Reviewerly/exHarmony huggingface dataset:
Art forum as an artistic association of students, teachers and artists

The article analyses the artistic and methodological component of the CRESCENDO ART FORUM cultural and educational project as a “case” of synergistic pedagogical practice. The project is held as an art forum, which name “encodes” the concept of crescendo. In the context of the philosophy of the event, it emphasizes such values of the project as the gradual increase in the impact of art on human development. The purpose of the event is to develop students’ critical and creative thinking; cultivate a conscious attitude towards learning; raise the professional level of teaching staff; and establish creative relations with artistic, educational and cultural institutions of other countries. The target audience of the project is students and teachers of institutions of art education. Therefore, the art forum is a special communication format that has united both the research and creative components of students (including students with special educational needs), teachers of institutions of art education, and famous creative personalities. The system of scientific and cognitive events of the art forum consists of three core stages (preparatory, main, and reflective) that may have certain specifics. А student conference with discussion panels becomes a key event at the main stage. The conference thematic areas are supplemented with current trends annually, so their number changes. According to the topics of the student speakers' reports, the programs of the discussion panels of the conference are formed into two age-related categories. The student conference “ARS LONGA” and the international seminar-workshop “Preliminary Art Education Today: Global Experience” for educators are held as a part of the main stage of the art forum. Its goal is to create a pedagogical intercultural space for communication on the development of art education. Adjacent events of the art forum are workshops, creative workshops, creative meetings with famous artists, an exhibition of artworks by students of art schools in online and offline modes, plein air with the participation of students of art schools, interactive tours from partners, and a round table. The art forum is a project open to further development. It promotes the freedom of creativity, thought, communication, and choice, in which a student as a future artist, a spectator, and a conscious citizen of his country is revealed under the “soft” pedagogical and artistic force of example.
The Game Theory in Quantum Computers: A Review

Game theory has been studied extensively in recent centuries as a set of formal mathematical strategies for optimal decision making.This discipline improved its efficiency with the arrival, in the 20th century, of digital computer science.However, the computational limitations related to exponential time type problems in digital processors, triggered the search for more efficient alternatives.One of these choices is quantum computing.Certainly, quantum processors seem to be able to solve some of these complex problems, at least in theory.For this reason, in recent times, many research works have emerged related to the field of quantum game theory.In this paper we review the main studies about the subject, including operational requirements and implementation details.In addition, we describe various quantum games, their design strategy, and the used supporting tools.We also present the still open debate linked to the interpretation of the transformations of classical algorithms in fundamental game theory to their quantum version, with special attention to the Nash equilibrium.
Blockchain implementation in the academic administration service system

An academic information system is a website that must be owned by every university to improve academic performance. FST Seminar is an academic administration service website for students of the Faculty of Science and Technology. This website contains submissions for seminars/conferences, appointments of supervisors/examiners, active course/scholarship letters, and letters of good conduct. The problem that occurs is when students submit a letter as an administrative file that they want to go to, verification of student submissions is still manual to the admin. It should be a good information system. When the user has filled in the data that has been submitted, the admin immediately gets the data and processes the submission from the user. With Blockchain, technology systems can record digital transactions before they are stored permanently, and existing data can be decentralized so that student submission data is available to the admin. The User-Centered Design method produces solutions based on student and admin needs and runs according to the functions tested with black box testing.
Comparative analysis of OCI and Google Cloud platform for the system of the University of Santo Tomas

