Reviewerly/exHarmony huggingface dataset:
A random matrix model for random approximate $t$-designs

For a Haar random set $\mathcal{S}\subset U(d)$ of quantum gates we consider the uniform measure $\nu_\mathcal{S}$ whose support is given by $\mathcal{S}$. The measure $\nu_\mathcal{S}$ can be regarded as a $\delta(\nu_\mathcal{S},t)$-approximate $t$-design, $t\in\mathbb{Z}_+$. We propose a random matrix model that aims to describe the probability distribution of $\delta(\nu_\mathcal{S},t)$ for any $t$. Our model is given by a block diagonal matrix whose blocks are independent, given by Gaussian or Ginibre ensembles, and their number, size and type is determined by $t$. We prove that, the operator norm of this matrix, $\delta({t})$, is the random variable to which $\sqrt{|\mathcal{S}|}\delta(\nu_\mathcal{S},t)$ converges in distribution when the number of elements in $\mathcal{S}$ grows to infinity. Moreover, we characterize our model giving explicit bounds on the tail probabilities $\mathbb{P}(\delta(t)>2+\epsilon)$, for any $\epsilon>0$. We also show that our model satisfies the so-called spectral gap conjecture, i.e. we prove that with the probability $1$ there is $t\in\mathbb{Z}_+$ such that $\sup_{k\in\mathbb{Z}_{+}}\delta(k)=\delta(t)$. Numerical simulations give convincing evidence that the proposed model is actually almost exact for any cardinality of $\mathcal{S}$. The heuristic explanation of this phenomenon, that we provide, leads us to conjecture that the tail probabilities $\mathbb{P}(\sqrt{\mathcal{S}}\delta(\nu_\mathcal{S},t)>2+\epsilon)$ are bounded from above by the tail probabilities $\mathbb{P}(\delta(t)>2+\epsilon)$ of our random matrix model. In particular our conjecture implies that a Haar random set $\mathcal{S}\subset U(d)$ satisfies the spectral gap conjecture with the probability $1$.
Transfer learning for photonic delay-based reservoir computing to compensate parameter drift

Abstract Photonic reservoir computing has been demonstrated to be able to solve various complex problems. Although training a reservoir computing system is much simpler compared to other neural network approaches, it still requires considerable amounts of resources which becomes an issue when retraining is required. Transfer learning is a technique that allows us to re-use information between tasks, thereby reducing the cost of retraining. We propose transfer learning as a viable technique to compensate for the unavoidable parameter drift in experimental setups. Solving this parameter drift usually requires retraining the system, which is very time and energy consuming. Based on numerical studies on a delay-based reservoir computing system with semiconductor lasers, we investigate the use of transfer learning to mitigate these parameter fluctuations. Additionally, we demonstrate that transfer learning applied to two slightly different tasks allows us to reduce the amount of input samples required for training of the second task, thus reducing the amount of retraining.
A traceable and revocable multi-authority attributed-based access control scheme for mineral industry data secure storage in blockchain

Abstract With the rapid advancements of the mineral industry, the data generated by this industry chain have increased dramatically. To reduce the growing pressure of data storage and security risks, we design a credible on-chain and off-chain collaborative dual storage system that integrates blockchain technology and Interplanetary file system (IPFS), also construct a traceable and revocable multi-authority ciphertext-policy attributed-based encryption (CP-ABE) algorithm to meet the demand of privacy protection and dynamic fine-grained access control. Furthermore, the multi-authority layered authorization with a central authority model distributes system overhead while enabling the platform can be regulated. More importantly, our scheme achieves accurate trace of the malicious users by white-box traceability and capable of implementing indirect immediate user and attribute revocation without requiring key or ciphertext updates. Finally, the proposed scheme is indistinguishability under chosen-plaintext attack (IND-CPA) secure in the standard model. And the performance analysis demonstrates that our scheme is feature-rich, practical and efficient.
A Review Paper on Cyber Harassment Detection Using Machine Learning Algorithm on Social Networking Website

