Reviewerly/exHarmony huggingface dataset:
Prediction of solar irradiance based on Python

The rapid development of modern industrial society has relied heavily on cheap and abundant fossil fuel energy. However, to achieve sustainable development, there is an increasing focus on developing new energy sources such as photovoltaics (PV) and wind energy. In the context of using solar irradiance to generate electricity, predicting the solarpower in advance is crucial for efficient utilization. This paper utilizes the pvlib-python model to predict three types of irradiance in clear sky conditions: POA_DNI, POA_GHI, and POA_DHI. Furthermore, we incorporate aerosol data from pvlib to improve the prediction accuracy.Three sites from BSRN are selected and the predicted data are compared with the observed data to evaluate the model's prediction effectiveness. The result reveals that the model performs best for POA_GHI and the actual cloud cover distribution has a significant impact on the prediction accuracy.
Expanding Promotion and Marketing of Village Products Through Smart Village System

Several villages in South Sumatra do not yet have a way to develop and advance their economy by selling local products that they make themselves, one of which is Burai Village. These products consist of agricultural products, handicrafts, special foods and typical clothing from the village. So far, the products they make are only sold and bought by people close to the village, both people from within the village and from outside the village. That way the income of village product sellers is not that big because the products they sell don't have a place to promote their products so that they can be known by many people and can be disseminated. To promote these village products, the author will make a Village Product catalog on the Sistem Desa Pintar (SIDESPIN) website, using the Web Engineering method and the Laravel framework to make it easier to develop the village product catalog features. With this research a village product catalog will be produced on the SIDESPIN website such as e-commerce which will be used to promote the village's products so that they can be known more widely.
Comparation of Federated and Centralized Learning for Image Classification

Federated Learning (FL) is a new approach in machine learning or it can also be called collaborative learning, which is a machine learning method that includes client devices to carry out the training process, so that clients do not need to send training data to the server but directly conduct training on their respective devices. respectively. The models generated from local training will be sent to the server for further global aggregation. Therefore, FL is referred to as machine learning which can maintain the privacy of the data owner, because the data is not submitted to the server and is still stored in each client's device. In this study, a performance comparison will be carried out to prove whether the latest approach which is Federated Learning, can produce the same accuracy performance as the traditional approach, that is Centralized Machine Learning, in the case of Image Classification. The comparison of two approaches would be conducted by using the Open Source Image Classification dataset, namely MNIST. The performance of two approaches would be presented by evaluation that is Accuracy. The result shows that Federated Learning almost overcome the performance of Centralized Learning in the case of Image Classification by provided Accuracy 76%.
Speaker Identification in Multiple Languages: Regional, Indonesian, and English with Short Utterance

One of the authentication models that are currently often used is based on biometrics, such as eye retina, fingerprint, and speech recognition. Moreover, textindependent speaker identification is one of the domains of speech recognition that has been widely studied. Short speech duration in the speaker identification process is one of the challenges in the field of speaker recognition. Accuracy is a great issue when speech duration shorter, besides identification system has to be general enough to process various languages with different dialects which have their own characteristic based on tribe and region. Therefore, the author of this study introduces the speaker identification system in multi languages that comprise of regional, Indonesian, and English with short utterance. Researchers used MFCC technique to extract voice features and CNN as the classification model. There are two kinds of dataset used, open dataset for regional and English language, and own dataset for Indonesian. Own dataset used is a voice recording of 18 persons of different gender who each read the text in several paragraphs of sentences in Indonesian. Whereas public dataset of regional language used consisted of 80 speakers, 41 Sundanese and 39 Javanese. As for English dataset, 126 male speakers and 125 female speakers were taken from LibriSpeech. Tests are carried out separately with variety of language and speech duration, about 3 seconds in English and regional languages, 1 and 3 seconds in Indonesian. The result, best accuracy obtained by each dataset is 95% (regional dataset), 94% (English dataset), and 98% (private dataset).
Persepsi Mahasiswa Tentang Penggunaan Teknologi Digital dalam Proses Pembelajaran

The purpose of this research is to find out students' perceptions regarding digital technology in the learning process and to find out whether digital technology really helps students in the learning process. The method used in this research is descriptive research with a quantitative approach. The selection of research subjects was carried out through a random sampling technique. The data collection technique was carried out by closed interviews through the Google form and the data analysis technique used in this study was questionnaire data analysis. The majority of students (75%) agree that digital technology makes a major contribution to the learning process on campus. This is consistent with the fact that technology makes it easier for educators and students in distance learning. Various digital platforms facilitate assessment, information exchange, and interaction between teachers and students. Digital technologies provide significant benefits in the learning process, including increasing interaction between teachers and students, providing access to additional resources, and facilitating collaborative learning through online platforms.&#x0D; Keywords: Student Perception, Digital Technology, Learning Process
Sustainability Reporting and Return on Assets of Selected Deposit Money Banks in Nigeria

