Reviewerly/exHarmony huggingface dataset:
Global-prior-guided fusion network for salient object detection

RGB image and depth map (RGB-D)-based salient object detection (SOD) has been well-studied in recent years, especially using deep neural networks. An RGB image provides rich local and semantic features, while the depth map provides global structural information. Many researchers have treated depth information as a supplement to RGB maps. However, depth maps in various datasets are not as precise as RGB information, as they are captured under various conditions. Therefore, thoroughly exploiting these features at different levels remains unresolved. Many cognitive theories, such as the topological perception theory, claim that global properties are prior to local ones and are important for human cognition. In this paper, we propose a novel global-prior-guided fusion network with global-prior extraction modules to fuse cross-modality features. Each module contains a cross attention guided by deeper global priors, and the global prior extracted by this module is used to guide the processing of local features in shallow layers. The global guided network first integrates the local and global cross features into the decoder of depth maps, and then the fused structural features of the decoder are finally fused into the saliency decoder. Experimental results show that our method outperformed other state-of-the-art methods in the RGB-D-based SOD task on seven datasets (i.e., DUT-RGBD, NJUD, LFSD, NLPR, RGBD135, SIP, and STERE) and in terms of most metrics. To thoroughly exploit the modules we designed, we extended our model to accomplish the tasks of RGB and video SOD with slight adaptions, and obtained results comparable to those of the state-of-the-art (SOTA) methods in both fields. • A new global-prior-guided cross-fusion module to fuse the structural features. • A new network structure with the global fusion strategy. • Our model achieves state-of-the-art performance in salient object detection.
Beyond-Classical Computing Using Superconducting Quantum Processors

The Google Quantum AI team's long-term goal is to realize a large-scale fault-tolerant quantum computer. Of the technologies available to implement the quantum processor at the core of such a system, solid-state superconducting circuits based on Josephson junctions (JJs) are among the strongest contenders. Recent demonstrations with this technology include computation beyond the capabilities of today's most powerful supercomputers [1] and execution of quantum error correction (QEC) codes that can detect and correct either bit or phase flip errors [2]. These exciting results were enabled by low-error-rate quantum processors with tens of qubits and the supporting infrastructure to run these devices. However, today's quantum computers still have orders of magnitude fewer qubits than is required for a useful fault-tolerant quantum computer; for this, an estimated million or more qubits will be needed and scaling-up today's technology to this extent is non-trivial. Here, we review the Google Quantum AI team's recent progress and future plans. Specifically, we describe our post-classical Sycamore quantum computer and discuss the Google Quantum AI roadmap to fault-tolerant quantum computing.
A novel multiphase segmentation method for interpreting the 3D mesoscopic structure of asphalt mixture using CT images

The three-dimensional reconstruction of asphalt mixture using X-ray computed tomography (CT) images is beneficial to establish the correlation between its mesoscopic structure and its macroscopic properties. Segmentation of CT images that distinguish the mineral aggregates and air voids in asphalt mixture is an essential step for this purpose, wherein up-to-date methods are often limited due to complex image quality and failed to identify the bituminous mortar with gray levels ranging between void and aggregate. In this paper, we propose a local threshold based and Monte-Carlo incorporated segmentation method to address the above problem effectively. The probability of each pixel identified as aggregate could be calculated in a CT image. Notably, the proposed method is able to identify the multiphase components of asphalt mixture and build the three-dimensional mesoscopic structure model to investigate the spatial variation of different components. The proposed method has been verified in a case study, wherein 400 sequenced CT images of an asphalt mixture sample are segmented to interpret its mesoscopic structure. Results show that the proposed method performs well to distinguish the multi-phase components of the asphalt mixture, particularly the bituminous mortar that is usually miscategorized as void or aggregate by previous binarization methods.
Image classification using convolutional neural network with wavelet domain inputs

