Reviewerly/exHarmony huggingface dataset:
Big data cluster tendency techniques with spectral features for efficient data partitions assessment

Cluster tendency assessment in big data poses a challenge, particularly for non-compact separated (non-CS) datasets with irregular boundaries. This paper introduces a novel Spectral-Based Visual Technique (SVT) to address this limitation. Determining the similarity features for the data objects is a crucial computation in data clustering. Distance measures such as Euclidean and cosine are widely employed in clustering applications. By pre-determining cluster tendency, the quality of clusters is obtained using the algorithms of Visual Assessment of Cluster Tendency (VAT) and cosine-based VAT (cVAT). Both VAT and cVAT utilize Euclidean and cosine distance measures to identify the similarity features of objects. For extensive data cluster tendency assessment, an extended concept of VAT, Clustering using Improved Visual Assessment of Tendency (ClusiVAT), is employed to derive clusters with scalable amounts of time and memory loads. However, it operates efficiently for Compactly Separated (CS) datasets. The research gap lies in the need to deliver the quality of big data partitions (or clusters) for non-compact separated (non-CS) datasets. Thus, this paper proposes a spectral-based visual cluster tendency technique to address the challenge of significant data clustering for non-CS datasets. Experimental analysis employs benchmarked datasets to illustrate the performance of the proposed work compared to other techniques.
Farming assistance web service

The Farming Assistance Web Service aims to revolutionize the agricultural sector by providing a comprehensive online platform to help farmers optimize their crop cultivation and overall farming practices. This web service leverages technology to offer a range of tools, information, and services accessible through web browsers or mobile devices. This innovative platform makes it possible for suppliers, retailers, and farmers to communicate effectively. Farmers can also contact the right merchants, and the farmers are notified through SMS when dealers submit an advertisement or an offer. This system uses Firebase to allow for push notifications to be sent to users across platforms (iOS, Android, and web) to engage and re-engage with the app's audience and also to utilize their farmer login to register their complaints with the proper dealers or authorities, and the authorities will regularly access that page using their login ids and passwords. Additionally, it offers seamless integration of data, including access to agricultural information, crop suggestions, pest control guidance, and weather forecasts.
Provably Avoiding Geographic Regions for Tor’s Onion Services

Tor, a peer-to-peer anonymous communication system, is one of the most effective tools in providing free and open communication online. Many of the attacks on Tor’s anonymity occur when an adversary can intercept a user’s traffic; it is thus useful to limit how much of a user’s traffic can enter potentially adversarial networks. Recent work has demonstrated that careful circuit creation can allow users to provably avoid geographic regions that a user expects to be adversarial. These prior systems leverage the fact that a user has complete control over the circuits they create. Unfortunately, that work does not apply to onion services (formerly known as “hidden services”), in which no one entity knows the full circuit between user and hidden service. In this work, we present the design, implementation, and evaluation of DeTor $$_{OS}$$ , the first provable geographic avoidance system for onion services. We demonstrate how recent work to build and deploy programmable middleboxes onto the Tor network allows us to take existing techniques like these and deploy them in scenarios that were not possible before. DeTor $$_{OS}$$ is immediately deployable as it is built using programmable middleboxes, meaning it does not require either the Tor protocol or its source code to be modified. This work also raises a number of interesting questions about extensions of provable geographical routing to other scenarios and threat models, as well as reinforces how the notion of programmable middleboxes can allow for the deployment of both existing and new techniques in novel ways in anonymity networks.
R2: Boosting Liquidity in Payment Channel Networks with Online Admission Control

Payment channel networks (PCNs) are a promising technology to improve the scalability of cryptocurrencies. PCNs, however, face the challenge that the frequent usage of certain routes may deplete channels in one direction, and hence prevent further transactions. In order to reap the full potential of PCNs, recharging and rebalancing mechanisms are required to provision channels, as well as an admission control logic to decide which transactions to reject in case capacity is insufficient. This paper presents a formal model of this optimisation problem. In particular, we consider an online algorithms perspective, where transactions arrive over time in an unpredictable manner. Our main contributions are competitive online algorithms which come with provable guarantees over time. We empirically evaluate our algorithms on randomly generated transactions to compare the average performance of our algorithms to our theoretical bounds. We also show how this model and approach differs from related problems in classic communication networks.
Short Paper: DeFi Deception—Uncovering the Prevalence of Rugpulls in Cryptocurrency Projects