The paper investigated sustainability reporting and return on assets of deposit money banks in Nigeria from 2013 – 2022 using data collected from the annual report of selected deposit money banks. Ordinary Least Square regression technique was used to evaluate the relationship between sustainability reporting and return on assets of the sampled banks.&#x0D; The empirical result obtained from the study showed that sustainability reporting has a significant and positive effect on return on assets of selected deposit money banks in Nigeria for the period of the study. It was recommended that deposit money banks in Nigeria should sustain sustainability practices in order to improve their financial performance.
Application of Enterprise Architecture to Decentralized Architecture in Internet Databases

Enterprise architecture is a holistic, hierarchical and abstract description of the essential elements of an organization to maximize shareholder value over time. Enterprise Architecture (EA) is a way to structure enterprise information system elements which can be a set of models and relationships between enterprise elements used in planning, designing, and realizing an enterprise structure, business processes, information systems, and related infrastructure in inside. The database application architecture describes the basic design of the database application to be built. The database architecture describes the interaction diagram between the components that make up the database management system. These components include hardware devices, software, computer networks, and users. Distribution Architecture or Decentralized Architecture has its own advantages and disadvantages.
Vulnerability Detection Based on Attention Mechanism and Deep Graph Convolutional Network

To address the problem of incomplete graph structure features due to the lack of contextual information in existing graph neural network-based vulnerability mining methods and the problem of over-smoothing that prevents the model from learning higher-order features of the graph structure resulting in poor prediction performance, a vulnerability detection method based on deep graph convolutional networks and attention mechanism, PSG-GCNIIAT, is proposed. PSG fuses order relational graphs on the basis of program dependency graphs so that code statements have the ability to sense their contextual information, and generates embedding vectors of code lines by abstracting syntax trees to achieve deep structure feature extraction of graph nodes. GCNIIAT uses a deep graph convolutional network, GCNII, combined with a graph attention mechanism to more effectively identify graph structure features of program slices associated with vulnerabilities. The experimental results show that the vulnerability detection model GCNIIAT proposed in this paper has obvious advantages over Vuldeepecker, GCN and GGNN in accuracy and F1 value indicators, and can effectively improve the performance of program vulnerability detection.
MT-YOLO: Combination of Multi-Scale Feature Extraction and Transformer in One-Stage Object Detection

Object detection is a very important area in computer vision that can be applied to many scenarios and plays a very important role, where YOLO has become the industry standard for effective object detection. In this study, we consider enhancing the capability of YOLO for global feature modeling and design a multi-scale dilated dense connectivity module based on YOLOv5 to extract feature information at different scales and levels to obtain spatially dependent information in images, while adding a Convolutional Block Attention Module(CBAM) to learn details of the detected objects. Then, we use three Transformer Encoder-like integrations to the top of the Neck instead of on the backbone network. In this way, more meaningful, fine-grained and feature-enriched mapping relations can be obtained to extract associations about different image regions. The validation results on the COCO 2017 dataset show that MT - YOLO-S is able to achieve 44.1 % AP while being able to reach a detection speed of 115 frames, in addition, our N/M size model achieves 35.2 % % and 49.5% AP performance in the same experimental setting.
High-security image encryption by multiplexing phase encoding in domains of dual optical transforms

A novel optical image encryption is proposed based on multiplexing of the random phase encoding with shift and rotation operations in domains of two transforms, extended fractional Fourier transform (eFrFT) and Fresnel transform. The original image is subjected to eFrFT with the action of the random phase mask. The mask is shifted and rotated to enhance the security of this encryption method. The image obtained from eFrFT is entered into Fresnel diffraction by the use of the phase mask to obtain the final encrypted image. We plan for the phase keys to be multiplexed in order to decrease the amount of keys that need to be stored in an application. Here, the displacement, rotation angle, and wavelength in this system can be used as additional keys to improve the security and reliability of the encryption system. Numerical experiments are conducted to verify the effectiveness and security of the method. The findings demonstrate that the keys are sufficiently sensitive for high security.
POSTER: AuthZit: Multi-modal Authentication with Visual-Spatial and Text Secrets