Commonly used convolutional neural networks (CNNs) usually compress high-resolution input images. Although it reduces the computation requirements into a reasonable range, the downsampling operation causes information loss, which affects the accuracy of image classification. How to adopt high-resolution image inputs to improve the quality of input information and thus improve the classification accuracy without changing the overall structure of the pre-defined CNN model or increasing the model parameters is an important issue. Here, a CNN model with wavelet domain inputs is proposed to provide a solving scheme. Specifically, the proposed method applies wavelet packet transform or dual-tree complex wavelet transform to extract information from input images with higher resolutions in the image pre-processing stage. Some subband image channels are selected as the inputs of conventional CNNs where the first several convolutional layers are removed, so that the networks directly learn in the wavelet domain. Experiment results on the Caltech-256 dataset and the Describable Textures Dataset with the ResNet-50 show that the classification accuracy of our method can have a maximum improvement of 2.15% and 10.26%, respectively. These validate the effectiveness of our proposed scheme. This code is publicly available at https://github.com/BeBeBerr/wavelet-cnn.
Selection of DevOps best test practices: A hybrid approach using ISM and fuzzy TOPSIS analysis

Abstract Testing is a complex phase in DevOps process due to need of an automated process that provides feedback at different strategies of continuous development and operations pipeline. Software organization face several challenges during the testing phase due to lack of understanding on testing best practices for the DevOps paradigm. The objective of this study is to prioritize DevOps best testing practices, which can facilitate the selection of testing practices during DevOps process. To perform this research, we have extended the work done by Hornbeek, using the 15 DevOps testing practices discussed in his study. First, we categorize the test practices against culture, automation, lean, measurement, and sharing (CALMS) pillars of DevOps adoption principles. Next, a questionnaire‐based survey was conducted to collect feedback from industry practitioners on the DevOps test practices and their categorization against CALMS criteria. Finally, we applied Interpretive Structure Modeling (ISM) to find the interrelationship between CALMS criteria, and fuzzy TOPSIS was used to prioritize the DevOps test practices that will assist practitioners to better manage the testing activities during DevOps process.
Improved Security Blockchain for IoT based Healthcare monitoring system

In recent years, healthcare analytics is considered as an important application of research topic in medical field. The patient data is the main concern to be noticed in healthcare analytics solutions. The privacy of data leads to securing the data with high quality of care and requires a best design methodology. In current scenario, many failed to provide security of data which stored in server accessible by third party. To improve the data security and privacy thereby reducing bandwidth and improving efficiency, a consortium method called Enhanced Proof of Work (E-PoW) consensus blockchain for IoT based healthcare monitoring system has been proposed.
A Distributed NWDAF Architecture for Federated Learning in 5G

For network automation and intelligence in 5G, the network data analytics function (NWDAF) has been introduced as a new network function. However, the existing centralized NWDAF structure can be overloaded if an amount of analytic data are concentrated. In this paper, we introduce a distributed NWDAF structure tailored for federated learning (FL) in 5G. Leaf NWDAFs create local models and root NWDAF construct a global model by aggregating the local models. This structure can guarantee data privacy since local models are created in NF, and can reduce network resource usage because the global model is created by collecting local models.
Transliteration based Generative Pre-trained Transformer 2 Model for Tamil Text Summarization

Automatic text summarization plays a significant role in natural language processing to extract the useful information from the huge amount of online contents. Especially in human&#x0027;s day-to-day life, the online news article provides the information about current events around the world through the various media to the public. The research work focuses on addressing the problem of Tamil text summarization for online Tamil news article to create the summary for news data. In this research work, transliteration based Generative Pre-trained Transformer 2 (GPT-2) model is proposed to summarize the online news articles for Tamil language by extracting the great number of relevant features namely sentence position, one hot encoding, number of entities, term frequency, and inverse document frequency. We proposed Attention based LSTM- NMT Transliteration model for performing translation between Tamil and English Languages and it is compared with other transliteration models for analyzing the performance of the model. In addition, the comparative study is made between three pre trained transformer models known as fine-tuned Generative Pre-trained Transformer 2 (GPT-2) model, Text-To-Text Transfer Transformer (T-5) model and Bidirectional Encoder Representations from Transformers (BERT) model. To evaluate the performance of all models, the dataset is created for Tamil language by gathering the data from various online sources. The experiments are done to evaluate the enhanced transliteration model based on Bilingual Evaluation Understudy Score and also to assess the GPT-2 based text summarization model based on the ROUGE evaluation measures. The performance analysis shows that the transliteration based Generative Pre-trained Transformer 2 (GPT-2) model achieves better summarization performance by reducing the occurrence of repetition.
A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.
Perfect NOT and conjugate transformations

