Reviewerly/exHarmony huggingface dataset:
Cognitive Competencies of Secondary School Teachers in Jordan from their Point of View

The study aimed to identify the level of cognitive competencies for secondary school teachers in Jordan from the teachers’ point of view and their relationship to gender and educational qualification variables. The descriptive approach was used, and the study sample consisted of (210) male and female teachers in the first semester of the academic year (2021-2022), in addition to using the questionnaire as a study tool. The study concluded that the level of cognitive competencies for secondary school teachers in Jordan was high, as the percentage of response to it reached (76.4%). . The results also showed that there were no statistically significant differences in the cognitive competencies according to the gender variable (males, females), in addition to the presence of statistically significant differences in the cognitive competencies of teachers according to the educational qualification variable and in favor of graduate degree holders (Masters and Ph.D.).
Knowledge Sharing in Peer Group: An Alternative of Internet Literacy Education Media for Children

The data of the Indonesian Internet Service Provider Association in 2018 mentioned that children and teenagers aged 15-19 years old are the majority internet users in Indonesia. The first time the children access to the internet is also getting earlier from year to year. In that context, parents’ role to guide and protect their children from the online risk is getting stronger. The problem is that the parents’ internet literacy level is often lower than the children. Such a condition then push the children to search other source of information when having difficulties in accessing to the internet. The previous research mentioned that elder brothers or sisters and friends often become the reference for children related to the digital media usage. Departed from this condition, the research is conducted to explore the opportunity of utilizing peer group in literacy internet education for children. This research has been conducted in eight months involving 40 students of SMPN 2 and 40 students of SMP Al Irsyad Purwokerto as respondents. The data are collected via the survey, FGD, and indepth interview. Data analysis is conducted using descriptive and interactive analysis. The result of research shows that 61.4% of respondents admit that they prefer to choose their friends to ask and to do the knowledge sharing when having difficulties in accessing to the internet. Some reasons appearing are because the parents’ technical skill are lower than the children’s, uncommunicative talk with parents, high level of parents’ busy activities, and children’s need to socialize with their friends as amusement especially in the middle of pandemic
E -Voting System using Blockchain

Abstract: Designing a voting system that ensures fairness, privacy, and security is a significant challenge. The lack of trust in election systems among large sections of society worldwide is a major concern for democracy. Flawed voting systems pose a threat to democracies, including governing bodies, co-operating societies, and student councils. Vote rigging, hacking of electronic voting machines (EVMs), election manipulation, and booth capturing are the key issues with the current electoral system. To address these issues, we propose using novel technologies such as blockchain and Merkel trees, which are wellknown for their security benefits. Our system builds on popular blockchain frameworks that provide blockchain as a service, while preserving participants' anonymity and enabling public scrutiny. Blockchain is a unique technology of our time that promises to enhance the resilience of digital voting platforms. It presents an opportunity to leverage the benefits of blockchain, such as cryptographic foundations and transparency, to achieve an efficient digital voting system.
Time-series Anomaly Detection via Contextual Discriminative Contrastive Learning

Detecting anomalies in temporal data is challenging due to anomalies being dependent on temporal dynamics. One-class classification methods are commonly used for anomaly detection tasks, but they have limitations when applied to temporal data. In particular, mapping all normal instances into a single hypersphere to capture their global characteristics can lead to poor performance in detecting context-based anomalies where the abnormality is defined with respect to local information. To address this limitation, we propose a novel approach inspired by the loss function of DeepSVDD. Instead of mapping all normal instances into a single hypersphere center, each normal instance is pulled toward a recent context window. However, this approach is prone to a representation collapse issue where the neural network that encodes a given instance and its context is optimized towards a constant encoder solution. To overcome this problem, we combine our approach with a deterministic contrastive loss from Neutral AD, a promising self-supervised learning anomaly detection approach. We provide a theoretical analysis to demonstrate that the incorporation of the deterministic contrastive loss can effectively prevent the occurrence of a constant encoder solution. Experimental results show superior performance of our model over various baselines and model variants on real-world industrial datasets.
Mini-VLAT: A Short and Effective Measure of Visualization Literacy

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants' Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.
Analyzing Activity and Suspension Patterns of Twitter Bots Attacking Turkish Twitter Trends by a Longitudinal Dataset

