Reviewerly/exHarmony huggingface dataset:
Analysis of Data Propagation in Blockchain Network

&lt;p&gt;Bitcoin is the first cryptocurrency based on blockchain technology emerged in 2008 to provide a peer-to-peer (P2P) electronic cash system without a financial third party. Much research has been done to analyze different aspects of blockchain-based networks and improve their performance. This dissertation presents an innovative approach to evaluating the churning process (the dynamic participation of nodes in the P2P network) in the Bitcoin network. We propose an analytical model based on the Continuous Time Markov Chain (CTMC) and queuing model to evaluate node churn impact on Bitcoin network performance and calculate the synchronization time needed for nodes when rejoining the net- work.&lt;/p&gt; &lt;p&gt;This dissertation also proposes an analytical model for the Bitcoin network’s churning process with relay nodes that is the first to the best of our knowledge. Relay nodes only distribute blocks in the network and have a higher number of connections. We introduce two different CTMCs for ordinary and relay nodes to model each type of node’s behavior separately. We analyze the transaction and block propagation in the network and calculate synchronization time. We show that the churn of relay nodes has a higher impact on traffic performance than ordinary nodes.&lt;/p&gt; &lt;p&gt;Moreover, we introduce an analytical model to evaluate the impact of node churn in the Bitcoin network when compact block protocol is in use. Compact block protocol aims to reduce bandwidth usage and probably latency by propagating a smaller version of blocks in the network. We model the node’s behavior with CTMC and calculate synchronization time including transaction deficit recovery.&lt;/p&gt; &lt;p&gt;Finally, we propose an analytical model to evaluate the impact of selfish behavior on Bitcoin network performance. We evaluate the Bitcoin network’s performance metrics, including network connectivity, block arrival rate, delivery time, and block response time in the presence of selfish miners. We also calculate the probability of intentional forking caused by selfish behavior compared to unintentional forking caused by network delay and show that intentional forking probability is higher than unintentional forking, which may result in ledger inconsistency. &lt;/p&gt;
ARTIFICIAL INTELLIGENCE (AI) IN HIGHER EDUCATION: TOOL OR TRICKERY?

With the release of ChatGPT in November 2022 by OpenAI, the field of higher education was rudely awakened to the revolutionary impact of generative AI on the traditional means of assessing academic performance.Newand ever-improving -"large language models" (LLMs) are now capable of on-demand generation of coherent, comprehensive and academic-grade content -be it in the form of (long-format) text, code or mathematical symbols.Many universities have, as yet, not officially positioned themselves with respect to these new developments and those that have, did so in strikingly diverse ways, from outright bans on any form of AI to investigating how AI can be leveraged by students and staff alike to enhance the academic learning process.This workshop aims to guide participants in the process of 1) gaining awareness of the potential, limitations and pitfalls of AI tools; 2) critically assessing these tools within the framework of participants' (domain-specific) practices for teaching, learning and assessment; 3) comparing observations and sharing opinions with co-practitioners in the domain; and 4) developing a position towards these new technologies and translating this position into (tentative) policy guidelines, either on a personal or institutional level.
The 5-puzzle doubly covers the soccer ball

This paper studies a sliding block puzzle defined on the two by three grid, which we call the 5-puzzle. Our main aim in this paper is to show a fine geometric property of the graph representing the transitions of the positions of the 5-puzzle. We show that by identifying every position with its 180° rotation and applying path contractions, the soccer ball graph is obtained.
STUDENTS’ PERCEPTION OF RPP DESIGN AND USE OF LEARNING MEDIA THROUGH THE MICRO-TEACHING PRACTICE

The aims of this research are 1) The students’ the teaching practice in microteaching, 2)The students’ perception of the Lesson Plan design in microteaching practice, 3) Students’ perception of the use of learning media in microteaching practice. This method of this research was use descriptive qualitative. Subject of the Research is the location of this research was at English Department, Universitas Negeri Surabaya. The subject of the research was the students at Universitas Negeri Surabaya, English Department of seventh semester. The researcher observed one classes consists of 87 students. Based on the data obtained, the researcher concludes that most of the students gave positive perceptions of the microteaching practice in the teaching and learning process, there were many statements that microteaching got a lot of knowledge, including how to teach properly and correctly, and microteaching can to increase self-confidence and being more excited.
Federated Learning with Client Selection and Gradient Compression in Heterogeneous Edge Systems

