Reviewerly/exHarmony huggingface dataset:
T$^{3}$SR: Texture Transfer Transformer for Remote Sensing Image Superresolution

Remote sensing image superresolution has made significant progress in recent years, aiming to restore natural and realistic high-resolution images from low-resolution images. However, most image superresolution remote sensing methods are improved only by deepening their network and expanding the network size, consuming substantial computing resources and imposing a bottleneck in development. Here, we propose an end-to-end image superresolution network called texture transfer transformer for remote sensing image superresolution (T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> SR). For the first time, T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> SR introduces image texture transfer into remote sensing, which achieves the most advanced results. Specifically, T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> SR divides image superresolution into two stages: texture transfer and feature fusion. First, to solve the problems of missing textures, artifacts, and blurring in a single image superresolution approach, we design a texture transfer module to serve the shallow texture transfer. Second, to further reduce the dependence of the model on the reference image, we propose a U-Transformer-based feature fusion scheme to reduce the dependence on the reference image. Finally, we conduct numerous experiments on standard public datasets to fully evaluate our approach. In addition to verifying the method's superiority based on the reference image paradigm, we also test the performance without the reference image. All results show that our method yields an abundant texture and finish with better visual results. Moreover, the best score is also obtained in the quantitative parameters of PSNR and SSIM. Compared with the best available approach, T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> SR has an improved performance by 0.79 dB and 0.33 dB in the datasets of WHU-RS19 and RSSCN7, respectively.
Self-Supervised Few-Shot Time-series Segmentation for Activity Recognition

Extracting valuable activity segments from continuously received sensor data is a key step for many downstream applications such as activity recognition, trajectory prediction, and gesture recognition. Numerous unsupervised and supervised approaches have been proposed for activity segmentation. However, current unsupervised methods generally suffer from subject and environment-dependent problems, and supervised methods require a great many labeled data which is time-consuming and expensive to be collected. To address these issues, we propose a Self-supervised Few-shot Time-series Segmentation framework called SFTSeg, which introduces few-shot learning to conduct activity segmentation only relying on several labeled target samples. To be applicable to time-series data, we design a line-level data augmentation method to build a consistency regularization for the few-shot learning framework, which can augment limited labeled target samples to enhance generalization capacity of the model. Also, we devise a time series-specific pretext task to construct a self-supervised loss with adaptive weighting, which can adopt unlabeled target data to enable the model to learn characteristics of the target data and further improve segmentation performance. The experiments illustrated that SFTSeg achieves obvious gains compared to state-of-the-art methods.
CuMARL: Curiosity-Based Learning in Multiagent Reinforcement Learning

In this paper, we propose a novel curiosity-based learning algorithm for Multi-agent Reinforcement Learning (MARL) to attain efficient and effective decision-making. We employ the centralized training with decentralized execution framework (CTDE) and consider that each agent has knowledge of the prior action distribution of others. To quantify the difference in agents’ knowledge, curiosity, we introduce conditional mutual information (CMI) regularization and use the amount of information for updating decision-making policy. Then, to deploy these learning frameworks in a large-scale MARL setting while acquiring high sample efficiency, we consider a Kullback-Leibler (KL) divergence-based prioritization of experiences. We evaluate the effectiveness of the proposed algorithm in three different levels of StarCraft Multi Agent Challenge (SMAC) scenarios using the PyMARL framework. The simulation-based performance analysis shows that the proposed technique significantly improves the test win rate compared to various state-of-the-art MARL benchmarks, such as the Optimistically Weighted Monotonic Value Function Factorization (OW_QMIX) and Learning Individual Intrinsic Reward (LIIR).
PPTA: Privacy-Preserving Task Assignment Based on Inner Product Functional Encryption in SAM

