Reviewerly/exHarmony huggingface dataset:
Hierarchy Clustering Implementation on YouTube's Top Data

Clustering is a method or process of grouping datasets into various clusters to produce variations in smaller clusters.Clustering has broad application fields such as data concept construction, pattern recognition, web search, simplification, security, and several other areas.Clustering methods are classified into two types, hierarchies and partitions.The hierarchical clustering method defines the cluster hierarchy by separating and combining them, whereas the partitioning method involves defining and evaluating sections based on criteria.Thus, the selected clustering algorithm must be efficient.This article focuses on clustering algorithms for obtaining and processing YouTube Channel Top Data.
PlaySafe: A Digital Rights Management System for Media Content Consumption

Digital right management (DRM) is widely used by online streaming service provider that allows them to manage and control the access rights of their copyrighted content.The existing DRM technology requires the developer to gain approval from the DRM provider company in order to access to their proprietary Software Development Kit and tools.This issue had caused some software developers not being able to get their approval to implement the DRM technology as the DRM's proprietary Software Development Kit and tools are not publicly available and often causing the delay of their software release.A DRM system, PlaySafe, is proposed as an alternative DRM system for copyrighted content management.The objective of this research is to design and develop a DRM system to facilitate media file encryption and decryption processes.The requirements elicitation and gathering process from the documentations provided by the existing DRM technology provider is being used as the requirements for the newly proposed DRM system.PlaySafe provides the content provider with features such as encryption, key rotation, automated HLS streaming packaging, and set streaming quality constraints which are cross platform, self-hosting supported and open source.The proposed backend system runs on a modular Docker container with virtualization which allows the content provider to host the system on their own local machines.Lastly, PlaySafe is an open-source project which allows anyone with the right knowledge to modify and add extra feature that they need since the source code is accessible by the public.
Face Recognition Using ArcFace and FaceNet in Google Cloud Platform For Attendance System Mobile Application

The attendance system process in Indonesia generally are still using a traditional method.Paper-based is used as a medium to perform attendance at every event.With this traditional method, there are still many shortcomings in terms of security and management.In terms of security, the traditional attendance system is still quite lacking due to the number of participants cheating by asking their relatives, such as examples of signatures that can still be imitated, or attendance checks can still be tricked because we can change them easily.Therefore, it is necessary to have an attendance system that can be carried out efficiently, safely, and easy to manage, with attendance being done online or using a smartphone.It can be implemented easier for event owners to manage the attendance track of participants, reduce the use of paper, which is quite significant, and secure the attendance system.CNN is an artificial neural network that is more often used in visual image analysis.CNN can distinguish visual images from one another with various aspects given.The models that we used for this application are ArcFace and FaceNet.Three different BackEnd Encoder and BackEnd recognized are used, RetinaFace, MTCNN, and OpenCV.From the experiment, we suggest the usage of ArcFace in RetinaFace for High Accuracy of recognition but with high-cost drawbacks, the longer computation time for encoding and recognition.As an alternative, ArcFace with MTCNN can be used with faster computation time but less accurately than RetinaFace.
The Application of Text-to-Speech Technology in Language Learning

Technology has improved temporarily from time to time.There are a lot of technologies that had been developed to enhance language learning, and one of them is text-to-speech technology.Text-to-speech technology is a form of system that can convert phoneme to audio.It has provided an impact in language learning since it was developed.This article presents how the application of text-to-speech technology is used in language learning, including the negative and positive side of text-to-speech technology in language learning.It reports on the results of a systematic review of articles that specifically examine the use of text-to-speech technology in language learning.The articles were published between 2012 and 2022 and collected from several databases, including Google Scholar, Elsevier, SAGE, Springer, ERIC, IEEE, and Taylor & Francis.The articles were then reviewed and selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach.The analysis results of 20 selected articles revealed that the use of text-to-speech assisted the process of knowledge transfer.Text-to-speech technology has also played a practical role in language learning, especially in improving students' language skills.The review also revealed that text-to-speech technology lacks in intonation, eye-contact, and real-time class interaction.But overall, despite that it has a slight negative impact, text-to-speech technology can be a breakthrough to support language learning.
MODEL PEMBELAJARAN MOVING PEER TUTORING UNTUK MENINGKATKAN LITERASI INFORMASI MAHASISWA