The University of Santo Tomas has decided to migrate their student information system over to the cloud. The Student Information System is part of the university&apos;s logistical infrastructure that allows administrators to keep track of student records. The aim is to explore other options that may better cater to the university&apos;s SIS system. The study explores Google Cloud Platform (GCP) as a competitor and compares it to Oracle Cloud. The study performs a partial replication of the OCI SIS development configuration done by the STePs and replicates the configuration over to the GCP. Several smokescreen tests are done to test the functionality of the 2 platforms and benchmark tests to analyze the performance of the 2 platforms. The data of the tests are then compared based on their functionality, efficiency, reliability, scalability, and security. With these comparisons, along with the provided documentations of the vendors, a feasibility is also performed. The feasibility study goes over the technical, economical, and operational feasibility of implementing either of the infrastructures as a service. The researchers were able to list which cloud platform performs better in each category and conclude which is the better performing cloud platform in specific categories and overall. With these findings the researchers were able to perform a feasibility study in terms of operational, technical, and economical. Based on the study, with regards to operational feasibility, Google Cloud Platform proves a better choice due to the better performance of their virtual machines. However, in terms of Database the Oracle Cloud Autonomous Database is greatly considered with it handling more transactions per second. With this, the researchers discussed the option for a hybrid implementation among the two platform&apos;s services. In terms of technical feasibility, Google Cloud Platform and Oracle Cloud Infrastructure are both functional and the tests prove they would work if needed. But with consideration of the benchmark results, the Google Cloud Platform proved to be the better choice. For economical feasibility, the Google Cloud Platform would be the better choice in terms of service provisions alone, with the assumption that the university does not go over 10 TB of data egress. But anything over the 10 TB of data egress, the Oracle Cloud Infrastructure would be the better option. These results are in respect and may vary over time depending on the currency exchange rate of the US Dollar and Philippine Peso.
A comprehensive comparative study of AWS and OCI for University of Santo Tomas - Information and Communications Technology network infrastructure

Cloud computing has rapidly evolved, introducing new technologies with vast potential. Businesses and organizations worldwide are increasingly adopting cloud computing, making it a leading and in-demand solution. The Office of the University of Santo Tomas Information and Communications Technology is in the early stages of migrating the on-premises infrastructure to Oracle Cloud Infrastructure. Albeit the satisfactory performance of the cloud service provider, the office is looking for an alternative that can provide additional features or services; and a more cost-effective, manageable, and robust cloud platform to cater to the needs of the university. The researchers proposed Amazon Web Services (AWS) as an alternative for Oracle Cloud Infrastructure (OCI). The study conducted multiple tests on AWS and OCI services, mainly Swingbench for the database, Iperf3 for the network performance and Dbench for the block storage performance. After a series of benchmarking and testing, the researchers concluded that Amazon Web Services could be an alternative to Oracle Cloud Infrastructure as it showed competitive results regarding cost-effectiveness and computing power, storage, and network performance. The researchers also noted that Oracle Cloud Infrastructure exceeds Amazon Web Services when it comes to database performance.
Statistical Optimization of AC/TiO2-Cu Composite via Response Surface Methodology (RSM)

AC/TiO2-Cu composite was synthesized via microwave-assisted sol gel method and acts as ternary composite photocatalyst for the removal of antibiotic from an aqueous solution under UV light irradiation through adsorption/photodegradation process. In this study, a statistical optimization experimental design was performed using a face-centered central composite design in response surface methodology for optimizing the synthesize parameters of AC/TiO2-Cu composite. The influence of experimental factors such as AC/TiO2 ratio, Cu dosage, and irradiation power with adsorption-photodegradation performance as the response were investigated. The adequacy result of the statistical model between the relationship of the significant factors and responses were successfully specify (p≤0.05), where all the experimental factors had significant effects for the response. At optimal conditions of x_1 = 0.50, x_2 = 0.31 g, and x_3 = 574 Watts, the findings show a maximum antibiotic removal of 91.10%. Also, the results showed the regression analysis with an R2 value of 0.9837 indicates satisfactory predictive ability between the experimental results and the predictive values.
The Correlation Between Parents-Teachers Cooperation and Students’ Digital Literacy Development

This correlational study investigates the parents' and teachers' cooperation regarding the students' digital literacy development in light of the transition to online learning. This study was conducted at SDN 200118 Padangsidimpuan, with 103 students recruited as a sample of this study. The data is collected from the questionnaires distributed to sample the class teachers and the students. The correlation between the two variables was analyzed with Pearson Product Moment Correlation in SPSS 23, in which the result shows the value of .565 and sig. 0.000 &lt; 0.05. Thus, it can be stated that the alternative hypothesis, which states that there is a relationship between the parent and teacher cooperation variable with the development of students' digital literacy, is accepted.
Assessing, Creating and Using Knowledge Graph Restrictions

