Reviewerly/exHarmony huggingface dataset:
Emaze App as an Assistive Tool in Cultural Arts Learning

The purpose of this study was to test the use of the Emaze application as a tool in learning Cultural Arts (Dance) at SMK Pariwisata Aisyiyah West Sumatra. This research adopts a quantitative approach with a Pre-Experimental Design design. Researchers themselves act as research instruments, supported by tools such as cameras and indicator sheets. The study population consisted of 11 grade X students at SMK Pariwisata Aisyiyah West Sumatra. Research samples were selected using the group or cluster sampling method. Data was collected through dance creativity tests, observation, and interviews. The data analysis steps include the stages of data clarification, data description, and drawing conclusions. The results indicate that the use of the Emaze application has a positive effect on increasing the creativity of dance movements in grade X students of SMK Tourism Aisyiyah West Sumatra. This resulted in increased achievement in Cultural Arts learning. Student learning creativity is also increased thanks to the use of 2D to 3D visual features from the Emaze application. This effectiveness is evident from the results of student practice examinations that achieve satisfactory results and are in accordance with the learning objectives as stated in the teaching module. The teacher also appreciated the use of the Emaze application as a tool in learning Cultural Arts (Dance) and showed interest in better understanding its use and integrating it through discussion methods in teaching.
Design of a web platform for registering tourist locations in San Martín Texmelucan using augmented reality

This project aims to create an online tool that allows users to register, explore, and promote tourist destinations in San Martín Texmelucan using augmented reality technology, enabling visitors to fall in love with and appreciate the greatness of each place. The web platform seeks to boost tourism in the area, enhance the tourist experience, and promote the local economy by attracting more tourists and highlighting local tourist destinations. Additionally, the platform aims to provide detailed and enriched information about registered tourist spots, including photos, videos, descriptions, and user reviews, so that visitors can make informed decisions and have a more enriching experience. With this initiative, the goal is to promote tourism in San Martín Texmelucan and provide a useful and engaging tool for tourists, as well as for entrepreneurs and businesses in the local tourism sector.
Enchanting Math AudibleBook: Insights from Teachers, University Students, and Young Learners

Reading is an essential activity of learning. In this study, the researchers investigated the opinions of elementary school teachers, university students majoring in elementary school teacher education, and elementary school students on the mathematics audible book "Asyiknya Bermain Setatak”. The researchers distributed questionnaires to collect the data. Data collection was carried out randomly using the random sample method. The respondents comprised 75 people (9 elementary school teachers, 53 university students majoring in elementary school teacher education, and 13 elementary school students) in Pekanbaru, Riau. The data were analyzed using Rasch Model with item response theory (IRT). Based on the data analysis, all statements are valid and reliable, and it means that the items can be used to judge the design of the audible book "Asyiknya Bermain Setatak".Based on the results of the study, it was found that the audible book math media was in accordance with the learning of elementary school students. The use of math audible book is also easy to do by students, teachers, and prospective teachers. Math audible books also provide positive benefits in learning mathematics. Based on this, it can be concluded that teachers should consider using audible books as a media for mathematics learning.
DEVELOPMENT OF A METHODOLOGY FOR ORGANIZING CLASSES OF GENERAL SECONDARY SCHOOLS BASED ON NEW TECHNOLOGIES

In this article, the role of the music teacher in the development of music lessons in general secondary schools and the positive solution of the demands and obligations imposed on him are analyzed.
Secure Neural Network Inference as a Service with Resource-Constrained Clients

Applying services computing to neural networks, a service provider may provide inference with a pre-trained neural network as a service. Clients use the service to get the neural network's output on their input. To protect sensitive data, secure neural network inference (SNNI) entails that only the client learns the output; the input remains the client's secret and the neural network's parameters remain the service provider's secret. Several SNNI approaches were proposed and evaluated in environments where both service providers and clients used powerful computers.
Optimal Deployment of Cloud-native Applications with Fault-Tolerance and Time-Critical End-to-End Constraints

Cloud environments are becoming increasingly interesting to host time-critical use cases with far more stringent latency requirements than conventional cloud-native applications, such as smart industrial control systems or cloud-enabled autonomous vehicles. In these emerging domains, fault tolerance mechanisms play a critical role, due to the catastrophic consequences a fault might lead to, in the real world. This work presents a formal model for designing and deploying time-critical, cloud-native applications under fault conditions. Our model considers the interactions and interferences among service components and the possible occurrence of faults. We present an optimization framework to solve the deployment problem of minimizing the resources needed to achieve fault-tolerance under precise end-to-end deadline constraints. The ability of the optimizer to deliver precise temporal and fault-tolerance guarantees is validated through extensive simulations.
LLD: A Last-Level Defense for Application Integrity and Confidentiality

