Reviewerly/exHarmony huggingface dataset:
KEAMANAN JARINGAN WLAN DENGAN METODE FIREWALL FILTERING MENGGUNAKAN MIKROTIK PADA SMP NEGERI 1 DOLOK MERAWAN

The development of the network has changed, which used to use cables, now many wireless networks are built or commonly called Wireless Local Area Networks (WLAN). The use of the internet is increasingly widespread, starting from government agencies, companies, schools and others. WLAN network currently has a very important role as various information that we are looking for we can easily get on the internet. As well as used to make it easier to do our work such as exchanging and storing data. The internet has benefits if Users use it properly. The internet can also have an impact if it is used improperly, namely students at SMP Negeri 1 Dolok Merawan accessing the website during class hours which can disrupt teaching and learning activities. Then it is necessary to apply the Firewall Filtering method using a proxy router to block and limit websites at certain hours to support better and more comfortable teaching and learning activities.&#x0D; &#x0D;
Research on intelligent auxiliary decision system for maize breeding

As an important part of breeding research, germplasm resources play a fundamental role in maize breeding. This study starts from the needs of corn breeding scientific research, according to modern breeding technology, using computer technology and systems engineering technology, to study the key technologies of corn breeding process informatization, and build a practical and reliable corn breeding intelligent auxiliary decision support system. The establishment of this system will help to mine massive germplasm resources information, shorten the breeding time of new varieties with high quality and high yield, promote the development of informatization and intelligence in the breeding process, improve the level of breeding technology, and improve the level of corn breeding in the future. and provide a reference basis for the utilization of excellent maize germplasm resources.
News text classification based on multi-head attention and parallel capsule networks

The recognition effect of news text sequence data is strongly related to the importance of each word and the dependency relationship between them. Although the capsule network can learn the correlation information between news text as a whole and local, it lacks the attention to the key words in the text and ignores the distant dependencies in the text. To remedy the above shortcomings, this paper proposes a news text classification model which is based on multi-head attention and parallel capsule networks, using a multi-head attention layer for feature extraction and then a parallel capsule network module as the classification layer. The model can retrieve wealthier text details. Experimental results demonstrate that the proposed model of this paper works better than the mainstream capsule network based text classification models in both single-label and multi-label classification tasks of news texts.
Assessing the reading skill increase in children with learning disability through scanning and skimming focused approach

The objective of this study is to explore the reading skill development of students with learning difficulties after they were taught in the same conventional way as regular students. Specifically, the study will look at how well the sample of the study (n=24) read after receiving this instruction. For this analysis, a technique known as descriptive quantitative research was used. The authors evaluated the learners' levels of competence both before and after students got the treatment to determine how much progress they had made as a result of the intervention. The results of the study showed that students had various degrees of reading ability before they were taught using the methodology that was put to the test. On the other hand, the vast majority of students are proficient to the point where they are delighted with their results on the post-test. Keywords: Reading Skill, Learning Disability, Scanning. Skimming
Students' Nutrition Knowledge and Teachers' Perceptions of Integrating Nutrition Messages into Junior High School Curriculum

This study aimed to measure nutritional knowledge and observe teachers' perceptions of delivering nutrition messages through an e-module within the junior high school curriculum.This study was conducted at SMPN 35 Medan and employed the quasi-experimental approach, and a one-group pretestposttest design was used.This study's population was students in grades 7 and 8.The minimum sample size was 102 students after adding 10% of the total sample size.Teachers' perceptions were taken from four subject teachers.Data were collected using a questionnaire survey and an interview.A focus group discussion was conducted with the teachers to gather information about their perceptions of using an e-module to deliver nutrition messages at school.The data were analyzed using the Wilcoxon test.The results showed that the student's average total knowledge score before receiving the nutrition education intervention was 22.66.It increased to 26.48 after students received the intervention.Teachers used the e-module to deliver content learning about nutritional messages in schools and employed the e-module to deliver the relevant subject matters of nutrition messages at school.The teachers realized the importance of delivering nutrition messages to the students.The e-module could broaden the teachers' horizons as they came to realize that nutrition messages could be delivered through various subjects.They initially believed that nutrition messages could only be taught in the subject of natural sciences.However, they discovered that nutrition messages could also be delivered in other subjects, such as mathematics, crafts, and physical education.
Provably Correct Smart Contracts: An Approach using DeepSEA