More and more data is created, but meaningfully answer questions with this data is not straightforward because we need a shared understanding of what the different data means and how it relates.
Central Tendency, Citation Distributions, and Springer Nature (Part 2)

“In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. Colloquially, measures of central tendency are often called averages. The most common measures of central tendency are the arithmetic mean, the median, and the mode.” — Wikipedia. In the UK, we teach school kids how to calculate the mean, median, and mode in Year 6 (kids aged 10-11), it’s simple stuff.
K-Cap 2019

At the end of November 2019 I could visit the sunny California to attend the Conference on Knowledge Capture (K-Cap). A lot of interesting workshops, tutorials and talks covered topics such as the representation of knowledge in Wikidata, approaches to transform tabular data to Linked Data, or the representation of scientific literature and processes as Knowledge Graphs. Additionally I could present our work on MontoloStats.
ISWC 2019

In the last week of October 2019 I attended the International Semantic Web Conference (ISWC) in Auckland, New Zealand. I had the honor to present my PhD plan at the ISWC Doctoral Consortium. In this blog post I talk a bit about the conference and focus on a few presentations I found particular interesting or relevant for my PhD regarding guidelines for the design and use of ontologies.
Alternate Learning based Sparse Semantic Communications for Visual Transmission

Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. In this paper, in order to tackle the non-differentiability of channels, we propose an alternate learning based SemCom system for visual transmission, named SparseSBC. Specially, SparseSBC leverages two separate Deep Neural Network (DNN)-based models at the transmitter and receiver, respectively, and learns the encoding and decoding in an alternate manner, rather than the joint optimization in existing literature, so as to solving the non-differentiability in the channel. In particular, a ``self-critic" training scheme is leveraged for stable training. Moreover, the DNN-based transmitter generates a sparse set of bits in deduced ``semantic bases", by further incorporating a binary quantization module on the basis of minimal detrimental effect to the semantic accuracy. Extensive simulation results validate that SparseSBC shows efficient and effective transmission performance under various channel conditions, and outperforms typical SemCom solutions.
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery

A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance of the receiver in complicated channel environments and state-of-the-art (SOTA) performance has been achieved. However, its robustness has not been investigated. In order to evaluate the robustness of DL-based information recovery models under adversarial circumstances, we investigate adversarial attacks on the SOTA DL-based information recovery model, i.e., DeepReceiver. We formulate the problem as an optimization problem with power and peak-to-average power ratio (PAPR) constraints. We design different adversarial attack methods according to the adversary's knowledge of DeepReceiver's model and/or testing samples. Extensive experiments show that the DeepReceiver is vulnerable to the designed attack methods in all of the considered scenarios. Even in the scenario of both model and test sample restricted, the adversary can attack the DeepReceiver and increase its bit error rate (BER) above 10%. It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR. These results suggest that defense measures should be taken to enhance the robustness of DeepReceiver.
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing

Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude. However, it is more complicated to construct reasonable certificates against semantic transformation (e.g., image blurring, translation, gamma correction) and their compositions. In this work, we propose \emph{General Lipschitz (GL),} a new framework to certify neural networks against composable resolvable semantic perturbations. Within the framework, we analyze transformation-dependent Lipschitz-continuity of smoothed classifiers w.r.t. transformation parameters and derive corresponding robustness certificates. Our method performs comparably to state-of-the-art approaches on the ImageNet dataset.
Joint Participation Incentive and Network Pricing Design for Federated Learning

Federated learning protects users' data privacy through sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks.
Efficient Training of One Class Classification-SVMs