Our objective is to protect the integrity and confidentiality of applications operating in untrusted environments. Trusted Execution Environments (TEEs) are not a panacea. Hardware TEEs fail to protect applications against Sybil, Fork and Rollback Attacks and, consequently, fail to preserve the consistency and integrity of applications. We introduce a novel system, LLD, that enforces the integrity and consistency of applications in a transparent and scalable fashion. Our solution augments TEEs with instantiation control and rollback protection. Instantiation control, enforced with TEE-supported leases, mitigates Sybil/Fork Attacks without incurring the high costs of solving crypto-puzzles. Our rollback detection mechanism does not need excessive replication, nor does it sacrifice durability. We show that implementing these functionalities in the LLD runtime automatically protects applications and services such as a popular DBMS.
PPS: A Packets Pattern-based Video Identification in Encrypted Network Traffic

Video identification in encrypted network traffic has become a trending field in the research area for user behavior and Quality of Experience (QoE) analysis. However, the traditional methods of video identification have become ineffective with the usage of Hypertext Transfer Protocol Secure (HTTPS). This paper presents a video identification method in encrypted network traffic using the number of packets received at the user's end in a second. For this purpose, video streams are captured, and feature is extracted from the video streams in the form of a series of Packets per Seconds (PPS). This feature is provided as input to a Convolutional Neural Network (CNN), which learns the pattern from the network traffic feature and successfully identifies the video even if the pattern differs from the training sample. The results show that PPS outperforms the other video identification techniques with a high accuracy of 90%. Moreover, the results show that CNN outperforms its counterpart regarding video identification with a 25% performance increase.
Enhancing Deployment Variability Management by Pruning Elements in Deployment Models

Since applications often need to be deployed in different variants, deployment technologies, such as Ansible and Terraform, support modeling variability. Unfortunately, applications typically need the combination of multiple deployment technologies, which have proprietary and non-interoperable variability concepts. Therefore, variable deployment models have been introduced to model deployment variability across different technologies by assigning variability conditions to elements to specify their presence. However, the manual modeling of these conditions is repetitive, error-prone, and time-consuming. In this paper, we propose to reduce the modeling effort by a pruning concept, i.e., the automated removal of elements due to consistency issues and semantic aspects. To validate the practical feasibility, we implemented a prototype based on Open-TOSCA Vintner. Moreover, we present a case study that shows that the number of conditions to be modeled is significantly decreased.
MPCFL: Towards Multi-party Computation for Secure Federated Learning Aggregation

In the rapidly evolving machine learning (ML) and distributed systems realm, the escalating concern for data privacy naturally comes to the forefront of discussions. Federated learning (FL) emerges as a pivotal technology capable of addressing the inherent issues of centralized data privacy. However, FL architectures with centralized orchestration are still vulnerable, especially in the aggregation phase. A malicious server can exploit the aggregation process to learn about participants' data. This study proposes MPCFL, a secure FL algorithm based on secure multi-party computation (MPC) and secret sharing. The proposed algorithm leverages the Sharemind MPC framework to aggregate local model updates for securely formulating a global model. MPCFL provides practical mitigation of trending FL concerns, e.g., inference attack, gradient leakage attack, model poisoning, and model inversion. The algorithm is evaluated on several benchmark datasets and shows promising results. Our results demonstrate that the proposed algorithm is viable for developing secure and privacy-preserving FL applications, significantly improving all performance metrics while maintaining security and reliability. This investigation is a precursor to deeper explorations to craft robust FL aggregation algorithms.
Energy-Efficient Resource Management for Real-Time Applications in FaaS Edge Computing Platforms

Edge computing and Function-as-a-Service are two emerging paradigms that enable a timed analysis of data directly in the proximity of cyber-physical systems and users. Function-as-a-service platforms deployed at the edge require mechanisms for resource management and allocation to schedule function execution and to scale the available resources in order to ensure the proper quality of service to applications. Large-scale deployments will also require mechanisms to control the energy consumption of the overall system, to ensure long-term sustainability. In this paper, we propose a technique to schedule function invocations on Edge resources by powering down idle edge nodes during period of low demands. In doing so, our technique aims at reducing the overall energy consumption without incurring in service level agreements violations. Experimental evaluations demonstrate that the proposed approach reduces service level agreement violations by at least 78.1% and energy consumption by at least 62.5% on average using synthetic and real-world datasets w.r.t. different baselines.
Architecture-Based FedAvg for Vertical Federated Learning

