Reviewerly/exHarmony huggingface dataset:
OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features

Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures and one specifically designed for sentiment analysis. We also evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification and cross-dataset generalization performance. The evaluation explores a novel dataset—namely, OutdoorSent—and other publicly available datasets. We observe that the incorporation of knowledge about semantic attributes improves the accuracy of all ConvNet architectures studied. Besides, we found that exploring only images related to the context of the study—outdoor, in our case—is recommended, i.e., indoor images were not significantly helpful. Furthermore, we demonstrated the applicability of our results in the United States city of Chicago, Illinois, showing that they can help to improve the knowledge of subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment, which are also correlated with median income, opening up opportunities in different fields.
Personalized Image Classification by Semantic Embedding and Active Learning

Currently, deep learning has shown state-of-the-art performance in image classification with pre-defined taxonomy. However, in a more real-world scenario, different users usually have different classification intents given an image collection. To satisfactorily personalize the requirement, we propose an interactive image classification system with an offline representation learning stage and an online classification stage. During the offline stage, we learn a deep model to extract the feature with higher flexibility and scalability for different users’ preferences. Instead of training the model only with the inter-class discrimination, we also encode the similarity between the semantic-embedding vectors of the category labels into the model. This makes the extracted feature adapt to multiple taxonomies with different granularities. During the online session, an annotation task iteratively alternates with a high-throughput verification task. When performing the verification task, the users are only required to indicate the incorrect prediction without giving the exact category label. For each iteration, our system chooses the images to be annotated or verified based on interactive efficiency optimization. To provide a high interactive rate, a unified active learning algorithm is used to search the optimal annotation and verification set by minimizing the expected time cost. After interactive annotation and verification, the new classified images are used to train a customized classifier online, which reflects the user-adaptive intent of categorization. The learned classifier is then used for subsequent annotation and verification tasks. Experimental results under several public image datasets show that our method outperforms existing methods.
Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language

Sign Languages (SLs) are visual–gestural languages that have developed naturally in deaf communities. They are based on the use of lexical signs, that is, conventionalized units, as well as highly iconic structures, i.e., when the form of an utterance and the meaning it carries are not independent. Although most research in automatic Sign Language Recognition (SLR) has focused on lexical signs, we wish to broaden this perspective and consider the recognition of non-conventionalized iconic and syntactic elements. We propose the use of corpora made by linguists like the finely and consistently annotated dialogue corpus Dicta-Sign-LSF-v2. We then redefined the problem of automatic SLR as the recognition of linguistic descriptors, with carefully thought out performance metrics. Moreover, we developed a compact and generalizable representation of signers in videos by parallel processing of the hands, face and upper body, then an adapted learning architecture based on a Recurrent Convolutional Neural Network (RCNN). Through a study focused on the recognition of four linguistic descriptors, we show the soundness of the proposed approach and pave the way for a wider understanding of Continuous Sign Language Recognition (CSLR).
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation

Two-time-scale stochastic approximation is a popular iterative method for finding the solution of a system of two equations. Such methods have found broad applications in many areas, especially in machine learning and reinforcement learning. In this paper, we propose a distributed variant of this method over a network of agents, where the agents use two graphs representing their communication at different speeds due to the nature of their two-time-scale updates. Our main contribution is to provide a finite-time analysis for the performance of the proposed method. In particular, we establish an upper bound for the convergence rates of the mean square errors at the agents to zero as a function of the step sizes and the network topology.
Metode Case Based Reasoning Untuk Identifikasi Penyakit Tanaman Padi

After wheat and maize, rice (Oryza sativa L.) is a very important food crop in the world. Rice is a rice-producing crop and is a very important food crop. Most of the world's population, especially in Asia, is still used as a staple food. During the cultivation process, many rice plants are susceptible to disease and pest attacks, including: leaf blight, tongguo grass, rice bursts, and dwarf grass. Generally, when rice plants are attacked by diseases and pests, farmers use pesticides directly or by means of countermeasures which are sometimes not suitable for diseases and pests that are infected in rice plants. As a result, prevention is not optimal and can even cause new diseases and pests. Assisting farmers in identifying early symptoms of plant diseases and rice pests, so that treatment of plant diseases and insect pests is more concentrated and maximally is the aim of this study. The processed data were 4 pests and diseases which were processed using case-based reasoning. This method will diagnose the types of rice and insect pests and its treatment steps with an accuracy of 95%. Therefore, this method is suitable for the diagnosis of rice disease.Â
Software implementation of game-theoretic models accompanying labor resource allocation processes

This work describes and discusses in detail two algorithms for constructing solutions for two models of transport problems for the distribution of labor resources: static and dynamic. At the beginning of the work, these models are described, then the structure of the solution implementation and its specific form are considered. The paper considers a compromise solution in the models of the distribution of labor resources for the transportation problem. A description of the software implementation of the models.
Tuning machine learning models to detect bots on Twitter