This study aims to test the implementation of moving peer tutoring method to improve information literacy competence of prospective accounting educator students. This type of research is quantitative with an experimental design. This method is implemented on twenty five people as participants and three people as peer tutors who provide information to each other. The pre-test and post-test data were collected through a questionnaire. The instrument reliability test was carried out through validity and reliability tests with 23 respondents being tested. The results of this study indicate that the implementation of moving peer tutoring method is able to improve information literacy competence of prospective accounting educators. A significant increase shows that students in higher education are still comfortable with their learning style with their peers who are relatively the same age. These results need to be re-examined in elementary and middle school students to obtain broader results.
Finding smart contract vulnerabilities with ConCert's property-based testing framework

We provide three detailed case studies of vulnerabilities in smart contracts, and show how property-based testing would have found them: 1. the Dexter1 token exchange; 2. the iToken; 3. the ICO of Brave's BAT token. The last example is, in fact, new, and was missed in the auditing process. We have implemented this testing in ConCert, a general executable model/specification of smart contract execution in the Coq proof assistant. ConCert contracts can be used to generate verified smart contracts in Tezos' LIGO and Concordium's rust language. We thus show the effectiveness of combining formal verification and property-based testing of smart contracts.
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs corresponding to some task) along with a new query input, and generate the corresponding output. Crucially, in-context learning happens only at inference time without any parameter updates to the model. While large language models such as GPT-3 exhibit some ability to perform in-context learning, it is unclear what the relationship is between tasks on which this succeeds and what is present in the training data. To make progress towards understanding in-context learning, we consider the well-defined problem of training a model to in-context learn a function class (e.g., linear functions): that is, given data derived from some functions in the class, can we train a model to in-context learn "most" functions from this class? We show empirically that standard Transformers can be trained from scratch to perform in-context learning of linear functions -- that is, the trained model is able to learn unseen linear functions from in-context examples with performance comparable to the optimal least squares estimator. In fact, in-context learning is possible even under two forms of distribution shift: (i) between the training data of the model and inference-time prompts, and (ii) between the in-context examples and the query input during inference. We also show that we can train Transformers to in-context learn more complex function classes -- namely sparse linear functions, two-layer neural networks, and decision trees -- with performance that matches or exceeds task-specific learning algorithms. Our code and models are available at https://github.com/dtsip/in-context-learning .
I Feel You

I Feel You is a speculative textile design project looking into the possibilities to create multi-sensory electronic textiles that imitate living beings. The theme of textile surfaces imitating living beings has emerged from yearning for touching and closeness: During the global pandemic and the ongoing wave of extinction, I imagined a world where we are increasingly physically separated from each other and other animals. What if, in the future, we are accompanied by robot pets and people? Against this backdrop, this work speculates on how textiles could create an illusion of being close to another living being and being touched by a living creature. The series of textile pieces consists of Jacquard woven multilayered textiles that, when combined with electronics, react to the human touch. The materials include linen, cotton, responsible mohair, wool, and silver-based electrically conductive threads. Together and separately the materials and woven structures strive to create multisensory, touchable worlds. The aim of the study was to discover what kinds of textile surfaces humans can identify through the sense of touch, what kind of touch is experienced as soothing, and how to bring reactivity that imitates living beings into woven textiles. This knowledge was used as a basis to create a series of speculative woven electronic textile pieces that react to touch. Traditional materials and techniques interweave with new technologies creating possibilities to design new types of interactions with textiles. When designing active haptic textile surfaces, traditional properties of textiles, such as materials, patterns, and woven structures cannot be separated from the design process, where all the aspects entangle and affect each other.
Watermarking Pre-trained Language Models with Backdooring

Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems. PLMs typically need to be fine-tuned on task-specific downstream datasets, which makes it hard to claim the ownership of PLMs and protect the developer's intellectual property due to the catastrophic forgetting phenomenon. We show that PLMs can be watermarked with a multi-task learning framework by embedding backdoors triggered by specific inputs defined by the owners, and those watermarks are hard to remove even though the watermarked PLMs are fine-tuned on multiple downstream tasks. In addition to using some rare words as triggers, we also show that the combination of common words can be used as backdoor triggers to avoid them being easily detected. Extensive experiments on multiple datasets demonstrate that the embedded watermarks can be robustly extracted with a high success rate and less influenced by the follow-up fine-tuning.
Type Checking Data Structures More Complex than Trees