Federated learning (FL) has recently gained tremendous attention in edge computing and Internet of Things, due to its capability of enabling distributed clients to cooperatively train models while keeping raw data locally. However, the existing works usually suffer from limited communication resources, dynamic network conditions and heterogeneous client properties, which hinder efficient FL. To simultaneously tackle the above challenges, we propose a heterogeneity-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Specifically, FedCG introduces diversity to client selection and aims to select a representative client subset considering statistical heterogeneity. These selected clients are assigned different compression ratios based on heterogeneous and time-varying capabilities. After local training, they upload sparse model updates matching their capabilities for global aggregation, which can effectively reduce the communication cost and mitigate the straggler effect. More importantly, instead of naively combining client selection and gradient compression, we highlight that their decisions are tightly coupled and indicate the necessity of joint optimization. We theoretically analyze the impact of both client selection and gradient compression on convergence performance. Guided by the convergence rate, we develop an iteration-based algorithm to jointly optimize client selection and compression ratio decision using submodular maximization and linear programming. On this basis, we propose the quantized extension of FedCG, termed Q-FedCG, which further adjusts quantization levels based on gradient innovation. Extensive experiments on both real-world prototypes and simulations show that FedCG and its extension can provide up to 6.4× speedup.
Considering the Variability of Soiling in Long-Term PV Performance Forecasting

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term photovoltaic (PV) forecasting. Independent engineering firms typically build forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions are constructed through a Monte Carlo analysis that includes various factors, such as satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within long-term performance uncertainty. In this work soiling information from 16 sites in the U.S. Southwest are combined with 24 years of rainfall data to generate 24 years of energy production with soiling losses and then subsequently generate probability of exceedance values (e.g., P50, P90, P95…). The results show that the size of the 90% confidence interval (P5–P95) can increase from –0.7% to 10.1% when interannual soiling variability and soiling rate uncertainty is included.
Optimizing Video Compression and Transmission for Real-Time Applications

Customers' expectations for content have significantly increased in recent times, leading to a perpetual sense of dissatisfaction. They now demand fast loading times and uninterrupted video playback when using on-demand video platforms like YouTube and Netflix. Despite various CNN-based methods proposed to improve compressed video quality, the existing VVC and transmission protocols are struggling to meet the demands of this expanding market of content consumers. To tackle this problem, we present the latest video coding standard, H.266/VVC, which has been developed by Team JVET. H.266 employs a quad-tree with a layered multitype tree structure, although this approach increases the computational requirements for future video coding advancements. However, it is essential for enhancing the acceptance and utilization of video coding technology. Furthermore, we introduce the “HESP” protocol for video coding-streaming, which enables the delivery of streaming services with extremely low latency and significantly reduced streaming bandwidth on a large scale.
Hybrid Optimization Using CC and PSO in Cryptography Encryption for Medical Images

Private patient data is extensively present in medical photographs. Patients and medical institutions would suffer catastrophic losses as a result of medical image theft and destruction. Because medical photos include sensitive patient data, security is the primary problem during the transmission of those images. When digitized photographs and the patient data they contain are transferred over public networks, image compression security is an important technique for protecting sensitive data. A variety of data encryption based on various techniques was examined by researchers due to the quick advancements in the fields of clinical picture encryption. In this research, we used an optimization strategy to create a novel cryptographic model. Particle swarm optimization (PSO) and the cuckoo search (CS) optimization approach are used to accomplish medical image cryptography. This article used a novel cryptographic framework with optimization techniques to explore the safety of medical pictures. The optimum key for enhancing the security of the encryption and decryption process will be chosen using optimization techniques developed for particle swarms and cuckoo search (CS) in cryptography with elliptic curves. The effectiveness of the proposed method is measured using the Peak Signal-to-Noise Ratio (PSNR) and the entropy.
Parametric Optimization of Friction Stir Welding (FSW) of Dissimilar Aluminum Alloys with Newly Developed Tool

Semantic Image Inpainting is a challenging and promising research problem where parts of an image are masked or corrupted and need to be filled based on the semantic and visual information available from the image. Image Inpainting can help revive ancient scriptures, paintings, and recover corrupted parts of images without the guidance of a subject matter expert. Existing computer vision techniques using neighborhood based gap filling provide unsatisfactory results. In this paper, a novel visual feature learning model driven by context-based pixel prediction is employed, which develops the missing part of the image by conditioning on the available data. We adopt a Generative model based approach to derive inference about the missing part of the image from the context of the entire image. A novel loss function based on the linear combination of generative loss and contextual loss provides promising results for semantic image inpainting. Experiments on datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism.
Introduction

