Reviewerly/exHarmony huggingface dataset:
The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation

One of the datasets used to predict heart disease is UCI dataset. unfortunately, the dataset contains missing data. the missing data dramatically affects the performance of the backpropagation classification method. One of the techniques used to handle missing data is feature selection. This study compares the ReliefF and the C4.5 algorithm in feature selection to handle missing data. The results of these algorithms are applied to the classification of heart disease using the Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are an accuracy of 82.653%, a precision of 82.7%, and a recall of 82.7%. The performance results of the C4.5 and backpropagation are an accuracy of 80.61%, a precision of 80.4%, and a recall of 80.6%. Based on the results it can be concluded that the ReliefF gives better performance results on backpropagation than the performance results of the C4.5. Although, the results of C4.5 are below ReliefF but the results are quite satisfactory because of the accuracy, precision and recall results obtained above 80%. This shows that ReliefF and C4.5 can select features that affect the UCI heart disease patient dataset.
Improved quantum error correction with randomized compiling

Current hardware for quantum computing suffers from high levels of noise, and so to achieve practical fault-tolerant quantum computing will require powerful and efficient methods to correct for errors in quantum circuits. Here, we explore the role and effectiveness of using noise tailoring techniques to improve the performance of error correcting codes. Noise tailoring methods such as randomized compiling (RC) convert complex coherent noise processes to effective stochastic noise. While it is known that this can be leveraged to design efficient diagnostic tools, we explore its impact on the performance of error correcting codes. Of particular interest is the important class of coherent errors, arising from control errors, where RC has the maximum effect -- converting these into purely stochastic errors. For these errors, we show here that RC delivers an improvement in performance of the concatenated Steane code by several orders of magnitude. We also show that below a threshold rotation angle, the gains in logical fidelity can be arbitrarily magnified by increasing the size of the codes. These results suggest that using randomized compiling can lead to a significant reduction in the resource overhead required to achieve fault tolerance.
PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PADA ULASAN PELANGGAN HOTEL DI TRIPADVISOR

Indonesia is an archipelagic country that has very beautiful nature, in addition to the natural beauty of cultural diversity is also one of the factors Indonesia has a tourist attraction. One of the effects of Indonesia's natural beauty and cultural diversity can be seen from the increase in hotel occupancy rates. This hotel analysis system design uses training data and test data from the tripadvisor website. Tripadvisor is a website that focuses on tourism. on tripadvisor there are a lot of services offered ranging from transportation, lodging, travel experiences, and restaurants. One of the useful features of tripadvisor is the review column, this review column can be used to do research. visitor reviews from the tripadvisor comments column can be used as a value. to visualize and see people's emotions how the services provided by the hotel to visitors. The research phase starts from scrapping data from the triapadvisor review column, preprocessing data, word weighting, SVM, and evaluation with a confusion matrix. The data taken from the review column is done by web scraping technique. This study uses data from 3000 reviews from 15 hotels. The results of the classification will then be evaluated with a confusion matrix. The highest accuracy result will be used as a model for classification. the classification results will be displayed in the form of detailed tables and diagrams that describe the percentage of sentiment classification results.
PENERAPAAN GATED RECURRENT UNIT UNTUK PREDIKSI ZAT PENCEMAR UDARA

Air pollution caused by air pollutant substances is one of the problems of great concern in big cities, including the city of Jakarta. On June 16, 2022, Jakarta has been named the city with the worst source of air pollution in the world. This of course makes the residents of Jakarta and its surroundings feel worried. The purpose of designing this system is to predict air pollutants in DKI Jakarta using the website-based Gated Recurrent Unit (GRU) method. Where the test results from the GRU method produce different predictive values. The MAPE evaluation resulted in good predictions using the GRU method for air pollutants of PM10, SO2, CO, and O3 types with an average MAPE value of less than 50%. However, there are quite bad results for the type of NO2 substance, because it produces a MAPE value of more than 50%. Meanwhile, in the RMSE evaluation, all air pollutants produced an average value of no more than 20% so that it can be said that the GRU method produces predictions that are quite accurate for predicting air pollutants in the DKI Jakarta area.
Upaya Meningkatkan Minat Membaca Masyarakat Desa Cibeuteung Udik Melalui Program ‘Saung Baca’