It is possible to download a piece of software over the internet and then verify its correctness locally using an appropriate trusted proof system. However, on a blockchain like Ethereum, smart contracts cannot be altered once deployed. This guarantee of immutability makes it possible for end users to interact collectively with a 'networked' piece of software, with the same opportunity to verify its correctness. Formal verification of smart contracts on a blockchain therefore offers an unprecedented opportunity for end users to collectively interact with a deployed instance of software that they can verify while not relying on a central authority. All that is required to be trusted beyond the blockchain itself is an appropriate proof system, a component which always needs to be in the trusted computing base, and whose rules and definitions can be public knowledge. DeepSEA (Deep Simulation of Executable Abstractions) could serve as such a proof system.
Near-neighbor preserving dimension reduction via coverings for doubling subsets of ℓ1

Randomized dimensionality reduction has been recognized as one of the cornerstones in handling high-dimensional data, originating in various foundational works such as the celebrated Johnson-Lindenstrauss Lemma. More specifically, nearest neighbor-preserving embeddings exist for ℓ2 (Euclidean) and ℓ1 (Manhattan) metrics, as well as doubling subsets of ℓ2, where doubling dimension is today the most effective way of capturing intrinsic dimensionality, as well as input structure in various applications. These randomized embeddings bound the distortion only for distances between the query point and a point set. Motivated by the foundational character of fast Approximate Nearest Neighbor search in ℓ1, this paper settles an important missing case, namely that of doubling subsets of ℓ1. In particular, we introduce a randomized dimensionality reduction by means of a near neighbor-preserving embedding, which is related to the decision-with-witness problem. The input set gets represented with a carefully chosen covering point set; in a second step, the algorithm randomly projects the latter. In order to obtain the covering point sets, we leverage either approximate r-nets or randomly shifted grids, with different tradeoffs between preprocessing time and target dimension. We exploit Cauchy random variables, and derive a concentration bound of independent interest. Our algorithms are rather simple and should therefore be useful in practice.
Digital-Based Archives Management Training For Sub-District Employees and Village Appliances In The Telaga Jaya District, Gorontalo Regency

Telaga Jaya Sub-district is one of the sub-districts in the Gorontalo Regency which consists of 5 villages namely: Bulota Village, Buhu Village, Bunggalo Village, Luwooo Village and Hutadaa Village whose area conditions are in flood-prone areas need to be aware of various possible flood disasters that can occur. can destroy residential residents and government offices. One of the important assets to be saved before being hit by a flood disaster is assets in the form of archives in government offices. Implementation Method The method of implementing the activity is to provide an understanding of the management and storage of archives manually and digitally. Submission of material is carried out by resource persons and lecturers who are members of the team proposing community service activities. In addition, activities in the context of community service use two methods, namely 1) Lectures or counseling. In this method, lecturers or extension workers deliver material about Introduction to Archives and Digital Archives Training. On that occasion the participants were freed to ask questions with the material giver. The details of this method are an explanation of how to manage archives with a digital system. 2) Interactive dialogue, in this case participants are positioned as subjects who can provide reviews related to problems in archive management. This dialogue serves as brainstorming from participants to obtain input in the form of problems, aspirations, proposals, ideas and solutions. 3) The distribution of questionnaires before and after the implementation of the counseling (pretest and posttest questionnaires). The purpose of distributing this questionnaire is to get a comparison of participants' knowledge and insights about Archive Management.
Design of Explanatory Text Learning Devices for Class VII Indonesian Language Subjects in Middle School

In the planning stage, the teacher must design learning devices. The learning device in this study is the Learning Implementation Plan (RPP) which contains competencies, objectives, materials, strategies, media, learning scenarios, and assessments. The formulation of the problem of this research, namely: How is the design of explanatory text learning tools for class VII Indonesian Middle School subjects and how is the implementation of the design of explanatory text learning tools for grade VII Middle School Indonesian subjects? This study involved expert weighers in the validation test and 35 class VII students of SMP Negeri 1 Sumber as research subjects. The stages of this research consist of designing learning tools based on the ASSURE design model. Data collection techniques through interviews and questionnaires. Data analysis techniques, namely analytic description. Based on the research results, the average validity of learning tools obtained from expert reviewers is 84.32% with a level of validity, which is quite valid or can be used, but needs minor revisions, the average student learning outcomes is 81.67, the percentage of responses teachers to the implementation of learning tools is 86.67% with a very strong relationship level, and the percentage of students' responses to the implementation of learning tools is 89.39% with a very strong relationship level. So, the designed explanatory text learning tool for Indonesian Language and Literature in SMP Class VII is quite valid and the response is very strong/good in its implementation
Web Based Sales Information System at Greenvest Source