Twitter bots amplify target content in a coordinated manner to make them appear popular, which is an astroturfing attack. Such attacks promote certain keywords to push them to Twitter trends to make them visible to a broader audience. Past work on such fake trends revealed a new astroturfing attack named ephemeral astroturfing that employs a very unique bot behavior in which bots post and delete generated tweets in a coordinated manner. As such, it is easy to mass-annotate such bots reliably, making them a convenient source of ground truth for bot research. In this paper, we detect and disclose over 212,000 such bots targeting Turkish trends, which we name astrobots. We also analyze their activity and suspension patterns. We found that Twitter purged those bots en-masse 6 times since June 2018. However, the adversaries reacted quickly and deployed new bots that were created years ago. We also found that many such bots do not post tweets apart from promoting fake trends, which makes it challenging for bot detection methods to detect them. Our work provides insights into platforms' content moderation practices and bot detection research. The dataset is publicly available at https://github.com/tugrulz/EphemeralAstroturfing.
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices

Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation. However, dependencies among microservices with uneven execution time may result in longer queues, idle resources, or Quality-of-Service (QoS) violations. In this paper we introduce Reclaimer, a deep reinforcement learning model that adapts to runtime changes in the number and behavior of microservices in order to minimize CPU core allocation while meeting QoS requirements. When evaluated with two benchmark microservice-based applications, Reclaimer reduces the mean CPU core allocation by 38.4% to 74.4% relative to the industry-standard scaling solution, and by 27.5% to 58.1% relative to a current state-of-the art method.
DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching

We present three multi-scale similarity learning architectures, or DeepSim networks. These models learn pixel-level matching with a contrastive loss and are agnostic to the geometry of the considered scene. We establish a middle ground between hybrid and end-to-end approaches by learning to densely allocate all corresponding pixels of an epipolar pair at once. Our features are learnt on large image tiles to be expressive and capture the scene's wider context. We also demonstrate that curated sample mining can enhance the overall robustness of the predicted similarities and improve the performance on radiometrically homogeneous areas. We run experiments on aerial and satellite datasets. Our DeepSim-Nets outperform the baseline hybrid approaches and generalize better to unseen scene geometries than end-to-end methods. Our flexible architecture can be readily adopted in standard multi-resolution image matching pipelines.
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities

Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based representation learning has shown powerful representation capacities when applied to industrial or medical imaging and outlier detection methods have been applied successfully to these images. In this work, we propose an unsupervised anomaly detection (UAD) method based on a latent space constructed by a siamese patch-based auto-encoder and perform the outlier detection with a One-Class SVM training paradigm tailored to the lesion detection task in multi-modality neuroimaging. We evaluate performances of this model on a public database, the White Matter Hyperintensities (WMH) challenge and show in par performance with the two best performing state-of-the-art methods reported so far.
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture

Most invariance-based self-supervised methods rely on single object-centric images (e.g., ImageNet images) for pretraining, learning features that invariant to geometric transformation. However, when images are not object-centric, the semantics of the image can be significantly altered due to cropping. Furthermore, as the model becomes insensitive to geometric transformations, it may struggle to capture location information. For this reason, we propose a Geometric Transformation Sensitive Architecture designed to be sensitive to geometric transformations, specifically focusing on four-fold rotation, random crop, and multi-crop. Our method encourages the student to be sensitive by predicting rotation and using targets that vary with those transformations through pooling and rotating the teacher feature map. Additionally, we use patch correspondence loss to encourage correspondence between patches with similar features. This approach allows us to capture long-term dependencies in a more appropriate way than capturing long-term dependencies by encouraging local-to-global correspondence, which occurs when learning to be insensitive to multi-crop. Our approach demonstrates improved performance when using non-object-centric images as pretraining data compared to other methods that train the model to be insensitive to geometric transformation. We surpass DINO[Caron et al.[2021b]] baseline in tasks including image classification, semantic segmentation, detection, and instance segmentation with improvements of 4.9 $Top-1 Acc$, 3.3 $mIoU$, 3.4 $AP^b$, and 2.7 $AP^m$. Code and pretrained models are publicly available at: https://github.com/bok3948/GTSA
Grammar-Based Test Suite Construction Using Coverage-Directed Algorithms Over Lr-Graphs

