Reviewerly/exHarmony huggingface dataset:
TRANSFORMASI KEMAMPUAN NUMERASI SISWA SMP SELAMA PELAKSANAAN ASESMEN KOMPETENSI MINIMUM PROGRAM KAMPUS MENGAJAR ANGKATAN 4

The purpose of this study was to analyze the transformation of Asesmen Kompetensi Minimum values ​​related to students' numerical abilities which were carried out at SMP Islam Terpadu Syarif Hidayatullah Sukorambi. This study used a qualitative descriptive method, where the research subjects were class VIII students, totaling 15 students. Data collection was carried out twice, namely pre-test and post-test during the Kampus Mengajar Program batch 4. Each question consisted of 20 items with 12 indicators. The highest increasing scores of the two are in indicators number 2, 5, and 9. Balanced or equal scores are found in indicators number 1, 4, 6, 7, and 8. There is a decrease in scores in indicators number 3 and 12. These results indicate that there is a significant transformation marked by an increase in score from the pre-test average of 19.42% to the post-test average of 28.33%. Both are in the range of values ​​≤40. From this, it can be concluded that the numeracy skills SMP Islam Terpadu Syarif Hidayatullah students belong to the low category so improvements are needed in improving numeracy skills accompanied by support from the school.
Linking Metacognition, Workplace Cognitive Competencies and Performance: An Integrative Review-Based Conceptual Framework

A key driver of workplace cognitive competencies is metacognition which has been shown to impact performance among nurses, teachers and firefighters, however, it is scarcely studied among managerial employees. The research investigating this relationship is also scattered across multiple domains limiting its’ utility for researchers and practitioners. This paper, therefore, presents an integrative review of the existing empirical literature from the Web of Science and Scopus database to trace the linkages of metacognition, workplace cognitive competencies and performance at work. The identified linkages are then formulated into a conceptual framework clarifying how various workplace cognitive competencies and performance may be linked to metacognition. The findings indicate linkages between metacognition and various workplace cognitive competencies such as problem-solving, decision-making, innovation, creativity and knowledge acquisition. The present research also establishes the link of metacognition and cognitive competencies with learning, individual and firm performance. The review paves way for metacognition to be considered as a distinct construct in the workplace, identifies gaps and provides direction for future research.
Sistem Penjualan Sembako Toko Sarwodadi

The structure of data processing and information systems that are still manual at the dc Sarwodadi store make the system run ineffective, so this causes various problems. Another problem is the system of selling, purchasing, ordering goods, and stocking goods which are still difficult to organize, causing errors in counting. For this reason, a sales application system was made which aims to manage and process data stored with a database system to prevent data redundancy causing losses to the store. This research method uses the SDLC method. The SDLC method is a method with the process of system development and design to operation and maintenance. Data storage using MySQL type database. The Sarwodadi dc Store Sales Application System has data processing facilities for purchasing, selling, ordering goods, and stocking goods along with report data. So that with the new system, 83.3% of participants stated that the whole system was feasible and good to use in helping the Sarwodadi store to process data, make reports, and make it easier to get information about processed data both for the Sarwodadi store it self
Segshare:Secure group file sharing in the cloud using enclaves

This paper introduces a solution called Tenant-Oriented Duplication Integrity Checking Scheme (TDIC) to address the challenges faced in the Software-as-a-Service (SaaS) environment.In SaaS, tenants often create multiple customized duplicates of their data and distribute them across different data nodes for reliability.However, untrustworthy service providers can manipulate or delete the tenants' data, and they may store fewer copies than required.To overcome these issues, TDIC proposes a challenge-response model based on tuples sampling.It also introduces a new authentication structure called Tenant Duplication Authentication Structure (TDAS) that relies on tenant physical tuples.By incorporating homomorphism labels, TDIC enables data duplication verification without the need for local copies.Moreover, TDIC utilizes periodic random sampling to reduce complexity in constructing verification objects on the service provider's side and minimize unnecessary communication overhead.Overall, TDIC offers an effective approach to ensuring data integrity in SaaS by introducing a tuples sample-based challenge-response model and leveraging TDAS with homomorphism labels.It simplifies the verification process and enhances security in the face of untrustworthy service providers.
Android Application for Testing English Proficiency