Abstract We obtain the perfect NOT transformation criteria, two necessary and sufficient conditions for realizing a perfect NOT transformation on a quantum state set S of a qubit. Furthermore, we discuss a probabilistic perfect NOT transformation when there is no perfect NOT transformation on a state set S . We also generalize the perfect NOT transformation to the conjugate transformation in the multi-level quantum system and a lower bound of the best possible efficiencies attained by a probabilistic perfect conjugate transformation are obtained.
A privacy protection scheme for electricity transactions in the microgrid day-ahead market based on consortium blockchain

• A privacy protection scheme for microgrid day-ahead market is proposed. • ElGamal algorithm is adopted to generate anonymous identities that can be traced. • Certificateless signcryption is improved to make the scheme more secure and efficient. • The consortium blockchain is adopted to make trading more secure and efficient. The electricity trading plan made by the users in the microgrid day-ahead market (DAM) contains the electricity consumption information of the future day. Aiming at the problem that the leakage of electricity consumption information threatens the privacy security of users and the leakage of electricity trading plan destroys the fairness of the DAM, a privacy protection scheme for electricity transactions in microgrid DAM based on consortium blockchain is proposed. Firstly, the scheme is based on the ElGamal algorithm that generates anonymous identities for users involved in transactions, preventing leakage of electricity consumption information and allowing the real identity of the fraudster to be traced in the event of fraudulent transactions. Secondly, a certificateless signcryption (CLS) algorithm is improved to realize the encryption and authentication of the user's electricity trading plan, which enhances the efficiency of the scheme. Finally, the scheme realizes decentralized trading of users in the microgrid in the DAM through the consortium blockchain and smart contracts, ensuring transaction security and improving transaction efficiency. The scheme is proved to have high security under different types of attacks by stochastic prediction machine model. Moreover, by calculating the computation time for different kinds of operations in the two stages of signcryption and unsigncryption, it is concluded that the computation cost of this scheme is lower than that of other schemes of the same type has higher efficiency.
Hiddenite: 4K-PE Hidden Network Inference 4D-Tensor Engine Exploiting On-Chip Model Construction Achieving 34.8-to-16.0TOPS/W for CIFAR-100 and ImageNet

Since the advent of the Lottery Ticket Hypothesis [1], which advocates the existence of embedded sparse models that achieve accuracies equivalent to the original dense model, new algorithms to find such subnetworks have been attracting attention. In particular, Hidden Network (HNN) [2] proposed a training method that finds such accurate subnetworks (Fig. 15.4.1). HNN extracts the sparse subnetwork by taking a logical AND of an initial model's random weights and a binary mask that defines the selected connections - a supermask. The importance of each connection, quantified as a score, is evaluated in the training phase; a supermask is learned by picking the connections with the top-k% highest scores. Although similar to pruning, supermask training is clearly different in that it never updates the initial random weights.
Boosting the transferability of adversarial examples via stochastic serial attack

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by imposing mild perturbation on clean ones. An intriguing property of adversarial examples is that they are efficient among different DNNs. Thus transfer-based attacks against DNNs become an increasing concern. In this scenario, attackers devise adversarial instances based on the local model without feedback information from the target one. Unfortunately, most existing transfer-based attack methods only employ a single local model to generate adversarial examples. It results in poor transferability because of overfitting to the local model. Although several ensemble attacks have been proposed, the transferability of adversarial examples merely have a slight increase. Meanwhile, these methods need high memory cost during the training process. To this end, we propose a novel attack strategy called stochastic serial attack (SSA). It adopts a serial strategy to attack local models, which reduces memory consumption compared to parallel attacks. Moreover, since local models are stochastically selected from a large model set, SSA can ensure that the adversarial examples do not overfit specific weaknesses of local source models. Extensive experiments on the ImageNet dataset and NeurIPS 2017 adversarial competition dataset show the effectiveness of SSA in improving the transferability of adversarial examples and reducing the memory consumption of the training process.
Sparse to Dense Dynamic 3D Facial Expression Generation

