Reviewerly/exHarmony huggingface dataset:
LRP: Predictive output activation based on SVD approach for CNN s acceleration

Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in a wide range of applications. CNNs contain millions of parameters, and a large number of computations challenge hardware design. In this paper, we take advantage of the output activation sparsity of CNNs to reduce the execution time and energy consumption of the network. We propose Low Rank Prediction (LRP), an effective prediction method that leverages the output activation sparsity. LRP first predicts the output activation polarity of the convolutional layer based on the singular value decomposition (SVD) approach of the convolution kernel. And then it uses the predicted negative value to skip invalid computation in the original convolution. In addition, an effective accelerator, LRPPU, is proposed to take advantage of sparsity to achieve network inference acceleration. Experiments show that our LRPPU achieves 1.48 x speedup and 2.02 x energy reduction compared with dense networks with slight loss of accuracy. Also, it achieves on average 2.57 x speedup over Eyeriss and has similar performance and less accuracy loss compared with SnaPFA.
Real-time Quantum Calculations of Phase Shifts On NISQ Hardware Platforms Using Wavepacket Time Delay

We introduce a new method to calculate phase shifts on noisy intermediate scale quantum (NISQ) hardware platforms using a wave packet edge time delay.The method uses the early and intermediate stages of the collision because the standard method based on the asymptotic out-state behavior is unreachable using today's NISQ platforms.The calculation was implemented on a 4-site transverse Ising model in one spatial dimension with and without a potential interaction.A time evolution operator describing the progression of the system was constructed and transmission and reflection coefficients were calculated based on the identified quantum Fourier transformed momentum states.The detailed analysis of the phase shift calculations on both IBM superconducting transmon and University of Maryland ion trap quantum computers shows the platform independence of the methodology.This successful implementation of this wave packet preparation and projection on momentum eigenstates can now be performed with actual quantum computing hardware platforms.This method provides a procedure for calculating phase shifts and opens the possibility of using noisy intermediate scale quantum devices to perform real-time quantum mechanics and quantum field theory scattering calculations.
Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of noise
Topical Text Classification of Russian News: a Comparison of BERT and Standard Models

The paper is devoted to the single-label topical classification of Russian news. The author compares the BERT features and standard character, word and structure-level features as text models. Experiments with OpenCorpora and eight news topics show that the BERT model is superior to standard ones, and achieves good classification quality for a small dataset of long news. Error analysis reveals the best classified topics: "economics", "culture", and "media". Comparison with the state-of-the-art research allows to consider BERT as a baseline for future investigations of analysis of texts in Russian.
Accurate determination of the first arrival time of elastic wave traveled through rock sample by machine learning

To automate time-consuming and experience-based analysis of elastic wave velocities in rock samples, we propose a method of determining the arrival time of experimental waveform data using machine learning. Our model is one-dimensional semantic segmentation based on U-net utilizing ResNet and attention mechanisms. The results demonstrate high accuracy of travel time estimation (0.0125 μs) of two types of waves (P- and S-waves). Our approach can estimate the travel time in any experimental device. This method reduces the interpretation time and human biases in analyzing laboratory data of elastic waves. Therefore, this approach contributes to the efficient evaluation of subsurface structure from seismic properties.
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?

Approximate computing is known for its effectiveness in improvising the energy efficiency of deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recently, the inexact nature of approximate components, such as approximate multipliers have also been reported successful in defending adversarial attacks on DNNs models. Since the approximation errors traverse through the DNN layers as masked or unmasked, this raises a key research question—can approximate computing always offer a defense against adversarial attacks in DNNs, i.e., are they universally defensive? Towards this, we present an extensive adversarial robustness analysis of different approximate DNN accelerators (AxDNNs) using the state-of-the-art approximate multipliers. In particular, we evaluate the impact of ten adversarial attacks on different AxDNNs using the MNIST and CIFAR-10 datasets. Our results demonstrate that adversarial attacks on AxDNNs can cause 53% accuracy loss whereas the same attack may lead to almost no accuracy loss (as low as 0.06%) in the accurate DNN. Thus, approximate computing cannot be referred to as a universal defense strategy against adversarial attacks.
Robust facial expression recognition system in higher poses

Facial expression recognition (FER) has numerous applications in computer security, neuroscience, psychology, and engineering. Owing to its non-intrusiveness, it is considered a useful technology for combating crime. However, FER is plagued with several challenges, the most serious of which is its poor prediction accuracy in severe head poses. The aim of this study, therefore, is to improve the recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model, advanced ensemble of AdaBoost, and saturated vector machine (SVM). The FER features are tracked from one frame to the next using the ellipsoidal tracking model, and the visible expressive facial key points are extracted using Gabor filters. The ensemble algorithm (Ada-AdaSVM) is then used for feature selection and classification. The proposed technique is evaluated using the Bosphorus, BU-3DFE, MMI, CK + , and BP4D-Spontaneous facial expression databases. The overall performance is outstanding.
Gender Neutralisation for Unbiased Speech Synthesising

