based approach to automatically construct the labeled data for machine learning sentiment classification is discussed in this paper.Experiments performed using the Universiti Utara Malaysia (UUM) Facebook posts show that using the combined lexicon-based and machine learning approach on two classifiers i.e.Naï ve Bayes and Support Vector Machine outperform the single approaches to produce more accurate sentiment classifications.
Prediction and Analysis of Moroccan Elections Using Sentiment Analysis

Sentiment analysis (SA) or opinion mining constitutes an important scientific field that uses advanced methods to mine population’s views and determine their feelings. In fact, SA is exploited by many fields. In politics, SA can mine the citizens’ opinions posted online and use this data as a means to predict the results of an ongoing election. Morocco will witness its legislative elections, early on September 8, 2021, and many Moroccans use web sources like social media and electronic journals to express their opinions in favor or against a certain party’s candidates. The objective of this paper is to mine and analyze citizens’ sentiments towards Morocco’s upcoming elections in general, as well as their favorite parties and candidates. The first step will be performing a sentiment analysis on posts or comments related to the elections from the Moroccan Hespress website using supervised machine learning (ML) techniques. We will apply these algorithms on 3503 comments being mined during two months; June and July 2021 in order to get the sentiment polarity of the commentators. The second step will be to implement some ML-based classifiers in order to determine the polarity of each comment. The third step a thematic analysis on both positive and negative comments that will reveal the public favorite parties and candidates, as well as the motives behind their choices. Finally, we discuss our analytical findings. The results of this study offer a precise view of Moroccans’ opinion about the Moroccan elections. Such extracted insight can be exploited by electoral candidates in their political campaigns to improve their campaign strategy and to attract the largest possible number of people to vote on their electoral program.
System characterization report on Planet’s SuperDove

First posted February 3, 2022 For additional information, contact: Director, Earth Resources Observation and Science (EROS) CenterU.S. Geological Survey47914 252nd StreetSioux Falls, SD 57198Contact Pubs Warehouse This report addresses system characterization of Planet’s SuperDove and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5.8 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 3-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SuperDove has a band-to-band geometric performance in the range of −1.701 m (−0.567 pixel) to 1.173 m (0.391 pixel) in easting and −4.950 m (−1.650 pixels) to 6.051 m (2.017 pixels) in northing, an image-to-image geometric performance of −1.17 m (−0.39 pixel) to 23.45 m (7.82 pixels) in easting and −10.61 m (−3.54 pixels) to −4.43 m (−1.48 pixels) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.043 to 0.020 in offset and 0.812 to 1.246 in slope, and a spatial performance in the range of 3.59 to 3.70 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.005 to 0.008.
Transfer Learning for Tabular Data

Deep learning models for tabular data are restricted to a specific table format. Computer vision models, on the other hand, have a broader applicability; they work on all images and can learn universal features. This allows them to be trained on enormous corpora and have very wide transferability and applicability. Inspired by these properties, this work presents an architecture that aims to capture useful patterns across arbitrary tables. The model is trained on randomly sampled subsets of features from a table, processed by a convolutional network. This internal representation captures feature interactions that appear in the table. Experimental results show that the embeddings produced by this model are useful and transferable across many commonly used machine learning benchmarks datasets. Specifically, that using the embeddings produced by the network as additional features, improves the performance of a number of classifiers.