This study examines the use of a highly effective training method to conduct one-class classification. The existence of both positive and negative examples in the training data is necessary to develop an effective classifier in common binary classification scenarios. Unfortunately, this criteria is not met in many domains. Here, there is just one class of examples. Classification algorithms that learn from solely positive input have been created to deal with this setting. In this paper, an effective algorithm for dual soft-margin one-class SVM training is presented. Our approach makes use of the Augmented Lagrangian (AL-FPGM), a variant of the Fast Projected Gradient Method. The FPGM requires only first derivatives, which for the dual soft margin OCC-SVM means computing mainly a matrix-vector product. Therefore, AL-FPGM, being computationally inexpensive, may complement existing quadratic programming solvers for training large SVMs. We extensively validate our approach over real-world datasets and demonstrate that our strategy obtains statistically significant results.
Benchmarking Metaheuristic-Integrated QAOA against Quantum Annealing

The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising Noisy Intermediate Quantum Algorithms (NISQ) in solving combinatorial optimizations and displays potential over classical heuristic techniques. Unfortunately, QAOA performance depends on the choice of parameters and standard optimizers often fail to identify key parameters due to the complexity and mystery of these optimization functions. In this paper, we benchmark QAOA circuits modified with metaheuristic optimizers against classical and quantum heuristics to identify QAOA parameters. The experimental results reveal insights into the strengths and limitations of both Quantum Annealing and metaheuristic-integrated QAOA across different problem domains. The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA, particularly for problems with rugged landscapes and limited quantum resources. Furthermore, the study provides guidelines for selecting the most appropriate approach based on the specific characteristics of the optimization problem at hand.
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection

Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware. Advances in photonic computing can have profound impacts on applications such as autonomous driving and defect detection that depend on fast, accurate and energy efficient execution of image segmentation models. In this paper, we investigate image segmentation on photonic accelerators to explore: a) the types of image segmentation DNN architectures that are best suited for photonic accelerators, and b) the throughput and energy efficiency of executing the different image segmentation models on photonic accelerators, along with the trade-offs involved therein. Specifically, we demonstrate that certain segmentation models exhibit negligible loss in accuracy (compared to digital float32 models) when executed on photonic accelerators, and explore the empirical reasoning for their robustness. We also discuss techniques for recovering accuracy in the case of models that do not perform well. Further, we compare throughput (inferences-per-second) and energy consumption estimates for different image segmentation workloads on photonic accelerators. We discuss the challenges and potential optimizations that can help improve the application of photonic accelerators to such computer vision tasks.
Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Data

Chatbots have become popular in educational settings, revolutionizing how students interact with material and how teachers teach. We present Curriculum-Driven EduBot, a framework for developing a chatbot that combines the interactive features of chatbots with the systematic material of English textbooks to assist students in enhancing their conversational skills. We begin by extracting pertinent topics from textbooks and then using large language models to generate dialogues related to these topics. We then fine-tune an open-source LLM using our generated conversational data to create our curriculum-driven chatbot. User studies demonstrate that our chatbot outperforms ChatGPT in leading curriculum-based dialogues and adapting its dialogue to match the user's English proficiency level. By combining traditional textbook methodologies with conversational AI, our approach offers learners an interactive tool that aligns with their curriculum and provides user-tailored conversation practice. This facilitates meaningful student-bot dialogues and enriches the overall learning experience within the curriculum's pedagogical framework.
Safe Non-Stochastic Control of Control-Affine Systems: An Online Convex Optimization Approach

We study how to safely control nonlinear control-affine systems that are corrupted with bounded non-stochastic noise, i.e., noise that is unknown a priori and that is not necessarily governed by a stochastic model. We focus on safety constraints that take the form of time-varying convex constraints such as collision-avoidance and control-effort constraints. We provide an algorithm with bounded dynamic regret, i.e., bounded suboptimality against an optimal clairvoyant controller that knows the realization of the noise a prior. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite real-world unpredictable disturbances such as wind gusts. To develop the algorithm, we capture our problem as a sequential game between a controller and an adversary, where the controller plays first, choosing the control input, whereas the adversary plays second, choosing the noise's realization. The controller aims to minimize its cumulative tracking error despite being unable to know the noise's realization a prior. We validate our algorithm in simulated scenarios of (i) an inverted pendulum aiming to stay upright, and (ii) a quadrotor aiming to fly to a goal location through an unknown cluttered environment.