Reviewerly/exHarmony huggingface dataset:
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Multi-label learning in the presence of missing labels (MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which increase the complexity of implementation. This work seeks to fulfill the potential of loss function in MLML without increasing the procedure and complexity. Toward this end, we propose two simple yet effective methods via robust loss design based on an observation that a model can identify missing labels during training with a high precision. The first is a novel robust loss for negatives, namely the Hill loss, which re-weights negatives in the shape of a hill to alleviate the effect of false negatives. The second is a self-paced loss correction (SPLC) method, which uses a loss derived from the maximum likelihood criterion under an approximate distribution of missing labels. Comprehensive experiments on a vast range of multi-label image classification datasets demonstrate that our methods can remarkably boost the performance of MLML and achieve new state-of-the-art loss functions in MLML.
Gradient-matching coresets for continual learning

We devise a coreset selection method based on the idea of gradient matching: The gradients induced by the coreset should match, as closely as possible, those induced by the original training dataset. We evaluate the method in the context of continual learning, where it can be used to curate a rehearsal memory. Our method performs strong competitors such as reservoir sampling across a range of memory sizes.
FedCostWAvg: A new averaging for better Federated Learning

We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community. Our method addresses the problem of how to aggregate multiple models that were trained on different data sets. Conceptually, we propose a new way to choose the weights when averaging the different models, thereby extending the current state of the art (FedAvg). Empirical validation demonstrates that our approach reaches a notable improvement in segmentation performance compared to FedAvg.
A Comparative Analysis to Obtain Unique Device Fingerprinting

Main focus of this paper is to obtain unique device fingerprint using JavaScript specifically Angular JS and Client JS techniques which together can make the comparative analysis more efficient and valuable. The term ‘Device Fingerprinting,’ is a phenomenon to collect information such as device name, java installed and its version, user agent, browser version, etc., of an individual computing device for the purpose of identification. Through this technique, even if the cookies are turned off we can obtain individual device fingerprints. Device fingerprinting is often immutable and tends to rapidly change, making it challenging to get a unique one. The majority of solutions available to obtain a unique device fingerprint are complex, and most solutions don’t have the sufficient efficiency to obtain the required. Thus, this comparative analysis to obtain unique device fingerprint using JavaScript techniques simplify this challenge and create an opportunity in analyzing and obtaining the data in much more efficient way..
Facial Emotion Characterization and Detection using Fourier Transform  and Machine Learning

We present a Fourier-based machine learning technique that characterizes and detects facial emotions. The main challenging task in the development of machine learning (ML) models for classifying facial emotions is the detection of accurate emotional features from a set of training samples, and the generation of feature vectors for constructing a meaningful feature space and building ML models. In this paper, we hypothesis that the emotional features are hidden in the frequency domain; hence, they can be captured by leveraging the frequency domain and masking techniques. We also make use of the conjecture that a facial emotions are convoluted with the normal facial features and the other emotional features; however, they carry linearly separable spatial frequencies (we call computational emotional frequencies). Hence, we propose a technique by leveraging fast Fourier transform (FFT) and rectangular narrow-band frequency kernels, and the widely used Yale-Faces image dataset. We test the hypothesis using the performance scores of the random forest (RF) and the artificial neural network (ANN) classifiers as the measures to validate the effectiveness of the captured emotional frequencies. Our finding is that the computational emotional frequencies discovered by the proposed approach provides meaningful emotional features that help RF and ANN achieve a high precision scores above 93%, on average.
The Interplay between Sensory Emojis and Background Color of an Email: A Construal Level Approach: An Abstract

