Reviewerly/exHarmony huggingface dataset:
E-Voting: Technology Requirements Mapping

Technology is the most important component in the implementation of e-voting, although technology is not the only one that contributes to the successful implementation of e-voting. However, in several countries that have successfully implemented evoting such as India, Brazil, the Philippines and Estonia, technology plays an important role in the success of these countries in its implementation. Likewise in countries that are not successful, such as America, Brazil, the Netherlands, England and others, technology has contributed to the failure of the implementation. In this study, a mapping of the requirements and details of technology for the implementation of e-voting was made. As a result, the technology required for preparation / pre-registration, registration, voting and counting activities is divided into two models of e-voting implementation, namely kiosk (machines stored in the public room / polling station) and remote voting. These technologies must meet the aspects of security and public perception, which is: easy to use by voters and committees, as well as transparency. The contribution of this research is a map of the requirements for e-voting technology to increase the number of voters and positive perceptions / public trust in e-voting technology and the development of e-voting technology.
An Enhanced Triadic Color Scheme for Content-Based Image Retrieval

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user’s query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>L</mi> </mrow> <mi>∗</mi> </msup> <msup> <mrow> <mi>a</mi> </mrow> <mi>∗</mi> </msup> <msup> <mrow> <mi>b</mi> </mrow> <mi>∗</mi> </msup> </math> based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort.
Delving into the Continuous Domain Adaptation

Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e.g., art, real, painting, quickdraw, etc. We argue that this is not realistic as it is implausible to define the real-world datasets using a few discrete attributes. Therefore, we propose to investigate a new problem namely the Continuous Domain Adaptation (CDA) through the lens where infinite domains are formed by continuously varying attributes. Leveraging knowledge of two labeled source domains and several observed unlabeled target domains data, the objective of CDA is to learn a generalized model for whole data distribution with the continuous attribute. Besides the contributions of formulating a new problem, we also propose a novel approach as a strong CDA baseline. To be specific, firstly we propose a novel alternating training strategy to reduce discrepancies among multiple domains meanwhile generalize to unseen target domains. Secondly, we propose a continuity constraint when estimating the cross-domain divergence measurement. Finally, to decouple the discrepancy from the mini-batch size, we design a domain-specific queue to maintain the global view of the source domain that further boosts the adaptation performances. Our method is proven to achieve the state-of-the-art in CDA problem using extensive experiments. The code is available at https://github.com/SPIresearch/CDA.
Discussion on the Basic Model of Intelligent Technology to Enable the Transformation of Learning Methods

In the era of rapid development of new technologies, the field of education is also constantly being sublimated with the help of technological development. With the introduction of technologies such as the Internet, artificial intelligence, virtual reality, cloud computing, and the Internet of Things, learners' learning methods have undergone changes. From the traditional mode of learning based on book knowledge and fixed school professors to the adaptive and personalized learning of learners in a smart environment. The adaptive learning mode in the intelligent environment is more conducive to cultivating people who are independent, individual and adaptable to social development. This paper will discuss the transformation of learning methods supported by new technologies and their advantages. It analyzes and sorts out the modes and paths of intelligent technology-enabled learning methods, and discusses the challenges of education development under the background of technology.
Self-supervised learning for modal transfer of brain imaging

Today's brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multimodal data. In addition, their supervision information is mined from large-scale unsupervised data by establishing auxiliary tasks, and the network is trained by constructing supervision information, which not only ensures the similarity between the input and output of modal images, but can also learn valuable representations for downstream tasks.
On constant-time quantum annealing and guaranteed approximations for graph optimization problems

Abstract Quantum annealing (QA) is a computational framework where a quantum system’s continuous evolution is used to find the global minimum of an objective function over an unstructured search space. It can be seen as a general metaheuristic for optimization problems, including NP-hard ones if we allow an exponentially large running time. While QA is widely studied from a heuristic point of view, little is known about theoretical guarantees on the quality of the solutions obtained in polynomial time. In this paper, we use a technique borrowed from theoretical physics, the Lieb–Robinson (LR) bound, and develop new tools proving that short, constant-time QA guarantees constant factor approximations ratios for some optimization problems when restricted to bounded degree graphs. Informally, on bounded degree graphs the LR bound allows us to retrieve a (relaxed) locality argument, through which the approximation ratio can be deduced by studying subgraphs of bounded radius. We illustrate our tools on problems MaxCut and maximum independent set for cubic graphs, providing explicit approximation ratios and the runtimes needed to obtain them. Our results are of similar flavor to the well-known ones obtained in the different but related QAOA (quantum optimization algorithms) framework.
Multi objective task scheduling based on hybrid metaheuristic algorithm for cloud environment

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.
Polymorphic wireless receivers