&#x0D; &#x0D; &#x0D; &#x0D; English, the official language of many states and international organizations, serves as the standard for instruction globally. English proficiency is another prerequisite from some employers for employment in the working world. To determine a person's level of English proficiency, the TOEIC exam is employed. Several companies use a candidate's TOEIC score as a proxy for their level of English ability. A series of assessments, including listening and reading tests, make up the TOEIC. It is imperative for someone to conduct independent study in order to improve their TOEIC score given the comparatively expensive cost of the TOEIC course and exam. The layout of this TOEIC test simulation application makes it simple to practice whenever you want, wherever because it can be utilized offline on a smartphone. The TOEIC test simulation application, which contains four main menus: tips and tricks, TOEIC simulation, solutions, and about, was made using the Eclipse editor and Java programming. The questions under the tips and tricks section can be answered in a snap using easy methods. The right or wrong answers to the exam questions that are shown in the simulation menu can be found by selecting the solution option. &#x0D; &#x0D; &#x0D; &#x0D;
Comparison Of Clustering Levels Of The Learning Burnout Of Students Using The Fuzzy C-Means And K-Means Methods

Learning burnout is an impact from work done in a manner Keep going continuously, causing fatigue physical and emotional. If boredom study no handled, got cause students no productive and inhibits potency student . So from that study this proposed method clustering for group level saturation study students. The clustering process in research this use Fuzzy C-Means and K-Means. According to the previous study, Fuzzy C-Means and K-Means can produce results in the best clusters. Destination of study this is to compare performance from method Fuzzy C-Means and K-Means. The dataset used in this study is the boredom of students. Testing was conducted with the use amount clusters 3,4,5. Test results system with method Fuzzy C-Means get score Meanwhile, the global silhouette coefficient is 0.278 for K-Means results testing get score The global silhouette coefficient is 0.287. Temporary for results Davies Bouldin Index, methods Fuzzy C-Means get score 0.224and the K-Means method get value 0.384 of value, the Fuzzy C-Means generates more clusters _ good from K-Means. However both of them have weak structure _ because some data has data distance between one more clusters far from distance between different data clusters, so that creates that data worth.
SISTEM KEAMANAN PINTU KELAS DI SMK KOMPUTAMA MAJENANG

The development of computer technology and information systems at this time has experienced a very rapid increase, which is in line with the community's need for information. Today's society tends to be fast in following the flow of technological developments demands the availability of fast, precise and accurate information. Every agency, good government and private agencies definitely need an information system that is capable of support its performance to obtain and produce information in a more efficient way effective and efficient. One example of technology that is currently developing is system technology security. At SMK Komputama Majenang itself already has CCTV at several points and security as a form of security in schools. But the security system at SMK Komputama Majenang still cannot be said to be strong. Because if you only rely on security, the room security itself is far from several rooms that are considered vital. From the problems that occurred, then the author can provide a solution to the need for a tool that can work simply yet effective for meeting the human need for room security monitors and the need for a tool that can provide early warning when a hazard occurs crime. Based on the discussion that has been carried out in the previous chapters, it can be Several conclusions were drawn, namely with this security system it can prevent actions crime early, with the existence of a magnetic sensor-based door security system.
How to safeguard online data collection against fraud

The COVID-19 pandemic has forced large numbers of researchers to move their studies from the laboratory to the internet.In some ways, this has been a good thing -decades ago, a global pandemic would have shut down research entirely.Now, scientists can use social media, online registries and crowdsourcing tools such as Amazon Mechanical Turk to recruit participants to take surveys or perform tasks, without investing much time or money.But as helpful as these online tools are, they are not without risks, says Clare Harrop, assistant professor of allied health sciences at the University of North Carolina at Chapel Hill.When she and her team tried to recruit participants for a study in 2019 by sharing a link to a survey on social media, they received a torrent of fraudulent responses -presumably from someone who wanted the $5 gift cards her team offered respondents as compensation.After the pandemic began and so much research moved online, Harrop and her colleagues knew that they needed to spread the word about what had happened to them, so they wrote a letter to the editor, published in Autism Research in January.Harrop spoke with Spectrum about her experience and shared some tips on how other researchers can protect themselves from fraud. Spectrum: Tell me about the project you were working on when you ran into this issue.Clare Harrop: This data collection was part of a large outcome measurement grant led by Brian Boyd to create a measure of behavioral inflexibility for autistic children and then extend that to children with neurogenetic conditions -Down syndrome, Prader-Willi syndrome and fragile X syndrome.Most of my research focuses on sex differences, and the majority of my work has been done in person, but for this project we had used a magnitude of methods: focus groups, online surveys and in-person assessments.S: What did the process of online data collection look like?
A Development of Multiplayer Educational Game as Learning Media to Introduce to Computer Hardware

The large number of students who like to play games and conventional learning media causes students' lack of interest in learning, and an interesting learning media is needed for students. This research was conducted to develop and determine the feasibility of learning media based on multiplayer educational games. This study used the ADDIE development model. The trial process was carried out with media tests, material tests, and direct implementation on 29 grade VII students of SMP Negeri 4 Ngawi. The results of this study show the feasibility rate carried out by media experts of 83% with a very decent category, the feasibility tested by material experts was 93% with a very proper class, and the results of the satisfaction questionnaire were filled out by students. calculated using SUS calculations showing a value of 80.6 with the EXCELLENT category including grade scale B, it can be concluded from these results that this multiplayer educational game-based learning media is suitable for use as a learning medium.
A Review: Are Some Diet Models Beneficial or Harmful for Type II Diabetes Mellitus?