This research investigates the effect of interaction between sensory emojis and background color of an email on consumers’ responses. More particularly, would emojis related to our senses (e.g. or ) be able to activate sensory imagery such as touch or auditory imagery? If sensory emojis produce sensory imagery into which appropriate background color (e.g. red vs blue) they have to be inserted in order to enhance consumers’ responses? Indeed, atmospheric variables such as color are important marketing tools that attract and shape consumers’ perceptions (Labrecque and Milne 2012).We leverage Construal level theory (CLT) that provides a theoretical framework to predict in which appropriate background color specific sensory emojis have to be inserted. We suggest that sensory emojis related to proximal (distal) senses such as touch (hear) have to be integrated in red (blue) background color. We explain these combinations through the fit between psychological distance associated with senses and the construal level activated by red or blue color. We run an experiment, in a real situation, that tests the interplay between sensory emojis (touch vs hearing) and background colors (red vs blue) inserted into a starter email of a chatbot. A residual means analysis (Umesh et al. 1996) suggest that inserted proximal (distal) sensory emoji into red (blue) background is the optimal combination that enhances consumers' responses. These first results contribute to literature on emoji by showing that color background play an important role in the use of emoji into an email. Moreover, we contribute to CLT by showing that this framework could predict the effect of interplay between sensorial elements such as sensory emojis and color. Regarding implications managerial, this research aims to provide concrete suggestions for marketers who use emoji and color in their digital content. There are several applications for different contexts such as email marketing or online websites.
Permutations involving squares in finite fields

&lt;abstract&gt;&lt;p&gt;Let $ p $ be an odd prime and let $ \mathbb{F}_p $ be the finite field of $ p $ elements. In 2019, Sun studied some permutations involving squares in $ \mathbb{F}_p $. In this paper, by the theory of local fields we generalize this topic to $ \mathbb{F}_{p^2} $, which gives a partial answer to the question posed by Sun.&lt;/p&gt;&lt;/abstract&gt;
Primitive Normal Values of Rational Functions over Finite Fields

In this paper, we consider rational functions $f$ with some minor restrictions over the finite field $\mathbb{F}_{q^n},$ where $q=p^k$ for some prime $p$ and positive integer $k$. We establish a sufficient condition for the existence of a pair $(\alpha,f(\alpha))$ of primitive normal elements in $\mathbb{F}_{q^n}$ over $\mathbb{F}_{q}.$ Moreover, for $q=2^k$ and rational functions $f$ with quadratic numerators and denominators, we explicitly find that there are at most $55$ finite fields $\mathbb{F}_{q^n}$ in which such a pair $(\alpha,f(\alpha))$ of primitive normal elements may not exist.
Evaluation of Neural Networks Defenses and Attacks using NDCG and  Reciprocal Rank Metrics

The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relies on traditional classification metrics, without adequate adaptation to adversarial scenarios. Most of these metrics are accuracy-based, and therefore may have a limited scope and low distinctive power. Other metrics do not consider the unique characteristics of neural networks functionality, or measure the effect of the attacks indirectly (e.g., through the complexity of their generation). In this paper, we present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Using additional information about the probability of the rank enabled us to define novel metrics that are suited to the task at hand. We evaluate our metrics using various attacks and defenses on a pretrained VGG19 model and the ImageNet dataset. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness.
Ex-Model: Continual Learning from a Stream of Trained Models

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.
Lecture Notes on Partially Known MDPs

In these notes we will tackle the problem of finding optimal policies for Markov decision processes (MDPs) which are not fully known to us. Our intention is to slowly transition from an offline setting to an online (learning) setting. Namely, we are moving towards reinforcement learning.
Stochastic and Worst-Case Generalized Sorting Revisited

