Reviewerly/exHarmony huggingface dataset:
Hybrid non-convex regularizers model for removing multiplicative noise

Obtaining natural and realistic restorations from the noisy images contaminated by multiplicative noise is a challenging task in image processing. To get over this conundrum, by introducing the non-convex potential functions into the total variation and high-order total variation regularizers, we investigate a novel hybrid non-convex optimization model for image restoration. Numerically, to optimize the resulting high-order PDE system, a proximal linearized alternating minimization method, based on the classical iteratively reweighted ℓ1 algorithm and variable splitting technique, is designed in detail. Meanwhile, the convergence of the constructed algorithm is also established on the basis of convex analysis. The provided numerical experiments point out that our new scheme shows superiorities in both visual effects and quantitative comparison, especially in terms of the staircase aspects suppression and edge details preservation, compared with some popular denoising methods.
Neural Network based Temporal Point Processes for Attack Detection in Industrial Control Systems

Industrial Control Systems safety is nowadays constantly jeopardized by a plethora of different attacks. The identification of malicious activities happened in the past and the prediction of future menaces is crucial more than ever. In this paper we propose a new methodology based on Marked Temporal Point Processes (MTTP) and Neural Networks in order to identify and predict threats. Our technique differs from classical Temporal Point Processes because it does not rely on prior assumptions on the form of the conditional intensity function. The latter, in fact, is learned leveraging two different Deep Learning approaches. The experimental evaluation, performed on a dataset obtained from gas pipelines, highlights that our methodology is suitable to represent logs and identify past and future threats in a completely unsupervised fashion. The results obtained confirm the validity of our approach.
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet

This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR (audio, text) dataset. The majority of the audio dataset have silent gaps at both ends which are clipped during dataset preprocessing for a more uniform mapping of audio frames and their corresponding texts. Mel Frequency Cepstral Coe cients (MFCCs) are used as audio features to feed into the model. The model having Bidirectional LSTM paired with ResNet and one-dimensional CNN produces the best results for this dataset out of all the models (neural networks with variations of LSTM, GRU, CNN, and ResNet) that have been trained so far. This novel model uses Connectionist Temporal Classification (CTC) function for loss calculation during training and CTC beam search decoding for predicting characters as the most likely sequence of Nepali text. On the test dataset, the character error rate (CER) of 17.06 percent has been achieved.
Learner and teacher perspectives on robot-led L2 conversation practice

Abstract This article focuses on designing and evaluating conversation practice in a second language (L2) with a robot that employs human spoken and non-verbal interaction strategies. Based on an analysis of previous work and semi-structured interviews with L2 learners and teachers, recommendations for robot-led conversation practice for adult learners at intermediate level are first defined, focused on language learning, on the social context, on the conversational structure and on verbal and visual aspects of the robot moderation. Guided by these recommendations, an experiment is set up, in which 12 pairs of L2 learners of Swedish interact with a robot in short social conversations. These robot–learner interactions are evaluated through post-session interviews with the learners, teachers’ ratings of the robot’s behaviour and analyses of the video-recorded conversations, resulting in a set of guidelines for robot-led conversation practice: (1) societal and personal topics increase the practice’s meaningfulness for learners; (2) strategies and methods for providing corrective feedback during conversation practice need to be explored further; (3) learners should be encouraged to support each other if the robot has difficulties adapting to their linguistic level; (4) the robot should establish a social relationship by contributing with its own story, remembering the participants’ input, and making use of non-verbal communication signals; and (5) improvements are required regarding naturalness and intelligibility of text-to-speech synthesis, in particular its speed, if it is to be used for conversations with L2 learners.
SSTRN: Semantic Style Transfer Reference Network for Face Super-Resolution

Reference super-resolution (RefSR) has achieved promising results in the single image super-resolution (SISR) field by providing additional details from the reference images. Existing RefSR methods usually tend to extract similar or aligned features from reference images to further enhance the resolution of the final result. Therefore, the efficiency of RefSR models highly depends on the conformity between extracted features from the low-resolution (LR) and reference images. In this paper, we propose a new reference image generation scheme via semantic style transfer to unleash our model from relevant feature extraction computations. The generated reference images have the most content similarity and identical alignment with the LR input that compensates for the lost details of the LR images. Despite previous RefSR methods that rely on extracting and transferring texture information from the reference image to LR input, provided reference images are enriched with the style information of high-resolution (HR) images. Extensive experiments indicate the effectiveness of the proposed reference images.
A BERT-based Text Sentiment Classification Algorithm through Web Data