Autonomous driving is the product of the deep integration of the automobile industry and the new generation of information technology. Among them, environmental perception technology is one of the vital essential technologies for realizing automatic driving. It is the premise of realizing safe and intelligent driving in complex road conditions. Existing environmental perception technologies for autonomous vehicles are generally based on sensors such as vision sensors, millimeter-wave RaDARs, and LiDARs. This chapter aims to gain an understanding and mastery of autonomous driving perception and critical developments.
Integrating Linguistic and Citation Information with Transformer for Predicting Top-Cited Papers

Academic literature contains many different types of data, including language, citations, figures, tables, etc. Released in 2017 for natural language processing, the Transformer model is widely used in fields as diverse as image processing and network science. Transformer models trained on large datasets have been shown to perform well on different tasks when trained on additional small amounts of new data. Most studies of classification and regression in the academic literature have designed individually customized features for specific tasks without fully considering data interactions. Customizing features for each task is costly and complex. This paper addresses this issue by proposing a basic framework that can be consistently applied to various tasks from the diverse information in academic literature. Specifically, we propose an end-to-end fusion method that combines linguistic and citation information of academic literature data utilizing two Transformer models. The experiments were conducted using a dataset on 67 disciplines extracted from the Web of Science, one of the largest databases about academic literature, and classified papers with the top 20% of citations five years after publication. The results show that the proposed method improves the F-measure by 0.028 compared to using only citation or linguistic information on average. Repeated experiments on 67 data sets also showed that the proposed model has the smallest standard deviation of F values. In other words, our proposed method shows the best average performance and is stable with a small variance of the F-value. We also conducted a comparative analysis between the dataset’s characteristics and the proposed method’s performance. The results show that our proposed method correlates poorly with the dataset’s characteristics. In other words, our proposed method is highly versatile. Based on the above, our proposed method is superior regarding F-value, learning stability, and generality.
Multi-Sensor Scene Segmentation

Semantic scene understanding is crucial for autonomous driving, robot navigation, and intelligent scene perception tasks. Semantic segmentation can identify and locate targets at the pixel level, providing essential situation awareness for navigation tasks. This chapter focuses on the most commonly used semantic segmentation task in autonomous driving, namely, lane line detection. From the perspective of multimodal fusion, the task of semantic segmentation is revisited. Due to the gap in different modalities, the alignment among of multi-sensor data is challenging for fusion, especially for scene segmentation. Several novel lane line detection algorithms are designed based on attention mechanisms, mutual information, and an end-to-end approach to solve this problem. This chapter first discusses the different fusion architectures and then utilizes the attention mechanism to achieve better results. Further, an adaptive fusion strategy is proposed to address robustness and bias in fusion. After experimental verification, the proposed methods have achieved good performance in segmentation tasks.
OpenMPD: An Open Multimodal Perception Dataset

Multimodal sensor fusion techniques have promoted the development of autonomous driving, while perception in a complex environment remains a challenging problem. This chapter proposes the Open Multimodal Perception Dataset (OpenMPD), a multimodal perception benchmark aimed at difficult examples. Compared with existing datasets, OpenMPD focuses more on those complex traffic scenes in urban areas with overexposure or darkness, crowded environments, unstructured roads, and intersections. It acquires the multimodal data through a vehicle with 6 cameras and 4 LiDAR for a 360-degree field of view and collects 180 clips of 20-s synchronized images at 20Hz and point clouds at 10Hz. In particular, we applied a 128-beam LiDAR to provide Hi-Res point clouds to understand the 3D environment and sensor fusion better. We sampled 15K keyframes at equal intervals from clips for annotations, including 2D/3D object detections, 3D object tracking, and 2D semantic segmentation. Moreover, we provide four benchmarks for all tasks to evaluate algorithms and conduct extensive 2D/3D detection and segmentation experiments on OpenMPD. Data and further information are available at http://www.openmpd.com/ .
An Image Enhancement Algorithm for Autonomous Underwater Vehicles: A Novel Approach

An imaginative technique is proposed that addresses the nature of underwater images by eliminating shadows and poorly differentiated relics that are normally tracked in them. The most common methods of capturing underwater images, resolution, reflection, and capture are affected by various effects of light. These effects can make your photo weaker or covertly louder. Improvement strategies are needed to overcome these degrading factors. This post presents a procedure with example calculations to handle the nature of submerged images, a generic histogram with limited contrast. The purpose of this document is to provide a minimum effort plan for underwater vehicles that can be used with simple electricity collection. The submerged vehicle is created using a smaller PC SOC known as the Raspberry Pi. This paper proposes computations that can be used to improve images captured by submerged vehicles. The sensor is also connected to the device using some GPIO pins to measure depth and detect hooks. Various techniques using Python 3 have been tried to improve the image. The aftermath of these tests was then analyzed. Three bounds, specifically PSNR, root-mean-square error, and entropy were examined to describe the impact of the proposed strategy using state-of-the-art methods. The proposed framework is compared to current strategies for editing underwater images.
PENGUKURAN USER EXPERIENCE (UX) DESAIN APLIKASI TROUBLE TICKET MENGGUNAKAN METODE SUPERGOLDEN RATIO

