Reviewerly/exHarmony huggingface dataset:
Extended Bayesian Personalized Ranking Based on Consumption Behavior

Bayesian Personalized Ranking (BPR) is a well-known recommendation framework that learns to rank items based on one-class implicit feedback. In some domains such as video and music streaming and news aggregator websites, users’ implicit feedback is not limited to one-class feedback as there are other types of feedback such as watching, listening and reading time which are continuous. This feedback reflects the consumption behavior of users. In this research we show that using this kind of implicit feedback on the top of one-class feedback, recommender systems are able to learn user preferences more precisely. We propose an extended form of BPR by including user consumption behavior to recommend news topics. The result shows that using the extended form of BPR with consumption information improves the performance based on four evaluation measures. The result also verifies that by considering a more granular feedback the extended BPR has better predictions.
Efficient Attention Pyramid Network for Semantic Segmentation

Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way.Recent studies witnessed that attention mechanisms achieve impressive performance in computer vision task.Current attention mechanisms based segmentation methods differ with each other in position and form of the attention mechanism, and perform differently in practice.This paper firstly introduces the effectiveness of multi-scale context features and attention mechanisms in segmentation tasks.We find that multi-scale and channel attention can play a vital role in constructing effective context features.Based on this analysis, this paper proposes an efficient attention pyramid network (EAPNet) for semantic segmentation.Specifically, to efficient handle the problem of segmenting objects at multiple scales, we design efficient channel attention pyramid (ECAP) which employ atrous convolution with channel attention in cascade or in parallel to capture multi-scale context by using multiple atrous rates.Furthermore, we propose a residual attention fusion block (RAFB), whose purpose is to simultaneously focus on meaningful low-level feature maps and spatial location information.At the same time, we will explore different channel attention modules and spatial attention modules, and describe their impact on network performance.We empirically evaluate our EAPNet on two semantic segmentation datasets, including PASCAL VOC 2012 and Cityscapes datasets.Experimental results show that without MS COCO pre-training and any post-processing, EAPNet achieved 81.7% mIoU on the PASCAL VOC 2012 validation set.With deeplabv3+ as the benchmark, EAPNet improve the model performance of more than 1.50% mIoU.
Multiparty Quantum Key Agreement With Strong Fairness Property

Multiparty Key Agreement (MKA) is the backbone for secure multiparty communication. Although numerous efficient MKA-cryptosystems are available in the classical field, their security relies on the assumption that some comput... | Find, read and cite all the research you need on Tech Science Press
Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.
An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.
Digitizing Teacher Education and Professional Development during the COVID-19 Pandemic

Video Conferencing. Video conferencing is a safe alternative to support online teaching and learning during the lockdown of educational institutions . It allows teachers to interact with each other as a large group and in small groups analogous with the process that would be undertaken in a face-to-face class. The use of break-out rooms via Zoom allowed for students to interact and share in pairs.
A Machine Learning based Knowledge Graph Framework for Heterogeneous Power Grid Systems

Knowledge graph system is becoming one of the acknowledged approaches for executing the transactional works of power grid applications. However, due to the heterogeneous data structure of recent power grid systems in the Big data environments, traditional machine learning (ML) based knowledge management approaches show limited adoptability. Hence, the paper proposed a comprehensive ML based knowledge graph framework for complex and heterogeneous power grid applications. The framework mainly involves two ML approaches which are a concept drift detection based incremental learning for homogeneous data and a reverse verification based transfer learning among heterogeneous data. The proposed framework is tested by multiple heterogeneous databases collected from power grid systems of state grid Anhui electric power research institute (China), and the results show improved practicability compare to traditional ML methods.
RETRACTED: Multimedia network teaching platform and its application in mechanical design

The comprehensive application and development of multimedia technology and network technology in education is a research hotspot for a long time now and for a long time. Mechanical design is an important technical basic course for mechanical and related majors in colleges and universities. It is practical and comprehensive. This paper takes mechanical design teaching as the starting point, designs and develops a multimedia network education platform that meets the mechanical design teaching of colleges and universities. The platform fully utilizes the comprehensive advantages of streaming media technology, WebRTC technology and Web mining technology to enable teachers and students to attend classes and communicate remotely online. Through the comparative experiments on the classes of Chengnan College, the results show that with the platform teaching, the students' motivation for learning in the mechanical design discipline is enhanced, the learning investment is increased, and the teachers are satisfied with the classroom atmosphere and teaching effects. This shows that the platform can improve the teaching effect.
Ethereum Blockchain-Based Peer-To-Peer Energy Trading Platform

Blockchain is one of the emerging security technologies that have enormous potential in diverse sectors such as financial organization, academic institutions, national government, business sphere. In this paper, we focus on the application of blockchain systems in the energy industry addressing potential challenges and limitations in this area. The deployment of the proliferation of distributed energy resources requires an efficient and reliable transactive energy (TE) management system in terms of peer-to-peer energy trading. Independence of the energy management system from financial transactions can cause an insecure and vulnerable energy exchange environment. The proposed system design focuses on eliminating gaps in the security by the integration of decentralized application technology with the TE management and Multi-Agent System. The paper discusses the Ethereum blockchain-based peer-to-peer energy trading platform based on the enforced smart contract that controls both financial transactions and energy interchange operations for power trading systems.
Co-movements between Bitcoin and other asset classes in India