DeFi has attracted legitimate investors and scammers alike. The paper presents an empirical investigation into the prevalence of rugpulls, a scam where cryptocurrency project developers exit without fully delivering and leave investors in the wind. Using forum data, 101 rugpulls from 6 different types of DeFi services are documented. ICOs form the majority of the rugpulls, most of which were active for less than six months before scamming out. ICOs rugpulled in 2021 were active for a much longer time than those that were rugpulled later on, perhaps pointing to new entrants intending to pull the rug. Through qualitative thematic analysis, we discover that these schemes primarily use authoritative and financial lures at the announcement stage of the project to mimic legitimate projects.
Optimizing database efficiency: Empowering systems with data mining

Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10–25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics.
Intelligent Chain: A Safer Decision-Making Framework with Blockchain-Based Incentives

AbstractMany strategies use ML algorithms for intelligent IDS as part of the development of IoT security. These algorithms are quite susceptible to being manipulated to provide biased results. A variety of contradictory threats are constantly present throughout the use of ML, such as the erroneous poisoning of learning data sets or the alteration of model parameters. So, in this post, we’ve suggested a blockchain-based approach to using intelligent contracts to secure the decision-making process of the learning model. We will dissect decision-making functions based on the categorization of SVM and MLP on the chain for a more comprehensive Blockchain integration.Keywords: Blockchainmachine learningsmart contractdecision functionSVMMLP Disclosure statementNo potential conflict of interest was reported by the authors.
Studi Optimalisasi Rute Pengangkutan Sampah Stationary Container System Berbasis Sistem Informasi Geospasial (SIG) di Kecamatan Balikpapan Timur

Waste transportation in East Balikpapan District currently does not have an effective waste transportation route, this has resulted in not optimal waste transportation, which is indicated by the large amount of waste accumulated at the TPS. Based on these problems, East Balikpapan requires optimizing waste management, especially in waste transportation, to reduce the accumulation of waste in TPS, which can cause environmental pollution. The research procedure used is to optimize the Waste transportation route in the East Balikpapan District using a qualitative descriptive method. Through this method, this research can improve the waste transportation system at the research location by providing improvements that are modelled using Geographic Information Systems (GIS). Determination of the most effective transportation route will be adjusted to the condition of the road being traversed, the distance travelled and the speed of the vehicle in the waste transportation process as well as the time it takes for the SCS (stationary container system) system to transport waste. Based on the existing conditions, transporting two dump trucks takes 7 hours, and one dump truck takes 6 hours and 30 minutes, with a distance of 69 km, 68 km and 104 km. The distance and time for transporting waste under existing conditions can be optimized with alternative routes, optimization results for the three dump truck fleets, namely the distance of 68 km with a time of 6 hours, a distance of 46 km in 5 hours and a distance of 68 km with a time of 5 hours in 2 trips per day.
Credit Card Fraud Prediction Based on Machine Learning Algorithms

The escalating use of the Internet has led to a surge in online shopping and e-commerce, resulting in a corresponding increase in credit card fraud incidents. Therefore, this research focuses on employing machine learning techniques, which offer enhanced precision and efficiency compared to manual detection, to identify fraudulent activities. To establish the association between credit card transaction attributes and the presence of fraudsters, this study initially gathers data from Kaggle, subsequently normalizing the collected data. Furthermore, the data exhibits severe imbalance, leading to overfitting concerns. To ascertain feature correlations, a correlation heatmap is constructed. Moreover, this investigation selects three models for analysis. Finally, the performance of each model is evaluated using a confusion matrix and derived metrics. The findings reveal that both the decision tree and random forest models exhibit optimal performance, achieving 100% across all indicators. The most influential factors in determining credit card fraud involve the ratio to median purchase price and the geographical proximity of the transaction location to the cardholder's residence.
Learning quantum finite automata with queries

Abstract Learning finite automata (termed as model learning ) has become an important field in machine learning and has been useful realistic applications. Quantum finite automata (QFA) are simple models of quantum computers with finite memory. Due to their simplicity, QFA have well physical realizability, but one-way QFA still have essential advantages over classical finite automata with regard to state complexity (two-way QFA are more powerful than classical finite automata in computation ability as well). As a different problem in quantum learning theory and quantum machine learning , in this paper, our purpose is to initiate the study of learning QFA with queries (naturally it may be termed as quantum model learning ), and the main results are regarding learning two basic one-way QFA (1QFA): (1) we propose a learning algorithm for measure-once 1QFA (MO-1QFA) with query complexity of polynomial time and (2) we propose a learning algorithm for measure-many 1QFA (MM-1QFA) with query complexity of polynomial time, as well.
Smart-Systems in STEM Education