At this time information technology is experiencing very fast development, along with human needs which also continue to grow, with the internet we will get millions of information, we can even do business selling via the internet. The purpose of this research is to make it easier for buyers and sellers to buy in an easy way by using e-commerce web-based applications. In scientific writing, the research method used by the author is descriptive research methodology. The system design stage is described as designing to build a system and configuring software and hardware components so as to produce a good system, the designed system is one component. The conclusion of this study is to provide information to the general public about the products being sold by presenting pictures and information
Efektivitas Metode Question Answer Relationships terhadap Kemampuan Membaca Pemahaman Peserta didik Kelas IV

Abstrak: Kemampuan membaca pemahaman merupakan kemampuan menemukan arti kata, makna tersurat dan makna tersirat serta membuat kesimpulan. Kesulitan dalam mengidentifikasi gagasan pokok dalam bacaan terutama dalam membuat simpulan dialami oleh siswa kelas IV. Keefektifan metode Question Answer Relationships (QAR) terhadap kemampuan membaca pemahaman siswa kelas IV di MI 6 Tahun Tambakboyo Blitar menjadi fokus pada penelitian yang menggunakan pendekatan kuantitatif dengan jenis true experiement. Teknik pengumpulan data menggunakan instrumen tes. Hasil penelitian ini menunjukkan bahwa nilai thitung lebih besar dari ttabel pada signifikansi 0,05 (th = 3,824 &gt;ttb = 1,171). Sehingga disimpulkan metode QAR efektif untuk meningkatkan kemampuan membaca pemahaman dibandingkan metode diskusi pada kelas kontrol.Abstract: Reading comprehension is the ability to capture the meaning of words, the ability to capture the explicit and implied meanings and the ability to draw conclusions. Fourth grade students have difficulty in identifying the main ideas in the reading, and they have not been able to draw conclusions. The purpose of this study is to determine the level of understanding before and after implementing Question-Answer-Relationship method and to measure its effectiveness in improving the reading comprehension of fourth grade students at MI (Islamic Primary School) 6 Tambakboyo Blitar. This study employed a quantitative approach, using a true experimental method. The data were collected using test instruments. The results of this study demonstrate that the value of t-observed is greater than t-table at a significance level of 0.05 (to = 3.824 &gt; tt = 1.171). Hence, it can be concluded that the QAR method is effective for improving reading comprehension skills compared to the discussion method in the control class.
Discontinuous phase unwrapping based on the minimization of Zernike gradient polynomial residual

In many certain optical metrology cases, the pupil is usually divided into multiple connected domains by secondary mirror spiders, thus producing segment piston errors and leaving a false phase unwrapping result. In this paper, a method based on minimization of Zernike gradient polynomial residual (MZGR) is proposed to estimate segment piston errors and correct erroneous phase unwrapping results. Simulations and experiments demonstrated that this method can obtain the segment piston errors precisely under complex aberration forms and varied obscurations, indicating reliable practicality. Comparison to the 4D commercial solution, the RMS (root-mean-square) of the residual decreased from 0.154 λ to 0.020 λ .
Identification of authorship and prevention of fraudulent transactions/cybercrime using efficient high performance machine learning techniques

Cyber safety is the best skill required among a group of employees in organisations. Many offenders hide behind anonymous masking, including dishonest purchases, brazen plagiarism of the work, breaching corporate safety and stealing private data. To understand and analyse the actual phenomenon encountered with data, requirements of scientific methods, machine learning techniques, processes are to be used. This paper aims to tackle these problems by providing a protection layer for users, where data is being gathered from cyber security sources, analytical complement with latest data-driven patterns provides effective security solutions. We then discuss machine learning, deep learning powered models for the detection of insider threats and identifying authorship identification of anonymised articles. The individual modules are trained on authorship attribution, mouse monitoring, keyboard monitoring and command tracing and reached promising results with good accuracies in the range of 65%-85% on average.
DAMIAN – data accrual machine intelligence with augmented networks for contextually coherent creative story generation