Bot generated content on social media can spread fake news and hate speech, manipulate public opinion and influence the community on relevant topics, such as elections. Thus, bot detection in social media platforms plays an important role for the health of the platforms and for the well-being of societies. In this work, we approach the detection of bots on Twitter as a binary output problem through the analysis of account features. We propose a pipeline for feature engineering and model training, tuning and selection. We test our pipeline using 3 publicly available bot datasets, comparing the accuracy of all trained models with the model selected at the end of our pipeline.
Structural and Textual Information Fusion for Symptom and Disease Representation Learning

Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.
Working Memory and Expertise

The question of what individual differences in working memory capacity reflect has been a topic of intensive interest in research for several decades. This research has shed light on mechanisms underlying working memory performance. At the same time, the chapter authors argue this research has been myopic in two respects. First, it has largely ignored the influence of other predictor variables on complex task performance. Second, it has overlooked the importance of the environment as a co-determinant of performance. In this chapter, answering the seven questions put to contributors of this volume, the authors discuss their own research on working memory capacity and describe an ecological view of the working memory system that they believe will advance both research on working memory and the usefulness of findings from this research.
User-centered Design and Evaluation of a Geovisualization Application Leveraging Aggregated QS Data

Individual movement traces recorded by users of activity tracking applications such as Strava provide opportunities that extend beyond delivering personal value or insight to the individual who engages in these “quantified-self” (QS) activities. The large volumes of data generated by these individuals, when aggregated and anonymized, can be used by city planners, Departments of Transportation, advocacy groups, and researchers to help make cities safer and more efficient. This opportunity, however, is constrained by the technical skills and resources available to those tasked with assessing bicycling behavior in urban centers. This paper aims to address the question of how to design cartographic interfaces to serve as mediated platforms for making large amounts of individual bicycling data more accessible, usable, and actionable. Principles of cartographic representation, geovisual analytics techniques, and best practices in user interface/experience design are employed to arrive at an effective visualization tool for a broad urban planning audience. We use scenario-based design methods to encapsulate knowledge of map use practice gleaned from the development process, and conduct a post-implementation two-part user study with seven domain experts to further assess the usability and utility of the interactive mapping tool.
Significant role of the specific prime number p = 257 in the improvement of cryptosystems

Cryptology is the significant science which is inseparable from the means of communication of secrets. In a safe manner, it has the main objective of transmitting (potentially sensitive) information between two interlocutors. One distinguishes mainly two “dual” disciplines within cryptology: (a) cryptography, which is interested in the security of information. (b) cryptanalysis, which seeks to attack it. One have a starting set of 256 elements, we add a new element to this set to form a set of 257 elements. In this paper, we consider a finite field that contains 257 elements. As with any field, a finite field is a set on which the operations of multiplication, addition, subtraction and division are defined and satisfy certain basic rules. The most common examples of finite fields are given by the integers modulo p when p is a prime number. For our case ℤ/pℤ, p = 257. We apply it to affine ciphers and show that this cipher looks like a permutation cipher. The idea based on this result, is to use the affine ciphers with the modulo 257 (as an initial permutation) in any specific algorithm of ciphering. Besides, one finishes with the decryption affine with the modulo 257 like an inverse permutation. This is to significantly increase the security of the specific encryption algorithm and to lengthen the 16-bits encryption key.
Perbedaan Persepsi dan Kemandirian Belajar Siswa SMA Terhadap Pembelajaran Daring Ditinjau Dari Gender

In learning mathematics, students' affective abilities are also important to pay attention to in achieving learning goals.The affective ability referred to is the perception self regulated learning.This study aims to obtain information: (1) knowing there are differences in perceptions between students in class XI and XII IPA on online learning (2) knowing that there are differences in perceptions between male students and female students regarding online learning (3) knowing there are differences in learning independence Between class XI and XII IPA students on online learning (4) there are differences in learning independence between male students and female students towards online learning.This research is a descriptive quantitative research.The research subjects were 231 respondents.Data collection using a questionnaire instrument to collect data on students' perceptions and learning independence.Based on the calculation results, the average percentage of students' perceptions of online learning was 38.44%.This means that almost half of students have a perception of learning mathematics using an online model.While the average percentage of students' learning independence was 46.96%.This means that almost half of the students are independent in learning mathematics.The results of the calculation of the Mann Whitney test show that (1) the significant value is 0.0055, so there is a difference in perceptions between class XI and XII IPA students on online learning (2) the Sig value is 0.095, so there is no difference in the perception of male students and female students towards online learning.(3) the Sig value is 0.095, so it can be concluded that there is no difference in self regulated learning between class XI and XII IPA students on online learning (4) the Sig value is 0.162, so there is no difference in self regulated learning between male students and female students on online learning.
Object Boundaries Detection Based Center Of Gravity