Purpose This study aims to analyze the dynamic relationship between the Bitcoin market and the conventional asset classes in India Design/methodology/approach This paper aims to cast light on the dynamic linkages between Bitcoin prices and other conventional asset classes in India by using the wavelet transform frameworks, which can allow us to analyze components of time series without losing the information. To do that, the techniques used with the data set include wavelet-based covariance, correlation, coherence spectrum, continuous power spectrum and Granger causality test. Findings The findings of the study suggest that interrelationships between Bitcoin and the key financial asset returns are statistically significant at low, medium and high frequencies. This study also finds the existence of the unidirectional connectedness between Bitcoin the other assets in India. Practical implications The outcome of the analysis calls for substantial policy implications for investors, portfolio management in India. This research on the existence of the interconnectedness between Bitcoin and other conventional asset classes in a specific country context, India can, therefore, make a significant contribution to the contemporary debate about the speculative nature of the cryptocurrencies. It casts light on whether Bitcoin provides any diversification and risk management benefits for Indian, as well as global investors. Originality/value To the best of the author’s knowledge, this is the first paper investigating the interrelatedness between Bitcoin and key conventional asset classes in India. This research makes methodological advancements by using the wavelet coherence transform. The findings provide empirical bases from which to deal with issues regarding hedging purposes and optimal portfolio allocation for an increasing number of investors in the Indian context. Therefore, the main contribution of this study to related literature in this field is significant.
A Survey of DevOps in the South African Software Context

This study investigated DevOps practices and experiences in the South African software development context, along with associated perceptions of benefits and challenges.Survey data collected from a sample of 80 software development professionals showed that more frequent builds, earlier detection of bugs and reduced project lead times were the top three benefits, while getting DevOps capable members into a team, finding experienced professionals to support DevOps practice and changing deep-seated company culture to support DevOps were the top three challenges.DevOps practices are still emerging.Although 85% of respondents report continuous integration as a frequent practice, only 54% report using continuous deployment frequently.The biggest reported impacts of DevOps on software development culture were in making development team members aware of operational faults, responsible for quality assurance, and responsible for deployments.Realisation of benefits from DevOps depends largely on a culture change.Results are useful for guiding organisations considering DevOps adoption.
Process Automation on the Blockchain: An Exploratory Case Study on Smart Contracts

While business process automation through information technology has progressed over the last decades, smart contracts have recently emerged as a promising new means of automation.However, in practice, the adoption of smart contract-based automation is in its infancy, raising the question if the technology genuinely offers a unique approach to process automation.Drawing on an exploratory case study of four start-ups, we investigate the potentials for automation that organizations achieve through smart contracts and how smart contracts differ from established automation technologies, such as workflow management systems, enterprise resource planning systems, and robotic process automation.We contribute to the literature on process automation by unveiling transparent and immutable, crossorganizational, and decentralized automation as characteristics that differentiate smart contracts from established automation technologies.Besides, we provide practitioners with an understanding of application scenarios, potentials, and drawbacks of smart contracts for process automation.
Towards Impartial Multi-task Learning

Abstract Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifically, for the task-shared parameters, we optimize the scaling factors via a closed-form solution, such that the aggregated gradient (sum of raw gradients weighted by the scaling factors) has equal projections onto individual tasks. For the task-specific parameters, we dynamically weigh the task losses so that all of them are kept at a comparable scale. Further, we find the above gradient balance and loss balance are complementary and thus propose a hybrid balance method to further improve the performance. Our impartial multi-task learning (IMTL) can be end-to-end trained without any heuristic hyper-parameter tuning, and is general to be applied on all kinds of losses without any distribution assumption. Moreover, our IMTL can converge to similar results even when the task losses are designed to have different scales, and thus it is scale-invariant. We extensively evaluate our IMTL on the standard MTL benchmarks including Cityscapes, NYUv2 and CelebA. It achieves the new state-of-the-art among loss weighting methods under the same experimental settings.
Light field image coding with flexible viewpoint scalability and random access

This paper proposes a novel light field image compression approach with viewpoint scalability and random access functionalities. Although current state-of-the-art image coding algorithms for light fields already achieve high compression ratios, there is a lack of support for such functionalities, which are important for ensuring compatibility with different displays/capturing devices, enhanced user interaction and low decoding delay. The proposed solution enables various encoding profiles with different flexible viewpoint scalability and random access capabilities, depending on the application scenario. When compared to other state-of-the-art methods, the proposed approach consistently presents higher bitrate savings (44% on average), namely when compared to pseudo-video sequence coding approach based on HEVC. Moreover, the proposed scalable codec also outperforms MuLE and WaSP verification models, achieving average bitrate saving gains of 37% and 47%, respectively. The various flexible encoding profiles proposed add fine control to the image prediction dependencies, which allow to exploit the tradeoff between coding efficiency and the viewpoint random access, consequently, decreasing the maximum random access penalties that range from 0.60 to 0.15, for lenslet and HDCA light fields.
RETRACTED: Application of Cloud Computing in Education Management Informatization Construction

