Reviewerly/exHarmony huggingface dataset:
Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation

Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing real-world users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in mission-critical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter.
Transition-based Parsing with Stack-Transformers

Modeling the parser state is key to good performance in transition-based parsing.Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g.stack-LSTM parsers, or local state modeling of contextualized features, e.g.Bi-LSTM parsers.Given the success of Transformer architectures in recent parsing systems, this work explores modifications of the sequence-to-sequence Transformer architecture to model either global or local parser states in transition-based parsing.We show that modifications of the cross attention mechanism of the Transformer considerably strengthen performance both on dependency and Abstract Meaning Representation (AMR) parsing tasks, particularly for smaller models or limited training data.
Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model

The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching.However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images.In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration.Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor.Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration.We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images.The results demonstrate that the proposed registration model can achieve subpixel registration.The relative registration accuracy improved by 1.6%-7.5%,whereas the overall performance improved by 4.5%-14.1%.
Differentiable Feature Aggregation Search for Knowledge Distillation

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design.
Anti-noise diffractive neural network for constructing an intelligent imaging detector array

To develop an intelligent imaging detector array, a diffractive neural network with strong robustness based on the Weight-Noise-Injection training is proposed. According to layered diffractive transformation under existing several errors, an accurate and fast object classification can be achieved. The fact that the mapping between the input image and the label in Weight-Noise-Injection training mode can be learned, means that the prediction of the optical network being insensitive to disturbances so as to improve its noise resistance remarkably. By comparing the accuracy under different noise conditions, it is verified that the proposed model can exhibit a higher accuracy.
Ensemble Machine Learning Model for Classification of Spam Product Reviews

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam product reviews is a challenging issue in NLP (natural language processing). Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron (MLP), K-Nearest Neighbour (KNN), and Random Forest (RF) and predicts the outcome of the review as spam or real (nonspam), based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers (MLP, KNN, and RF) and state-of-the-art boosting approaches like Generalized Boost Regression Model (GBM), Extreme Gradient Boost (XGBoost), and AdaBoost Regression Model in terms of classification accuracy.
Estimation of Driver’s Gaze Region From Head Position and Orientation Using Probabilistic Confidence Regions

Visual attention is one of the most important aspects related to driver distraction. Estimating the driver’s visual attention can help a vehicle understand the awareness state of the driver, providing important contextual information. While estimating the exact gaze direction is difficult in the car environment, a coarse estimation of the visual attention can be obtained by tracking the head pose. Since the relation between head pose and gaze direction is not one-to-one, this paper proposes a formulation based on probabilistic models to create salient regions describing the driver’s visual attention. The area of the estimated region is small when the model has high confidence, which is directly learned from the data. We use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gaussian process regression</i> (GPR) to implement the framework, comparing the performance with different regression formulations such as linear regression and neural network based methods. We evaluate these frameworks by studying the tradeoff between spatial resolution and accuracy of the probability map using naturalistic recordings collected with the UTDrive platform. We observe that the GPR method produces the best result creating accurate estimations with localized salient regions. For example, the 95% confidence region is defined by an area covering 3.77% region of a sphere surrounding the driver.
The turtle and the mouse: how constructivist learning theory shaped artificial intelligence and educational technology in the 1960s

Drawing on historical epistemology and considerations on the function of scientific modelling, this article investigates how in the mid-twentieth century electronic and programmable animal models became tools for exploring the inaccessible ontology of the human mind. The article examines how machines have informed our understanding of the learning process and, vice versa, how the concept of learning has been used to design new technology. The main focus is on Seymour Papert, an artificial intelligence researcher and advocate of Jean Piaget’s constructivist learning theory, who developed computer software for children based on the animated turtle LOGO. The sources used include archival material from Austria, Switzerland and the United States, as well as several publications and grey literature. The article demonstrates that there were close links between artificial intelligence, constructivism and educational technology, and how these links benefited from the cultural transformation of the late 1960s.
Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability

Building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems.In general, rule-based and linear models lack accuracy, while deep learning interpretability is based on rough approximations of the underlying inference.However, recently, the rule-based Tsetlin Machines (TMs) have obtained competitive performance in terms of accuracy, memory footprint, and inference speed on diverse benchmarks (image classification, regression, natural language understanding, and game-playing).TMs construct rules using human-interpretable conjunctive clauses in propositional logic.These, in turn, are combined linearly to solve complex pattern recognition tasks.This paper addresses the accuracy-interpretability challenge in machine learning by introducing a TM with integer weighted clauses -the Integer Weighted TM (IWTM).The intent is to increase TM interpretability by reducing the number of clauses required for competitive performance.The IWTM achieves this by weighting the clauses so that a single clause can replace multiple duplicates.Since each TM clause is formed adaptively by a Tsetlin Automata (TA) team, identifying effective weights becomes a challenging online learning problem.We solve this problem by extending each team of TA with another kind of automaton: the stochastic searching on the line (SSL) automaton.We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability.On average, IWTM uses 6.5 times fewer literals than the vanilla TM and 120 times fewer literals than a TM with real-valued weights.Furthermore, in terms of average memory usage and F1-Score, IWTM outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs.IWTM finally outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of Area Under Curve, while performing competitively on COMPAS.
Efficient Decomposition of Unitary Matrices in Quantum Circuit Compilers

Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For the selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition, which generates O(344n) controlled-not gates for an n-qubit input gate. In addition, we implement optimizations to take advantage of the potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition. Comparing our implementation to Qubiter and the UniversalQCompiler (UQC), we show that our implementation generates circuits that are much shorter than those of Qubiter and not much longer than the UQC. At the same time, it is also up to 10 times as fast as Qubiter and about 500 times as fast as the UQC.
An Efficient Face Mask Detector with PyTorch and Deep Learning

INTRODUCTION:The outbreak of a coronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives.The Covid-19 affected numbers are rising at a tremendous pace.Because of that, many countries face an economic catastrophe, recession, and much more.One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world.But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes.As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.OBJECTIVES: To develop a Face Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.METHODS: A Neural Network model called ResNet is trained on the dataset.Furthermore, this work makes use of the inbuilt Face Detector after training.Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.
Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

Abstract With the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.
Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.
Multiplicative Filter Networks

Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields. Key to these recent successes has been the use of new elements such as sinusoidal nonlinearities, or Fourier features in positional encodings, which vastly outperform simple ReLU networks. In this paper, we propose and empirically demonstrate that an arguably simpler class of function approximators can work just as well for such problems: multiplicative filter networks. In these networks, we avoid traditional compositional depth altogether, and simply multiply together (linear functions of) sinusoidal or Gabor wavelet functions applied to the input. This representation has the notable advantage that the entire function can simply be viewed as a linear function approximator over an exponential number of Fourier or Gabor basis functions, respectively. Despite this simplicity, when compared to recent approaches that use Fourier features with ReLU networks or sinusoidal activation networks, we show that these multiplicative filter networks largely outperform or match the performance of these recent approaches on the domains highlighted in these past works.
Evaluating the Impact of Different Symmetrical Models of Ambient Assisted Living Systems

In recent years, numerous attempts have been made to enhance the living standard for old-aged people. Ambient Assisted Living (AAL) is an evolving interdisciplinary field aimed at the exploitation of knowledge and communication technology in health and tele-monitoring systems to combat the impact of the growing aging population. AAL systems are designed for customized, responsive, and predictive requirements, requiring high performance of functionality to ensure interoperability, accessibility, security, and consistency. Standardization, continuity, and assistance of system development have become an urgent necessity to meet the increasing needs for sustainable systems. In this article, we examine and address the methods of the different AAL systems. In addition, we analyzed the acceptance criteria of the AAL framework intending to define and evaluate different AAL-based symmetrical models, leveraging performance characteristics under the integrated fuzzy environment. The main goal is to provide an understanding of the current situation of the AAL-oriented setups. Our vision is to investigate and evaluate the potential symmetrical models of AAL systems and frameworks for the implementation of effective new installations.