We describe and evaluate two algorithms that construct test suites from a set of valid paths that cover all edges in a labeled directed graph corresponding to an LR-automaton that accepts the language of the grammar. Vertices in this graph correspond to states in the LR-automaton; two vertices are connected by an edge iff the top of the LR-automaton’s stack can change from one state to the other, either by shifting symbol, or by reducing with a grammar rule. Both algorithms can successfully generate a test suite, even if the LR-automaton construction leads to shift/reduce or reduce/reduce conflicts. We also describe mutations to the positive paths that generate negative tests ithout needing any further verification by an oracle. Our experiments show that our algorithm constructs test suites that more effectively cover deeply nested grammar structures than the generic cover algorithm with test suites of a similar size. It is also more robust against random choices made to resolve ambiguity in the construction of the tests, while the code coverage is more uniform. Finally, it shows that the negative test suites constructed by path mutation identify more faults in a set of student grammars than those constructed by rule mutation.
Approximation and decomposition in lattices of clutters

A clutter on Ω is a family of subsets of Ω that are incomparable under inclusion. Given a collection Σ of clutters on Ω and another clutter Λ on Ω, we study the problem of approximating and decomposing Λ in terms of the clutters in Σ. We want to guarantee when these approximations exist not only for a single element Λ, but for all Λ that belong to a family of clutters X. We use the lattice structure of the collection of clutters to show that the existence of such approximations and decompositions for every clutter Λ is equivalent to the fact that Σ contains a given family of irreducible elements determined by Σ and X. We explicitly compute these irreducible families in some cases and apply our results to clutters arising from discrete objects such as matroids, graphs and secret sharing schemes.
EFL SPEAKING STUDENT READINESS TO USE MOBILE-ASSISTED LANGUAGE LEARNING

In recent years, mobile-assisted language learning has been on the rise as an instructional approach. While some innovative media platforms have been highly studied, Thus, this study focused on how students' readiness and learning outcomes the use of mobile phones to facilitate individualized instruction in public speaking as part of an English as a Foreign Language course. Data were collected from (n=42) students in non-English department classes who participated in this study. They were asked to answer an online questionnaire that surveyed their readiness and outcomes using Google Foam in their speaking skills. All participants performed two class projects aimed at aiding them in exploring mobile-assisted language learning using their own mobile devices. However, the students self-reported some opportunities and benefits for English Foreign Language Learners (EFLL) among these MALL users. The results of this study suggest that EFL-speaking students' readiness affects mobile-assisted language learning. Teachers should consider students' motivation, attitudes, and technology skills when creating mobile-assisted language learning programs. Instructors should also train and motivate pupils to use mobile-assisted language learning resources by improving their technology skills. Furthermore, need for further technical support and facilitating students' mastery of speaking performance.
Implementation of Feature Selection and Data Split using Brute Force to Improve Accuracy

This study seeks to classify data using feature selection and brute force.The dataset contains irrelevant characteristics, therefore feature selection influences computing time and the classification model.UCI's YouTube Spam Collection was used for testing.This dataset contains five datasets with 1,956 legitimate messages from five popular videos (Shakira, Katy Perry, Psy, Eminem, and LMFAO).Using weight information gain, the feature selection technique finds the best attributes.The dataset will then be separated into two parts: training with a 70:30 ratio and testing with a 30:70 ratio.Comparing using C4.5 and Nave Bayes.The FS+BF+C4.5 approach has an accuracy of 69.90%, 63.37%, 98.32%, 50.89%, and 91.75 for five videos (Psy, Katy Perry, LMFAO, Eminem and Shakira).Standard C4.5 technique accuracy is 66.99%, 59.41%, 95.80%, 50.89%, and 88.66%.Naive Bayes accuracy is 61.17, 51.49, 89.08, 50.00, and 79.38.FS+BF+C4.5 obtains an overall average accuracy of 74.85%, 2.5% and 8.6% higher than C4.5 and Naive Bayes (72.35 percent and 66.22 percent).Using feature selection and brute force with the C4.5 approach can reduce classification error compared to the normal C4.5 and Naive Bayes methods.
Changes in communication; the impact of digitalisation on children’s communication and language use

