Reviewerly/exHarmony huggingface dataset:
THE EFECT OF THA RAFT AND PMI STRATEHIES ON THE ACHIEVEMENT OF HEOHRAPHY AND ANALYTICI THINKINH AMONH FOURTH-HRADE LITERARY STUDENTS

The current research aims to know (the effect of the RAFT and PMI strategies on the achievement of geography and analytical thinking among the literary fourth grade students) and for that the two hypotheses were formulated 1- There is no statistically significant difference at the level (0.05) between the average scores of the fourth literary grade students of the three groups The first experimental group that studies the subject of geographical foundations and its techniques using the (RAFT) strategy, the second experimental group that studies the strategy (PMI), and the control group that studies the same material in the (traditional) way in the post-achievement test 2- There is no statistically significant difference at the level (0.05).) Between the average scores of the students of the fourth literary grade, the three groups, the first experimental group that studies the subject of geographical foundations and its techniques using the strategy (RAFT), the second experimental group that studies using the strategy (PMI), and the control group that studies the same subject in the (traditional) way in the analytical thinking test. (90) students, and the experimental research method was used for three groups. Analytical thinking tools and the number of its paragraphs reached (28) test items, and the validity and stability of the two tools were extracted. In increasing the achievement and analytical thinking for as long as possible in the minds of students more than the usual method that has long been forgotten after a period of time. current on female gender. Key words: Impact, RAFT, PMI, Achievement, Geography, Analytical Thinking, Fourth, Grade Literary Students.
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features

Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying the strength of positional information remain unreliable and frequently lead to erroneous results. To address this issue, we propose novel metrics for measuring (and visualizing) the encoded positional information. We formally define the encoded information as PPP (Position-information Pattern from Padding) and conduct a series of experiments to study its properties as well as its formation. The proposed metrics measure the presence of positional information more reliably than the existing metrics based on PosENet and a test in F-Conv. We also demonstrate that for any extant (and proposed) padding schemes, PPP is primarily a learning artifact and is less dependent on the characteristics of the underlying padding schemes.
A Forward Speculative Interference Attack

This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from IEEE Computer Architecture Letters.
BLOCKCHAIN BASED E-VOTING SYSTEM IMPLEMENTATION AND EVALUATION

People may express their views via voting in elections, which are an essential part of a democratic society. Because our democracy depends on free and fair elections, they must be conducted in a way that the public can feel confident in. Voting methods have seen a great deal of change in this context. Efforts to make the system more secure, verifiable, and open are driving this advancement. Blockchain technology is a potential new tool for the development of secure apps because of its strong cryptographic foundations. All transactions are recorded and distributed on the Blockchain, which has resembled a data structure from its inception. Data stored in a distributed decentralised database is protected against unauthorised manipulation, tampering, or alteration in any form. Voting is safe and simple with our decentralised, transparent, and secure method. It securely mines blocks and converts votes into transactions. For one thing, the ability to cast a vote from any place is one of the many perks of a block chain voting system. Using blockchain technology, the voting process may be made more secure, transparent, immutable,
The Impact of the Nonprofit Sector on Employment and Unemployment in Developed Economies: Dynamic Panel Data Analysis

In the last quarter of the last century, the reshaping of the state apparatus under the domination of liberal economics and globalization led to a rapid and radical transformation of the nonprofit sector.With growing incomes, expenditures, staff and volunteers, these organizations have become major economic actors in many countries today.Indeed, economic research and technical reports on the sector have started to attract attention in the last few decades.When we look at these studies, we can say that the sector has a very active presence in labor markets, especially in developed countries.However, these studies do not econometrically evaluate the direction and extent to which the sector actually affects employment in countries.Therefore, the subject of this study was to investigate the data set of the 16 developed countries for the period of 2008 to 2018 using the least squares dummy variable corrected estimator.It was observed that there is a positive relationship between the employment rate and the gross value added of nonprofit organizations, and there is no statistically significant relationship between the employment rate and the world giving index.In addition, a negative relationship was found between the unemployment rate and the gross value added of nonprofit organizations and the world giving index.
Applying Fishbowl Strategy to Enhance Student’s Reading Comprehension Skill

