Reviewerly/exHarmony huggingface dataset:
Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm

Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm. Firstly, this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features, which helps to improve the feature extraction effect. Meanwhile, adaptive synthetic sampling (ADASYN) is introduced to deal with class imbalanced data, and integrated with the Adaboost classifier. Finally, this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts. Experimentally, this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision. Moreover, this article compares and discusses the state of art methods with our method in four aspects: data acquisition, data preprocessing, feature extraction, and classifier design. Both experiment and discussion validate the effectiveness of our model.
APPLICATION AND ATTRIBUTE ANALYSIS IN THE MODEL OF CLASSIFYING HEART DISEASE

The heart is the central center in the human circulatory system. A malfunction of the heart that is not functioning is a condition in which the heart cannot carry out its duties properly. Selection of features that can reduce a very large dataset and in a data set that is not suitable can use a reduction model. The classification process is strongly influenced by an attribute. Various types of inappropriate redundancy have a negative effect on classification accuracy. Heart disease data was taken from the UCI Machine Learning Repository dataset. In this study, the researchers used the K-Nearest Neighbor (KNN) algorithm where the K-Nearest Neighbor algorithm can classify the results of heart disease accurately. The results are as follows 1.67358 rank one 1.33949 rank two, 1.27260 rank three, 1.2528 rank four, 1.24193 rank last
Smart Contracts and Blockchain based E-Voting

Voting is a fundamental right for every citizen in a country to express their views and select their representatives for shaping the society. However, the current voting process is vulnerable to security breaches and frauds. To address this issue, e-voting systems have emerged using the blockchain technology. By utilizing this technology, the voting process can become more secure, transparent, and fraud-proof. For every vote, separate blocks are created and decentralized to maintain transparency and accuracy in the voting process. The blockchain technology, working on a peer-to-peer network, guarantees secure and transparent record-keeping and verification of each vote. Additionally, the system’s immutability property ensures the voting process’s protection. The data is stored in encrypted blocks with hash values, and smart contract concepts detect fake votes and multiple voting attempts. This research work presents an in-depth explanation of how the blockchain algorithm records voting results and voter information at every election location, highlighting the technology’s fundamental structure and characteristics for electronic voting.
Personality Detection of Twitter Social Media Users using the Support Vector Machine Method

Personality is a person's psychological tendency to carry out certain social behaviors, whether in the form of feelings, thoughts, attitudes and will or actions. Big Five is the most popular and widely used personality model, therefore this proposal uses the Big Five Personality model. In this technological era, humans interact using social media. One of the fastest growing social media is Twitter. Twitter is a social media used by all groups. Every human being has a different personality. Personality detectors are needed for employee recruitment to dig up information about the personality of prospective employees. So personality detection or BigFive Personality can be done through tweets that are shared on Twitter. With this, it is necessary to detect personality using the Support Vector Machine (SVM) method. From the results of the study, it was found that the maximum performance parameter combination in detecting personality on Twitter users was a combination of Linear parameters and C = 10 which obtained an accuracy of 0.979. The data used is the result of crawling on the Twitter site with 146 user usernames and 38853 tweets.
Defining Anonymity Properties of Datasets with the Compliance Assertion Language (COMPASS)

Organizations that manage Personally Identifiable Information cannot share this information directly due to legal restrictions. Meanwhile, there are several solutions that support the anonymization of these types of data to make these available to a wider audience. For these solutions, it is important that the corresponding anonymization modules guarantee legal properties. In general, due to the scale and complexity of the software, it is difficult to prove that it does not violate these properties in some cases. This article proposes a new approach that addresses this challenge. The approach provides a software component that checks the output of an anonymization module against editable legal constraints—the Privacy-Enhancing Verification Component (PE-VC). An organization can formulate these constraints separately from the software using the new Compliance Assertion Language. Because the PE-VC is a carefully developed and verified module that can be used without modification for different anonymization modules, an auditor only needs to check the specified assertions and not the software itself. This approach ensures a much higher level of confidence in the correctness of the output of an anonymization software.
Clifford Circuits can be Properly PAC Learned if and only if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">RP</mml:mtext></mml:mrow><mml:mo>=</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">NP</mml:mtext></mml:mrow></mml:math>

