Reviewerly/exHarmony huggingface dataset:
Mnemonic Dictionary Learning for Intrinsic Motivation in Reinforcement Learning

Reinforcement learning for hard-exploration tasks remains challenging due to the long-term dependence and sparse-and-delay rewards in complex environments. In these challenging tasks, intrinsic motivation has become a dominant paradigm to enable the agent to explore the environment when no external reward feedback is available. In this work, inspired by studies from the human memory mechanism, we present a mnemonic dictionary learning (MDL) model for intrinsic motivation in reinforcement learning. The MDL model leverages sparse dictionary learning to incremental abstract the exploration histories into a compact memory-like dictionary, providing an excellent intrinsic motivation model. This mnemonic dictionary model not only drives the agent to explore novel stats in the environments indicated by the memory reconstruction error but also helps the agent to remember the key states and structure of the environments using its learned bases and reconstruction coefficients. The proposed MDL model can serve as a generative module for existing exploration methods. Extensive experimental results on typical sparse-reward tasks demonstrate its effectiveness and applicability over several competing algorithms. We will release the source code and trained models to facilitate further studies in this research direction.
A Comparative Study of Self-Supervised Speech Representations in Read and Spontaneous TTS

Recent work has explored using self-supervised learning (SSL) speech representations such as wav2vec2.0 as the representation medium in standard two-stage TTS, in place of conventionally used mel-spectrograms. It is however unclear which speech SSL is the better fit for TTS, and whether or not the performance differs between read and spontaneous TTS, the later of which is arguably more challenging. This study aims at addressing these questions by testing several speech SSLs, including different layers of the same SSL, in two-stage TTS on both read and spontaneous corpora, while maintaining constant TTS model architecture and training settings. Results from listening tests show that the 9th layer of 12-layer wav2vec2.0 (ASR finetuned) outperforms other tested SSLs and mel-spectrogram, in both read and spontaneous TTS. Our work sheds light on both how speech SSL can readily improve current TTS systems, and how SSLs compare in the challenging generative task of TTS. Audio examples can be found at https://www.speech.kth.se/tts-demos/ssr_tts
Implementation Of The Inter Tk Package, Sub-Process And Os In The Network Management Application Development With Python Programming Language

The development of technology is currently running very rapidly, especially in the field of computer networks that make it possible to connect all people from various places. The need for information triggers the rapid development of network infrastructure technology by several vendors or brands. The need for network infrastructure is of course inseparable from the large number of users who use the internet service, causing new problems because the number of users is not proportional to the number of technicians available. For general users, they can wait for the handling schedule from the technician, while for agencies that need a network to complete their work, they don't have that time. To overcome this, you can use a special program to check the network regularly where everyone can use it so that it can reduce free customer service at network providers. One programming language that can be used open source for the development of these applications is python. Python is a multipurpose interpretive programming language with a design philosophy that focuses on code readability. Python programming is still rarely used because it still uses the terminal as the main access where the user has to type certain code to execute the desired result, such as the Command Prompt Windows (CMD) program. The use of python-based applications can be made more comfortable for users by using a GUI (Graphical user interface) display using the TKInter library, which is a standard Python package for creating GUI displays. Tkinter has an interface to the Tk toolkit that can work as an object-oriented layer on top of Tk. Tkinter has many components that can be used to create user-friendly interfaces. Apart from Tkinter, the application uses a subprocess and os library which functions to access the terminal and execute certain comments in the terminal. The built application has functions for pinging, nslookup, ipconfig, network info, active connection and network processes. Ping and Nslookup require data to be entered in text input on the same line, for ping you can input the IP address of the intended network or website address (requires internet connection) while nslookup can be filled in with the website address.
Data Interoperating Architecture (DIA): Decoupling Data and Applications to Give Back Your Data Ownership