The \emph{generalized sorting problem} is a restricted version of standard comparison sorting where we wish to sort $n$ elements but only a subset of pairs are allowed to be compared. Formally, there is some known graph $G = (V, E)$ on the $n$ elements $v_1, \dots, v_n$, and the goal is to determine the true order of the elements using as few comparisons as possible, where all comparisons $(v_i, v_j)$ must be edges in $E$. We are promised that if the true ordering is $x_1 < x_2 < \cdots < x_n$ for $\{x_i\}$ an unknown permutation of the vertices $\{v_i\}$, then $(x_i, x_{i+1}) \in E$ for all $i$: this Hamiltonian path ensures that sorting is actually possible. In this work, we improve the bounds for generalized sorting on both random graphs and worst-case graphs. For Erd\H{o}s-Renyi random graphs $G(n, p)$ (with the promised Hamiltonian path added to ensure sorting is possible), we provide an algorithm for generalized sorting with an expected $O(n \log (np))$ comparisons, which we prove to be optimal for query complexity. This strongly improves over the best known algorithm of Huang, Kannan, and Khanna (FOCS 2011), which uses $\tilde{O}(\min(n \sqrt{np}, n/p^2))$ comparisons. For arbitrary graphs $G$ with $n$ vertices and $m$ edges (again with the promised Hamiltonian path), we provide an algorithm for generalized sorting with $\tilde{O}(\sqrt{mn})$ comparisons. This improves over the best known algorithm of Huang et al., which uses $\min(m, \tilde{O}(n^{3/2}))$ comparisons.
Acceleration techniques for optimization over trained neural network  ensembles

We study optimization problems where the objective function is modeled through feedforward neural networks with rectified linear unit (ReLU) activation. Recent literature has explored the use of a single neural network to model either uncertain or complex elements within an objective function. However, it is well known that ensembles of neural networks produce more stable predictions and have better generalizability than models with single neural networks, which suggests the application of ensembles of neural networks in a decision-making pipeline. We study how to incorporate a neural network ensemble as the objective function of an optimization model and explore computational approaches for the ensuing problem. We present a mixed-integer linear program based on existing popular big-$M$ formulations for optimizing over a single neural network. We develop two acceleration techniques for our model, the first one is a preprocessing procedure to tighten bounds for critical neurons in the neural network while the second one is a set of valid inequalities based on Benders decomposition. Experimental evaluations of our solution methods are conducted on one global optimization problem and two real-world data sets; the results suggest that our optimization algorithm outperforms the adaption of an state-of-the-art approach in terms of computational time and optimality gaps.
Rabbit, toad, and the Moon: Can machine categorize them into one class?

Recent machine learning algorithms such as neural networks can classify objects and actions in video frames with high accuracy. Here, I discuss a classification of objects based on basal dynamic patterns referencing one tradition, the link between rabbit, toad, and the Moon, which can be seen in several cultures. In order for them to be classified into one class, a basic pattern of behavior (cyclic appearance and disappearance) works as a feature point. A static character such as the shape and time scale of the behavior are not essential for this classification. In cognitive semantics, image schemas are introduced to describe basal patterns of events. If learning of these image schemas is attained, a machine may be able to categorize rabbit, toad, and the Moon as the same class. For learning, video frames that show boundary boxes or segmentation may be helpful. Although this discussion is preliminary and many tasks remain to be solved, the classification based on basal behaviors can be an important topic for cognitive processes and computer science.
Infinite Neural Network Quantum States

We study infinite limits of neural network quantum states ($\infty$-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics. Ensemble averages of Renyi entropies are expressed in terms of neural network correlators, and architectures that exhibit volume-law entanglement are presented. A general framework is developed for studying the gradient descent dynamics of neural network quantum states (NNQS), using a quantum state neural tangent kernel (QS-NTK). For $\infty$-NNQS the training dynamics is simplified, since the QS-NTK becomes deterministic and constant. An analytic solution is derived for quantum state supervised learning, which allows an $\infty$-NNQS to recover any target wavefunction. Numerical experiments on finite and infinite NNQS in the transverse field Ising model and Fermi Hubbard model demonstrate excellent agreement with theory. $\infty$-NNQS opens up new opportunities for studying entanglement and training dynamics in other physics applications, such as in finding ground states.
Face and Face Mask Detection Using Convolutional Neural Network

