Reviewerly/exHarmony huggingface dataset:
Distribution Testing with a Confused Collector

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learning classifier that has not learned to distinguish all elements of the domain. The confused collector holds an unknown clustering of the domain and an input distribution $\mu$, and provides two oracles: a sample oracle which produces a sample from $\mu$ that has been labeled according to the clustering; and a label-query oracle which returns the label of a query point $x$ according to the clustering. Our first set of results shows that identity, uniformity, and equivalence of distributions can be tested efficiently, under the earth-mover distance, with remarkably weak conditions on the confused collector, even when the unknown clustering is adversarial. This requires defining a variant of the distribution testing task (inspired by the recent testable learning framework of Rubinfeld & Vasilyan), where the algorithm should test a joint property of the distribution and its clustering. As an example, we get efficient testers when the distribution tester is allowed to reject if it detects that the confused collector clustering is "far" from being a decision tree. The second set of results shows that we can sometimes do significantly better when the clustering is random instead of adversarial. For certain one-dimensional random clusterings, we show that uniformity can be tested under the TV distance using $\widetilde O\left(\frac{\sqrt n}{\rho^{3/2} \epsilon^2}\right)$ samples and zero queries, where $\rho \in (0,1]$ controls the "resolution" of the clustering. We improve this to $O\left(\frac{\sqrt n}{\rho \epsilon^2}\right)$ when queries are allowed.
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer

Previous logits-based Knowledge Distillation (KD) have utilized predictions about multiple categories within each sample (i.e., class predictions) and have employed Kullback-Leibler (KL) divergence to reduce the discrepancy between the student and teacher predictions. Despite the proliferation of KD techniques, the student model continues to fall short of achieving a similar level as teachers. In response, we introduce a novel and effective KD method capable of achieving results on par with or superior to the teacher models performance. We utilize teacher and student predictions about multiple samples for each category (i.e., batch predictions) and apply cosine similarity, a commonly used technique in Natural Language Processing (NLP) for measuring the resemblance between text embeddings. This metric's inherent scale-invariance property, which relies solely on vector direction and not magnitude, allows the student to dynamically learn from the teacher's knowledge, rather than being bound by a fixed distribution of the teacher's knowledge. Furthermore, we propose a method called cosine similarity weighted temperature (CSWT) to improve the performance. CSWT reduces the temperature scaling in KD when the cosine similarity between the student and teacher models is high, and conversely, it increases the temperature scaling when the cosine similarity is low. This adjustment optimizes the transfer of information from the teacher to the student model. Extensive experimental results show that our proposed method serves as a viable alternative to existing methods. We anticipate that this approach will offer valuable insights for future research on model compression.
Exact confidence intervals for the mixing distribution from binomial mixture distribution samples

We present methodology for constructing pointwise confidence intervals for the cumulative distribution function and the quantiles of mixing distributions on the unit interval from binomial mixture distribution samples. No assumptions are made on the shape of the mixing distribution. The confidence intervals are constructed by inverting exact tests of composite null hypotheses regarding the mixing distribution. Our method may be applied to any deconvolution approach that produces test statistics whose distribution is stochastically monotone for stochastic increase of the mixing distribution. We propose a hierarchical Bayes approach, which uses finite Polya Trees for modelling the mixing distribution, that provides stable and accurate deconvolution estimates without the need for additional tuning parameters. Our main technical result establishes the stochastic monotonicity property of the test statistics produced by the hierarchical Bayes approach. Leveraging the need for the stochastic monotonicity property, we explicitly derive the smallest asymptotic confidence intervals that may be constructed using our methodology. Raising the question whether it is possible to construct smaller confidence intervals for the mixing distribution without making parametric assumptions on its shape.
JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables

Machine learning has played a pivotal role in advancing physics, with deep learning notably contributing to solving complex classification problems such as jet tagging in the field of jet physics. In this experiment, we aim to harness the full potential of neural networks while acknowledging that, at times, we may lose sight of the underlying physics governing these models. Nevertheless, we demonstrate that we can achieve remarkable results obscuring physics knowledge and relying completely on the model's outcome. We introduce JetLOV, a composite comprising two models: a straightforward multilayer perceptron (MLP) and the well-established LundNet. Our study reveals that we can attain comparable jet tagging performance without relying on the pre-computed LundNet variables. Instead, we allow the network to autonomously learn an entirely new set of variables, devoid of a priori knowledge of the underlying physics. These findings hold promise, particularly in addressing the issue of model dependence, which can be mitigated through generalization and training on diverse data sets.
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation

Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and as a result, they have already been used for tasks such as unsupervised semantic segmentation. In this paper, we investigate self-supervised representations for instance segmentation without any manual annotations. We find that the features of different SSL methods vary in their level of instance-awareness. In particular, DINO features, which are known to be excellent semantic descriptors, lack behind MAE features in their sensitivity for separating instances.
ARIA: On the interaction between Architectures, Aggregation methods and Initializations in federated visual classification

Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the clients and a server. Despite individual studies on how client models are aggregated, and, more recently, on the benefits of ImageNet pre-training, there is a lack of understanding of the effect the architecture chosen for the federation has, and of how the aforementioned elements interconnect. To this end, we conduct the first joint ARchitecture-Initialization-Aggregation study and benchmark ARIAs across a range of medical image classification tasks. We find that, contrary to current practices, ARIA elements have to be chosen together to achieve the best possible performance. Our results also shed light on good choices for each element depending on the task, the effect of normalisation layers, and the utility of SSL pre-training, pointing to potential directions for designing FL-specific architectures and training pipelines.
Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping

This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms by cooptimizing gate sequences, algorithm parameters, and qubit mapping. Demonstrations on near-term quantum devices validate the effectiveness of these strategies, leveraging both algorithmic and hardware advantages. Our approach increases the approximation ratio by an average of 4.49$\times$ without error mitigation and 84.8% with error mitigation, while reducing CX gate count and circuit depth by 28.8% and 33.4%, respectively, compared to Qiskit and Tket. These findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters, with broader implications for enhancing algorithm performance on near-term quantum devices.
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach

Differentially Private Stochastic Gradient Descent with gradient clipping (DPSGD-GC) is a powerful tool for training deep learning models using sensitive data, providing both a solid theoretical privacy guarantee and high efficiency. However, using DPSGD-GC to ensure Differential Privacy (DP) comes at the cost of model performance degradation due to DP noise injection and gradient clipping. Existing research has extensively analyzed the theoretical convergence of DPSGD-GC, and has shown that it only converges when using large clipping thresholds that are dependent on problem-specific parameters. Unfortunately, these parameters are often unknown in practice, making it hard to choose the optimal clipping threshold. Therefore, in practice, DPSGD-GC suffers from degraded performance due to the {\it constant} bias introduced by the clipping. In our work, we propose a new error-feedback (EF) DP algorithm as an alternative to DPSGD-GC, which not only offers a diminishing utility bound without inducing a constant clipping bias, but more importantly, it allows for an arbitrary choice of clipping threshold that is independent of the problem. We establish an algorithm-specific DP analysis for our proposed algorithm, providing privacy guarantees based on R{\'e}nyi DP. Additionally, we demonstrate that under mild conditions, our algorithm can achieve nearly the same utility bound as DPSGD without gradient clipping. Our empirical results on Cifar-10/100 and E2E datasets, show that the proposed algorithm achieves higher accuracies than DPSGD while maintaining the same level of DP guarantee.
A Comprehensive Review on Deep Learning Approach for Prostate Cancer Gleason Grading

This comprehensive review explores the transformative role of deep learning in revolutionizing the diagnosis of prostate cancer through a refined Gleason grading approach. Prostate cancer diagnosis has significantly benefited from advancements in deep learning techniques, enabling more accurate and precise Gleason grading—a critical component in assessing the severity of prostate tumors. The abstract delves into the latest developments in deep learning algorithms and their application to Gleason grading, highlighting the potential to enhance diagnostic accuracy, improve prognostic predictions, and ultimately contribute to more effective treatment strategies for prostate cancer patients. The synthesis of current research findings in this review underscores the pivotal role that deep learning plays in reshaping the landscape of prostate cancer diagnosis and emphasizes the promising future prospects for integrating these innovative technologies into clinical practice.
Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.
Use of Machine-Learning in Engineering Students’ Trajectories