Federated Learning (FL) has emerged as a promising solution to address privacy concerns by collaboratively training Deep Learning (DL) models across distributed parties. This work proposes an architecture-based aggregation strategy in Vertical FL, where parties hold data with different attributes but shared instances. Our approach leverages the identical architectural parts, i.e. neural network layers, of different models to selectively aggregate weights, which is particularly relevant when collaborating with institutions holding different types of datasets, i.e., image, text, or tabular datasets. In a scenario where two entities train DL models, such as a Convolutional Neural Network (CNN) and a Multi-Layer Perceptron (MLP), our strategy computes the average only for architecturally identical segments. This preserves data-specific features learned from demographic and clinical data. We tested our approach on two clinical datasets, i.e., the COVID-CXR dataset and the ADNI study. Results show that our method achieves comparable results with the centralized scenario, in which all the data are collected in a single data lake, and benefits from FL generalizability. In particular, compared to the non-federated models, our proposed proof-of-concept model exhibits a slight performance loss on the COVID-CXR dataset (less than 8%), but outperforms ADNI models by up to 12%. Moreover, communication costs between training rounds are minimized by exchanging only the dense layer parameters.
StarFlow: fine-grained execution of workflows in Hybrid Cloud HPC for data stream applications

Nowadays we are witnessing the diffusion of the Function-as-a-service paradigm, that has emerged due to its ability to deploy fine-grained units of computation on heterogeneous resources on-demand, reducing costs and improving resource utilization. In this work, we discuss our objective: extending the runtime support of a structured parallel programming framework, FastFlow, to empower it with the mechanisms needed to exploit FaaS functionalities. To achieve this objective, we also integrates workflows as a way to specify and describe, at a very high level of abstraction, the topology and the requirements of complex parallel data stream applications.
Enhancing Data Privacy in Federated Data Spaces: Hierarchical Secret-Share based Pseudonymization

Sharing Data Spaces in federated ecosystems can bring several advantages to companies and administrations. However, data security, privacy, and ethics issues need to be properly managed. In particular, pseudonymization techniques allow hiding portions of data accordingly to the access policies of the Federation. In this work, we deal with pseudonymization in Federated Data Spaces, providing an overview of the available approaches in the literature and our solution to create multi-level pseudonymization functionalities able to map different access policies in the same Federation.
Energy Efficient Cloud Architecture Solutions: A Real Case

The technology sector contributes a large amount of power consumption, specifically the cloud providers with their large data centers. Major cloud providers work toward sustainable data centers; however, organizations not building correct cloud architectures could contribute to large amounts of wasted power. This paper aims to review the research conducted into increasing the energy efficiency of cloud-based systems and further simulate a real-world architecture solution, analyzing its energy efficiency and providing improvement solutions. The literature review provides insights into cloud-based architectures and resources. The simulations show that the use case could save upwards of 74% of power by using a proper scaling broker and upwards of 85.4% with an improved virtual machine consolidation algorithm.
Architecting Cloud Solutions for Improved Consulting Practices: Leveraging Simulation for Cost, Performance, and Energy Efficiency

With the emergence of cloud computing in the IT industry, more companies than ever are moving resources to the cloud. Cloud consultants currently help companies by relying on experience. Still, this approach often leads to a poor migration job, typically resulting in over-provisioning expensive resources for the clients' setups. This paper investigates the integration of cloud simulators into the consulting process to assist consultants in making data-driven recommendations to their clients. The simulations could provide metrics on cost-effectiveness, performance, and energy efficiency. The paper is initiated by a systematic review of state-of-the-art cloud simulators, followed by an industry survey to address the identified knowledge gaps. This formed the basis for a practical use case identified through collaborative efforts with Eficode, a consultancy company. The evaluation results reveal that simulating cloud architecture models has significant potential to help the consultants by facilitating a data-driven approach, allowing the consultants to help companies achieve cost savings and optimize resource allocation during cloud migration.
Is Securing an Untrusted Cloud with Cryptography Still Economically Unviable?

This paper explores the economic implications of outsourcing data and computation to a cloud provider in comparison to maintaining an on-premise data center. Our investigation is twofold: contrasting a private data center with a trusted cloud, and the evaluation of the costs associated with securing an untrusted (e.g., honest-but-curious) cloud. We revisit prior cost models based on the current cost of CPU cycles and cryptographic operations. To evaluate the cost of securing a public cloud, we compare execution in AWS EC2 with AWS Clean Rooms, which facilitates encrypted querying. Our study offers a nuanced perspective on the economic feasibility of different cloud computing paradigms, providing essential insights for organizations contemplating the transition from local infrastructure to public cloud providers. Additionally, our analysis sheds light on the cost implications of securing against trusted insider threats, emphasizing the importance of aligning cloud computing decisions with an organization's risk tolerance and security posture. Outsourcing computation to a trusted cloud provider is economical today as cloud costs have declined since 2010, but securing against an untrusted cloud remains economically unviable despite advances in secure query processing and new cloud services.
Tiny-Impute: A Framework for On-device Data Quality Validation, Hybrid Anomaly Detection, and Data Imputation at the Edge