Diabetes mellitus (DM) is a chronic disease affecting millions worldwide.According to the current literature, type II DM is related to some trending diet models such as high-protein, low-carbohydrate (CHO), ketogenic, gluten-free, intermittent fasting nutrition models, the Mediterranean diet, etc.The purpose of this review article was to shed light on the relationship between some trending diet models and type II DM, potential effect mechanisms and complications according to the evidence from current literature.High-protein, low-CHO, ketogenic, gluten-free and intermittent fasting nutrition models can potentially decrease inflammation and body weight, improve the lipid profile and gut microbiota, increase the secretion of incretin hormones and regulate immune response thus increase insulin sensitivity in tissues.However, these trending diet models may result in acute and chronic complications such as; hypoglycemia, fatigue, lethargy, nutrient deficiencies, acute abnormalities and related chronic disorders.Some of these complications may be critical for life.In addition, the Mediterranean diet has very important beneficial effects on the prevention of type II DM and no evidence in the literature shows this diet model has side effects.In conclusion, applying optimal nutritional principles is the most recommended nutritional approach for reducing risk and managing type II DM.
A Cyber Attack Simulation for Teaching Cybersecurity

With the rising number of cyberattacks, such as ransomware attacks and cyber espionage, educating non-cybersecurity professionals to recognize threats has become more important than ever before. However, traditional training methods, such as phishing awareness campaigns, training videos and assessments have proven to be less effective over time. Therefore, it is time to rethink the approach on how to train cyber awareness. In this paper we suggest an alternative approach – a serious game – to educate awareness for common cyberattacks. While many serious games for cybersecurity education exist, all follow a very similar approach: showing people the effects of a cyber attack on their own system or company network. For example, one of the main tasks in these games is to sort out phishing mails. We developed and evaluated a new type of cybersecurity game: an attack simulator, which shows the entire setting from a different perspective. Instead of sorting out phishing mails the players should write phishing mails to trick potential victims and use other forms of cyberattacks. Our game explains the intention of each attack and shows the consequences of a successful attack. This way, we hope, players will get a better understanding on how to detect cyberattacks.
Graph Neural Network: The Next Frontier in Deep Learning

Graph Neural Network (GNN) is a graph based neural network that adopts the structure of graph to solve the complex problem. This poster highlight the types of GNN along with it application in the different fields like social networks, web connection data, molecular structures, geographic maps and so on. It also highlights the effectiveness of the GNN model in predicting and solving a wide range of real-world problems. Overall, the poster provides an overview of the current state of the art in GNN research and highlights its potential for further applications.
Combating Hate: How Multilingual Transformers Can Help Detect Topical Hate Speech

Automated hate speech detection is important to protecting people’s dignity, online experiences, and physical safety in Society 5.0. Transformers are sophisticated pre- trained language models that can be fine-tuned for multilingual hate speech detection. Many studies consider this application as a binary classification problem. Additionally, research on topical hate speech detection use target-specific datasets containing assertions about a particular group. In this paper we investigate multi-class hate speech detection using target-generic datasets. We assess the performance of mBERT and XLM-RoBERTA on high and low resource languages, with limited sample sizes and class imbalance. We find that our fine-tuned mBERT models are performant in detecting gender-targeted hate speech. Our Urdu classifier produces a 31% lift on the baseline model. We also present a pipeline for processing multilingual datasets for multi-class hate speech detection. Our approach could be used in future works on topically focused hate speech detection for other low resource languages, particularly African languages which remain under-explored in this domain.
Enhancing Declarative Temporal Model Mining in Relational Databases: A Preliminary Study

Propositionalisation tampers the running time of state-of-the-art algorithms in declarative temporal model mining, as they exhaustively generate the clauses instantiated with the results of frequent itemset mining algorithms. Existing algorithms also exploit non-indexed data representations, thus negatively affecting the overall running time. This paper proposes a novel temporal model mining algorithm, Bolt, twinning confidence and support metrics as heuristics for candidate pruning with data structures enabling fast temporal data scanning. Bolt outperforms both state-of-the-art and renditions of existing mining algorithms using KnoBAB as a library.
Multilingual Simplification of Medical Texts