The COVID-19 outbreak has posed a severe healthcare concern in Malaysia. Wearing a mask is the most effective way to prevent infections. However, some Malaysians refuse to wear a face mask for a variety of reasons. This work proposes a real-time face and face mask detection method using image processing technique to promote wearing face mask. Haar Cascade is used for the face detection to extract the features of the human faces as a method of approach. On the other hand, the face mask detection utilizes convolutional neural network (CNN) to train a model using the MobileNetV2 training model designed using Python, Keras and Tensorflow. OpenCV package was used as the interface for the algorithms to be connected to a web camera. Based on the performance metric calculation of detection rate analysis of the experimental results, the face detection rate is at 90% true and 10% false detection, which shows very good detection rate. Furthermore, the training accuracy and validation accuracy for the face mask detector are efficiently near to 1.0, proving a steady accuracy over the time. Training loss and validation loss are almost near to zero and decreasing over time, reassuring the algorithm performance is accurate and efficient for a datasets of 4000 images.
Speed limits of the trace distance in the interaction picture for open  quantum system

We investigate the speed limit of the state transformation in open quantum systems described by the Lindblad type quantum master equation. We obtain universal bounds of the total entropy production described by the trace distance between the initial and final states in the interaction picture. Beyond a previous study by Vu and Saito [Phys. Rev. Lett. 128, 010602 (2022)] applicable only to the completely mixed initial state, our results are applicable to an arbitrary initial state. Our bounds can be tighter than the bound of Vu and Hasegawa [Phys. Rev. Lett. 126, 010601 (2021)] which measures the distance by the modified Wasserstein distance $d_T$: For a system of which Hilbert space is two-dimensional, the trace distance is greater than or equal to $d_T$. For this reason, our results can significantly improve Vu-Hasegawa's bound.
Formation of competitiveness of future teachers in the university education system.

The purpose of the article is a theoretical and practical justification of the process of forming the competitiveness of a future teacher. Research methods: theoretical analysis and generalization, modeling, in-depth interviewing. The results of the study: the essence of the competitiveness of the future teacher is determined, the developed model of the process of forming the desired quality is presented, the results of diagnostics are presented. Based on the results of the study of the formation of the competitiveness of future teachers, it was concluded that it is necessary to develop and implement a holistic model of the formation of the desired quality through mentoring and coaching techniques, the development of personal qualities in training training, the development of a system of professional counseling and career guidance. The authors define the competitiveness of the future teacher, substantiate some conditions for its formation. The process of globalization and the variability of the environment today require that society ensure sustainability for itself and its environment throughout life. Therefore, the paradigm of competitiveness of an individual and a specialist is currently changing. For graduates, it is important not only to achieve academic results, but also how they can take into account the likely level of competition in the labor market and be ready to respond flexibly to these changes. The authors of the article, analyzing the psychological and pedagogical literature, come to the conclusion that along with the development of professional qualities, it is necessary to develop habits of leadership, analytical abilities, involvement in public life, active citizenship, as competitive advantages of a specialist.
Generative Adversarial Networks for Synthetic Data Generation: A  Comparative Study

Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. So far much of this work has been applied to use cases outside of the data confidentiality domain with a common application being the production of artificial images. Here we consider the potential application of GANs for the purpose of generating synthetic census microdata. We employ a battery of utility metrics and a disclosure risk metric (the Targeted Correct Attribution Probability) to compare the data produced by tabular GANs with those produced using orthodox data synthesis methods.
The Construction of University Course Teaching under the Background of Cloud Computing

As the basic task of talent training in colleges and universities, cultivating honest talents is of great significance at the national level. However, his continuous curriculum education suggestions have deepened the connotation and extension of higher education curriculum reform. From the perspective of cloud computing technology, this paper conducts a design and research on the teaching of college courses. Through cloud computing technology and data analysis, the actual construction path of university curriculum education and teaching has been determined.
A Neuronal Noise Critique of Integrated Information Theory

Integrated Information Theory (IIT) is an audacious attempt to pin down the abstract, phenomenological experiences of consciousness into a rigorous, mathematical framework. We show that IIT's stance in regards to neuronal noise is inconsistent with experimental data demonstrating that neuronal noise in the brain plays a critical role in learning, visual recognition, and even categorical representation. IIT predicts that entropy due to noise will reduce the information integration of a physical system, which is inconsistent with experimental data demonstrating that decision-related noise is a necessary condition for learning and visual recognition tasks. IIT must therefore be reformulated to accommodate experimental evidence showing both the successes and failures of noise.