Reading is essential for students' information development. Despite this, learners occasionally struggled to understand the report text. As a result, the point of the study was to ascertain whether it was a difference in reading achievement between students who used the fishbowl strategy and those who did not. All eleventh-graders from SMAN 04 Kayuagung participated in the study. Two classes consisting of 64 students were chosen utilizing a technique of purposive sampling. A quasi-experimental design was used in this research. A t-test was used to examine the data. The findings revealed that the experimental group's average post-test score (67.56) was better than the control group’s (53.22). The outcome of the t-obtained was greater than the t-table. It meant that there was a significant difference in students' Reading comprehension achievement after using the fishbowl strategy. Alternatively, the fishbowl strategy might enhance students’ reading comprehension skills of report text at SMAN 04 Kayuagung.
The Android Based Social Welfare Survey Application

Socioeconomic welfare is a complex problem, so more work is needed to address the lack of population welfare. One way by reviewing communities by surveying methods is that the survey method itself has the advantage that the results of the survey can be translated into number and this can make it easier for data grouping. But the current system of surveying is conducted has many flaws, ranging from an easy to split-down survey sheet damaged to difficult recapitulation because data must be regenerated. Lacking the conventional survey system, it is necessary for a system that can solve the problems that arise and enable survey activity to run smoothly. Researchers choose Research and Development Method. The goal of this study is to produce an app that can be used on an android device. The framework base researcher use is Framework7. Framework7 is the choice because framework7 have user friendly interface characteristics. The development of the conventional survey system is an android - based application for the social welfare of the population to make it easier for officials to survey in the field and help village staff to log the survey data, so that it can be reported to the data center.
LEXpander: applying colexification networks to automated lexicon  expansion

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually-curated sets of root words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e. semantic networks connecting words based on shared concepts and translations to other languages. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on various word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.
Research Challenges of Library and Information Science in retrieving content based Multimedia Information

Since several decades, the field of information retrieval (IR) has been active.Initially, IR field was concerned with effective and efficient analysis of information needs based on a set of keywords.It is still being used today in search engines.Multimedia formats are available in large amounts, including video, audio, and images.Multimedia Information Retrieval (MIR) deals with how to process queries about multimedia objects.The present paper briefly discusses the various components and classifications of the MIR.It discusses analytically LIS perspective on text, image, audio, and video information techniques.Finally, it suggested a research gap in MIR where the LIS domain seriously contributes in the development and upgrading of existing applications.
Review: Metode-Metode Ekstraksi Ciri dan Klasifikasi Identifikasi Pembicara

Identifying a person's identity still often uses an ID card (KTP, SIM, passport, etc.). This method has a weakness because the ID Card is easily damaged and lost. Biometric recognition systems provide a solution by using human body parts as identity recognition. Sounds are readily available biometric information. Voice pattern recognition is used for the speaker identification process to obtain the identity of someone speaking. This paper reviews several feature extraction and classification methods that are often used in speaker identification. The selection of feature extraction methods and classification functions in computation and the level of accuracy of the speaker identification system. Based on the survey dataset applied with the feature extraction method, the Mel Frequency Cepstral Coefficients (MFCC) method has high accuracy even with noise input. Then in classification, the Gaussian Mixture Model (GMM) method is most often used because it can work in noise. Recently, a hybrid classifier has been developed, which increases the accuracy value.
LDRNet: Enabling Real-time Document Localization on Mobile Devices

While Identity Document Verification (IDV) technology on mobile devices becomes ubiquitous in modern business operations, the risk of identity theft and fraud is increasing. The identity document holder is normally required to participate in an online video interview to circumvent impostors. However, the current IDV process depends on an additional human workforce to support online step-by-step guidance which is inefficient and expensive. The performance of existing AI-based approaches cannot meet the real-time and lightweight demands of mobile devices. In this paper, we address those challenges by designing an edge intelligence-assisted approach for real-time IDV. Aiming at improving the responsiveness of the IDV process, we propose a new document localization model for mobile devices, LDRNet, to Localize the identity Document in Real-time. On the basis of a lightweight backbone network, we build three prediction branches for LDRNet, the corner points prediction, the line borders prediction and the document classification. We design novel supplementary targets, the equal-division points, and use a new loss function named Line Loss, to improve the speed and accuracy of our approach. In addition to the IDV process, LDRNet is an efficient and reliable document localization alternative for all kinds of mobile applications. As a matter of proof, we compare the performance of LDRNet with other popular approaches on localizing general document datasets. The experimental results show that LDRNet runs at a speed up to 790 FPS which is 47x faster, while still achieving comparable Jaccard Index(JI) in single-model and single-scale tests.
Strategies for Internet of Things data privacy and security using systematic review