The article “Smart-Systems in STEM Education” explores the significance of integrating “Smart-systems” technologies into STEM (Science, Technology, Engineering, and Mathematics) education. The article highlights the role of “Smart-systems” in preparing students for the future by providing practical experiences and fostering critical thinking and problem-solving skills. It discusses various technologies associated with “Smart-systems,” such as robotics, IoT, AI, and data analytics, and their applications in STEM education. The article also presents a range of resources available for educators and students, including online courses, educational websites, maker spaces, and competitions. By leveraging these resources, educators can create engaging learning environments that inspire students to explore and pursue careers in emerging fields. The article emphasizes the benefits of incorporating “Smart-systems” in STEM education, including the development of technological literacy, interdisciplinary learning, and the cultivation of skills necessary for the digital age. Ultimately, embracing “Smart-systems” in STEM education empowers students to become the next generation of innovators and problem solvers who can contribute to a rapidly evolving technological landscape.
Quality Assessment and Assurance of Machine Learning Systems: A Comprehensive Approach

Machine learning (ML) is opening up new opportunities for the development of innovative systems across a wide range of industries. However, assessing and ensuring the quality of systems with ML components introduces unique challenges related to inherent characteristics of such components like data centricity and unpredictable behavior. Traditional software quality assessment and assurance methods may not be sufficient for ML systems: (1) they focus on software code, while ML systems’ quality is influenced by the characteristics of the data and the algorithms used to create ML components; (2) they do not cover the emerging quality characteristics specific to ML systems, such as interpretability, explainability, fairness and trustworthiness. This PhD project aims to develop a comprehensive approach for assessing and assuring the quality of ML systems, with a focus on bias detection and prevention. The research will (1) explore the problem of bias in production ML systems; (2) analyze the gaps in existing software quality models and methods related to bias detection and prevention; and (3) propose an improved approach to quality assessment and assurance to address the challenges associated with bias in ML systems. The results of this PhD project are expected to contribute to the development of better models and methods for assessing and assuring the quality of ML systems, as well as have practical implications for industries that rely on ML systems to automate complex tasks, facilitate decision-making processes and gain insights from large amounts of data.
Cut2Self: A single image based self‐supervised denoiser

Abstract Despite the recent upsurge of self‐supervised methods in single image denoising, achieving robustness and efficiency of performance is still challenging due to some prevalent issues like identity mapping, overfitting, and increased variance of network predictions. Recent self‐supervised approaches prescribe a dropout‐based single‐pixel masking strategy in this regard. However, real camera noise is signal‐dependent, and typically poses trivial changes to the images. Hence, such a strategy still preserves contextual information about target location even after dropping them out, leading to an identity mapping and overfitting problems in practice. Here, Cut2Self, a new denoising method to address this issue, which cuts out random block‐regions instead of singleton pixels to provide the higher possibility to remove contextual information from the neighbouring pixels, thus reducing identity mapping chances while being resilient against overfitting is proposed. Cut2Self creates distinct training pairs for each training iteration by randomly cutting out square regions of input and sending them to the denoising network. Thus, iteration‐wise different network predictions are generated, which are then assembled to generate the final denoised output. Cut2Self is evaluated with synthetic and real‐world noise, visualising its consistent denoising performance compared to other supervised, unsupervised, and self‐supervised methods.
Vulnerability Detection of Smart Contracts Based on Bidirectional GRU and Attention Mechanism

The paper is devoted to methods of detecting vulnerabilities of smart contracts using machine learning. The purpose of the study is to improve the accuracy of detecting the reentrancy vulnerability of smart contracts by implementing new machine learning models. A thorough analysis of the current literature was performed and the shortcomings of the existing tools for detecting vulnerabilities of smart contracts were identified. In particular, insufficient accuracy and low adaptability of existing models to new vulnerabilities were noted. To solve these problems, a new model based on a kind of recurrent neural networks with a gating mechanism, namely a bidirectional GRU with an attention mechanism, was proposed to detect the reentrancy vulnerability at the Solidity code level. Using the Word2vec model, the source code of smart contracts was transformed into an array of vectors and used as input to the neural network. Precision, recall and F-beta score were used to evaluate the developed model. 500 smart contract source codes from the Ethereum blockchain were used to train the model, 250 of which had the reentrancy vulnerability. The constructed model was compared with Simple RNN, LSTM, BLSTM, BGRU and BLSTM-ATT models. The obtained results showed that the developed model is ahead of the listed models. The closest values of the metrics were obtained by the BLSTM-ATT model, while the developed BGRU-ATT model uses significantly fewer parameters that need to be optimized, which reduces the training time of the model to detect new vulnerabilities.
Frequency‐structure‐aware modelling for unsupervised zero‐shot cross‐domain heterogeneous face translation

