Reviewerly/exHarmony huggingface dataset:
Peningkatan Hasil Belajar Siswa pada Materi Teks Berita Melalui Model Pembelajaran Silent Game

The completeness of students' cognitive learning outcomes on news text material, especially in linguistic sub-rules, is very low because the learning model is not yet precise. This article aims to improve students' cognitive learning outcomes on news text material in the linguistic rules sub-material with the silent game learning model. The method used is Classroom Action Research with two cycles. Each cycle is carried out in one meeting. The results showed a significant increase in cognitive learning outcomes. Furthermore, there was an increase in the absorption of students from cycle I to cycle II of 18.66. Meanwhile, learning completeness increased by 75% and completeness of objectives and material subjects by 100%. It concludes that the silent game learning model can improve the students' cognitive learning outcomes.
Changes in Communication in Higher Education during the Pandemic: Lecturers’ Attitudes

Abstract: The analysis dedicated to the problems that lecturers in Bul¬garian universities encounter during the third year of COVID-19 pandemic; more precisely to the communication during the educational process that changes drastically due to the transition of the process to a digital environ¬ment. The main hypothesis is that the communication during the online learning is perceived by lecturers as not sufficiently effective being com¬puter mediated and also because it has less opportunities for feedback, which generates problems in the learning process as such. The analysis is based on an online survey with lecturers in different Bulgarian universities during the third year of the pandemic. Keywords: online research, online learning, pandemic, higher education, communication.
A Gentle Introduction to Cognitive Map Based on Input Output Linguistic Variables

This paper studies on feedback graph model of linguistic variables which is generated from Hedge algebra.We also introduce a visual graphic model for input - output cognitive maps.
From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people&amp;rsquo;s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public&amp;rsquo;s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.
Deep learning classification applied to traffic accidents prediction

In this paper, YOLOv4 neural networks are trained with the goal of detecting and classifying objects from a street as seen from a drone. These have been trained on the VisDrone dataset, which is firstly validated through a custom-made graphic user interface. Then, several tests regarding performance, dataset composition and contrast have been carried out on the trained models. Results are compared to those from other previously existing models in order to evaluate their performance and analyse their shortcomings. The trained models are then used to detect and classify objects in a city scenario in real-time. Finally, an algorithm is proposed to track the objects, infer their future trajectories and predict potential collisions from the expected trajectories.
Mind-Set Based Signage: Applying Mind Genomics to the Shopping Experience

The paper presents a new approach to optimizing the shopper experience, combining easy-to-implement tools for understanding shopper mind-sets at the granular, specific level (Mind Genomics; www.BimiLeap.com)with a simple, rapid way which assigns any shopper or prospective shopper to the relevant mind-set for that granular topic (www.PVI360.com).The approach begins with a simple study of the motivating power of relevant messages, and thus uncovers mind-sets or groups of respondents showing similar patterns of what motivates them.Then, using the same data, the approach creates a simple questionnaire comprising six questions taken from the original study, the pattern of answers to which assign a new person to a mind-set.Once the mind-set of the shopper is 'identified' for the granular topic using the PVI (personal viewpoint identifier) it is a matter of giving the shopper the appropriate motivating message, either at the time of shopping in brick and mortar store or e-store, or sending the message on the Internet in the form of an advertisement or individualized coupon.
Before Truth: Lonergan, Aquinas, and the Problem of Wisdom, Jeremy D.Wilkins, The Catholic University of America Press, 2018(ISBN 978‐0‐8132‐3147‐1), xx + 414 pp., hb $65

Reviews in Religion & TheologyVolume 29, Issue 4 p. 304-307 Review Before Truth: Lonergan, Aquinas, and the Problem of Wisdom, Jeremy D. Wilkins, The Catholic University of America Press, 2018(ISBN 978-0-8132-3147-1), xx + 414 pp., hb $65 Nathan Bradford Williams, Nathan Bradford Williams orcid.org/0000-0001-8590-6546 University of TorontoSearch for more papers by this author Nathan Bradford Williams, Nathan Bradford Williams orcid.org/0000-0001-8590-6546 University of TorontoSearch for more papers by this author First published: 19 October 2022 https://doi.org/10.1111/rirt.14185Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume29, Issue4October 2022Pages 304-307 RelatedInformation
Subscription Management SaaS-based System

Abstract: Btag is a Cloud-Based Web App that is designed to maintain the membership of any subscription-based business. The problem of management is not unknown in the world of business. We have to make sure that such problems are resolved to boost economic growth and to also add more businesses to the grid. Btag software does that for us. We have thought and analyzed through all possible lenses how to build good membership-based software. The system does what it's made for. The system will store the data of all the members. The member data includes the name, Aadhaar number of the user, sex, phone number, email, and status. There are three different statuses of any member which are Enquiry, Active and Inactive. At any given time a member will be in either of these three statuses. Each status denotes certain characteristics that help the business in better management. If the user has come for the very first time then the user will be in the Enquiry mode given the member hasn't paid yet. Once the member pays the status changes to Active. If the member has any dues then the status will change to Inactive. One can also search all the different members in the ecosystem based on different criteria which are as follows status, sex, name, phone number, etc. Based on the data collected by the user and the payment trends. This work also gives us statistics and relevant data which can help a business in driving sales. This will help revolutionize the subscription-based business scenario.
Computable criteria for ballisticity of random walks in elliptic random environment