The Internet of Things (IoT) now referend to as the Internet of Everything (IoE) has been in existence long before it was identified as a concept. It was introduced with the emergence of the Fourth Industrial Revolution and was aimed at improving people’s lives and economies across the globe by connecting physical items to the internet so they can be able to deliver specific services implicitly. The nature of IoT requires that all the systems ensure data privacy and security because much of data that is uploaded into and used by the system is personal and private. Thus, the aim of this research was to identify the tools and strategies that can be used for IoT data privacy and security while also providing a brief but intensive understanding of the concept of IoT and data privacy and security challenges faced by IoT systems. This qualitative research study utilised a pragmatic paradigm and data was collected and analysed using text-based secondary data sources and a PRISMA protocol through systematic review. A PRISMA flow diagram was utilised to assess the eligibility of the sources used for this research. The findings showed that hacking is a major challenge that affects IoT systems and that there are strategies that can be used to protect data such as authentication, encryption technology, and anonymisation amongst many. Additional findings found that the strategies have not yet been found effective, but standards have been set upon the results expected from them. The conclusion is that for the identified strategies to be proven effective, they must be implemented and tested in IoT systems, so further investigation can be conducted if they prove to be ineffective.
Model-Based Reinforcement Learning Is Minimax-Optimal for Offline Zero-Sum Markov Games

This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a $\gamma$-discounted infinite-horizon Markov game with $S$ states, where the max-player has $A$ actions and the min-player has $B$ actions. We propose a pessimistic model-based algorithm with Bernstein-style lower confidence bounds -- called VI-LCB-Game -- that provably finds an $\varepsilon$-approximate Nash equilibrium with a sample complexity no larger than $\frac{C_{\mathsf{clipped}}^{\star}S(A+B)}{(1-\gamma)^{3}\varepsilon^{2}}$ (up to some log factor). Here, $C_{\mathsf{clipped}}^{\star}$ is some unilateral clipped concentrability coefficient that reflects the coverage and distribution shift of the available data (vis-\`a-vis the target data), and the target accuracy $\varepsilon$ can be any value within $\big(0,\frac{1}{1-\gamma}\big]$. Our sample complexity bound strengthens prior art by a factor of $\min\{A,B\}$, achieving minimax optimality for the entire $\varepsilon$-range. An appealing feature of our result lies in algorithmic simplicity, which reveals the unnecessity of variance reduction and sample splitting in achieving sample optimality.
Neuromorphic Networks as Revealed by Features Similarity

The study of neuronal morphology is important not only for its potential relationship with neuronal dynamics, but also as a means to classify diverse types of cells and compare than among species, organs, and conditions. In the present work, we approach this interesting problem by using the concept of coincidence similarity, as well as a respectively derived method for mapping datasets into networks. The coincidence similarity has been found to allow some specific interesting properties which have allowed enhanced performance (selectivity and sensitivity) concerning several pattern recognition tasks. Several combinations of 20 morphological features were considered, and the respective networks were obtained by maximizing the literal modularity (in supervised manner) respectively to the involved parameters. Well-separated groups were obtained that provide a rich representation of the main similarity interrelationships between the 735 considered neuronal cells. A sequence of network configurations illustrating the progressive merging between cells and groups was also obtained by varying one of the coincidence parameters.
Research on Factors Influencing the Training of Artificial Intelligence Applied Talents Based on DEMATEL-TAISM