Abstract Unsupervised heterogeneous face translation requires obtaining heterogeneous images with the same identities at training time, limiting the use in unconstrained real‐world scenarios. Taking a step further towards unconstrained heterogeneous face translation, the authors explore unsupervised zero‐shot heterogeneous face translation for the first time, which is expected to synthesize images that resemble the style of target images and whose identities in the source domain have been preserved but never seen in the target domain during training. Essentially, asymmetry between heterogeneous faces under the zero‐shot setting further exacerbates the distortion and blurring of the translated images. The authors therefore propose a novel frequency‐structure‐guided regularization, which can jointly encourage to capture detailed textures and maintain identity consistency. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, the authors verify the effectiveness of the proposed framework.
Digital Technologies as a Means of Forming Subject-Methodical Competence Future Primary School Teachers

The article updates the problem of the application of digital technologies in various professional methods and their integration in the educational process of institutions of higher pedagogical education. According to the authors, the formation of subject-methodical competence of future primary school teachers is not possible without the formation of abilities: navigate in the information space, use existing and, if necessary, create new electronic resources; use modern digital technologies in the educational process. For the formation of professional skills and abilities in students of pedagogical specialties, the quality of educational content of a methodological nature, created by means of digital technologies, is essential. In accordance with the purpose of digital technologies and their application in professional teaching methods, the most effective sets of programs and applications were determined for students to create educational and methodological support for the educational process in primary school. Such as: mind maps (summary diagram of interactive lessons), presentations (demonstration of educational materials), comics (visualization of educational information in frames-pictures), word clouds (visualization of educational content in keywords), infographics (visualization and structuring of a large amount of educational information), virtual boards (coordination of work in groups in class, during the web quests, organization of student communication), interactive tasks and online tests (creation of exercises to acquire skills, development of tasks and tests to reveal knowledge and skills).
Design and Implementation of Didactic Process Based on Simulation

The problem of designing didactic processes has not been solved to this day, although some specific issues are considered. The article presents a generalized approach model based on the reference ADDIE model including the organisational level to the design and implementation of the didactic process. The model uses Bloom’s taxonomy and Gardner’s Theory of Multiple Intelligences. The model has been modified towards a dynamic design oriented on quality, efficiency and adaptive learning. A competency-based decomposition is used. The lowest level of decomposition is the activities level where the information flows as well as the learning and forgetting are taken into account. The competences-objectives-activities-data linking is discussed. The level of activities is represented in the form of an electrical-like network. The network represents differential equations describing dynamic learning and forgetting as well as the structure and information flows of the didactic process. The element models can be easily extended. The networks can be simulated and optimized. The results can be used during the design process. The simulation result as well as the structure of network equations enables inference. The article presents the results of simulations of some aspects of didactic processes including scheduling and sequencing, gap effect, workload, predicting achievements and effectiveness.
Penggunaan Jar Test untuk Penentuan Dosis dan Komposisi Optimum Komposit Koagulan Poly Aluminium Chloride (PAC) – Kitosan dalam Menurunkan TSS dan COD

Laundry waste is a pollutant that has pollutant parameters such as TSS and COD. One alternative processing is flocculation-coagulation using synthetic coagulants. The advantage of synthetic coagulants is that they are able to remove high concentrations of pollutants, but the disadvantages are that they produce quite a lot of chemical sludge and the pH of the water becomes acidic because the higher the concentration, the more coagulant is used. Previous research explains that chitosan at low doses has good penyisihan efficiency so that when combined with PAC it is hoped that it can increase penyisihan efficiency with less sludge. The purpose of this study was to determine the optimum dosage and composition for PAC and chitosan coagulant composites using the Jar Test. The Jar Test method for determining the optimum dose uses fast stirring at 140 rpm for 2 minutes and then slow stirring at 60 rpm for 15 minutes and settling for 30 minutes. Determination of the optimum dose by adding a coagulant composite of 300 ppm, 500 ppm, and 700 ppm and by varying the composition at each dose, namely 1:1, 1:3, and 3:1 between PAC-chitosan. The best conditions from the study obtained an optimum dose of 700 ppm and an optimum composition of 3:1 between PAC and chitosan resulting in COD and TSS penyisihan efficiencies of 70.04% and 90.82%.