Abstract In recent years, with the rapid development and continuous optimization of computer network information technology, cloud computing technology has already developed from the experimental initial stage to the stage of large-scale computer enterprise application and campus LAN universal application. And driven by scientific and technological innovation, cloud computing provides users with better and safer data management, calculation, storage and other services because of its unique resource sharing and convenient management and maintenance, which has attracted wide attention and favor from all walks of life. The emergence of cloud computing technology has ignited new thinking for our education and teaching. If computer multimedia and Internet, as the previous round of technologies, have brought about changes in teaching presentation, teaching resource acquisition and education transmission, the new technologies based on cloud computing will not only continue to deepen the previous educational changes, but also bring more profound fission to education. Under the guidance of new technology, the ever-changing and innovative “future education” will be more fascinating, and provide us with a broader development space for the construction of educational informationization. With the help of the service advantages of cloud computing, combined with the modern teaching concept, it can be applied to information teaching, which is just conducive to improving the teaching level of higher vocational education and realizing efficient teaching work. At present, most universities in China use cloud computing for teaching management, breaking the traditional mode of university resource management, fully combining with contemporary advanced concepts and technologies, and keeping pace with the times.
Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI

Computer-aided diagnosis based on features extracted from medical images relies heavily on accurate lesion segmentation before feature extraction. Using 994 unique breast lesions imaged with dynamic contrast-enhanced (DCE) MRI, several segmentation algorithms were investigated. The first method is fuzzy c-means (FCM), a well-established unsupervised clustering algorithm used on breast MRIs. The second and third methods are based on the convolutional neural network U-Net, a widely-used deep learning method for image segmentation—for two- or three-dimensional MRI data, respectively. The purpose of this study was twofold—1) to assess the performances of 2D (slice-by-slice) and 3D U-Nets in breast lesion segmentation on DCE-MRI trained with FCM segmentations, and 2) compare their performance to that of FCM. Center slice segmentations produced by FCM, 2D U-Net, and 3D U-Net were evaluated using radiologist segmentations as truth, and volumetric segmentations produced by 2D U-Net (slice-by-slice) and 3D U-Net were compared using FCM as a surrogate truth. Five-fold cross-validation was conducted on the U-Nets and Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used as performance metrics. Although 3D U-Net performed well, 2D U-Net outperformed 3D U-Net, both for center slice (DSC p=4.13&times;10<sup>-9</sup>, HD p=1.40&times;10<sup>-2</sup>) and volume segmentations (DSC p=2.72&times;10<sup>-83</sup>, HD p=2.28&times;10<sup>-10</sup>). Additionally, 2D U-Net outperformed FCM in center slice segmentation in terms of DSC (p=1.09&times;10<sup>-7</sup>). The results suggest that 2D U-Net is promising in segmenting breast lesions and could be an effective alternative to FCM.
Latin American Smart University: Key Factors for a User-Centered Smart Technology Adoption Model

The quality of administrative, academic, and extension processes is influenced by the management of technology in universities. Smart technologies such as artificial intelligence (AI), cloud computing, the Internet of things (IoT), and big data continue to emerge with great prominence. Universities must use tools that connect learning with the use of new technologies, bridging gaps with the outside world. Smart education is provided by an educational environment supported by smart technologies and devices. A smart university is primarily based on the integration of smart technologies into the educational process. The emergence of this concept allows the application of a large number of components that involve the adaptation of the traditional educational model using these technologies. Many of the ideas of adoption in Latin American universities generate demotivation toward change and do not imply the expected impact in their processes. One way to prevent this situation is to monitor the adoption process itself. The generation of tools that can measure the level of adoption by universities is a necessity as smart technologies are incorporated and become more powerful. Focusing on the users of the processes is an objective of adoption measurement. A User-Centered Smart University Model is the goal of this transformation process, established on factors such as an individual’s perception, safety and risks, and organizational support.
Selective Encryption of the Versatile Video Coding Standard

versatile video coding (VVC) is the next generation video coding standard developed by the joint video experts team (JVET) and released in July 2020. VVC introduces several new coding tools providing a significant coding gain over the high efficiency video coding (HEVC) standard. It is well known that increasing the coding efficiency adds more dependencies in the video bitstream making format-compliant encryption with the standard more challenging. In this paper we tackle the problem of selective encryption of the VVC standard in format-compliant and constant bitrate. These two constraints ensure that the encrypted bitstream can be decoded by any VVC decoder while the bitrate remains unchanged by the encryption. The selective encryption of all possible VVC syntax elements is investigated. A new algorithm is proposed to encrypt in format-compliant and constant bitrate the transform coefficients (TCs) together with other syntax elements at the level of the entropy encoder. The proposed solution was integrated and assessed under the VVC reference software model version 6.0. Experimental results showed that encryption drastically decreases the video quality while the encryption is robust against several types of attacks. The encryption space is estimated in the range of 15% to 26% of the bitstream size resulting in a lightweight encryption process. The web page of this work is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gugautie.github.io/sevvc/</uri> .