Abstract: Cyber bullying is nothing but bullying with the use of digital technology. Cyber bullying can take place on social media platforms, messaging platforms, gaming platforms and mobile phones etc. Cyber bullying is a big issue that is encountered by the individual on internet that affects teenagers as well as adults. It has lead to nuisances like suicide, mental health problems and depression. Therefore regulation of content on Social media platforms has become a growing need. Also detecting Cyber bullying detection at early stages can help to alleviate impacts on the victims. In the following research we make use of data from two different data sets. The first one is tweets from Twitter and second one is comments based on personal attacks from Wikipedia forums. The approach is to build a model based on detection of cyber bullying in text data using Natural Language Processing and Machine learning. We try to build a model which will provide accuracy up to 90% for tweets and accuracy up to 80% for Wikipedia forums. Cyber bullying detection will be done as a binary classification problem where we are detecting two major form of cyber bullying: hate speech on Twitter and Personal attacks on Wikipedia and classifying them as containing Cyber bullying or not. The end result of this proposed work may reduce negative impacts on the victims as a result of early detection.
Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk

Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.
Deteksi Dini Anak Disleksia dengan metode Support Vector Machine

Dyslexia is a brain disorder caused by genetics. People with dyslexia can live a normal life and even have certain advantages if they get the correct education. People with dyslexia often get the predicate stupid because teachers do not know the case of their students. Early detection of dyslexic children can be done with a series of tests so that the system can conclude that the data is dyslexic or not. Support Vector Machine is a data classification method to share dyslexia test results or not. This system is trained with test results data that are already available using the SVM method. This study uses gamification data to detect dyslexic children or not. SVM proves a good level of accuracy in predictions up to 94%.
Analisis Sentimen Customer Feedback Tokopedia Menggunakan Algoritma Naïve Bayes

Products and customers have a close relationship, therefore UMKM need to build good relationships with customers. The most common way that companies or UMKM do is to look at the reviews given, this is called customer feedback. The results of customer feedback to companies or UMKM can improve service and product quality. The problem that arises is how to process the many reviews given, especially reviews from marketplaces like Tokopedia. Therefore, a method is needed to see user reviews of the products being sold, whether positive or negative. The method that will be used is sentiment analysis. Sentiment analysis is the process of understanding and extracting and automatically processing text data and can produce sentiments that are displayed in a sentence. The steps taken were taking House of Smith customer review data at Tokopedia, manual labeling to get positive and negative data reviews, data preprocessing, TF-IDF weighting and classification using the Naïve Bayes algorithm. The results of sentiment testing using the Naïve Bayes algorithm with TF-IDF weighting quality accuracy of 83% with visualization of the distribution of words that appear the most are the words 'good', 'comfortable' and 'use' for positive reviews. The most frequent negative reviews were 'material' and 'thin' which indicated that some buyers felt that the product had a thin material.
Narrative Planning in Large Domains through State Abstraction and Option Discovery

Low-level game environments and other simulations present a difficulty of scale for an expensive AI technique like narrative planning, which is normally constrained to environments with small state spaces. Due to this limitation, the intentional and cooperative behavior of agents guided by this technology cannot be deployed for different systems without significant additional authoring effort. I propose a process for automatically creating models for larger-scale domains such that a narrative planner can be employed in these settings. By generating an abstract domain of an environment while retaining the information needed to produce behavior appropriate to the abstract actions, agents are able to reason in a lower-complexity space and act in the higher-complexity one. This abstraction is accomplished by the development of extended-duration actions and the identification of their preconditions and effects. Together these components may be combined to form a narrative planning domain, and plans from this domain can be executed within the low-level environment.
Exploiting hierarchical dependence structures for unsupervised rank fusion in information retrieval

The goal of rank fusion in information retrieval (IR) is to deliver a single output list from multiple search results. Improving performance by combining the outputs of various IR systems is a challenging task. A central point is the fact that many non-obvious factors are involved in the estimation of relevance, inducing nonlinear interrelations between the data. The ability to model complex dependency relationships between random variables has become increasingly popular in the realm of information retrieval, and the need to further explore these dependencies for data fusion has been recently acknowledged. Copulas provide a framework to separate the dependence structure from the margins. Inspired by the theory of copulas, we propose a new unsupervised, dynamic, nonlinear, rank fusion method, based on a nested composition of non-algebraic function pairs. The dependence structure of the model is tailored by leveraging query-document correlations on a per-query basis. We experimented with three topic sets over CLEF corpora fusing 3 and 6 retrieval systems, comparing our method against the CombMNZ technique and other nonlinear unsupervised strategies. The experiments show that our fusion approach improves performance under explicit conditions, providing insight about the circumstances under which linear fusion techniques have comparable performance to nonlinear methods.
A GAN-Based Face Rotation for Artistic Portraits