The explosions of mobile communications and the Internet of Things (IoT) have spawned a new distributed computing paradigm—spatial crowdsourcing, in which workers actively participate in spatiotemporal computing tasks for earning commissions, facilitating the development of urban sharing economic services. Furthermore, to reduce users’ storage space and computational overhead, the server assignment model (SAM) is widely used, which means that crowdsourcing platforms collect sensitive information about tasks and workers, e.g., locations and interests, to perform task assignments accurately. However, in the real world, crowdsourcing platforms are not fully trustworthy and may reveal sensitive information about workers and tasks, which can reduce users’ motivation to use crowdsourcing services. Therefore, how to assign tasks efficiently and securely is still an urgent problem to be solved. In this article, we propose a privacy-preserving task assignment scheme (PPTA), in which the crowdsourcing platform efficiently implements the nearest task assignments without revealing sensitive information about tasks and workers. In PPTA, we utilize inner product functional encryption to achieve circular range queries and multikeyword queries. Considering that workers usually prefer to query the nearest tasks for reducing travel costs, we use the grid location intersection to enable the nearest task assignment. In particular, we design a SAM algorithm, which can improve task assignment rates in multitask and multiworker scenarios. In addition, our scheme can implement user accountability and user revocation, which enhances the security and practicality of the scheme. Finally, we demonstrate the privacy preservation through security theoretical proofs and show the efficiency by constructing extensive comparative experiments, which respectively illustrate the security and the effectiveness of our scheme.
Modalities of Participation:

Participatory design (PD) approaches strive for inclusion and empowerment in the process of design, no matter the experience or abilities of participants. In practice, however, conventional PD methods can be inappropriate for those who communicate in non-verbal ways, which leads to exclusion. This paper asks what participation looks like when we place value on modalities of expression that are beyond the verbal. Responding to PDC 2022’s ‘Senti-Pensar’ theme, we ask; How can we better attune to different forms of expression in a socially just way? What are ‘other’ dimensions to participation? How do we value these, even if they are difficult to describe but somehow felt? Retroanalysing the second author's work in the context of dementia care and design through the first author's framework for non-verbal participatory design, we reflect on how we can recognise and support participation other.
Artificial Empathetic Intelligence for Leadership in Energy and Environmental Design Buildings

The modern era of advanced communications and electronic structures yields with steady steps the enhanced implementation of smart “leadership in energy and environmental design” (LEED) buildings with artificial intelligence (AI) structures. These structures need development through various studied steps. In this paper, we propose a holistic design of these buildings ranging from the compatibility of people with these kinds of structures to the impact on their health (in times of pandemics such as Covid-19). Moreover, we propose a viable scheme of an AI environment (inside LEED buildings), which includes virtual empathetic companion and colleague versions. This scheme relies on a proposed algorithmic logic of iterations for the best possible machine learning. Specifically, we first describe the iterative architecture of the proposed AI entity consisting of 5 sectors. Then, we present the proposed machine learning pipeline (MLP) that comprises these 5 sectors. Afterward, we give the 7-stage empathetic essence that should reside inside the MLP. Finally, we exhibit a proposed experiment besides our case study under development. Then, after implementation, the acquired technological experience can lead to a more exquisite version of AI software robots that could be adaptable to several labor and safety protocols.
Resume Classifier and Summarizer

The tool developed by us is for categorizing and summarizing the resumes. The text was cleaned by transformation to a better encoding scheme, removal of punctuation marks, stop words, and everything was reduced to lower case to avoid redundancy. Following the rigorous cleaning, the text was passed to our model for classification, which accurately predicts the category of resume. The overall output of our model would be the class of the input resume along with a summarized version of the resume for the process of classification, different models were tried out including Logistic Regression, multinomial Naive Bayes, Support Vector Machines. After repeated cleaning and training, our model was seen to perform well with an overall accuracy of 70% using Support Vector Classifier.
Role and Analysis of Various SEO Strategies to Improve Website Ranking

The introduction of the World Wide Web (WWW, W3), was done by the British computer scientist Tim Berners-Lee, in 1990. Soon after the launch of W3, it became clear that standard surfing was insufficient for the internet users to find the information they were looking for. It has had a significant impact on how individuals receive information. The development of search engines met this requirement. Search engines are becoming one of the most effective tools for sharing information. The Search Engine optimization (SEO) is a set of techniques for increasing a website’s visibility and presence on a Search Engine’s Results Page (SERP). Keyword research/selection, search engine indexing, On-page optimization, and Off-page optimization are the four major areas of SEO techniques. In search engines, sorting and selecting distinct websites is a time-consuming operation. Search Engine optimization (SEO) is a method that extracts needful data out of billions of webpages with the use of various strategies. In this article, a website analysis is performed first, and then several SEO tactics are used to improve the visibility and effectiveness of the website. Improved visibility of the website leads to an increased ranking of the keywords, count of users, count of sessions, count of referrals, and likes on the Facebook pages. This paper is soliciting theoretical and case studies contributions of consultmyraid.com, as well as discussion and treatment of issues, and SEO solutions. Analysis of various strategies that are used to improve the effectiveness of the website is using Google analytics tool at various levels. The analysis is done quarterly to check for the enhancement in various factors like keywords, users, sessions, referrals, and Facebook likes. The analysis shows that there is a rise in the website ranking using selected keywords with gains of 77%, 98%, 97%, 97%, and 52% on the analyzed parameters of keywords, users, sessions, referrals, and Facebook likes, respectively.
Role of Zero-Knowledge Proof in Blockchain Security