In this paper, we propose a solution to the task of generating dynamic 3D facial expressions from a neutral 3D face and an expression label. This involves solving two sub-problems: (i)modeling the temporal dynamics of expressions, and (ii) deforming the neutral mesh to obtain the expressive counterpart. We represent the temporal evolution of expressions using the motion of a sparse set of 3D landmarks that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To better encode the expression-induced deformation and disentangle it from the identity information, the generated motion is represented as per-frame displacement from a neutral configuration. To generate the expressive meshes, we train a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement. This allows us to learn how the motion of a sparse set of landmarks influences the deformation of the overall face surface, independently from the identity. Experimental results on the CoMA and D3DFACS datasets show that our solution brings significant improvements with respect to previous solutions in terms of both dynamic expression generation and mesh reconstruction, while retaining good generalization to unseen data. The code and the pretrained model will be made publicly available.
Effects of COVID-19 on the Perception of Virtual Education in University Students in Ecuador; Technical and Methodological Principles at the Universidad Técnica Particular de Loja

The confinement and migration from face-to-face to open access, online or blended/hybrid education modality caused because of the coronavirus crisis has forced a readaptation of education with enormous deficiencies at all levels. This work analyzes the viewpoint of a group of students from the Universidad Técnica Particular de Loja (Ecuador) regarding the current state of emergency from a descriptive and correlational quantitative methodological conception, based on the application of an instrument made up of six thematic blocks: socio-demographic situation, use of ICT, importance of ICT, methodology, didactic techniques, and study modality. The main results show that students are not yet convinced that a virtual modality is better than face-to-face. However, there are groups that value positively the use of ICTs mainly for recalling information, self-learning, and motivation. The techniques most valued by students are the traditional ones: teacher explanation and individual work. However, they give a low value to individualization as a methodological principle under which these techniques are based.
Sequence of Actions Recognition Using Continual Learning

Continual Learning is the basic fundamental idea behind Artificial Intelligence and Machine Learning. With C.L, the machine will autonomously learn and translate the data into meaningful information like humans. Additionally, it can be defined as a model that automatically keeps on fine-tuning itself by acquiring knowledge over time throughout its lifespan. Moreover, this paper tries to solve a long-remained problem of human action prediction using continual learning. The human action prediction is an indispensable component for featuring a seamless human-robot interaction in the future. And along with that, such models and implementations have tremendous scope in almost every field in the future ranging from security to elderly or child care or law and order maintenance and automation and whatnot. Numerous implementations use simple machine learning models, which wear out over the period. Using continual learning, a model can be designed which improves itself without requiring any human intervention. And the model will be able to produce substantially improved results because of continual learning. The model will not only be able to get better with the objects coming in its way in the future but also will be able to improve the model and get additionally polished results.
Position control of a planar cable-driven parallel robot using reinforcement learning

Abstract This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.
Implementation of three efficient 4-digit fault-tolerant quantum carry lookahead adders

Abstract Adders are one of the most interesting circuits in quantum computing due to their use in major algorithms that benefit from the special characteristics of this type of computation. Among these algorithms, Shor’s algorithm stands out, which allows decomposing numbers in a time exponentially lower than the time needed to do it with classical computation. In this work, we propose three fault-tolerant carry lookahead adders that improve the cost in terms of quantum gates and qubits with respect to the rest of quantum circuits available in the literature. Their optimal implementation in a real quantum computer is also presented. Finally, the work ends with a rigorous comparison where the advantages and disadvantages of the proposed circuits against the rest of the circuits of the state of the art are exposed. Moreover, the information obtained from such a comparison is summarized in tables that allow a quick consultation to interested researchers.