We consider random walks in i.i.d. elliptic random environments which are not uniformly elliptic. We introduce a computable condition in dimension $d=2$ and a general condition valid for dimensions $d\ge 2$ expressed in terms of the exit time from a box, which ensure that local trapping would not inhibit a ballistic behavior of the random walk. An important technical innovation related to our computable condition, is the introduction of a geometrical point of view to classify the way in which the random walk can become trapped, either in an edge, a wedge or a square. Furthermore, we prove that the general condition we introduce is sharp.
Towards a framework for the democratisation of deep semantic segmentation models

Semantic segmentation models based on deep learning techniques have been successfully applied in several contexts. However, non-expert users might find challenging the use of those techniques due to several reasons, including the necessity of trying different algorithms implemented in heterogeneous libraries, the configuration of hyperparameters, the lack of support of many state-of-the-art algorithms for training them on custom datasets, or the variety of metrics employed to evaluate semantic segmentation models. In this work, we present the first steps towards the development of a framework that facilitates the construction and usage of deep segmentation models.
Identifying Items for “Self-Reflection” as a Religious Factor to Succeed in the Pre-Sea Training

Self-reflection has become a necessary skill to achieve the goal students set for themselves to be successful in pre-sea training. It helps students to improve their actions in critical situations, primarily when supported by religious practices. This study aimed to identify items that represent self-reflection as a religious factor for success in pre-sea training. A three-round Delphi approach was adopted in which ten experts were selected who met the necessary condition to participate in the study. Data were analyzed quantitatively using SPSS version 23 for median and IQR. The study found that self-reflection can be implemented through meditation and self-evaluation, the act of repentance, and returning to the source of religion. However, the experts felt that isolation is unsuitable for them as a ship's crew. They are advised to find other methods of meditation and self-assessment, such as self-contemplation. Students should return to the sources of religion, including the Quran and Sunnah, by reading books and online references and also seek the help of knowledgeable and pious people. However, experts pointed out how difficult it is for a Muslim to reach out to this pious person when he is the only Muslim on board. This study has developed 18 practices for students to implement self-reflection in the form of meditation and self-contemplation.
Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks

Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at https://github.com/PDillis/stylegan3-fun.
Approximate Membership Query Filters with a False Positive Free Set

In the last decade, significant efforts have been made to reduce the false positive rate of approximate membership checking structures. This has led to the development of new structures such as cuckoo filters and xor filters. Adaptive filters that can react to false positives as they occur to avoid them for future queries to the same elements have also been recently developed. In this paper, we propose a new type of static filters that completely avoid false positives for a given set of negative elements and show how they can be efficiently implemented using xor probing filters. Several constructions of these filters with a false positive free set are proposed that minimize the memory and speed overheads introduced by avoiding false positives. The proposed filters have been extensively evaluated to validate their functionality and show that in many cases both the memory and speed overheads are negligible. We also discuss several use cases to illustrate the potential benefits of the proposed filters in practical applications.
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance

Visuals captured by high-flying aerial drones are increasingly used to assess biodiversity and animal population dynamics around the globe. Yet, challenging acquisition scenarios and tiny animal depictions in airborne imagery, despite ultra-high resolution cameras, have so far been limiting factors for applying computer vision detectors successfully with high confidence. In this paper, we address the problem for the first time by combining deep object detectors with super-resolution techniques and altitude data. In particular, we show that the integration of a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network into standard recognition pipelines can considerably increase the detection efficacy in real-world settings. We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED. We find that the proposed approach can consistently improve over ablated baselines and the state-of-the-art performance for both datasets. In addition, we provide a systematic analysis of the relationship between animal resolution and detection performance. We conclude that super-resolution and altitude knowledge exploitation techniques can significantly increase benchmarks across settings and, thus, should be used routinely when detecting minutely resolved animals in aerial imagery.
Variational Auto-Encoder Architectures that Excel at Causal Inference

Estimating causal effects from observational data (at either an individual -- or a population -- level) is critical for making many types of decisions. One approach to address this task is to learn decomposed representations of the underlying factors of data; this becomes significantly more challenging when there are confounding factors (which influence both the cause and the effect). In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects. We propose a progressive sequence of models, where each improves over the previous one, culminating in the Hybrid model. Our empirical results demonstrate that the performance of all three proposed models are superior to both state-of-the-art discriminative as well as other generative approaches in the literature.
Relative Performance of Various Types of Repositories for MySQL Archive Backup and Restore Operations