We present a GAN-based model that rotates the faces in artistic portraits to various angles. We build a dataset of artistic portraits for training our GAN-based model by applying a 3D face model to the artistic portraits. We also devise proper loss functions to preserve the styles in the artistic portraits as well as to rotate the faces in the portraits to proper angles. These approaches enable us to construct a GAN-based face rotation model. We apply this model to various artistic portraits, including photorealistic oil paint portraits, watercolor portraits, well-known portrait artworks and banknote portraits, and produce convincing rotated faces in the artistic portraits. Finally, we prove that our model can produce improved results compared with the existing models by evaluating the similarity and the angles of the rotated faces through evaluation schemes including FID estimation, recognition ratio estimation, pose estimation and user study.
An Evolving Fuzzy Neural Network Based on Or-Type Logic Neurons for Identifying and Extracting Knowledge in Auction Fraud

The rise in online transactions for purchasing goods and services can benefit the parties involved. However, it also creates uncertainty and the possibility of fraud-related threats. This work aims to explore and extract knowledge of auction fraud by using an innovative evolving fuzzy neural network model based on logic neurons. This model uses a fuzzification technique based on empirical data analysis operators in an evolving way for stream samples. In order to evaluate the applied model, state-of-the-art neuro-fuzzy models were used to compare a public dataset on the topic and, simultaneously, validate the interpretability results based on a common criterion to identify the correct patterns present in the dataset. The fuzzy rules and the interpretability criteria demonstrate the model’s ability to extract knowledge. The results of the model proposed in this paper are superior to the other models evaluated (close to 98.50% accuracy) in the test.
An Empirical Study of Factors Influencing the Perceived Usefulness and Effectiveness of Integrating E-Learning Systems during the COVID-19 Pandemic Using SEM and ML: A Case Study in Jordan

The purpose of this research paper is to identify and test the factors influencing the perceived usefulness and perceived effectiveness of adopting an e-learning system from the perspective of teachers in public and private schools as well as the United Nations Relief and Works Agency for Palestinian Refugees in the Near East (UNRWA) in Jordan during the first wave of the COVID-19 pandemic in the academic year 2019/2020. Based on the findings and best practices, the study intends to make appropriate recommendations to decision-makers. Its significance stems from the use of scientific tools of research and investigation, and it aims to ensure the quality and effectiveness of Jordanian schools’ e-learning systems. The study’s hypotheses were verified by electronically collecting 551 questionnaires from teachers in Jordan. To test the study hypotheses, the empirical validity of the research model was set up, and the data were analyzed with SPSS version 21.0. Structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods were used to test the study hypotheses and validate the properties of the instrument items. Nineteen variables and one mediating variable were studied. The study found that independent variables pertaining to technology (relative advantage, compatibility, top management support, communication technologies, competitive pressure, technology competence, information intensity, and work flexibility) and moderating variables pertaining to the teacher’s personal income and those pertaining to school (school size, education program, and work sector) had a positive effect on teachers’ perceived usefulness of adopting e-learning systems during the COVID-19 pandemic. On the other hand, independent variables pertaining to technology (complexity and collaboration technology), moderating variables pertaining to the teacher (age, education level, and gender), and moderating variables pertaining to school (educational stage, number of students) were not supported.
Minimum Power Adversarial Attacks in Communication Signal Modulation Classification with Deep Learning