In order to analyze the sentiment tendency of public opinion, this paper conducts a textual sentiment classification research through web data. In the research, this paper uses the BERT (Bidirectional Encoder Representation from Transformers) model to replace the commonly used word2vec model as a text vectorization tool, which has stronger semantic representation capabilities and can realize polysemous words. For the multi-label classification problem of reviews, the BR (Binary Relevance) algorithm is used to transform the problem into multiple binary classification problems, which is directly and efficient for processing multi-label data. Design the BiLSTM-Attention model, which combines the bidirectional long and short-term memory network and the attention mechanism to achieve further extraction of text features. After multiple sets of comparative experiments, the effectiveness of the BiLSTM-Attention model is verified through performance evaluation. In order to further improve the performance of the model, the problem of unbalanced data set is solved by adjusting the loss function and various parameters so that a better classification effect is achieved.
Attendance Management System using Facial Recognition

Student attendance in a large classroom is difficult to handle in a hands-on way. Due to the very huge number of students present during the class they always have the chance for a representative to arrive. Marking attendance in traditional ways has been a challenge. However, these procedures are not very much reliable. In this study an automated logging and attendance management system is being proposed with the help of face detection and recognition methods. This research concerns improving the automated attendance management system that uses face recognition technology to record student attendance. The research is intended to make the attendance management system less complex, more time-saving, more efficient, and simpler to use. Here the detection of face is carried out using face recognition algorithm and later the image which is processed will then be compared to the existing images in the folder and the attendance will be marked and stored in the excel timely. While comparing with the existing traditional method, this new automated system helps in reducing people's workload. The proposed research includes phases such as image capture through webcam, face detection, face comparisons and recognition and marking attendance.
Developing a Forecasting model for uranium occurrence in GII, Northeastern Desert, Egypt using artificial neural networks

In the resources sector, artificial neural networks (ANNs) are becoming more and more well-liked. Using datasets of uranium occurrence as input data, ANN technology offers answers to problems. In this paper, a new artificial neural network (ANN) model and Triangulation Irregular Network (TIN) were used for forecasting of uranium occurrence in Gattar II (GII) area, Northeastern Desert, Egypt. The multilayer perceptron ANN model was trained with the Levenberg–Marquardt algorithm, for calculating the uranium (U) occurrence in GII area based on 185 datasets. TIN method showed a clear distribution for uranium ore grade, total gamma ray (total γ-ray) and thorium (Th) content at the studied area. The proposed ANN model achieves coefficient of determination (R2) of 0.993, root mean square error (RMSE) of 67.197%, mean relative error (MRE) of −2.66%, and mean absolute relative error (MARE) of 12.54%.
Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge-it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://github.com/yikaiw/CEN.
Blockchain Technology in Teaching, Learning, and Academic Administration as a Long-term Solution to Emergencies in Higher Education

The application of blockchain, particularly through avenues in teaching and learning and academic administration, has created scope for a new business model in higher education. Although higher education institutions in many African countries including Botswana are yet to adopt blockchain in their institutional processes, the chapter points to seven unique areas of practice in which the technology could be infused. Distributed ledgers and smart contracts are among the blockchain functions that could assist in improving efficiency, security, and privacy in the way academic administration is executed especially in student identification, processing of student admission applications, degree certification, document authentication, and quality assurance. A distinct highpoint of the chapter is the attention it calls to student experience in the learning and teaching process, by highlighting that it stands to benefit from the collaborative learning model that blockchain enables. At a wider level, the conversation about blockchain-based pedagogy coalesces with the push by many African governments, in particular in Botswana, towards a digitally transformed society. Already, many colleges and universities have placed the focus on achieving ‘smart campuses’ as a strategic goal. Blockchain adoption is one such step towards a smart campus. The chapter theorises that higher education institutions cannot legitimately talk about smart campuses while still holding steadfast to manual systems and processes.
Predictive geometrization of grade indices of an iron-ore deposit