The backup and restore operations are essential part of database system's administration process. Usually, the size of the database is a major factor that determines numerous practical issues, among which is what backup and recovery methods to be used. However, in addition, there are various types of storage systems (repositories) available in MySQL 8.0, for example MyISAM, Merge, Memory, InnoDB, CSV, Blackhole, Archive and Federated, which in general sense determine how to store each table into at least one file. Each of these storage systems have their own positive and negative characteristics, so when constructing logical archives, it is vital to compare the performance of those various types of repositories.
Exploring language styles of teacher and students' verbal interaction

This research aims to find out the teacher in the classroom uses the verbal interaction between students and teacher, and language styles. The research was conducted at one of the Islamic Boarding High Schools in Maros regency, South Sulawesi, Indonesia. This research used a descriptive qualitative method, and the data was collected using observation and interviews of the teaching and process in the classroom. The results of this study demonstrated that verbal interaction and language style in a classroom are functioning effectively because the instructor can manage the situation and make the situation function properly by asking largely students for feedback. Nine types were explained in the findings: intrapersonal interaction, intrapersonal interaction, small group interaction, public interaction, formal style, informal style, group project, peer instruction, and discussion or debates. Based on the result above, it could be concluded that the verbal interaction between students and teacher and language styles in the classroom was running well.
MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification

Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems. It can be readily used also for experiments with dereverberation, denoising, and speech enhancement. We tackled the ever-present problem of the lack of multi-channel training data by utilizing data simulation on top of clean parts of the Voxceleb dataset. The development and evaluation trials are based on a retransmitted Voices Obscured in Complex Environmental Settings (VOiCES) corpus, which we modified to provide multi-channel trials. We publish full recipes that create the dataset from public sources as the MultiSV corpus, and we provide results with two of our multi-channel speaker verification systems with neural network-based beamforming based either on predicting ideal binary masks or the more recent Conv-TasNet.
Hybrid Acceleration Scheme for Variance Reduced Stochastic Optimization Algorithms

Stochastic variance reduced optimization methods are known to be globally convergent while they suffer from slow local convergence, especially when moderate or high accuracy is needed. To alleviate this problem, we propose an optimization algorithm -- which we refer to as a hybrid acceleration scheme -- for a class of proximal variance reduced stochastic optimization algorithms. The proposed optimization scheme combines a fast locally convergent algorithm, such as a quasi--Newton method, with a globally convergent variance reduced stochastic algorithm, for instance SAGA or L--SVRG. Our global convergence result of the hybrid acceleration method is based on specific safeguard conditions that need to be satisfied for a step of the locally fast convergent method to be accepted. We prove that the sequence of the iterates generated by the hybrid acceleration scheme converges almost surely to a solution of the underlying optimization problem. We also provide numerical experiments that show significantly improved convergence of the hybrid acceleration scheme compared to the basic stochastic variance reduced optimization algorithm.
Multimedia Information Time Balance Management in Mobile Cloud Environment Supported By Case Study

Mobile cloud computing is used to define and determine computing services with a structure model. The data and resource of any service will be retrieved from cloud computing through internet service, some tools, and user interface (web-based or application). Mobile Cloud Computing (MCC) is a hybrid of cloud computing and mobile computing. Multimedia Information is the core of Mobile Cloud information because of the sizable information of multimedia particularly video streaming. Mobile Cloud mostly handles and processes that information. MCC is one of the business expressions with the real environment in the IT world. The concept of the MCC is still in the beginner stage of advancement. So, the handle of the innovation in a careful way especially in the bearing of future research should be provide. In this paper, an algorithm is throttled load balancing for mobile clouds has been presented within an example of Multimedia information. The results has shown that the load balancing of cloud computing environment. In this scenario, load balancing techniques in mobile cloud computing can be employed and can successfully manage time through the cloud.
On the Problems of Modern Education. II. Learning Technologies

The article examines the fundamental provisions of the main regulators of the educational process: the Federal Law on Education in the Russian Federation No. 273-ФЗ of December 29, 2012 and the Federal State Educational Standard of Secondary General Education — Order No. 413 of May 17, 2012 (with subsequent changes). The necessity of their significant adjustment is substantiated. The ideas put into the educational process by the Ministry of Education and Science of the Russian Federation during the reforms of 2004–2012 and the explanations of A. Fursenko (at that time the Minister) are discussed. The positive and negative aspects of the USE are considered. The demands to abandon grades, exams and homework are analyzed, and a dangerous movement towards general dilettantism is pointed out. Problems in the process of moving away from the Bologna system of higher education and the position of the Ministry of Science and Education of the Russian Federation and Minister V. Falkov are discussed. Problems with textbooks and teaching methods are noted. The “subject-targeted” education model is considered, as well as the “cognitive-roleplay” form of education, implemented in the USSR. It is noted that we have a unique opportunity to make a modern shift to critical and analytical thinking instead of dogmatic, as the giants of the Renaissance did in their time.