Given a dataset of input states, measurements, and probabilities, is it possible to efficiently predict the measurement probabilities associated with a quantum circuit? Recent work of Caro and Datta \cite{2020Caro} studied the problem of PAC learning quantum circuits in an information theoretic sense, leaving open questions of computational efficiency. In particular, one candidate class of circuits for which an efficient learner might have been possible was that of Clifford circuits, since the corresponding set of states generated by such circuits, called stabilizer states, are known to be efficiently PAC learnable \cite{rocchetto2018stabiliser}. Here we provide a negative result, showing that proper learning of CNOT circuits with 1/ poly(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>) error is hard for classical learners unless <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">RP = NP</mml:mtext></mml:mrow></mml:math>, ruling out the possibility of strong learners under standard complexity theoretic assumptions. As the classical analogue and subset of Clifford circuits, this naturally leads to a hardness result for Clifford circuits as well. Additionally, we show that if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">RP = NP</mml:mtext></mml:mrow></mml:math> then there would exist efficient proper learning algorithms for CNOT and Clifford circuits. By similar arguments, we also find that an efficient proper quantum learner for such circuits exists if and only if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">NP &amp;#x2286; RQP</mml:mtext></mml:mrow></mml:math>. We leave open the problem of hardness for improper learning or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi><mml:mo class="MJX-tex-caligraphic" mathvariant="script" stretchy="false">(</mml:mo><mml:mn class="MJX-tex-caligraphic" mathvariant="script">1</mml:mn><mml:mo class="MJX-tex-caligraphic" mathvariant="script" stretchy="false">)</mml:mo></mml:mrow></mml:math> error to future work.
Interpolating discriminant functions in high-dimensional Gaussian latent mixtures

Abstract This paper considers binary classification of high-dimensional features under a postulated model with a low-dimensional latent Gaussian mixture structure and nonvanishing noise. A generalized least-squares estimator is used to estimate the direction of the optimal separating hyperplane. The estimated hyperplane is shown to interpolate on the training data. While the direction vector can be consistently estimated, as could be expected from recent results in linear regression, a naive plug-in estimate fails to consistently estimate the intercept. A simple correction, which requires an independent hold-out sample, renders the procedure minimax optimal in many scenarios. The interpolation property of the latter procedure can be retained, but surprisingly depends on the way the labels are encoded.
Safe Trajectory Planning for Safety Critical Drone Delivery

Ensuring the safety of a drone over the course of its flight is essential. This work presents a pre-flight planner that can make supported claims about the safety of the system in the case of engine failure, which is cited to be the most damaging failure mode. The developed algorithm utilizes geographical data to generate the shortest trajectory under safety constraints while guaranteeing no violations of restricted airspace. This trajectory is optimized using the A* (A star) algorithm. The generated trajectory performs better in terms of proximity to safe landing spots throughout the course of the flight when compared to a selection of alternative trajectories, while minimizing the total length, as expected.
Enhancing Usability in AR and Non-AR Educational Technology: An Embodied Approach to Geometric Transformations

The present study investigates user interaction with the augmented reality (AR) educational application, BRICKxAR/T. BRICKxAR/T uses AR to facilitate an embodied approach for learning geometric transformations and their mathematics through a virtual and physical interactive environment with the goal of helping in cognitive offloading and spatial thinking related to 3D transformations and matrix algebra. Two usability studies were conducted to examine users’ interaction and experience with two versions of the educational application BRICKxAR/T (AR and non-AR). By using both quantitative and qualitative methodology, usability studies assessed users’ interactions and experience with specific in-app functions and general evaluation of the app’s companion materials (i.e., introduction and app interaction videos). Findings were used to provide recommendations that could be implemented to enhance the application. Implications of findings for a variety of other educational technologies will be discussed.
Behavior-Aware English Reading Article Recommendation System Using Online Distilled Deep Q-Learning