Graphs are a generalized concept that encompasses more complex data structures than trees, such as difference lists, doubly-linked lists, skip lists, and leaf-linked trees. Normally, these structures are handled with destructive assignments to heaps, which is opposed to a purely functional programming style and makes verification difficult. We propose a new purely functional language, λGT, that handles graphs as immutable, first-class data structures with a pattern matching mechanism based on Graph Transformation and developed a new type system, FGT, for the language. Our approach is in contrast with the analysis of pointer manipulation programs using separation logic, shape analysis, etc. in that (i) we do not consider destructive operations but pattern matchings over graphs provided by the new higher-level language that abstract pointers and heaps away and that (ii) we pursue what properties can be established automatically using a rather simple typing framework.
DISTANCE LEARNING: ITS PECULIARITIES IN HIGHER EDUCATION DURING WORLD PANDEMIC COVID-19

The work is devoted to the study of the issue of distance learning as a form of organization of the educational process in higher education. It is noted that distance learning has become one of the answers to the challenges of global reality of pandemic COVID-19. It is shown that distance learning was not a completely new form of organization of the educational process in higher education. Five generations of distance learning in historical retrospect and itscharacteristics and unique traits are presented. It is highlighted that the current stage of distance learning development can be considered as the next sixth generation, which is directly related to the global reality of pandemic COVID-19. The most important features of distance learning at this stage, namely its massiveness, lack of alternatives, and the improvement of distance education platforms are also presented. Besides an analysis of the experience adopted at different countries concerning the distance learning in the higher education institutions during the world pandemic COVID-19 is done.
High Probability Convergence of Clipped-SGD Under Heavy-tailed Noise

While the convergence behaviors of stochastic gradient methods are well understood \emph{in expectation}, there still exist many gaps in the understanding of their convergence with \emph{high probability}, where the convergence rate has a logarithmic dependency on the desired success probability parameter. In the \emph{heavy-tailed noise} setting, where the stochastic gradient noise only has bounded $p$-th moments for some $p\in(1,2]$, existing works could only show bounds \emph{in expectation} for a variant of stochastic gradient descent (SGD) with clipped gradients, or high probability bounds in special cases (such as $p=2$) or with extra assumptions (such as the stochastic gradients having bounded non-central moments). In this work, using a novel analysis framework, we present new and time-optimal (up to logarithmic factors) \emph{high probability} convergence bounds for SGD with clipping under heavy-tailed noise for both convex and non-convex smooth objectives using only minimal assumptions.
Platformed professional(itie)s and the ongoing digital transformation of education

Over the past decades, a growing body of research has identified a substantial restructuring of the education field, caused by global governance transformations such as the rising empowerment of international organizations and policy networks (e.g., Dale & Robertson, 2007), trends of marketization (e.g., Rönnberg, Lindgren & Lundahl, 2019), or the growing dominance of accountability-and test-related policies (e.g., Lingard, Martino, Rezai-Rashti & Sellar, 2015;Grek, Maroy & Verger, 2021).Much of that research has analyzed the various effects of these transformations on educational institutions and classroom practices, and hereby also fostered our understanding of both their global nature and their local manifestations (e.g., Verger, Altinyelken & Novelli, 2018).It is within that wider group of research that studies on the impacts of governance transformations on educational professional(itie)s can be situated.Little surprisingly, the strongest focus has hereby so far been on teachers, and the conflictual interplay between professionalization and de-professionalization (for an early thematic review see Race, 2002).For instance, in countries such as the US and the UK, which have strongly intensified high-stakes accountability policies in education over the past decades, many scholars have identified a rising de-professionalization (e.g., in the form of diminishing autonomy and trust) and demoralization of teachers (Wronowski & Urick, 2021;Holloway, Sørensen & Verger, 2017).Other work has put emphasis not only on the collegial and individual, but equally on the organizational level of professionality, as well as on micro-level contextual variation, to address the actual
Randomness and Determinism, is It Possible to Quantify These Notions?