Abstract Integrating cognitive radio (CR) technique with wireless networks is an effective way to solve the increasingly crowded spectrum. Automatic modulation classification (AMC) plays an important role in CR. AMC significantly improves the intelligence of CR system by classifying the modulation type and signal parameters of received communication signals. AMC can provide more information for decision making of the CR system. In addition, AMC can help the CR system dynamically adjust the modulation type and coding rate of the communication signal to adapt to different channel qualities, and the AMC technique help eliminate the cost of broadcast modulation type and coding rate. Deep learning (DL) has recently emerged as one most popular method in AMC of communication signals. Despite their success, DL models have recently been shown vulnerable to adversarial attacks in pattern recognition and computer vision. Namely, they can be easily deceived if a small and carefully designed perturbation called an adversarial attack is imposed on the input, typically an image in pattern recognition. Owing to the very different nature of communication signals, it is interesting yet crucially important to study if adversarial perturbation could also fool AMC. In this paper, we make a first attempt to investigate how we can design a special adversarial attack on AMC. we start from the assumption of a linear binary classifier which is further extended to multi-way classifier. We consider the minimum power consumption that is different from existing adversarial perturbation but more reasonable in the context of AMC. We then develop a novel adversarial perturbation generation method that leads to high attack success to communication signals. Experimental results on real data show that the method is able to successfully spoof the 11-class modulation classification at a model with a minimum cost of about − 21 dB in automatic modulation classification task. The visualization results demonstrate that the adversarial perturbation manifests in the time domain as imperceptible undulations of the signal, and in the frequency domain as small noise outside the signal band.
The Science of Rejection: A Research Area for Human Computation

We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Theory for Equivariant Quantum Neural Networks

Most currently used quantum neural network architectures have little-to-no inductive biases, leading to trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in classical machine learning address this crux by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks whose action commutes with that of the symmetry. In this work, we import these ideas to the quantum realm by presenting a general theoretical framework to understand, classify, design and implement equivariant quantum neural networks. As a special implementation, we show how standard quantum convolutional neural networks (QCNN) can be generalized to group-equivariant QCNNs where both the convolutional and pooling layers are equivariant under the relevant symmetry group. Our framework can be readily applied to virtually all areas of quantum machine learning, and provides hope to alleviate central challenges such as barren plateaus, poor local minima, and sample complexity.
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Robustness has been studied in many domains of AI, yet with different interpretations across domains and contexts. In this work, we systematically survey the recent progress to provide a reconciled terminology of concepts around AI robustness. We introduce three taxonomies to organize and describe the literature both from a fundamental and applied point of view: 1) robustness by methods and approaches in different phases of the machine learning pipeline; 2) robustness for specific model architectures, tasks, and systems; and in addition, 3) robustness assessment methodologies and insights, particularly the trade-offs with other trustworthiness properties. Finally, we identify and discuss research gaps and opportunities and give an outlook on the field. We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge humans can provide, and discuss the need for better understanding practices and developing supportive tools in the future.
Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models. Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community on simple models. In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization. Direct application of Bayesian model reduction, however, gives rise to approximation errors. Therefore, a novel iterative pruning algorithm is presented to alleviate the problems arising with naive Bayesian model reduction, as supported experimentally on the publicly available UCI datasets for different inference algorithms. This novel parameter pruning scheme solves the shortcomings of current state-of-the-art pruning methods that are used by the signal processing community. The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training. Next to these benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.
Security of quantum key distribution with imperfect phase randomisation

The performance of quantum key distribution (QKD) is severely limited by multiphoton emissions, due to the photon-number-splitting attack. The most efficient solution, the decoy-state method, requires that the phases of all transmitted pulses are independent and uniformly random. In practice, however, these phases are often correlated, especially in high-speed systems, which opens a security loophole. Here, we address this pressing problem by providing a security proof for decoy-state QKD with correlated phases that offers key rates close to the ideal scenario. Our work paves the way towards high-performance secure QKD with practical laser sources, and may have applications beyond QKD.
A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study

In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study. Our goal was to use multiple longitudinal respiratory traits to cluster the participants into subgroups with similar longitudinal respiratory profiles in order to identify clinically relevant disease phenotypes. To appropriately account for distinct structures and types of these longitudinal markers, we proposed a novel joint model for clustering mixed-type (continuous, discrete and categorical) multivariate longitudinal data. We also developed a Markov Chain Monte Carlo algorithm to estimate the posterior distribution of model parameters. Analysis of the CHILD Cohort data and simulated data were presented and discussed. Our study demonstrated that the proposed model serves as a useful analytical tool for clustering multivariate mixed-type longitudinal data. We developed an R package BCClong to implement the proposed model efficiently.