Automated text simplification aims to produce simple versions of complex texts. This task is especially useful in the medical domain, where the latest medical findings are typically communicated via complex and technical articles. This creates barriers for laypeople seeking access to up-to-date medical findings, consequently impeding progress on health literacy. Most existing work on medical text simplification has focused on monolingual settings, with the result that such evidence would be available only in just one language (most often, English). This work addresses this limitation via multilingual simplification, i.e., directly simplifying complex texts into simplified texts in multiple languages. We introduce MultiCochrane, the first sentence-aligned multilingual text simplification dataset for the medical domain in four languages: English, Spanish, French, and Farsi. We evaluate fine-tuned and zero-shot models across these languages, with extensive human assessments and analyses. Although models can now generate viable simplified texts, we identify outstanding challenges that this dataset might be used to address.
Success factor focus and attention in study and work - a call for action at institutes of higher education in a more and more digitalized world

Digitization offers new opportunities in the context of students' learning opportunities and strategies, but also risks and challenges. Undisturbed attention is becoming a scarce resource due to multiple opportunities for influence and distraction (e.g., new social me- dia, "attention economy") and is increasingly limiting the learning success of students. "Stolen Focus", low "Attention Span", increased levels of stress and signals of exhaustion and depression in the face of performance and learning demands that tend to remain the same or comparable over the years are signs of an attention crisis and challenge us faculty to actively address this issue in courses. The coming generations (Z etc.) need assistance to regain or improve their self-efficacy in their studies with media literacy, attention, focus and deeper learning. The authors see this situation as a call for action, aiming to improve students' perception (also meta-awareness) and awareness of the impact of the digitalized and accelerated world on their daily routines, learning and working. The ability to work and learn in a focused way is seen and expected by employers as one of the most important competencies of graduates in a recent study. The planned project intends to face this challenge by developing a teaching design with different components, consisting of teaching materials (online/offline), a playbook and a "focus for success diary" and is intended to be applied in a regular Bachelor program, where the authors teach the same course in different locations of the university. Overall, the project should help learners to be better prepared for the demands of the modern world and to be more aware and successful in their learning processes. The social and psychological consequences of digitalization, acceleration and densification will thus become an integral part of the training and the solutions developed will offer a contribution in terms of social sustainability in the context of our ESG efforts.
Determination Of A Task Force For New Police Officers At The State Police School Using A Fully Recurrent Neural Network

Artificial Neural Networks (ANN) can be used to solve specific problems such as prediction, classification, processing data, and robotics. Based on the exposure, this study tried to develop a system by applying ANN models Fully Recurrent Neural Network (FRNN) to deal with the problems of classification determination unit for New Police Officer at NES Kupang include DitSabara, DitPolAir, and SatBrimob, which has been using the system manually, the ANN system Fully Recurrent Neural Networks can provide accurate information to the NES Kupang to determine the right decision. Fully Recurrent Neural Network structures have been presence of feedback that can make faster iteration thus making the update parameters and convergence speed become faster. The learning method used is Backpropagation Through Time . The system is implemented using the C# program. Input vectors used consisted of 7 variables.The results showed t the developed system will rapidly converge and able to achieve the most optimal error value (minimum error) when using one hidden layer with 17 units of the number of neurons . The best accuracy can be obtained using the LR of 0.001 , target of 0.1 and momentum 0.95, with 25 test data of data, the system accuracy reaches 96%, while the real data, the accuracy reached 83.33%.
E-Voting on the Blockchain

Abstract: It is a challenging task to build an electronically secure voting system. In 2005, the US Pentagon canceled a proposed online voting system that would allow foreign military members to vote in elections, saying it was impossible to verify the authenticity of the ballots. However, there is a new call to send a blockchain voting in the wild. The blockchain acts as a nonrefundable public book for transactions. An important contract of employment (i.e. genuine votes) is achieved 'with the consent of the miners to ensure new records are added. A new job record is generated whenever new submissions are required, such as votes, by the voter adding his or her vote information to the blockchain If it is confirmed that the transaction is valid, the new vote is added to the end of the blockchain and remains there in eternity. The brilliance of this system is that no centralize authority is needed to approve the votes; instead, a majority consensus is required. Because they can count the votes themselves and because of the blockchain audit trail, everyone can verify that no votes were tampered with or illegal votes were introduced, everyone agrees on the final result. The applicability of blockchain to voting is discussed in this study.