Designing a fallback authentication mechanism that is both memorable and strong is a challenging problem because of the trade-off between usability and security. This challenge is particularly pronounced for accounts that require infrequent authentication, where the authentication secrets must remain secure and easy to recall without frequent reinforcement. Inspired by people’s strong visual-spatial memory and memory via association, we introduce a novel system AuthZit to help address this problem. AuthZit encodes authentication secrets as paths through a 3D map of places in real life navigated in the first person and birds-eye perspective, along with a textual secret tagged with this path. We evaluated the usability and security of our design in two dimensions: memorability after one month and speed through a user study with 20 participants. Our results suggest that (1) user authentication secrets in AuthZit are memorable, and (2) authentication using AuthZit was not significantly slower to enter.
Error Handling

Abstract Chapter 5 discusses how SYCL extends C++ to manage error handling when using accelerators. Understanding the difference between synchronous and asynchronous SYCL exceptions and how to handle them is critical to writing production applications and libraries.
Data Management

Abstract Chapter 3 discusses methods to manage data in our quest to exploit data parallelism for performance gains. It introduces Unified Shared Memory (USM) and the higher-level buffer abstraction and explores when to use each method to manage the data in our accelerated programs.
Unified Shared Memory

Abstract Chapter 6 discusses Unified Shared Memory (USM) in detail. USM is a pointer-based strategy for data management. This chapter describes the three types of USM allocations, how to allocate and deallocate USM memory, and how different types of USM data movement can be explicitly in our applications or implicitly controlled by the system.
Gaussian Processes

The Gaussian process as a tool for, predominantly, regression tasks in machine learning has only been growing in popularity over recent years. Although not reaching the same widespread usage as neural network-based technology, it is also considered a key methodology for the machine learning pratictioner. In this short chapter, a basic introduction to the approach will be provided; following which, several extensions to the fundamental Gaussian process will be presented; finally, a case study involving wind turbine power curves will serve to highlight the engineering benefits of this approach. Two particular strengths will be brought to the fore: firstly, the automated quantification of uncertainty (under a Bayesian paradigm) inherent to the Gaussian process approach; secondly, the flexibility of the model compared to the relatively small number of tunable hyperparameters to be learnt.
Defining Kernels

Abstract Chapter 10 explores various ways to define kernels in detail, helping us choose the most natural kernel form for our C++ coding needs. It compares kernels defined as lambda expressions, as named function objects, and via interoperability with other APIs.
Buffers

Abstract Chapter 7 discusses the buffer abstraction in detail. Buffers are a higher-level abstraction that hides the underlying details of memory management from an application. This chapter describes how to allocate and access the contents of a buffer both in our host programs and in our kernels running on a device.
POSTER: Advancing Federated Edge Computing with Continual Learning for Secure and Efficient Performance

Federated Learning (FL) and the Internet of Things (IoT) have transformed data processing and analysis, overcoming traditional cloud computing limitations. However, challenges such as catastrophic forgetting in continuous training scenarios arise. To address these, we propose an FL framework that supports continual learning while enhancing system security. We preserve critical knowledge through the incorporation of Knowledge Distillation (KD), addressing the issue of catastrophic forgetting. In addition, we have integrated encryption techniques to secure the updated parameters of clients from potential threats posed by attackers.
Efficient Arithmetic for Polynomial Multiplication in Post-quantum Lattice-Based Cryptosystem on RISC-V Platform

With the development of quantum computers, NIST started post-quantum cryptography standardization to design post-quantum-secure cryptographic algorithms. Saber is a cryptosystem in third-round public-key encryption and key-establishment algorithm finalists. Because of its power of 2 number theoretic transform (NTT)-unfriendly ring, originally, Karatsuba polynomial multiplication algorithm (KPMA) and Toom-Cook polynomial multiplication algorithm (TCPMA) are used to speed up its computation-intensive matrix-vector multiplications. In later studies, NTT-based methods are applied to Saber on ARM platforms and result in a 61% speed-up. This work aims at adapting Saber with existing polynomial multiplication algorithms (PMAs), including non-NTT-based and NTT-based PMAs, to two energy-efficient RISC-V development boards, SiFive HiFive1 Rev B as well as Terasic T-Core. A 32-bit multiplier adapting Barrett reduction is designed to solve the overflow problem caused by RISC-V platform limitation. Experiment results show that the computation complexity depends on the algorithm choice and the underlying platform. NTT-based algorithms analytically have obvious advantages compared with non-NTT-based PMAs. However, the on-board cycle count on T-Core shows that NTT-based algorithms may have no comparability with non-NTT-based algorithms due to the high complexity overflow solutions. In addition, using the newly designed 32-bit multiplier can result in a 36.4% speed-up in practice. These results suggest several criteria for selecting algorithms on different platforms. This project can serve as a reference for future exploratory studies.
Trust Management Framework for Containerized Workloads Applications to 5G Networks