The Bali Provincial Information and Statistics Communication Service requires a Trouble Ticket application to report and document internet problems that exist in the offices of the Bali Provincial government. In addition to having functions as needed, the Trouble Ticket application must also pay attention to UI and UX. The method that has been widely used to create UI designs is the Golden Ratio. In addition to the Golden Ratio, there is a Ratio that has not been developed in the field of UI design, the Ratio is the Supergolden Ratio. Supergolden Ratio is the ratio obtained from the Ratio Limit Narayana Sequence which is worth 1.4656. In this study, a Trouble Ticket application design will be designed using the Supergolden Ratio method. The Trouble Ticket and UEQ application designs are given to users to determine the user experience (UX) when using the Trouble Ticket application design. The UEQ results show that the user experience of the application design that is designed using the Supergolden Ratio method produces positive results. Aspects of attractiveness, clarity, efficiency, accuracy, and simulation showed excellent results, while the novelty aspect showed good results.
MACHINE LEARNING METHODS FOR EARLY-STAGE DIAGNOSIS OF PARKINSON'S DISEASE THROUGH HANDWRITING DATA

Parkinson's disease (PD) deteriorates human cognitive and motor functions, causing slowness of movements and postural shakiness. PD is currently incurable, and managing symptoms in its late stages is difficult. PD diagnosis also has gaps in accuracy due to several clinical challenges. Thus, early-stage detection of PD through its symptoms, such as handwriting abnormality, has become a popular research area using machine learning. Since most related studies focus on advanced algorithms, this study aims to determine the classification accuracies of simpler classical models using the NewHandPD-NewMeander dataset. This study used the 9 features extracted from the meanders drawn by healthy participants and participants diagnosed with Parkinson’s disease and 3 features about the individual. The same features were reduced to the 8 best according to univariate selection and recursive feature elimination. The machine learning algorithms used for the models in this study are Logistic regression, Multilayer perceptron, and Naive Bayes. Additionally, hyperparameter optimization was done. Results have shown that feature selection improved the performances of the default model, while optimization had varying effects depending on the feature selection method used. Among 15 models built, Multilayer perceptron, which utilized top 8 features from univariate selection with default hyperparameters (MLPU8), performed best. It yielded an accuracy of 84.4% in cross-validation, 87.5% in holdout validation, and an F1-score of 87.5%. Remaining models had accuracies ranging from 81.4% - 84.4% in cross-validations and 82.5% - 85.0% in holdout validations. Other studies done on diagnosing PD using similar handwritten datasets resulted in lower accuracies of 87.14% and 77.38% despite utilizing complex algorithms for its models. This proved that the 15 models built using simple architecture can outperform complex classification methods. The 15 models built accurately classify meander data and can be used as an early assessment tool for detecting PD.
AI Decision Assistant ChatBot for Software Release Planning and Optimized Resource Allocation

This paper presents Alfred, a web-based artificial intelligence (AI) chatbot powered by the Rasa framework and designed to facilitate Agile software release planning by using two machine learning (ML) models to estimate the time for each task selected by the project managers and recommend the optimal resource to be assigned to each task. Extreme gradient boosting (XGBoost) is used for time estimation, and the model’s performance evaluated using accuracy, adjusted coefficient of determination (R2), and fitting a line between the estimation and actual values. The results obtained were 82% accuracy and 98% adjusted R2 score. K-nearest neighbors (K-NN) is used for resource recommendation in a one-shot learning manner. The application integrates a dashboard with useful visualizations and an interactive smart assistant AI-based chatbot, providing project managers with a tool to make more informed decisions. Additionally, an approach is used to indicate the model’s confidence in tasks estimation of given tasks based on the amount of available data showing the history in the system to give project managers more insights. The approach uses a keywords frequency indication algorithm based on the summary tokens to group the task estimates into one of three categories of confidence: high, medium, or low.
Changing distributions and preferences in learning systems

In this talk, I’ll describe some recent work outlining how distribution shifts are fundamental to working with human-centric data. Some of these shifts come from attempting to "join" datasets gathered in different contexts, others may be the result of people’s preferences affecting which data they provide to which systems, and even more can arise when peoples’ preferences themselves are shaped by ML systems’ recommendations. Each of these types of shift require different modeling and analysis to more accurately predict the behavior of ML pipelines deployed in a way where they interact repeatedly with people who care about their predictions.