Students’ trajectories show the student path in the educational system from the beginning to the end of their studies. There are several statistical tools to achieve its understanding and subsequent decision-making by the institution. Each stage of the student’s trajectory can be described by educational, socio-economic, demographic and cultural variables. The purpose of the research
Developing methodologies to correlate perceived sound qualities of violins with controlled construction parameters

The holy grail for violin makers is to find correlations between construction parameters and sound qualities. This is challenging for two main reasons: it is difficult to build violins reliably enough to ensure that the change in the sound is indeed a result of the change of construction parameters; when listening to the violins being played, differences seem to be smoothed out by the players who adapt very quickly. Therefore, while players had so far been preferred in our experiments to maximize the ecological validity and take into account the complexity of the interaction between the player and the instrument, we have decided to test whether other methods, which reduce the influence of the player but are quite artificial, may be useful to explore the influence of some construction parameters on the tone. In the context of two sets of violins built with controlled thickness variations of their plates, we will compare the results of listening tests based on real recordings with a player and with a bowing machine as well as synthesized recordings (from the convolution of an excerpt recorded with piezo sensors and radiation measurements in an anechoic chamber) and discuss them in the light of audio descriptors on the recordings and vibroacoustical measurements on the violins.
The concepts of «cultural explosion» by Y. M. Lotman, scientific revolutions by T. Kuhn and «three-stage development of science» by V. S. Stepin: comparative analysis

Today, the culture of our country and the whole world, primarily due to the explosive development of electronic technologies and their innovative potential, is at a stage of development, defined by many as a «revolutionary leap» or «phase transition». The scholars studying humanities have already accumulated a significant array of conceptual and theoretical constructions aimed at analyzing and understanding the functioning of socio­cultural realities in such «transitional» conditions. Many of them in new situations open up new facets of reading and understanding. The concepts considered by us are Yu. M. Lotman, T. Kuhn and V. S. Stepin as independent phenomena have repeatedly become objects of research analysis; however, so far no attempts have been made to compare them in general.
Astral Body: A Virtual Reality Game for Body Ownership Investigation

As one of the most disruptive human-computer interaction techniques, Virtual Reality (VR) provides a novel way to examine human movements, e.g. when investigating Body Ownership (BO) in the field of cognitive sciences, especially when the visual output diverges from real-world actions. Previous research in BO uses questionnaires and brain imaging, where the former is a highly subjective metric, and the latter is very costly in time, money, and personnel. To answer the question How can a VR serious game help overcome current challenges of BO assessment?, we designed Astral Body, a VR game that helps cognitive science researchers assess people’s level of BO. In the game, players are asked to grab ‘flying collectibles’ coming from a portal in space. Researchers can inject different types and levels of asynchrony into the arms of the visualized avatar, thus affecting the players’ BO experience and perception. Players, in turn, can also report whenever they perceive possible mismatched avatar behavior. In addition, researchers can analyze player data, including looking for unconscious responses, e.g. small adjustments in physical movements to mitigate injected asynchrony. Preliminary results from playtesting and qualitative analysis of Astral Bodyindicate that a VR game can effectively help researchers investigate BO phenomena.
Enhancing Educational Certificate Verification With Blockchain and IPFS: A Decentralized Approach Using Hyperledger Fabric

Educational certificate counterfeiting poses a significant global challenge, demanding urgent attention and robust solutions. This paper addresses the problem of forgery and verification of educational certificates by leveraging blockchain technology as an anti-counterfeiting measure. A decentralized architecture for educational certificate verification systems is proposed and implemented using the Hyperledger Fabric as a blockchain platform and the Inter Planetary File System (IPFS). By utilizing the unique features of these technologies, we aim to establish a reliable and secure system for verifying authenticity of educational certificates without relying on third-party intermediaries. Our approach involves storing educational certificate files on a decentralized file system, namely IPFS, ensuring their integrity and accessibility. To enable efficient verification, each certificate is associated with a certificate ID, which retrieves the necessary validating information from the Hyperledger Fabric network. This integration ensures that educational certificates are securely stored on the blockchain, preventing any tampering attempts and enabling easy validation. The experimental results demonstrate that educational certificates are stored on the Hyperledger Fabric network in a manner that ensures their immutability, making it impossible to alter them. Furthermore, the system allows for verification of their authenticity without the need for any third-party intervention. By combining the power of blockchain technology with IPFS, our solution provides a robust approach to tackle the growing issue of educational certificate counterfeiting. This research contributes to the field by offering a decentralized architecture that enhances the security and trustworthiness of educational certificate verification systems.
User Experience Analysis of Neobank Application Using Heart Metrics