Machine learning can encode and amplify negative biases or stereotypes already present in humans, resulting in high-profile cases. There can be multiple sources encoding the negative bias in these algorithms, like errors from human labelling, inaccurate representation of different population groups in training datasets, and chosen model structures and optimization methods. Our paper proposes a novel approach to speech processing that can resolve the gender bias problem by eliminating the gender parameter. Therefore, we devised a system that transforms the input sound (speech of a person) into a neutralized voice to the point where the gender of the speaker becomes indistinguishable by both humans and AI. Wav2Vec based network has been utilised to conduct speech gender recognition to validate the main claim of this research work, which is the neutralisation of gender from the speech. Such a system can be used as a batch pre-processing layer for training models, thus making associated gender bias irrelevant. Further, such a system can also find its application where speaker gender bias by humans is also prominent, as the listener will not be able to judge the gender from speech.
Self-Distilled Supervised Contrastive Learning for diagnosis of breast cancers with histopathological images

Convolutional neural networks (CNN) and its variants have been widely used for developing the histopathological image based computer-aided diagnosis (CAD). However, the annotated data are scarce in clinical practice, and limited training samples generally cannot well train the CNN model, resulting in degraded predictive performance. To this end, we propose a novel Self-Distilled Supervised Contrastive Learning (SDSCL) algorithm to improve the diagnostic performance of a CNN-based CAD for breast cancer. In particular, the original histopathological images are first decomposed into H and E stain views, which are served as the augmented sample pairs in the supervised contrastive learning (SCL). Due to the complementary characteristic of the two stain views, this data-driven SCL guide the CNN model to efficiently learn the discriminative features, alleviating the problem of small sample size. Furthermore, self-distillation is embedded into the SCL framework, in which the CNN model jointly distills itself and conducts SCL to further improve feature representation. The proposed SDSCL is evaluated on two public breast histopathological datasets, which outperforms all the compared algorithms. Its average classification accuracy, precision, recall, and F1 scores are 94.28%, 94.64%, 94.58%, 94.34%, respectively, on Bioimaging dataset, and 80.44%, 81.92%, 80.57%, 80.10% on Databiox dataset. The experimental results on two datasets indicate that SDSCL has the potential for the histopathological image based CAD.
Study of water resources parameters using artificial intelligence techniques and learning algorithms: a survey

Abstract Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response &amp; correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELM GWL model was the most accurate model with RMSE, MAPE, and R 2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation.
A parallel algorithm based on quantum annealing and double-elite spiral search for mixed-integer optimal control problems in engineering

Many engineering processes can be summarized as mixed-integer optimal control problems (MIOCPs) owing to the needs for optimizing mixed-integer dynamic control policies. However, MIOCP is a challenging NP-hard problem with great computational complexity, resulting in slow convergence or premature convergence by most current heuristics. Accordingly, this study explores a new and effective hybrid Quantum Annealing-Double-Elite Spiral Search (QA-DESS) algorithm to address this issue. To be specific, QA algorithm specializes in solving integer optimization with high efficiency due to the unique quantum-tunnelling-based annealing mechanism. For the optimization of continuous decisions, a DESS algorithm which adopts adaptive Cauchy mutation and double-elite evolutionary mechanism to enhance global searching is designed. The hybrid QA-DESS algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to find the optimal mixed-integer decisions by interactive parallel computing of QA and DESS. Simulation results on benchmark functions and practical engineering MIOCPs verify that the proposed optimization algorithm is more excel at finding promising results than other 7 heuristics (including traditional and state-of-the-art algorithms).
Application of Intelligent Education Using Interactive Modeling in Higher Ideological and Political Education Platform

In order to improve the efficiency of ideological and political intelligent teaching, this paper uses interactive modeling technology to build an ideological and political education platform. In the classroom interactive image processing, this paper uses the maximum circumscribed moment of the contour to extract the rectangular block area and uses the convolutional neural network to distinguish the defect and the text for the extracted rectangular block area. In addition, this paper comprehensively judges the detection results of each detection area and designs a corresponding detection algorithm for each part to detect defects. From the experimental research results, it can be seen that the interactive ideological and political teaching platform proposed in this paper has a good interactive teaching effect and can effectively improve the effect of ideological and political teaching.
Vision-Aided Brush Alignment Assembly System for Precision Conductive Slip Rings