Distributed Ledger Technology is also known as blockchain technology. It brings the characteristics like transparency, decentralization, immutability, and the distributed ledger in one package. The user identity of the public blockchain is anonymous on the network and cannot control the confidentiality and privacy of their data. The Anonymity of a user does not mean privacy. The importance of user and data privacy and confidentiality in blockchain technology is realized and is yet to be addressed by blockchain protocols. Zero-Knowledge-Proof can be the best solution to this problem. It is relatively new and is developing very rapidly. Zero-Knowledge-Proof can provide strong privacy if the security model is well understood and used carefully. Zero-Knowledge-Proof makes sure that no one accesses the secured data except the user. Paper discusses the types and the various applications of the Zero-Knowledge-Proof in the blockchain.
Trust Aware Multi-Objective Metaheuristics for Workflow Scheduling in Cloud Computing

Nowadays, in the world of modern businesses, it has become a trend to switch to cloud computing by deploying their workflow applications. Nevertheless, workflow scheduling is a challenging task because of the dependency among its tasks, high computational intensity, and heterogeneous behavior of resources. Execution time and trust for virtual machines on which the tasks are to be scheduled is the most difficult job during resource allocation and raises considerable problems. Thus, management of reducing of Makespan time and allocation of tasks to the virtual machine having the highest trust value at the same time is necessary. It becomes a multi-objective problem. Regarding the current issue, we address a multi-objective function of trust and the execution time for the selection of virtual machines for the allocation of workflow tasks. We have proposed MOGAACO, a multi-objective function-based approach using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) for scheduling workflows. For the generation of initial pheromone, ACO invokes GA. With the designed multi-objective function three parameters are evaluated, Load Balance Level (LBL), Makespan, Resource Utilization (RU). CloudSim simulator is used for simulation of experimental results and the proposed algorithms perform better than the basic ACO, GA, and heuristic algorithm MIN-MIN.
Survey of Different Recommendation Systems to Improve the Marketing Strategies on E-commerce

Making accurate prediction by recommendation system is a great challenge now-a-day. Up surging technology has led different applications and made customers as supreme for commercial organization to promote the business. Many researchers have described and explained various recommendation algorithms of their own as per the need, but developing an efficient recommendation system for accurate prediction has become the basic requirement of E-commerce. In this research work, authors provide the recent survey of all machine learning based recommender systems which helps in improving marketing strategies of E-commerce. Authors cover the fundamental nomenclature and concepts of recommender systems and also outline the key latest research on machine learning-based recommender systems for much more accurate predictions.
Deep Learning Based Yoga Pose Classification

Yoga’s popularity is increasing daily. Doing Yoga regularly helps physically, mentally, and spiritually. Many people practice yoga without any formal training. However, practicing yoga incorrectly or even without proper supervision can lead to serious health problems such as stroke, nerve damage, and so on. As a result, there is a need for scientific posture analysis ensuring appropriate yoga posture is an essential aspect to consider. Pose identification strategies are effective in identifying posture and assisting people in performing yoga more effectively. Due to the paucity of data and real-time positioning, position detection is a difficult task. In this paper, we have done an extensive literature survey and offer a method for real-time pose estimation that uses the deep neural network(DNN) model to detect and fix errors in a person’s stance.
An Innovative Multi-Factor Authentication Approach