Based on the accumulated reviews, it is known that the symptoms of disrupted user experience are supported by several reviews which show dissatisfaction in operating the Neobank application, which may have an effect on decreasing user satisfaction. This problem is something that must be monitored by PT. Neo Commerce Bank so that the Neobank application remains sustainable. This research has a mission to find out what will be emphasized for the development of the Neobank application based on the HEART Metrics measurement tool with the Importance Performance Analysis method implemented. Determining the number of respondents in this study implemented the formula created by Slovin and obtained a population of 99 respondents who are residents of the island of Java who use the Neobank application. Until the results of the importance and performance elaboration, there are several statement items that need to be evaluated, but there are items that need to be evaluated and prioritized immediately based on the results of the calculation of the suitability level and Cartesian diagram, namely the user experience that it is not uncommon to experience errors when operating a Neobank with a level conformity 91.91%. The items in the aforementioned statement have the potential to divert the user experience in using the Neobank application.
Groundwater Depth Prediction Based on Wavelet Decomposition-LSTM Network

The time series of groundwater depth has the characteristics of trend, abrupt, and non-stationary. Based on the advantages of wavelet decomposition and long short-term memory neural network (LSTM), a new coupling model (wavelet decomposition-LSTM) for groundwater depth prediction is proposed. Firstly, wavelet decomposition is applied to decompose the groundwater data into high-frequency periodic signals and low-frequency trend signals, to reduce the complexity of the time series. Secondly, the decomposed high-frequency and low-frequency signals are taken as inputs to train the model respectively, and then the total prediction value is acquired. The improved model reduces the limitations of LSTM processing complex signals and improves prediction accuracy. Taking the No. 5 well of Lu Wangfen Town and the No. 3 well of Muye Town as the research object, the achieved results from the proposed model were compared with the results of the LSTM model and the back-propagation (BP) neural network model. This comparison shows that the performance of the new model is better than the others, and the average relative errors of the coupling model are 2.11% and 2.49% respectively. The proposed method has high prediction accuracy and generalization ability and is a more effective method for groundwater depth prediction.
Machine Learning-Based Phishing E-mail Detection Using Persuasion Principle and NLP Techniques

At present time phishing attack is one of the biggest problem in our regular life. Phishing email is a type of cyberattack, which is a serious problem for financial damage in commercial organization. In phishing email used a special trick to the user that can access his digital assets. Generally phishing attack targeted to normal users by sending email that contain malicious links which are used to spoofed websites, by which the attacker can collect different sensitive information. This paper proposes a machine learning base comparison approach using persuasion principle and Natural Language Processing (NLP) base features (TF-IDF). Here we use 7 different classifiers for phishing email detection. We prepared a emails dataset collected from the Enron corpus and the Nazario phishing email corpus which are well-known and the most popular. We performed the comparative result analysis of different classification algorithms and shown the heighst performance with 99% accuracy value of AdaBoost classifier for phishing email detection.
A Novel Knowledge Augmented Model Customization Approach for Arabic Offensive Language Detection

Multiple attempts to develop systems for detecting online Arabic offensive language have been explored in previous studies. However, most of these attempts do not consider the variation of Arabic dialects, cultures, and offensive phrases. In contrast, this study aims to extract knowledge from multiple offensive language datasets to build a cross-dialect and culture knowledge-based repository. This knowledge-based repository is utilized to develop novel system architecture based on customizing the AraBERT model in a unique method to preserve dialectal knowledge and offensive cultural knowledge within the contextual word embedding of BERT architecture. Performance evaluation procedures consist of statistical evaluation metrics and a behavioral checklist. Results report more effective predictions by the customized model than the uncustomized one, particularly for offensive text. The customization process allows the model to gain more knowledge of informal text in general, and a better understanding of dialectal Arabic.