Today's wireless technologies are largely based on inflexible designs, which make them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF , a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet , which enables the solution of key waveform inference problems, and (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.
Rapid Application Development in Cloud Computing with IoT

Project management teams have been following the traditional rigorous methods for design, process and documentation for decades. Flexible management changes this direct approach. No wonder this method has become more and more common over time. Project managers have also attracted people’s attention, especially in areas such as software development, where technology, goals and tasks are among constantly changing teams. The rapid application development (RAD) model can improve project efficiency, create products on time and improve customer satisfaction. RAD has become a major trend in the field of software development. This is a relatively new method that ensures the seamless and rapid development of new applications to meet the growing development needs of companies from different industries. In the field of project management, “RAD” is a Dujour method. The RAD model is based on native and iterative development procedures and does not contain specific designs. The software development process itself involves the programming required to develop the product. The RAD model reduces the total development time by reusing and developing components at the same time, thereby speeding up delivery. WORK can only work if highly qualified engineers use it, and the customer promises to achieve the initial goal within a certain period of time.
Increasing the Motivation of Agricultural Universities Students to Studying English with the Help of Project Method

The article is devoted to the question of using the project method in teaching students of agricultural higher education institutions a foreign language.It is shown that the entry of the Internet into everyday life entails the entry of Internet resources into the learning process.The use of the project method in the educational process raises student's motivation, as it is one of the types of modern technologies in education.The article deals with the types of projects and their objectives, depending on the language background of students of a non-linguistic university.The outcome of the work is the development of a sequence of students' work on creating their own creative project.
ABOS: an attention-based one-stage framework for person search

Abstract Person search is of great significance to public safety research, such as crime surveillance, video surveillance and security. Person search is a method of locating and identifying the queried person from a complete set of images. The main cause of false recall and missed detection in person search is the presence of person occlusion in the images. In order to improve the accuracy of person search when the person to be queried is occluded, this paper proposes an attention-based one-stage framework for person search (ABOS) using an anchor-free model as a baseline. The method uses the channel attention module to express different forms of occlusion and take full advantage of the spatial attention module to highlight the target region of the occluded pedestrians. These attention modules integrate deep and shallow features to guide the network to pay attention to the visible area of the occluded target and extract the semantic information of the pedestrians. Experimental results on CUHK-SYSU and PRW datasets show that the proposed person search method based on attention mechanism in this paper has better performance than existing methods, achieving 93.7 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> of mAP on CUHK-SYSU dataset and 46.4 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> of mAP on PRW dataset, respectively.
An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning

Nowadays, phishing is a major problem on a global scale.Everyone must use the internet in today's society in order to cope up in the real world.As a result, internet crime like phishing has become a serious issue throughout the world.This type of crime can be committed by anyone; all they need is a computer.Additionally, hacking may now be learned quickly by anyone with programming and mathematical skills.The adoption of various techniques by anti-phishing toolbars, such as machine learning, may enable users to quickly identify a fake website.As a result, researchers are now particularly interested in the problem of detecting fraudulent websites.Machine learning techniques have been offered throughout the entire process to more precisely identify fraudulent websites.To find the best accurate outcome, classification with random parameter tuning and ensemble based approaches are utilized.A user-friendly interface has also been suggested to make the system more accessible to the public.
Enhancement of Low-Light Image using Homomorphic Filtering, Unsharp Masking, and Gamma Correction

Now-a-days, a digital image can be found almost everywhere, and digital image processing plays a huge role in analyzing and enhancing the image so that it can be delivered in a good condition.Color distortion and loss of image details are the common problems that were faced by low-light image enhancement methods.This paper introduces a low-light image enhancement method that applied the concept of homomorphic filtering, unsharp masking, and gamma correction.The aim of the proposed method is to minimize the two problems stated while producing images of better quality when compared to the other low-light image enhancement methods.An objective evaluation was done on the proposed method, comparing the results produced by the enhanced method with other two existing low-light image enhancement methods.The results obtained showed the proposed method outshines the other two existing low-light image enhancement method in maintaining the image details and producing a natural looking image, achieving the lowest Mean Square Error (MSE) and Lightness Order Error (LOE) scores, and has the highest Features Similarity Index color (FSIMc), Features Similarity Index (FSIM), Structure Similarity Index (SSIM), and Visual information fidelity (VIF) scores.Future studies that should be made on this research are to implement dehaze and denoise functionality into the low-light image as well as enabling it to be applicable in real-time scenarios.
Meta-Style: Few-Shot Learning Dataset for Social Media Field