The alignment precision of manual brush assembly for a precision conductive slip ring is critical to its performance of reliability and service lifetime. Currently, the alignment precision cannot be guaranteed since it largely depends on the operator’s experiences and skill level. In this paper, a machine vision-aided method is proposed to measure the ring groove positions as the brush alignment objective, and track the relative brush position deviation during the manual brush alignment assembly. A vision-aided brush alignment assembly system is also developed to provide quantitative position deviation for the precise alignment of the brush and the ring groove, ensuring higher alignment accuracy and efficiency. The experimental results indicate that, with the developed system, the brush alignment assembly accuracy can be controlled within ±0.02 mm.
Decentralized Crowdfunding Platform using Smart Contracts

Crowdfunding is an online cash raising technique which has been started to encourage the ideas of innovative minds of people by investing the required quantity of money on them. Using crowdfunding, people can put resources into spearhead businesses through a platform. The issue faced during the today’s world of crowdfunding technique is that, intermediator doesn't give the assurance to the investor who contributed money for the project and also the investor doesn't have any control over the cash they invested. To overcome this major problem, a blockchain based technique is used to provide a private, secure and decentralized platform for crowdfunding. The main objective is to create a smart contract to let investors contribute to any innovational ideas where the assurance of the money is guaranteed.
Using Bayesian Networks to Provide Educational Implications: Mobile Learning and Ethnomathematics to Improve Sustainability in Mathematics Education

There are many Western apps that help students strengthen their mathematics skills through learning and game apps. A research project was designed to create an IOS Math App to provide Grade 6 Emirati students with the opportunity to explore mathematics, then, using Bayesian Networks, to examine the educational implications. The learning app was developed using ethnomathematics modules based on the Emirati culture. Students were required to navigate through several modules to examine various mathematical concepts in algebra and geometry. The survey was written for Grade 6 English language learners. Based on the Bayesian Networks, the findings suggested that if students are allowed to explore mathematics based on familiar cultural norms and practices, students with grades A and C would get higher grades. However, if students are not allowed to explore mathematics in line with their local culture, more students would receive an F grade, while some students with an A grade would get lower grades. Additionally, the findings showed that incorporating cultural elements had a positive change in students’ liking and learning mathematics, even without a mobile device. However, not allowing a mobile device and not allowing culture had an increased negative impact on students’ liking and learning mathematics. The last finding suggested that students valued ethnomathematics examples over a mobile device to learn mathematics. The research findings could help to improve sustainability in mathematics education by promoting ethnomathematics and mobile learning.
An 0.92 mJ/frame High-quality FHD Super-resolution Mobile Accelerator SoC with Hybrid-precision and Energy-efficient Cache

With the rise of contactless communication and streaming services, Super-resolution (SR) in mobile devices has become one of the most important image processing technologies. Also, The popularity of high-end Application Processor (AP) and high resolution display in mobile drives the development of the lightweight mobile SR-CNNs [1], [2], which show the high reconstruction quality. However, the large size and wide dynamic range of both images and intermediate feature maps in CNN hidden layers pose challenges for mobile platforms. Constraints from the limited power <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(&lt; 100\text{mW})$</tex> and shared bandwidth <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(&lt; 2\text{GB}/\mathrm{s})$</tex> on mobile platform, a low power and energy-efficient architecture is required.
Supply Chain Financial Risk Management under the Background of Wireless Multimedia Communication and Artificial Intelligence

The application of information technology and various electronic communication equipment has grown rapidly. At the same time, information technologies such as the Internet and communication networks have become increasingly mature and widely used, making e-commerce transactions simpler and the roles of enterprises in the supply chain increasingly diversified. At this stage, supply chain finance has become an important way for small- and medium-sized enterprises to finance, and it is a key step in commercial trade. However, the risk control of this model is difficult to be effectively contained. How to control its financial risk to the lowest level is the research goal of this paper. This paper analyzes and calculates the supply chain financial risks of different enterprises through a questionnaire method, a case analysis method, and a comparison method and obtains relevant data. The data results show that the entropy value of the net interest rate is 0.97, which indicates that it has a larger market share and less risk. Through wireless multimedia communication technology and artificial intelligence algorithms, the system calculation of supply chain financial risk management is much simpler. In this regard, the research proposes a scientific system for building supply chain financial risk management.
Biomedical Argument Mining Based on Sequential Multi-Task Learning

Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification. Current approaches employ conventional multi-task learning framework for jointly addressing the latter two subtasks, and achieve some success. However, explicit sequential dependency between these two subtasks is ignored, which is crucial for accurate biomedical argument mining. Moreover, relation identification is conducted solely based on the argument component pair without considering its potentially valuable context. Therefore, in this paper, a novel sequential multi-task learning approach is proposed for biomedical argument mining. Specifically, to model explicit sequential dependency between argument component classification and relation identification, an information transfer strategy is employed to capture the information of argument component type that is transferred to relation identification. Furthermore, graph convolutional network is employed to model dependency relation among the related argument component pairs. The proposed method has been evaluated on a benchmark dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.