The purpose of this service is to increase interest in reading for early childhood children in Cibeteung Udik Village, Kp. Mosque Block. As a result of this activity, KKN students were able to complete the program, namely by establishing a 'Saung Baca' and increasing children's interest in reading.
Anomaly Detection in IoT Data

This work describes the performance-evaluation of various unsupervised classical machine learning algorithms in time series outlier detection. The aim is to test the robustness of known classical models that act as baselines in anomaly detection. IoT offers flexibility for various anomalies detection algorithms to be tested since the data collected is voluminous and the types of anomalies found are diverse. By deploying fine-tuned, long-established models, researchers can improve on the quality of the data they release from or use in various studies. This work also provides an insight into how time series data properties such as non-stationarity can affect anomaly detection and how operations such as windowing can be used to mitigate the effects and achieve desirable results. The experiments done show that, with some fine-tuning and data pre-processing, classical outlier detection methods’ performance can be enhanced and utilized in IoT data quality control.
RETRACTED: Obodoekwe et al. Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy. Electronics 2022, 11, 2128

The journal retracts the article entitled “Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy” [...]
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation

Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. Due to its efficiency and flexibility, Genetic Programming can be seen as the standard search technique for Symbolic Regression. However, the conventional Genetic Programming algorithm requires storing all data in a central location, which is not always feasible due to growing concerns about data privacy and security. While privacy-preserving research has advanced recently and might offer a solution to this problem, their application to Symbolic Regression remains largely unexplored. Furthermore, the existing work only focuses on the horizontally partitioned setting, whereas the vertically partitioned setting, another popular scenario, has yet to be investigated. Herein, we propose an approach that employs a privacy-preserving technique called Secure Multiparty Computation to enable parties to jointly build Symbolic Regression models in the vertical scenario without revealing private data. Preliminary experimental results indicate that our proposed method delivers comparable performance to the centralized solution while safeguarding data privacy.
Randomized semi-quantum matrix processing

Quantum computers have the potential to speed-up important matrix-arithmetic tasks. A prominent framework for that is the quantum singular-value transformation (QSVT) formalism, which uses Chebyshev approximations and coherent access to the input matrix via a unitary block encoding to design a target matrix function. Nonetheless, physical implementations for useful end-user applications require large-scale fault-tolerant quantum computers. Here, we present a hybrid quantum-classical framework for Monte-Carlo simulation of generic matrix functions more amenable to early fault-tolerant quantum hardware. Serving from the ideas of QSVT, we randomize over the Chebyshev polynomials while keeping the matrix oracle quantum. The method is assisted by a variant of the Hadamard test that removes the need for post-selection. As a result, it features a similar statistical overhead to the fully quantum case of standard QSVT and does not incur any circuit depth degradation. On the contrary, the average circuit depth is shown to get smaller, yielding equivalent reductions of noise sensitivity, as we explicitly show for depolarizing noise and coherent errors. We apply our technique to four specific use cases: partition-function estimation via quantum Markov-chain Monte Carlo and via imaginary-time evolution; end-to-end linear system solvers; and ground-state energy estimation. For these cases, we prove advantages on average depths, including quadratic speed-ups on costly parameters and even the removal of the approximation-error dependence. All in all, our framework provides a pathway towards early fault-tolerant quantum linear algebra applications.
Exploring Security Commits in Python