As mobile networks grow, managing the security of communication between all its internal components becomes more challenging because of the increasing number of services, network functions, and stakeholders involved. Among the different dimensions of security, trust is one that 5G communication service providers (CSP) must prioritize to provide secure services for their users. This requires a strategy for assessing trust between network assets at scale, considering the complexity of interactions, service layers, and deployment options. To address this problem, we propose a trust management framework that deals with requests to perform operations on a Kubernetes® cluster based on trust labels. The context of the assets is one of the attributes used in the framework, used to describe the environment in which the assets operate. In addition, we consider past interactions between the assets. Since trust is dynamic through time, it needs to be evaluated continuously. This paper focuses on defining parameters for trust that are tailored to the unique properties of the various assets involved in the operations to be secured. The proposed model is implemented in Kubernetes® using its admission control module. Finally, an adversary model and a threat assessment are defined to evaluate the security of the trust framework.
Intelligent traffic control system using computer vision algorithms

Traffic congestion has become one of the major issues in Bangladesh. The vehicle density on the road is slowly becoming greater than the road capacity and resulting in difficult commutes. This traffic delay leads to wastage of valuable time which impacts the economic development of the country. One of the main reasons for this type of road congestion is due to poor traffic management. This paper presents implementation of an intelligent traffic control system using computer vision algorithms. In this research, we propose a smart traffic management system by measuring the traffic density of the road by real time detection and image processing. The vehicle detection system counts the number of vehicles approaching a traffic signal to determine the congestion of the traffic on the road. Then traffic controller uses an algorithm to control the timings of the traffic signals, red, green and yellow, based on the number of vehicles on the road. Our system was developed by capturing real traffic video using smartphone, vehicle detection system tested in the computer and the traffic signal was implemented in Arduino hardware. Vehicle detection accuracy was increased by training a more extensive dataset with Faster R-CNN (Region-based Convolutional Neural Network) and YOLOv5 (You Only Look Once version 5) models.
A note on factorisation patterns of division polynomials of elliptic curves over finite fields

Let $E$ be an elliptic curve defined over a finite field $\mathbf{F}_{q}$, $q = p^{d}$, $p > 3$, and a prime number $\ell > 3$ such that $q \equiv 1 \pmod{\ell}$ and $\ell \mid \# E(\mathbf{F}_{q})$. In this paper we study the possible factorisation patterns over $\mathbf{F}_{q}[x]$ of the $\ell^{k}$-division polynomials associated to $E$ with $k \geq 2$, extending the work of Verdure [6] for $k=1$.
How Does Vocabulary Size Affect Maritime English Achievement? A Correlational Study of Cadets in Sorong Merchant Marine Polytechnic

English, particularly in the context of maritime communication, has significantly contributed to the education field. Conversely, the efficacy of its educational instruction in Indonesia has been lacking. Several variables influence students' proficiency in acquiring foreign languages; one such factor is their vocabulary size. This research aimed to investigate the relationship between the vocabulary size of cadets and their achievement in maritime English. The research design employed in this study is ex post facto. The researcher employed a vocabulary test and assessed maritime English achievement as the primary instrument in their study. In this instance, the researcher administered a written examination to assess the cadets' vocabulary size. Subsequently, the researcher analyzed the data obtained from the assessment to determine the cadets' performance in maritime English. The findings indicated a statistically significant correlation between the two variables. Hence, the size of one's vocabulary indicates the achievement cadets can attain in their proficiency in maritime English.
Monitoring System Development and Evaluation of Character Building in Semarang Polytechnic Through The Utilization of Information Technology

The character can be interpreted as a character with psychological qualities, morals, or manners that distinguish one person from another (Poerwadarminta Dictionary Ministry of National Education, 2010: 44) . Character builder (character building) is the process of carving or sculpting the soul in such a way so that its 'shape' is unique, engaging, and different or distinguishable from others. Monitoring and evaluation systems Character Development in Cadets Polytechnic Semarang cruise via utilization technology information use method waterfall. It expands the system with a do approach systematically and sequentially, starting from the need system to stage analysis, design, and coding. Evaluation and testing of running information systems are needed for information system continuity alone so that the application can be used and utility to application high, needed testing use black box, interview as well as charging questionnaire to the user the usual single sign-on. Research results with testing black box show that results testing can say has worked with excellent and proper by a hope that the users' system has described; it proved with the enhanced weighted average value in a manner whole before and after system development so can conclude the system already used with good however it needs exists additional menus on soft skills and physical cadets.