Data has become a valuable resource that drives new business models and creates enormous business value. However, under the current data ownership model, many data-driven applications arbitrarily collect and overuse user data for commercial purposes, resulting in increased incidents of data breaches and data misuse. In this paper, we present Data Interoperating Architecture (DIA) that decouples applications and user data to give back data ownership to users. We design Data Interoperating System (DIS) based on blockchain, identity system, and identifier system to solve the key issues of data interoperation: identity management, data identification, data discovery, and data ownership protection. DIS provides services without collecting or accessing data, keeping data under the control of its owner. We formalize the interactions among components in DIA as Data Interoperating Protocol (DIP), which facilitates applications and personal data stores to be compatible with DIS. We develop a prototype of DIS and evaluate the system performance under adopted techniques. Experimental results show that DIS is effective and efficient in supporting real-world data interoperation.
Classifying Packages for Building Linux Distributions

Linux distribution provides users an open source operating system to fulfil a variety of purposes and missions. Currently, there is no package categorization to lead Linux distribution developers and manufacturers to select the components, and no architecture information to guide the construction process. This paper proposes to automatically sort out the packages and outline the boundaries for the component selection in terms of package categories, and provide a guidance for the architecture design in the construction. A package data set is created, and presented as a graph with the packages as nodes and package dependencies as edges. The realistic data set has the unbalanced or long-tail problem, and some labels tend to be similar with noises due to unavoidable reasons. This paper explores different models and approaches, adapts and optimizes the label representation techniques, introduces the contrastive learning mechanism, and improves the direct GraphSage model. The metrics of prediction started with less than 50%, and reaches more than 90%. This demonstrates the validity of the proposed approach. The data set is publicly available on Internet.
Detecting and Preventing ROP Attacks using Machine Learning on ARM

As the ARM processor is receiving increased attention due to the fast growth of mobile technologies and the internet-of-things (IoT), it is simultaneously becoming the target of several control flow attacks such as return-oriented programming (ROP), which uses code present in the software system in order to exploit memory bugs. While some research can detect control flow attacks on architectures like x86, the ARM architecture has been neglected. In this paper, we investigate whether ROP attack detection and prevention based on hardware performance counters (HPC) and machine learning can be effectively transferred to the ARM architecture. Given the observation that ROP attacks exhibit different micro-architectural events compared to benign executions of a software, we evaluate whether and which HPCs, which track these hardware events, are indicative on ARM to detect control flow attacks. We collect data exploiting real-world vulnerable applications running on ARM-based Raspberry Pi machines. The collected data then serves as training data for different machine learning techniques. We also implement an online monitor consisting of a modified program loader, kernel module and a classifier, which labels a program’s execution as benign or under attack, and stops its execution once the latter is detected. An evaluation of our approach provides detection accuracy of 92% for the offline training and 75% for the online monitoring, which demonstrates that variations in the HPCs are indicative of attacks on ARM architectures. The performance overhead of online monitoring evaluated on 8 real-world vulnerable applications exhibits a moderate 6.2% slowdown on average. The result of our evaluation indicates that the behavioral changes in micro-architectural events of the ARM platform can play a vital role in detecting memory attacks.
Design and Development of An E-Voting Model for Village Head Elections Based on Android

Voting or voting in a democratic country is an important part of the means of selecting leaders. Village head elections are generally still carried out conventionally, elections still use paper to elect village heads (Kades) which are carried out by punching one of the candidate images. The problem that arises is that voters must come to the polling place in order to be able to collect data and vote, for Citizens who cannot attend the polling place or do not exercise their right to vote will add to the data on the abstentions list that is present at the election. In village head elections (Pilkades) the vote counting process is still done manually so it takes quite a long time. This study aims to build an e-voting simulation system which is expected to provide knowledge of technological developments in the field of general elections. This system development method uses Waterfall. Furthermore, if you have obtained the results, you will test the system that was built by testing it with the black box testing method to see in terms of functionality all features are running smoothly or not. The results of testing the E-voting application for village head elections show that the system can run well and has been implemented so that it can be used on Android-based smartphone devices.
As-Rigid-As-Possible Shape Interpolation

We present an object-space morphing technique that blends the interiors of given two- or three-dimensional shapes rather than their boundaries. The morph is rigid in the sense that local volumes are least-distorting as they vary from their source to target configurations. Given a boundary vertex correspondence, the source and target shapes are decomposed into isomorphic simplicial complexes. For the simplicial complexes, we find a closed-form expression allocating the paths of both boundaryand interior vertices from source to target locations as a function of time. Key points are the identification of the optimal simplex morphing and the appropriate definition of an error functional whose minimization defines the paths of the vertices. Each pair of corresponding simplices defines an affine transformation, which is factored into a rotation and a stretching transformation. These local transformations are naturally interpolated over time and serve as the basis for composing a global coherent least-distorting transformation.
Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem

Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.
Scaling survival analysis in healthcare with federated survival forests: A comparative study on heart failure and breast cancer genomics

Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are often incomplete, censored, distributed, and confidential, especially in healthcare settings where privacy is critical. The scarcity of data can severely limit the scalability of survival models to distributed applications that rely on large data pools. Federated learning is a promising technique that enables machine learning models to be trained on multiple datasets without compromising user privacy, making it particularly well-suited for addressing the challenges of survival data and large-scale survival applications. Despite significant developments in federated learning for classification and regression, many directions remain unexplored in the context of survival analysis. In this work, we propose an extension of the Federated Survival Forest algorithm, called FedSurF++. This federated ensemble method constructs random survival forests in heterogeneous federations. Specifically, we investigate several new tree sampling methods from client forests and compare the results with state-of-the-art survival models based on neural networks. The key advantage of FedSurF++ is its ability to achieve comparable performance to existing methods while requiring only a single communication round to complete. The extensive empirical investigation results in a significant improvement from the algorithmic and privacy preservation perspectives, making the original FedSurF algorithm more efficient, robust, and private. We also present results on two real-world datasets – a heart failure dataset from the Lombardy HFData project and Fed-TCGA-BRCA from the Falmby suite – demonstrating the success of FedSurF++ in real-world healthcare studies. Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy.
Syntactical Aspects in Translating Fluid Mechanic Terms from English to Indonesian

Modern science such as fluid mechanics has registers for efficient communication among peers.Inaccurate and unacceptable translations of registers may cause failure in communication across languages.For an accurate and acceptable translation, a translator needs a solid foundation in the aspects of phonology, morphology, syntax, and semantics.This study deals with the syntactic patterns of English and Indonesian that affect the translation of fluid mechanic terms.Applying the qualitative method, this study analyzes the translation of registers by focusing on nominal phrases and plural nouns.The findings show that the translation of nominal phrases changes the word structure from English and Indonesian.Meanwhile, in the translation of plural nouns, there are inconsistencies in the translation results for achieving naturalness.
Implementation of Computerized Curricular Assistive Tools (CCAT) And Its Effect on Student Preparedness for the New NCLEX-RN Test Plan

Undergraduate students achieving a first-time pass on the NCLEX is the ultimate outcome post-graduation. First-time pass rates on the NCLEX allow students to transition into their nursing careers smoothly; for nursing schools to receive the gold standard of a quality nursing educational program and reduce the nursing shortage. Therefore, it is the utmost responsibility and the highest concern of nursing programs to prepare their students to be successful on the NCLEX-RN exam. As technology demands evolve in nursing education curriculum and professional nursing practice, exploring CCAT as a strategy to better prepare students for the NCLEX-RN exam is imperative. Therefore, this evidence-based improvement project aims to investigate the effectiveness of integrating CCAT into the senior-level curriculum to better prepare students for the NCLEX-RN exam and achieve an increased first-attempt pass rate. The results of this study will provide recommendations based on the current evidence regarding CCAT as a strategy to prepare senior nursing students for this high-stakes exam.
Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition

Abstract Neural networks are commonly defined as ‘black-box’ models, meaning that the mechanism describing how they give predictions and perform decisions is not immediately clear or even understandable by humans. Therefore, Explainable Artificial Intelligence (xAI) aims at overcoming such limitation by providing explanations to Machine Learning (ML) algorithms and, consequently, making their outcomes reliable for users. However, different xAI methods may provide different explanations, both from a quantitative and a qualitative point of view, and the heterogeneity of approaches makes it difficult for a domain expert to select and interpret their result. In this work, we consider this issue in the context of a high-energy physics (HEP) use-case concerning muonic motion. In particular, we explored an array of xAI methods based on different approaches, and we tested their capabilities in our use-case. As a result, we obtained an array of potentially easy-to-understand and human-readable explanations of models’ predictions, and for each of them we describe strengths and drawbacks in this particular scenario, providing an interesting atlas on the convergent application of multiple xAI algorithms in a realistic context.
EDUCATING YOUTH ON PROJECT SUSTAINABILITY - PROJECT ENGAGEMENT AND RECOGNITION OF THE GREEN DEAL