In a modern digital environment, there is a continual need for new and better authentication methods, particularly in the context of Multi-Factor Authentication systems. A new authentication framework has been proposed that leverages Big Data and Machine Learning to create a powerful new authentication method for Multi-Factor Authentication systems. In a previous paper, a design was proposed for a main component of the framework, a chatbot software that automatically generates authentication challenges and evaluates user responses, along with the necessary functionalities for the design to satisfy the framework. In this paper, we provide a detailed implementation plan for this design such that it will be able to complete this new authentication framework. In addition, in order to demonstrate the viability of this design, we conducted three experiments which each test a critical functionality in a controlled environment.
Assessment of Hyperspectral, Multispectral, Radar, and Digital Elevation Model data in structural lineaments mapping: A case study from Ameln valley shear zone, Western Anti-Atlas Morocco

The analysis and the exploitation of remote sensing datasets have an effective contribution to geological studies, particularly the lineament extraction process could play an important role in tectonic features identification, well as new ore deposits, hydrogeological, and petroleum resources prospection. In this study, we applied an automatic approach to map structural lineaments in the Ameln valley area of the western Anti-Atlas province. The aim of this work is the mapping of structural lineaments in the region, then the comparison of the results of multi-source datasets in automatic lineaments extraction, where six images have been used including, HyMap, ASTER, ALOS PALSAR DEM, Sentinel-2A, and Sentinel-1A VH and VV polarizations. The principal component analysis was applied on optical images, a 7x7 Kernel matrix in four directions, N–S, NE-SW, E-W, and SE-NW for HyMap hyperspectral data image in order to achieve a clear and detailed contrast of structural features. ALOS PALSAR DEM was used for lineaments mapping after applying hill shading in the main four directions. Combined with the field investigations, the high resolution HyMap data image revealed relatively the highest number of lineaments (2091 lineaments), with lengths varying from a few hundred meters (100 m) to a kilometer wide (1.2 km). Additionally, the NNE–SSW, NE-SW, and ENE-WSW are the main obtained lineaments trends, where the NE-SW lineaments set highlights the main faults orientation in the study area. Whereas, a good number of lineaments was detected using elevation data of ALOS PALSAR and VH polarization data of Sentinel-1A that allowed the best discrimination of trending fracture system, leading to achieve a detailed map of the structural discontinuities in the study region. The lineaments mapping results highlight the significant contribution of the late Ediacaran extensional event in the actual tectonic structuration of the eastern Kerdous inlier.
Run-time adaptation of quality attributes for automated planning

Self-adaptive systems typically operate in heterogeneous environments and need to optimize their behavior based on a variety of quality attributes to meet stakeholders' needs. During adaptation planning, these quality attributes are considered in the form of constraints, describing requirements that must be fulfilled, and utility functions, which are used to select an optimal plan among several alternatives. Up until now, most automated planning approaches are not designed to adapt quality attributes, their priorities, and their trade-offs at run time. Instead, both utility functions and constraints are commonly defined at design time. There exists a clear lack of run-time mechanisms that support their adaptation in response to changes in the environment or in stakeholders' preferences. In this paper, we present initial work that combines automated planning and adaptation of quality attributes to address this gap. The approach helps to semi-automatically adjust utility functions and constraints based on changes at run time. We present a preliminary experimental evaluation that indicates that our approach can provide plans with higher utility values while fulfilling changed or added constraints. We conclude this paper with our envisioned research outlook and plans for future empirical studies.
A novel capacity expansion and recognition acceleration method for dot-dispersing coded targets in photogrammetry

Abstract In close-range photogrammetry, some improved designs have been developed to increase the coding capacity of Schneider-like concentric circular coded targets. However, another typical type, Hattori-like dot-dispersing coded targets (HCTs), is rarely expanded. In this paper, we give our detailed solution to recognize a kind of non-public HCT used in video-simultaneous triangulation and resection systems. Then, an advanced HCT (AHCT) with multiple templates and simplified <?CDATA ${P^2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> -Invariant is proposed, which achieves capacity expansion and recognition acceleration. First, the cross-ratio invariant is used in template recognition, and then the affine transformation is used in decoding. During the template recognition process, multiple templates are designed to expand the coding capacity, and a simplified <?CDATA ${P^2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> -Invariant is adopted to accelerate the recognition. Experiments are carried out with different shooting angles ranging from 0° to 80°, with smaller coded targets, with mixed coded targets of different sizes and with large outdoor scenes, and the results show that AHCTs can not only preserve the stability of the HCTs, but also achieve the capacity growth of coded targets at a faster recognition rate by 14.6%. The AHCTs with large capacity can be applied in scenarios with a large size or complex structure, and the acceleration advantage can be considered in real-time dynamic domains.