Cognitive computing refers to the usage of computer models to simulate human intelligence and thought process in a complex situation. Artificial intelligence (AI) is an augmentation to the limits of human capacity for a particular domain and works as an absolute reflection of reality. AI is where a computer program is able to efficiently make decisions without previous explicit knowledge and instruction. The concept of cognitive intelligence was introduced. The most interesting use case for this would be an AI bot that doubles as a digital assistant. This is aimed at solving core problems in AI like open domain question answering, context understanding, aspect-based sentiment analysis, text generation, etc. The work presents a model to develop a multi-resolution RNN to identify local and global context, develop contextual embedding via transformers to pass into a seq2seq architecture and add heavy regularisation and augment data with reinforcement learning, and optimise via recursive neural networks.
Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

&lt;span&gt;Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.&lt;/span&gt;
Facial Mask Wearing Condition Detection Using SSD MobileNetV2

Wearing a facemask is one of the main ways to prevent the spread of respiratory diseases such as Covid-19, so it is helpful to monitor people’s facemask-wearing status through vision-based systems. In this paper, a system has been developed that divides people’s face mask-wearing conditions using image processing into three classes: without a facemask, correct facemask-wearing, and incorrect facemask-wearing. For this purpose, the SSD-MobileNetV2 neural network has been used, and several hyperparameter sets have been compared for the best possible accuracy. A lightweight custom convolutional neural networks (CNN) has also been used as the second stage to improve the classification accuracy, so this stage can be used in cases where higher accuracy is required. The proposed neural network was implemented on a Raspberry-Pi3, and this system can control an entrance gate using a servo motor automatically.
Impact of MR sequences choice on deep learning segmentation of muscles

Medical image segmentation is a critical step for many medical studies. We address the problem of muscle segmentation on MRI images using Dixon sequences and explore the impact on the segmentation results when combining the four Dixon sequences available. Different combinations were put to test using two UNet-based architectures. One used an early fusion and input the images in the same encoder, while the other used late fusion, which learns the features from the images in separated encoders and then concatenates and decodes them as a whole. Our results show that the T1 water-only image is the most appropriate image for muscle segmentation in our database and that both early and late fusion approaches did not yield significantly different results. Thus, appropriate check of most adequate contrast to consider is feasible and recommended to exquisitely match to the observed population and the early fusion architecture appears to be the most efficient design to do so when dealing with such muscle segmentation task.
Spatial Attention based Semantic Decouple Network for Breast Tumor Segmentation

Breast tumor segmentation is of great significance for establishing a computer aided diagnosis system. Although existing studies have achieved some promising results for breast tumor segmentation with a single-center dataset, it is still challenging to improve the generalization capability of the segmentation model with multicenter datasets. To address this issue, we propose a spatial attention based semantic decouple network (SADnet) to segment the breast cancer tumors with datasets from different sites, which estimates the potential semantic clusters in breast cancer images based on multi-head spatial attention modules. The decoupled semantic clusters can overcome the influence of semantics interference on the segmentation and therefore promotes the segmentation performance. The experimental results show that SADnet for breast cancer segmentation achieves dice similarity coefficient (DSC) of 0.706 and sensitivity (SEN) of 0.755 on the external test set, which are 2.2% and 4.4% higher than state-of-the-art (SOTA) methods, respectively. In addition, it achieves an average surface distance (ASD) of 2.863, which is 11.2% lower than SOTA methods. These results show that the clustering strategy proposed in this paper is beneficial for multi-center breast tumor segmentation.
Distributed Ledger Technology and its Potential Applications - Financial Sector

The concept of blockchain has shown tremendous potential and growth in the last decade. The terms such as blockchain and digital ledger technology are used interchangeably. Many research efforts have been madto explain and harness the benefits of digital ledger technology for the financial sector. Digital currency or cryptocurrency is a technological currency based on the block chain concept.
Analysis of the Effect of Information and Communications Infrastructure on Foreign Direct Investment in Emerging Market Countries

This study examines the effect of information and communication technology infrastructure on foreign direct investment in 20 emerging market economies. In the 90s, developing countries had policies to make it easier for investors. Emerging markets countries are significant and potential markets. There is data that emerging market countries have increased from 35.90% to contributing 46.62% of FDI of the world’s total in 2019. Using 25 years of data (1995-2019), use the panel data regression method to see the influence of ICT infrastructure on FDI. Find strong empirical evidence showing that the influence of ICT infrastructure on FDI is significantly positive in emerging market economies. In addition, we also examine the relationship between the variables GDP, inflation, and Trade openness to FDI. GDP, and Trade openness have significant positive results for FDI, while inflation has significant negative results for FDI in emerging market economies.