Due to the differences of students' English proficiency and the rapid changes in reading interests, online personalized English reading recommendation is a highly challenging problem. Although some works have been proposed to address the dynamic change of recommendation, there are two issues with these methods. First, it only considers whether students have read the recommended articles. Second, these methods often fail to capture the real-time changing interests of users. To address the above challenges, a deep Q-network based recommendation framework was proposed. The authors further use the user's behavior and scores as reward information to get more user's feedback. In addition, a personalized adaptive module was introduced to capture the short-term interests on the fly and utilized the consistent loss of KL divergence to distill the knowledge from the online model. Extensive experiments on the offline and online dataset in the IWiLL website demonstrate the superior performance of the method.
ANALISIS TROJAN DAN SPYWARE MENGGUNAKAN METODE HYBRID ANALYSIS

Malicious Software or malware is software created to damage a computer system. The increase in internet users is also in line with the increase in the use of software. However, there are still many users who still use pirated software because it is relatively free and easy to obtain. Pirated software is usually embedded with dangerous malware such as Trojans and spyware. All crimes of spreading this malware are always related to stealing credit card information, internet banking and other cybercrimes. To prove that the software installed and used on a computer is malicious software, digital forensics is required by analyzing the software. Hybrid analysis technique is a combination of static and dynamic analysis which is suitable for analyzing malware activity. Based on the results of the analysis that has been carried out, the ryuk.bin trojan has evolved and the malware forms new malware files when it is run and also changes and destroys the original files on the system.
Implementation of Mel-Frequency Cepstral Coefficient as Feature Extraction using K-Nearest Neighbor for Emotion Detection Based on Voice Intonation

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN as an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts on YouTube. Some of the methods used in this study are pitch shifting for data augmentation, MFCC for feature extraction on audio data, basic statistics for taking the mean, median, min, max, standard deviation for each coefficient, Min max scaler for the normalization process and KNN for the method classification.Findings/result: Because testing is carried out separately for each gender, there are two classification models. In the male model, the highest accuracy was obtained at 88.8% and is included in the good fit model. In the female model, the highest accuracy was obtained at 92.5%, but the model was unable to correctly classify emotions in the new data. This condition is called overfitting. After testing, the cause of this condition was because the pitch shifting augmentation process of one tone in women was unable to solve the problem of the training data size being too small and not containing enough data samples to accurately represent all possible input data values.Originality/value/state of the art: The research data used in this study has never been used in previous studies because the research data is obtained by downloading from Youtube and then processed until the data is ready to be used for research.
Robust Control of a Customized Lane Change Maneuver

A full acceptation of Autonomous Vehicles (AV) could be achieved by the customization of different driving services. One of these could consist of choosing the driving style and consequently its impact on the generated trajectory shapes, particularly in overtaking maneuvers. A previous work introduced a driving style adaptive path planning method able to reproduce different behaviors during overtaking maneuvers. This paper focuses on the integration of the aforementioned path planning algorithm in the control scheme of an autonomous vehicle. The generated adapted path is the reference of a robust feedback feed-forward robust controller. The overall path generation and control are validated in simulation as well as in real-world conditions. The obtained results show a good correlation between simulation and experimental results.
Weakly-Supervised Multimodal Learning for Predicting the Gender of Twitter Users

Social media platforms, e.g. Twitter, are significant sources of information, with various users posting vast amounts of content every day. Analyzing such content has the potential to offer valuable insights for commercial and research purposes. To gain a comprehensive understanding of the information, it is crucial to consider the demographics of users, with gender being a particularly important factor. Nevertheless, the gender of Twitter’s users is not usually available. Predicting the gender of Twitter’s users from tweet data becomes more challenging. In this paper, we introduce a weakly supervised method to automatically build the supervision data. The experimental result show that our weak supervision component could generate well-annotated data automatically with an accuracy rate exceeding 85%. Furthermore, we conduct a comparative analysis of various multimodal learning architectures to predict the gender of Twitter users using weak supervision data. In the study, five multimodal learning architectures: 1) Early Fusion, 2) Late Fusion, 3) Dense Fusion, 4) Caption Fusion, and 5) Ensemble Fusion, are proposed. The experimental results on the evaluation data indicate that Caption Fusion outperforms the other multimodal learning architectures and baselines.
Clustering Approach Using Multiobjective Non-Dominated Sorting Teaching Learning Based Optimization with Kernel Fuzzy C-Means Algorithm (NSTLBO-KFCM)