Abstract The identification of key influencing factors in the training of applied talents in artificial intelligence includes two stages, namely, the establishment of factor index system and the identification of key influencing factors. Firstly, referring to a large number of literatures on the influencing factors of talent training and the sustainability evaluation index framework at home and abroad, and in combination with the characteristics of the development of China's artificial intelligence industry, this paper summarizes the influencing factors from the multi-dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence application-oriented talent training, this paper constructs the influencing factor index system of artificial intelligence applied talents training. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and the degree of influence, the degree of being influenced, the centrality degree, and the reason degree are calculated; Using the improved AISM method and based on the idea of game confrontation, a set of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained from the two opposite extraction rules of result priority and cause priority, respectively. Finally, the relevant suggestions are put forward in order to provide reference for promoting the training of applied talents in China's artificial intelligence industry.
An Intelligent Fraud Detection Model based on Deep Learning Ensembles

Abstract Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions' revenues and reputation. The increased use of the Web and the provision of online services open up the pathway for exposing these systems to numerous threats and jeopardise their effective functioning. Naturally, financial frauds are increased in number and forms imposing various requirements for their efficient and immediate detection. These requirements are related to the performance of the adopted models as well as the timely response of the decision making mechanism. Machine Learning (ML) and data mining are two research domains that can provide a number of techniques/algorithms for fraud detection and setup the road for mitigation actions. However, these methods still need to be improved with respect to the detection of unknown fraud patterns and the incorporation of big data processing mechanisms. This paper presents our attempt to build a hybrid system, i.e., an ensemble scheme for combining two deep learning models and efficiently detect potential financial frauds. We elaborate on the combination of an autoencoder and a Long Short-Term Memory (LSTM) recurrent neural network trained upon datasets which are processed through the use of an oversampling technique. Oversampling is adopted to handle heavily imbalanced datasets which is the `natural' scenario due to the limited number of frauds compared to the humongous volumes of transactions. The proposed approach tends to capture much more fraud events in comparison with other conventional ML techniques. Our experimental evaluation exposes that our model exhibits a good performance in terms of accuracy, recall &amp; precision.
Chatbot Using Python

Abstract: A chatbot enables a user to simply ask questions in the same manner that they would respond to humans. The most well-known chatbots currently are voices chatbots: SIRI and Alexa. However, chatbots have been adopted and brought into the daily application at a high rate on the computer chat platform. NLP also allows computers and algorithms to understand human interactions through various languages. Recent advances in machine learning have greatly improved the accurate and effective of natural language processing, making chatbots a viable option for many organizations. This improvement in NLP is firing a great deal of additional research which should lead to continued improvement in the effective of chatbots in the years to come.A bot is trained on and according to the training, based on some rules on which it is trained, it answers questions. It is called ruled based approach. The language by which these bots can be created is Artificial Intelligence Markup Language (AIML). It is a language based on XML which allows the developer to write the rules which the bot will follow. In this research paper, We are trying to understand these chatbots and understanding their shortcomings. question or statement submitted by a user and allow the user to control over the content to be displayed Keywords: AI; ML; Wordnet; Chatbot; NLP
RGB-D Neural Radiance Fields: Local Sampling for Faster Training

Learning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advances in implicit neural representation from images using neural radiance fields(NeRF) have shown promising results. Some of the limitations of previous NeRF based methods include longer training time, and inaccurate underlying geometry. The proposed method takes advantage of RGB-D data to reduce training time by leveraging depth sensing to improve local sampling. This paper proposes a depth-guided local sampling strategy and a smaller neural network architecture to achieve faster training time without compromising quality.
EFFECT OF FLOWER WRITING STRATEGY ON WRITING SKILLS OF FABLE STORY TEXT IN STUDENTS OF CLASS VIII MTs

This study aims to describe the effect of using the flower writing strategy on the writing skills of the eighth grade students of MTS Persis 50 Lembang.Â The data of this research is in the form of a score of writing skills for fable stories without and using mind mapping techniques.Â This type of research is quantitative research conducted using an experimental design.Â The results of this study are that the skills of writing fable texts for class VIII MTS Persis 50 Lembang students do not use mind mapping techniques but still have more than adequate qualifications and the skills of writing fable texts for class VIII MTS Persis 50 Lembang students using flower writing strategies are good qualifications.Â Based on the t-test, it was concluded that the use of flower writing strategy had an effect on the writing skill of the fable text.