Python has become the most popular programming language as it is friendly to work with for beginners. However, a recent study has found that most security issues in Python have not been indexed by CVE and may only be fixed by 'silent' security commits, which pose a threat to software security and hinder the security fixes to downstream software. It is critical to identify the hidden security commits; however, the existing datasets and methods are insufficient for security commit detection in Python, due to the limited data variety, non-comprehensive code semantics, and uninterpretable learned features. In this paper, we construct the first security commit dataset in Python, namely PySecDB, which consists of three subsets including a base dataset, a pilot dataset, and an augmented dataset. The base dataset contains the security commits associated with CVE records provided by MITRE. To increase the variety of security commits, we build the pilot dataset from GitHub by filtering keywords within the commit messages. Since not all commits provide commit messages, we further construct the augmented dataset by understanding the semantics of code changes. To build the augmented dataset, we propose a new graph representation named CommitCPG and a multi-attributed graph learning model named SCOPY to identify the security commit candidates through both sequential and structural code semantics. The evaluation shows our proposed algorithms can improve the data collection efficiency by up to 40 percentage points. After manual verification by three security experts, PySecDB consists of 1,258 security commits and 2,791 non-security commits. Furthermore, we conduct an extensive case study on PySecDB and discover four common security fix patterns that cover over 85% of security commits in Python, providing insight into secure software maintenance, vulnerability detection, and automated program repair.
Bans vs. Warning Labels: Examining Support for Community-wide Moderation Interventions

Social media platforms like Facebook and Reddit host thousands of independently governed online communities. These platforms sanction communities that frequently violate platform policies; however, user perceptions of such sanctions remain unclear. In a pre-registered survey conducted in the US, I explore user perceptions of content moderation for communities that frequently feature hate speech, violent content, and sexually explicit content. Two community-wide moderation interventions are tested: (1) community bans, where all community posts and access to them are removed, and (2) community warning labels, where an interstitial warning label precedes access. I examine how third-person effects and support for free speech mediate user approval of these interventions. My findings show that presumed effects on others (PME3) is a significant predictor of backing for both interventions, while free speech beliefs significantly influence participants' inclination for using warning labels. I discuss the implications of these results for platform governance and free speech scholarship.
Mercer Large-Scale Kernel Machines from Ridge Function Perspective

To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from Fundamentality of ridge functions. We consider the main theorem of the recent paper by Rachimi and Recht, 2008, Random features for large-scale kernel machines in terms of the Approximation Theory. We study which kernels can be approximated by a sum of cosine function products with arguments depending on $x$ and $y$ and present the obstacles of such an approach. The results of this article may have various applications in Deep Learning, especially in problems related to Image Processing.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors

Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities. However, previous work does little to explore what environment state information is provided to LLM actors via language. Exhaustively describing high-dimensional states can impair performance and raise inference costs for LLM actors. Previous LLM actors avoid the issue by relying on hand-engineered, task-specific protocols to determine which features to communicate about a state and which to leave out. In this work, we propose Brief Language INputs for DEcision-making Responses (BLINDER), a method for automatically selecting concise state descriptions by learning a value function for task-conditioned state descriptions. We evaluate BLINDER on the challenging video game NetHack and a robotic manipulation task. Our method improves task success rate, reduces input size and compute costs, and generalizes between LLM actors.
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft

In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in many unstructured real-world settings, such a reward signal is unknown and humans cannot reliably craft a reward signal that correctly captures desired behavior. To solve tasks in such unstructured and open-ended environments, we present Demonstration-Inferred Preference Reinforcement Learning (DIP-RL), an algorithm that leverages human demonstrations in three distinct ways, including training an autoencoder, seeding reinforcement learning (RL) training batches with demonstration data, and inferring preferences over behaviors to learn a reward function to guide RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest that the method can guide an RL agent to learn a reward function that reflects human preferences and that DIP-RL performs competitively relative to baselines. DIP-RL is inspired by our previous work on combining demonstrations and pairwise preferences in Minecraft, which was awarded a research prize at the 2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at https://sites.google.com/view/dip-rl.
LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction

Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/implicit solutions to this problem struggle to recover self-occluded geometry and/or faithfully reconstruct topological shape structures. To resolve this dilemma, we introduce LIST, a novel neural architecture that leverages local and global image features to accurately reconstruct the geometric and topological structure of a 3D object from a single image. We utilize global 2D features to predict a coarse shape of the target object and then use it as a base for higher-resolution reconstruction. By leveraging both local 2D features from the image and 3D features from the coarse prediction, we can predict the signed distance between an arbitrary point and the target surface via an implicit predictor with great accuracy. Furthermore, our model does not require camera estimation or pixel alignment. It provides an uninfluenced reconstruction from the input-view direction. Through qualitative and quantitative analysis, we show the superiority of our model in reconstructing 3D objects from both synthetic and real-world images against the state of the art.
Leveraging Knowledge Graphs for Zero-Shot Object-agnostic State Classification

We investigate the problem of Object State Classification (OSC) as a zero-shot learning problem. Specifically, we propose the first Object-agnostic State Classification (OaSC) method that infers the state of a certain object without relying on the knowledge or the estimation of the object class. In that direction, we capitalize on Knowledge Graphs (KGs) for structuring and organizing knowledge, which, in combination with visual information, enable the inference of the states of objects in object/state pairs that have not been encountered in the method's training set. A series of experiments investigate the performance of the proposed method in various settings, against several hypotheses and in comparison with state of the art approaches for object attribute classification. The experimental results demonstrate that the knowledge of an object class is not decisive for the prediction of its state. Moreover, the proposed OaSC method outperforms existing methods in all datasets and benchmarks by a great margin.
Security and Privacy Issues of Federated Learning

Federated Learning (FL) has emerged as a promising approach to address data privacy and confidentiality concerns by allowing multiple participants to construct a shared model without centralizing sensitive data. However, this decentralized paradigm introduces new security challenges, necessitating a comprehensive identification and classification of potential risks to ensure FL's security guarantees. This paper presents a comprehensive taxonomy of security and privacy challenges in Federated Learning (FL) across various machine learning models, including large language models. We specifically categorize attacks performed by the aggregator and participants, focusing on poisoning attacks, backdoor attacks, membership inference attacks, generative adversarial network (GAN) based attacks, and differential privacy attacks. Additionally, we propose new directions for future research, seeking innovative solutions to fortify FL systems against emerging security risks and uphold sensitive data confidentiality in distributed learning environments.
Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education

The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely human-written or entirely AI-generated. In this study, we investigated AI content detection in a rarely explored yet realistic setting where the text to be detected is collaboratively written by human and generative LLMs (i.e., hybrid text). We first formalized the detection task as identifying the transition points between human-written content and AI-generated content from a given hybrid text (boundary detection). Then we proposed a two-step approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other. Through extensive experiments, we observed the following main findings: (1) the proposed approach consistently outperformed the baseline methods across different experiment settings; (2) the encoder training process can significantly boost the performance of the proposed approach; (3) when detecting boundaries for single-boundary hybrid essays, the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a 22% improvement in the In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.
Saturating automata for game semantics

Saturation is a fundamental game-semantic property satisfied by strategies that interpret higher-order concurrent programs. It states that the strategy must be closed under certain rearrangements of moves, and corresponds to the intuition that program moves (P-moves) may depend only on moves made by the environment (O-moves). We propose an automata model over an infinite alphabet, called saturating automata, for which all accepted languages are guaranteed to satisfy a closure property mimicking saturation. We show how to translate the finitary fragment of Idealized Concurrent Algol (FICA) into saturating automata, confirming their suitability for modelling higher-order concurrency. Moreover, we find that, for terms in normal form, the resultant automaton has linearly many transitions and states with respect to term size, and can be constructed in polynomial time. This is in contrast to earlier attempts at finding automata-theoretic models of FICA, which did not guarantee saturation and involved an exponential blow-up during translation, even for normal forms.