After the development in the field of visual data analysis, it has become necessary to obtain objects inside the image, which must be with a high degree of accuracy and clarity for the purpose of identifying the object inside that body in order to obtain clear living organisms, as well as medical professionals can clarify the types of diseases and their forms to find Treatments suitable for her.In this paper was used a set of previously used and proven mechanisms and techniques that help to remove impurities from the images and reveal the edges to distinguish the object's boundaries and the length of the body and calculate the center of gravity and through the mechanism of the body's rotation to identify these things so that specialists can determine what these things are and how to deal with them And develop possible solutions to it, whether medical, geographical or otherwise.
Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.
An Efficient Pairing-Free Identity Based Proxy Blind Signature Scheme with Message Recovery

In recent years, due to their potential applications, proxy blind signatures became an active research topic and are an extension of the basic proxy signature. A proxy blind signature scheme enables a proxy signer to produce a blind signature on behalf of an original signer. Such schemes are useful in many practical applications such as e-commerce, e-voting, e-tendering systems. Many proxy blind signature schemes have been proposed in the literature. In order to improve the efficiency and to adopt resource constrained devices, in this paper, we propose a pairing free ID-based proxy blind signature scheme with message recovery. The proposed scheme is proven secure against the random oracle model under the hardness assumption of the elliptic curve discrete logarithm problem. We compare our scheme with the other proxy blind signature schemes. The efficiency analysis shows that our scheme is more efficient in terms of computational and communicational point of view. Also due to the message recovery property, our scheme can be deployed easily in low band width devices.
Application of Artificial Intelligence Technology in College Physical Education

Abstract With the rise of a new round of scientific and technological revolution, the vigorous development of artificial intelligence technology has gradually entered human daily life. Physical education, as one of the daily teaching activities of human beings, is an indispensable part of the current teaching evaluation and students’ quality assessment in Colleges and universities. With the development of the times, there are many shortcomings in the traditional teaching practice, and the unique advantages of artificial intelligence technology will largely make up for the shortcomings of traditional teaching. At present, the integration of physical education and artificial intelligence technology is still in the preliminary exploration stage. Therefore, this paper will first introduce the development history and main content of artificial intelligence technology. Then, starting from the reality of physical education in Colleges and universities, this paper analyzes the problems existing in traditional physical education teaching methods. Secondly, the paper puts forward the teaching methods of artificial intelligence technology such as virtual reality technology, big data analysis technology and intelligent recognition technology as the innovation points, in order to solve the shortcomings of traditional sports teaching method. The last part is the conclusion, analyzes the application prospect of artificial intelligence technology in college physical education.
The FEDHC Bayesian Network Learning Algorithm

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.
Constructivism Learning Environment by Using Augmented Reality in Art History Course

Augmented Reality (AR) is a potential new aid in education as it has the ability to enhance student engagement towards the content of any subject.The computer technology in AR allows the users to combine the physical and the virtual world.The involvement of AR in higher education is widely researched and rapidly evolved but underutilised.Teaching history courses with the conventional method in a class can be very challenging, especially to get the student engagement.The conventional method used in class not only limits the student reception and comprehension but also made them into passive students.Learning through AR results in functional knowledge transfer to students as it includes exploration of practical and theoretical knowledge.This research aims to examine student engagement in learning animation history course by using AR.Qualitative method was used by approaching the students of animation history as the focus group in order to examine their engagement, attention, and interest based on Constructivism Learning Theories.As a result, AR enhances student engagement in Art History class, through active and constructive learning.The digital content in AR improves the learning condition as well as promotes and supports exploratory behaviour with multiple modes of presentation such as textual information assisted with video and audio, 3D interactivity, peer interaction and coaching.
Log Pattern Mining for Distributed System Maintenance

Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length (MDL) principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method.
A Conceptual Framework and Evaluation Tool for Mobile Learning Experiences

There is an identified need in the research literature for the design, implementation, and evaluation of a conceptual framework for creating contextual, interactive mobile learning. This article details how the conceptual framework was implemented and tested in an online learning course for physicians, nurses, and healthcare professionals at medical organizations across Ontario. The conceptual framework and evaluation instruments were revised based on identified best practices and feedback from study participants. This provided a practical, evidence-based tool for informing the effective design of mobile learning. Results indicated that the design of the framework to include context-specific content, guided participation delivery, flexible and intuitive usability, formal online and informal mobile structure, and access to communities of practice all resulted in practical, applicable learning outcomes and a high degree of learner satisfaction.