The European Green Deal is a blueprint for a more sustainable life throughout the European Union and its member states.It is based on environmental protection and sensible consumption.However, implementing Green Deal policies necessitates the participation of local populations who are affected by pollution.This paper presents the significance that youth hold in contributing to the implementation and development of the European Green Deal and the circular economy as well as the key role they play in local self-government in order that the aims of the European Green Deal be achieved.To do so, three pilot studies (29 youth were engaged) and one comprehensive research (201 youth were engaged) were conducted on university and secondary school students in Republic of Serbia in 2022.Based on these, youth were found to not possess environmentally friendly habits, as well as adequate education on sustainability challenges.Theoretical and practical implication has been discussed.
Postfix Form to Infix Form

Even though infix expression is easy for humans to understand, it is hard for the computers to comprehend. According to Sayan Mahapatra from GeekforGeeks , “Prefix and Postfix expressions can be evaluated faster than an infix expression. This is because we don’t need to process any brackets or follow operator precedence rule.” Using postfix expression eliminates the problem of confusion due to precedence. For example, an infix expression such as 2$3&amp;amp;4. One does not know if 2$3 should be applied first or 3&amp;amp;4 without knowing the precedence . In addition, Postfix expression can be easily calculated with the Stack base algorithm .
A novel modified dandelion optimizer with application in power system stabilizer

&lt;p&gt;This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA) and the original dandelion optimizer (DO). The simulation results show that the proposed method, which is a modified dandelion optimizer, provides promising performance.&lt;/p&gt;
Machine learning classification analysis model community satisfaction with traditional market facilities as public service

&lt;p&gt;Traditional markets are public service facilities that can be utilized by the&lt;br /&gt;community. The market function is used place where sellers and buyers meet&lt;br /&gt;in conducting transactions. This study aims to build a machine learning&lt;br /&gt;classification analysis model in measuring community satisfaction with&lt;br /&gt;traditional market facilities. The analytical methods used include Fuzzy.&lt;br /&gt;multiple linear regression (MRL), artificial neural network (ANN), and&lt;br /&gt;decision tree (DT). Fuzzy is used to generate a pattern of rules in determining&lt;br /&gt;the level of satisfaction. MRL serves to measure and test the correlation of&lt;br /&gt;rules that have been formed. The ANN method is used to carry out the&lt;br /&gt;classification analysis process based on learning. In the final stage. DT is used&lt;br /&gt;to describe the decision tree of the analysis process. This study presents the&lt;br /&gt;results of machine learning analysis which is very good in determining&lt;br /&gt;satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy&lt;br /&gt;logic which can develop a classification rule pattern of 32 patterns. MRL also&lt;br /&gt;shows a significant correlation level of 81.1% based on the indicator variables.&lt;br /&gt;Overall, the machine learning classification analysis model can provide&lt;br /&gt;knowledge to be considered in the management of traditional markets as&lt;br /&gt;public service facilities.&lt;/p&gt;
Sentiment analysis of Chinese game reviews based on SKEP

As China's game industry enters the era of the stock market, improving the quality of game products in all aspects is gradually becoming an important part of the development of China's game industry. There exists a large amount of game review data on the Internet, and sentiment analysis of them can provide important help for game makers to make decisions. However, the huge scale of game reviews and the extensive use of metaphors and sarcasm make it difficult to conduct sentiment analysis. In this paper, we experiment with a publicly available Chinese game review dataset using the ERNIE pre-training model equipped with the SKEP method, which improves the model's masking strategies and pre-training objectives to specialize the model's sentiment analysis ability. In addition, we compared the performance of ERNIE1.0, RoBERTa, MacBERT, BERT-wwm, and Nezha. The precision, recall, and F1 score of SKEP are higher than those of the models for comparison studies, which confirms the effectiveness of SKEP in this area.