The article presents results obtained in attempts to quantify randomness characteristics for real numerical sequences or strings, using relative entropy. The characterization of the randomness of a series of real numbers is proposed to guide researchers in investigating phenomena towards deterministic or stochastic models. A numerical string's relative entropy is calculated using the histograms corresponding to the analysed strings, compared to the maximum entropy for the same histogram. It is shown that the entropy values have an asymptotic behaviour, but the relative entropy decreases with the increase in the number of histogram classes. Compared to other methods of characterizing the randomness of strings, which are not many, most of them being based on statistical tests, the method proposed in this article determines a better resolution for the classification of strings and, in addition, it can designate them as belonging to a class of randomness similar to that of some known strings, such as finite substrings of prime numbers, pseudorandom strings generated by common programs, trigonometric strings, etc. The attempt to quantify the randomness of real numerical strings, the results of which are presented in this article, is a first step in characterizing the randomness of experimental numerical strings, this being the final goal of the investigations.
STABLE DIFFUSION TEXT-IMAGE GENERATION

A text-to-image technology called Stable Diffusion will enable billions of individuals to produce beautiful works of art in in a few seconds. It can run on GPUs available in consumer gadgets and is an improvement in both performance and quality. It will advance democratic. image generation by enabling both researchers and the general public to use it under various conditions. We anticipate the open ecosystem that will grow up around it as well as new models to really probe the limits of latent space. Keywords— laion5B, Perceptual Image Compression,Latent Diffusion Models, Conditioning Mechanisms , Text to image generation, Image Modification
An Innovative Leukemia Detection System using Blood Samples via a Microscopic Accessory

Leukemic patients are in a rapid increase.Hence, the use of microscopic images of blood samples through visual inspection to identify blood disorders has increased, opening the door for computerized techniques for detecting leukemia.This project applies computer vision techniques to increase the accuracy and speed of detection from peripheral blood.It also enhances visualization by providing an appropriate supplement to traditional microscopy.A microcomputer (Raspberry Pi) was well programmed in Python for analyzing images with the help of a Raspberry Pi camera and a touch screen as an alternative to the eyepiece.To achieve diversity and seek for more accuracy, image datasets for this project were obtained from various resources.These datasets were then analyzed through image processing techniques to detect leukemia cells.This detection process involves resizing cells to a standard size, noise removal by linear scaling filter, global-local contrast enhancement, segmentation of white blood cells (WBCs) using marker-controlled watershed algorithm, overlapping detection and separation using watershed and k-means clustering algorithms, and extraction with selection of the most relevant features from cells.These features were then imported into the Support Vector Machine (SVM) model which resulted in an accuracy of 93.2773%.A standalone desktop application with a suitable graphical user interface (GUI) was implemented.It was then uploaded into the Raspberry Pi, some code lines were rewritten for dealing with the camera, the hardware was designed and implemented, and then formal experiments were conducted resulting in the detection of leukemia in 5 samples out of 6.This implies that precise detection can be implemented with different data taken in various imaging conditions.
The Role of Using Virtual Reality in Learning in an Education Environment

This study aims to examine the literature on the use of Virtual Reality (VR) in education.The author discusses articles published in the last 10 years that discuss the use of Virtual Reality as a learning medium at all levels of education.A total of 50 articles were analysed comprehensively.The results of the study show that virtual technology allows humans to access simulations of the reality of situations that may not be done directly so that the action taken is to simulate it through virtual technology so that it remains relevant to actual conditions [3].This virtual reality is very suitable to support efforts to solve various problems faced by humans related to limited access to real world conditions which are actually due to certain things.Related parties can use this technology to achieve their goals so that from all the limitations of existing circumstances, humans can still maximize everything they want to achieve or everything they want to do.
Noise Injection Node Regularization for Robust Learning

We introduce Noise Injection Node Regularization (NINR), a method of injecting structured noise into Deep Neural Networks (DNN) during the training stage, resulting in an emergent regularizing effect. We present theoretical and empirical evidence for substantial improvement in robustness against various test data perturbations for feed-forward DNNs when trained under NINR. The novelty in our approach comes from the interplay of adaptive noise injection and initialization conditions such that noise is the dominant driver of dynamics at the start of training. As it simply requires the addition of external nodes without altering the existing network structure or optimization algorithms, this method can be easily incorporated into many standard problem specifications. We find improved stability against a number of data perturbations, including domain shifts, with the most dramatic improvement obtained for unstructured noise, where our technique outperforms other existing methods such as Dropout or $L_2$ regularization, in some cases. We further show that desirable generalization properties on clean data are generally maintained.