The research on few-shot learning has received extensive attention because there are few labeled samples in real application scenarios, which could promote the transformation of industrial intelligence. However, the existing datasets for few-shot learning mainly focus on common objects, which limits the application of few-shot learning to specific domains like the social media field. In this paper, we construct a few-shot learning dataset Meta-Style for the social media field, which is composed of the photos of different styles of online celebrities. Compared with the existing general few-shot datasets, it can better reflect the performance and application value of few-shot learning algorithm in the field of social media. In addition, to evaluate the performance of few-shot learning algorithms in this field, the performance of common few-shot learning algorithms on Meta-Style is reproduced and reported. More importantly, a baseline method is proposed, that is, latent prototype estimation based on global perception and neighborhood adaptation, which obtains the state-of-the-art result on Meta-Style. The algorithm was also awarded the fifth place in the few-shot learning track of the 2021 iFLYTEK A.I. Developer Competition. Meta-Style and subsequent updates will be released at https://github.com/pcchvol/Meta-StyleV1.
Design a hybrid Optimization and Homomorphic Encryption for Securing Data in a Cloud Environment

Cloud Computing (CC) is denoted as web-based computing that offers devices or users a shared pool of information, resources, or software.It permits small companies and end-users for making the use of different computational resources such as software, storage, and processing ability offered via other companies.But the main problem in CC is data security because of malware and attacks.So this paper developed a novel Hybrid Bat and Cuckoo-based Pallier Homomorphic Encryption (HBC-PHE) scheme for enhancing the data security of the cloud from malware and attacks.Initially, collected datasets are stored in the cloud using the python tool, and collected datasets are transferred into the developed HBC-PHE framework.At first, generate the key for each dataset and separate the private key for all datasets.Moreover, convert the plain text into ciphertext using the bat and cuckoo fitness function in PHE.Finally, cloud-stored data are encrypted successfully and the attained performance outcomes of the developed framework are associated with other existing techniques in terms of confidential rate, decryption time, encryption time, efficiency, and throughput.Additionally, the developed model gained a throughput of 654Kbps, decryption time of 0.05ms, encryption time of 0.08ms, and efficiency of 98.34% for 500kb.As well, the designed model gained a confidential rate of 98.7% and a computation time of 0.03s for using a 500 kb.
Services personalization in digital academic libraries: a Delphi study

Purpose This paper aims to explore the realm of literature about personalization of digital library services. This paper focuses on users’ unique needs and will identify different types of personalized services. Therefore, this study has identified different types of services personalization in the context of digital academic libraries. Design/methodology/approach In this research, the systematic review method has been used to obtain the relevant indicators of different types of personalization in the context of libraries. To explain basic indicators, a Delphi method has been used. The Delphi panel’s members consisted of 15 experts (faculty members, researchers, professional users and software designers). A purposeful sampling and the Delphi fulfillment process were performed in three rounds. After collecting data, descriptive statistics (mean and standard deviation), inferential statistics (binomial distribution test) and the Kendall coordination coefficient were used to determine the consensus rate among experts. Findings A total of 103 indicators were extracted for different types of personalization through a systematic literature review. Of these, 90 indicators were considered significant in the experts’ view. Generally, content personalization, interactive personalization, collaborative personalization and information retrieval personalization are the main components of personalization types, each of which has its own indicators. Originality/value This study has dealt with the issue of what is personalized in the context of digital academic library. The findings should be helpful and effective in the development of a holistic view on personalization of services in digital libraries.
Low precision decentralized distributed training over IID and non-IID data

Decentralized distributed learning is the key to enabling large-scale machine learning (training) on the edge devices utilizing private user-generated local data, without relying on the cloud. However, practical realization of such on-device training is limited by the communication and compute bottleneck. In this paper, we propose and show the convergence of low precision decentralized training that aims to reduce the computational complexity and communication cost of decentralized training. Many feedback-based compression techniques have been proposed in the literature to reduce communication costs. To the best of our knowledge, there is no work that applies and shows compute efficient training techniques such as quantization, pruning etc., for peer-to-peer decentralized learning setups. Since real-world applications have a significant skew in the data distribution, we design "Range-EvoNorm" as the normalization activation layer which is better suited for low precision training over non-IID data. Moreover, we show that the proposed low precision training can be used in synergy with other communication compression methods decreasing the communication cost further. Our experiments indicate that 8-bit decentralized training has minimal accuracy loss compared to its full precision counterpart even with non-IID data. However, when low precision training is accompanied by communication compression through sparsification we observe a 1-2% drop in accuracy. The proposed low precision decentralized training decreases computational complexity, memory usage, and communication cost by ∼4× and compute energy by a factor of ∼20×, while trading off less than a 1% accuracy for both IID and non-IID data. In particular, for higher skew values, we observe an increase in accuracy (by ∼0.5%) with low precision training, indicating the regularization effect of the quantization.