In the digital information age, the relationship between language and text has been placed on a new footing, and this is of great importance, as it can be a major step forward in our development and culturalisation.These technical achievements have the potential to make a significant impact on the development of humanity, which we all feel.It is reflected in our culture, in our scientific disciplines, and in our individual lifestyles.The electronic environment has also brought many changes to verbal, vocal, and non-verbal communication.The concept of discourse cannot be avoided in the study of electronic information flows.On one hand, it emphasises the analysis of the use of language; it is based on the natural language used by the subject and focuses on regularities rather than rules.On the other hand, it is a speech event that is indirectly produced according to the expectations of a situation.With regard to the grouping of written and spoken discourse, the differentiation between informal and formal written and spoken language is further related.The topic of my writing is the study of language use and communication in today's children.Their communication is quite different from that of previous generations.They have a different milieu, a different medium.These differences are partly due to digitalisation and partly to the way they lead their lives in a rapidly changing world.This has a natural impact on both communication and rhetoric.The changes in the world, the digital environment and social phenomena have had a huge impact on the way we speak and communicate.This is natural, but how does it affect our children and young people?Do they in the communication with their peers find ways how to express themselves in each situation in the perception of the constant presence of the digital medium?
On the Moments of the Variance-Gamma Distribution

We obtain new closed-form formulas for the moments and absolute moments of the variance-gamma distribution. We thus deduce new formulas for the moments and absolute moments of the product of two correlated zero mean normal random variables.
Adopting Cloud Computing and Accounting Practices : A Review of Artifice Intelligence Perspective

Cloud computing is revolutionizing the way businesses operate in the digital age. This technology allows businesses to store, process, and analyze data from multiple sources and locations, resulting in improved efficiency and cost savings. However, while leveraging this technology has significant advantages, accounting professionals must understand the implications for their operations to ensure accuracy and compliance. This paper explores the impact of cloud computing on accounting practices, including the precision of calculations, financial accounting of services used, verification of internal controls, compliance, and cyber security considerations. Understanding the implications of cloud-based systems is essential for businesses to ensure accounting accuracy and maintain compliance with laws and regulations in the digital age.
GINGER WRITER AS AN ONLINE REPHRASE TOOL WITH AI-POWERED SUGGESTIONS OF ALTERNATIVE SENTENCES IN ENGLISH WRITING

This is a descriptive-qualitative study analyzes the use of Ginger Writer as an online rephrase tool based on AI-powered suggestions in English writing. The result of analysis shows that the free edition of the Ginger Writer application is used for the implementation. Some techniques of rephrasing applied b the tools are: (1) rephrasing by using equations/synonyms of words. (2) rephrasing by changing/rearranging the sequence of words based on the relevant rules. (3) rephrasing by altering the word's form, (4) rephrasing by using the active or passive form, (5) rephrasing by changing the word class, and (6) rephrasing word into phrase, phrase into clause, and vice versa. Ginger Writer makes writing high-quality work effortless for users. Not only adds missing words, completes fragmented phrases, and corrects spelling problems related to the context of sentences, Ginger Writer also adds missing words, completes fragmented sentences, and corrects spelling errors. This online rephrasing tool is considerably fast but teachers and students should double-check their work. To some extent, manual paraphrasing is considered better and, even the best in quality, compared to the automatic ones.
Performance Analysis of Conventional MPPT Techniques for a Solar PV System

The effectiveness and accuracy of two different MPPT methods of solar photovoltaic (PV), namely Perturb &amp; Observe (P&amp;O) and Constant Current (CC) have been investigated in this study. The main objective of this study is to test the impacts of temperature and solar irradiance (insolation) variations on solar PV output power which are achieved through these methods. Usually, solar insolation and temperature are the two most important influencing factors that have a significant impact on the output power (Pout). Here, the two conventional MPPT techniques have been applied to analyze the response through power characteristics curves of solar PV systems subject to changing environmental conditions. The MPPT techniques have been implemented on the solar PV system using MATLAB®. Additionally, the output power curves at various irradiances are shown in this work. The research demonstrates that the P&amp;O-based MPPT method outperforms the CC-based MPPT method.