Clustering has evolved over a period and has become more sensitive and precise with the outcomes. No clustering algorithm is suited for all types of data sets, but partitional clustering lays foundation for almost all-important clustering algorithms. Single solution can be achieved in case of single objective clustering, but in multiobjective clustering the solution becomes set of solution vector. Multiobjective clustering has made the pattern recognition possible in more than one dimension. The solution strategy of Multiobjective clustering is conceptualized on Pareto dominance. A multiobjective NSTLBO-KFCM is implemented in this paper using non-dominated sorting technique. The clustering algorithm is then compared with multiobjective non dominated sorting genetic algorithm third generation-based kernel fuzzy C-means (NSGAIII-KFCM) algorithm and multiobjective particle swarm optimization-based kernel fuzzy C-means (MPSO-KFCM) algorithm. The algorithm is also compared with non-dominated sorting teaching learning-based optimization with fuzzy C-means (NSTLBO-FCM) algorithm and the results show that NSTLBO-KFCM is superior clustering algorithm.
Which CDN to Download From? A Client and Server Strategies

Content Delivery Networks (CDNs) has been evolved to enable different video streaming services to deliver media content over the Internet with less latency and improved quality. However, using only a single CDN is highly vulnerable to outages and crashes, resulting in a poor viewer experience. Regardless of viewers, traffic, or media content, a single CDN will be never sufficient to satisfy viewers' quality of experience (QoE) requirements. To avoid single CDN issues, leveraging multiple CDNs from multiple providers, refers to multi-CDN, helps in improving performance, increasing geographic coverage, and alleviating outages. An essential part in multi-CDN solutions is the decision to select the best performing CDN in real-time, depending on periodic measurements of CDNs and video players. While multi-CDN architecture provides tremendous benefits, it has not been well investigated and integrated with the industry. This paper highlights various decision strategies for real-time CDN selection that helps content providers select the right solution aligned with their goals and business.
A tough, anticorrosive hydrogel consisting of bio-friendly resources for conductive and electromagnetic shielding materials

This paper explores the use of sodium alginate, polyvinyl alcohol and polyethylene glycol. Chemical cross-linking creates a high-toughness support structure, forming a corrosion-resistant, high-toughness and electromagnetic shielding hydrogel.
AutoAI using IBM Watson

Users are given the ability to automate the process of constructing and deploying machine learning models through the use of a tool called AutoAI.It automates the most time-consuming portions of the data science process, such as feature engineering, model selection, and hyperparameter tuning, by making use of a combination of artificial intelligence and machine learning approaches [4].AutoAI is also capable of providing explanations for its suggestions and forecasts, which makes it much simpler for users to comprehend the thought process that underlies the model's outputs.This tool has the potential to drastically reduce the amount of time and resources required to develop efficient machine learning models, which makes it a useful asset for businesses as well as researchers who are trying to leverage the power of AI in their work.
GNN Cleaner: Label Cleaner for Graph Structured Data

Graph Neural Network (GNN) has emerged as a predominant tool for graph data analysis. Despite their proliferation, the low-quality labels of many real-world graphs will undermine their performance dramatically. Existing studies on learning neural networks with noisy labels mainly focus on independent data and thus cannot fully exploit the structural information of graph data. Currently, there are few studies of robustness to noisy labels for graph-structured data even if this problem is commonly seen in real-world settings. To remedy this deficiency, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GNN Cleaner</i> which utilizes structural information of graph data to combat noisy labels. More specifically, a pseudo label is computed from the neighboring labels for each node in the training set via a modified version of label propagation. Additionally, a novel method is developed to learn to correct the labels adaptively and dynamically. Extensive experiments show that GNN Cleaner can train GNNs robustly and correct both the synthetic and real-world noisy labels even if the noise is severe. Moreover, GNN Cleaner is model-agnostic and can be combined with various GNNs to improve their robustness against label noise.