In the landscape of Internet of Things (IoT) systems, data quality degradation can occur continuously for several reasons, such as sensor malfunctions, intermittent network availability, device maintenance, or incomplete data collection. This paper proposes three efficient data quality validation and imputation algorithms that identify and replace noisy and missing values with better-quality data. We extensively stress-tested and evaluated our algorithms by deploying them on microcontrollers and small CPU-based IoT boards with memory limited to as little as 32KB. We simulated a sensor data stream using five real-world datasets, including data collected from the Newcastle Urban Observatory. In this setup, each algorithm excelled in different areas, consistently demonstrating high performance across 100 samples in terms of high energy efficiency (0.014 J), low computation time (85.94 ms), and low error rates (0.0019 MAE, 0.0027 RMSE). Remarkably, we found that on average, our algorithms running on hardware, costing less than $10, showed performance on par with state-of-the-art methods on high-end devices. The results also demonstrated that our algorithms enabled on-device cleansing of live streaming sensor data, eliminating the dependency on cloud services and allowing for real-time data quality validation and processing at the edge.
Cloud Security Frameworks

The expansion of cloud computing applications and services has introduced a number of control and audit frameworks designed to provide a standard around data access, cloud security, and digital enablement. While certification frameworks and security standards have become the de facto industry guidance for IT governance, there is a notable absence of a fundamental rating and review system for the frameworks themselves. This research evaluates cloud provider security standards set forth by ISO/IEC 27001 and 27002, FedRAMP, and SOC 2 against a third-party benchmark, the Cloud Controls Matrix (CCM) created by the Cloud Security Alliance (CSA), as well as the CSA's "Treacherous Twelve" of the top threats to cloud computing security. Recent revisions of ISO/IEC-27001 and 27002 and FedRAMP significantly improve their coverage of CCM controls; SOC 2 provides less coverage overall but remains a viable alternative to the aforementioned certifications.
SASER in Practice: Early Insights from Two Case Studies Connected to Industry

Abstract Systems Engineering (SE) research with Industry as a laboratory (IaL) has multiple benefits: it safe‐guards relevance, facilitates co‐design, and allows researchers to gain practical insights. However, it faces a challenge in aligning traditional academic research with fast‐paced industrial processes. The Spiral Approach for Systems Engineering Research (SASER) was recently proposed to address this challenge. In this paper, we demonstrate and discuss for the first time the application of SASER in the context of (1) the High‐tech industry, and (2) Small and Medium Enterprises (SMEs). Despite the context differences of the two case studies, SASER shows promising results for both the researchers and the industrial partners in keeping the partners interested, enabling shorter feedback loops, and preventing analysis paralysis. Even though there are just two case studies and this is an early stage research application of SASER, we firmly believe that it shows merit in facilitating SE academic research in the context of IaL.
PENDEKATAN STM MELALUI PENGGUNAAN APLIKASI ANDROID DALAM PEMBELAJARAN IPS DI SMP KOTA BANDUNG

Research of STM (Science, Technology and Society) approach to social studies learning has not been developed much. So far, STM has been used more in science learning. This research was designed as a positive response in facing the Industrial Revolution 4.0 which had a revolutionary impact on the education. Consistently integrated efforts are needed to develop science and technology-based learning innovations that are closely related to societal problems which are the subject of social studies. The research method used is a survey. The android application is used as a means to improve social studies learning practices in junior high schools, especially for online learning. Social studies learning activities ultimately lead to the 21st century 4C skills (Collaboration, Critical Thinking, Creativity, and Communication). Social studies learning is expected to not only equip students with knowledge, but also with the attitudes and skills that are qualified in the 21st century.
Artificial Intelligence (AI)-Powered Chatbots for Marketing and Online Shopping

The COVID-19 pandemic is changing the way we connect, communicate, and collaborate. the crisis rapidly re-shape both the “what” and the “how” of companies. Businesses now move to doing business online, that involves selling of products, generating leads and connecting with customers. The chatbot, a software application designed for human-like conversations, emerged as a trend that is helping to close the social distance gap between customers and businesses. Many business have incorporated the tool in their communication strategy, this paper aims to understand to what extent, tries to put-forward an understanding of chatbots marketing and the use of chatbots in online shopping process. Furthermore, the paper tries to speaks extensively about the design in such a way that it will peak its performance for user acquisition.