Purposeis development of the methods to predict indices of iron-ore deposits relying upon the improvement of available techniques as well as formulation of new geometrization procedures and identification of the most adequate decision-making way to assess geological data as the basis for geometrization and prediction. Methods are to develop a self-organizing prediction algorithm based upon combination of the available techniques and formulation of new mathematical methods; consider various means to assess them in the context of iron-ore deposit; and select the most efficient one. Use of geostatistical methods makes it possible to evaluate and process output geological information. The methods help assess mineral reserves of a mining enterprise. Findings. Dependencies of magnetite ore content upon geological factors have been derived in the context of an open pit of PIVDGZK JSC. The deposit has been geometrized; predictive mining and geometric model of the deposit site has been deve-loped. Factors have been determined influencing the distribution nature of the indices. Graphs to arrange grade indices of the deposit have been constructed. The graphs have helped predict their placement within the deposit. Originality. A method to predict mining and geological indices of iron-ore deposit has been developed relaying upon a self-organizing algorithm. Correlation between grade indices of minerals and different geological factors has been determined making it possible to describe spatial distribution of grade indices of the deposit. Practical implications. Geometrization methods for iron-ore deposits have been formulated. The methods help schedule mining operations accurately while improving their efficiency. The developed predictive self-organizing algorithm is the flexible tool used for various mining and geological conditions to provide scheduling and assessing of different mining methods. The self-organizing as well as geostatic evaluation techniques is quite a promising research tendency.
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks are trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model's performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning. The code is released on github.com/YunpengZhai/PEG.
A fully dynamic forward-secure group signature from lattice

Abstract A forward-secure group signature (FSGS) ensures the unforgeability of signatures in the past time period despite signing secret key is leaked in the current time period. As we know, traditional FSGS schemes are mostly relying on number-theoretic assumptions unable to resist quantum attacks. Therefore, we present an efficient lattice-based fully dynamic (i.e. users can flexibly join or quit the group) forward-secure group signature (DFSGS) by combining an improved version of FSGS scheme proposed by Ling. Based on an efficient zero-knowledge argument, we construct argument of knowledge of the committed value and the plaintext that help with privacy protection. Our DFSGS scheme is proved to be anonymous and forward-secure traceable relying on short integer solution and learning with errors assumptions in random oracle model. Moreover, the lengths of group public key and signature of our DFSGS scheme have been improved, and the length of user secret key has no connection with the quantity of group members.
Distributed, dynamic and trustworthy access control for telehealth systems

Summary With its ability to “minimize human intervention when generating, exchanging and consuming data,” the Internet of Things (IoT) is increasingly deployed in all sectors, in particular in the healthcare sector. With IoT, telehealth has become the new paradigm in healthcare, as it helps keep patients connected with wearable devices and other remote patient monitoring tools in order to help practitioners work more efficiently. However, telehealth implies that patients share their personal and physiological data remotely with the hospital staff, which can put the patient's privacy at risk. Thus, the establishment of access control is mandatory. Therefore, the objective of this article is to achieve a distributed and reliable access control for telehealth systems using smart contract‐enabled blockchain technology. To do so, we propose three different smart contract‐based access control approaches. We evaluate the three propositions and compare them with related work in terms of latency of access‐request response and gas consumption related to contract deployment, function execution and different responses, furthermore, we give a security analysis.
Quantitative Approximation by Kantorovich-Choquet Quasi-Interpolation Neural Network Operators Revisited

In this chapter we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ . When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions. It follows [19].
Evaluation of China’s High-Advanced Industrial Policy: A PMC Index Model Approach

High-advanced industries upgraded by digital empowerment have gradually become an important support industry. Therefore, various provinces in China have issued relevant policies to support the prosperous of the digital economy and high-advanced industries. The collection and analysis of high-advanced industrial policy help to scientifically evaluate industrial policies and formulate scientific policy optimization paths. Based on a total of 168 high-advanced industrial policy documents from 26 cities in the Yangtze River Delta region from 2009–2021, this study adopts the PMC-Index model to evaluate the high-advanced industry policies in the digitalization context quantitatively. 12 representative high-advanced industry policy texts were selected for specific analysis. In addition, this study visualizes the measurement results of the internal structure and policy effectiveness of policies by PMC-Surface diagrams and then concludes that the design of high-advanced industry policies was relatively reasonable overall, with 11 policies rated as “Good Consistency” and only one “Acceptable Consistency.” The sample policies lack reasonable arrangements for different period plans, lack incentives, or have relatively single incentives. The policy influence among cities in the Yangtze River Delta urban agglomerations is small, and the integration trend is not apparent. There is a particular gap in the scores between Shanghai, Zhejiang, Jiangsu, and Anhui province. This study provides references and suggestions for formulating and revising high-advanced industrial policies.
Generalized Symmetrical Sigmoid Function Induced Neural Network Multivariate Approximation

Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the generalized symmetrical sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.
Generalized Symmetrical Sigmoid Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations

Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators.