Reviewerly/exHarmony huggingface dataset:
Social Engineering Attacks Prevention: A Systematic Literature Review

Social engineering is an attack on information security for accessing systems or networks. Social engineering attacks occur when victims do not recognize methods, models, and frameworks to prevent them. The current research explains user studies, constructs, evaluation, concepts, frameworks, models, and methods to prevent social engineering attacks. Unfortunately, there is no specific previous research on preventing social engineering attacks that effectively and systematically analyze it. Current prevention methods, models, and frameworks of social engineering attacks include health campaigns, human as security sensor frameworks, user-centric frameworks, and user vulnerability models. The human as a security sensor framework needs guidance that will explore cybersecurity as super-recognizers, likely policing act for a secure system. This paper intends to critically and rigorously review prior literature on the prevention methods, models, and frameworks of social engineering attacks. We conducted a systematic literature review based on Bryman & Bell’s literature review method. We found a new approach in addition to methods, frameworks, models and evaluations to prevent social engineering attacks based on our review, which is using a protocol. We found the protocol to effectively prevent social engineering attacks, such as health campaigns, the vulnerability of social engineering victims, and co-utile protocol, which can manage information sharing on a social network. We present this systematic literature review to recommend ways to prevent social engineering attacks.
Knowledge-Enriched Distributional Model Inversion Attacks

Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. Such attacks have triggered increasing concerns about privacy, especially given a growing number of online model repositories. However, existing MI attacks against deep neural networks (DNNs) have large room for performance improvement. We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data. In particular, we train the discriminator to differentiate not only the real and fake samples but the soft-labels provided by the target model. Moreover, unlike previous work that directly searches for a single data point to represent a target class, we propose to model a private data distribution for each target class. Our experiments show that the combination of these techniques can significantly boost the success rate of the state-of-the-art MI attacks by 150%, and generalize better to a variety of datasets and models. Our code is available at https://github.com/SCccc21/Knowledge-Enriched-DMI.
AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, ×16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.
The climate change perspective of photovoltaic power potential in Brazil

Photovoltaic power potential (PPV) is part of the strategies in Brazil to satisfy the population's energy demand and contribute to reduction of global warming in the climate change context. This study assesses climate change's impact on PPV using a set of 47 state of the art Earth System Models from CMIP6 under two climate change (SSP2-4.5 and SSP5-8.5) scenarios. Changes in solar radiation, temperature and PPV are calculated for the near-term (2021–2050) and end-century (2071–2100) future, concerning the historical period (1981–2010). For the future, the sign of climate change is consistent in both scenarios, but with more intense magnitudes in the SSP5-8.5 for the end-century, and indicate a brightening in North region and warming in the whole country. For the near-term future, PPV projects the maximum increases (≈3%) in the North under both scenarios. The most notable decreases in PPV are noticed by the end-century (−5%) over the country, except for the extreme north. The sensitivity to the increase in temperature of current PPV systems' technology would not allow increases in the yield of PPV, canceling the positive effect of the brightening.
PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods

Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.
HoloLens 1 vs. HoloLens 2: Improvements in the New Model for Orthopedic Oncological Interventions

This work analyzed the use of Microsoft HoloLens 2 in orthopedic oncological surgeries and compares it to its predecessor (Microsoft HoloLens 1). Specifically, we developed two equivalent applications, one for each device, and evaluated the augmented reality (AR) projection accuracy in an experimental scenario using phantoms based on two patients. We achieved automatic registration between virtual and real worlds using patient-specific surgical guides on each phantom. They contained a small adaptor for a 3D-printed AR marker, the characteristic patterns of which were easily recognized using both Microsoft HoloLens devices. The newest model improved the AR projection accuracy by almost 25%, and both of them yielded an RMSE below 3 mm. After ascertaining the enhancement of the second model in this aspect, we went a step further with Microsoft HoloLens 2 and tested it during the surgical intervention of one of the patients. During this experience, we collected the surgeons' feedback in terms of comfortability, usability, and ergonomics. Our goal was to estimate whether the improved technical features of the newest model facilitate its implementation in actual surgical scenarios. All of the results point to Microsoft HoloLens 2 being better in all the aspects affecting surgical interventions and support its use in future experiences.
Lifting the Curse of Multilinguality by Pre-training Modular Transformers

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages.We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant.In contrast with prior work that learns languagespecific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-MOD) models from the start.Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance.Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
Automatic Speech Recognition (ASR) Systems for Children: A Systematic Literature Review

Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech signals into text. Over the last few decades, an enormous amount of research work has been done in the research area of speech recognition (SR). However, most studies have focused on building ASR systems based on adult speech. The recognition of children’s speech was neglected for some time, which means that the field of children’s SR research is wide open. Children’s SR is a challenging task due to the large variations in children’s articulatory, acoustic, physical, and linguistic characteristics compared to adult speech. Thus, the field became a very attractive area of research and it is important to understand where the main center of attention is, and what are the most widely used methods for extracting acoustic features, various acoustic models, speech datasets, the SR toolkits used during the recognition process, and so on. ASR systems or interfaces are extensively used and integrated into various real-life applications, such as search engines, the healthcare industry, biometric analysis, car systems, the military, aids for people with disabilities, and mobile devices. A systematic literature review (SLR) is presented in this work by extracting the relevant information from 76 research papers published from 2009 to 2020 in the field of ASR for children. The objective of this review is to throw light on the trends of research in children’s speech recognition and analyze the potential of trending techniques to recognize children’s speech.
Online education in higher education: emerging solutions in crisis times

The COVID-19 pandemic caused changes in higher education institutions, mainly due to the temporary closure of face-to-face activities in universities worldwide. The transformation from face-to-face to online education was one of the emerging solutions to this crisis. This research aims to analyse and describe the adaptation process from face-to-face to online education and the perceptions of faculty and students during the pandemic in a literature review study. We developed this study following the methodology supported by the PRISMA statement and the PICoS strategy, retrieving scientific literature from Web of Science, Scopus, ERIC and PsycINFO. Of the 241 studies that the search yielded, 29 have been included. The results showed that online education was an enabling alternative for the development of higher education, but numerous weaknesses in the transition to online education were identified. The study concludes that online learning is a future direction in higher education. Therefore, institutions should invest more in online education platforms and improve faculty training plans.
Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights

Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students' spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students' cognitive skills. Students' data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes.
A survey on binary metaheuristic algorithms and their engineering applications

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
Systematic Literature Review on the Use of Metaverse in Education

Metaverse is a concept that has existed in our lives for years. However, it has become widely known by gaining new meanings thanks to the developments in blockchain and virtual reality technologies and the marketing techniques of social media companies. The aim of this study is to examine the studies on the use of Metaverse technology in education. In the study, it is also aimed to analyze the changing understanding of technology use in education over the years and to investigate the reflections of the concept of Metaverse on education. This systematic literature review is based on the reporting criteria of PRISMA 2020 (Page et al., 2021) and the scope of the study consists of 22 academic studies including the keyword "metaverse" and the keywords "education" or "teaching" or "learning" in academic studies scanned in Scopus, WoS, Eric and Proquest indexes. In this study, the distribution of the studies used in the metaverse in education by year, country, field, type of research, and the context of the technology used is included. In order to contribute to the research on the Metaverse in the field of education, it has been tried to determine the opportunities and risks that teaching in the Metaverse environment can offer to the field of education. In addition, it is thought that this study will shed a light on future studies.
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the probe-to- gallery, almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal per-son Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code and dataset are released at: https://github.com/VCM-project233/MITML.
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://github.com/IVIPLab/CTCNet.
Asynchronous federated learning on heterogeneous devices: A survey

Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.
An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning

DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions; making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior.
Arabic Natural Language Processing

The Arabic language continues to be the focus of an increasing number of projects in natural language processing (NLP) and computational linguistics (CL).This tutorial provides NLP/CL system developers and researchers (computer scientists and linguists alike) with the necessary background information for working with Arabic in its various forms: Classical, Modern Standard and Dialectal.We discuss various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing from enabling technologies and resources, to common tasks and applications.The tutorial will explain important concepts, common wisdom, and common pitfalls in Arabic processing.Given the wide range of possible issues, we invite tutorial attendees to bring up interesting challenges and problems they are working on to discuss during the tutorial.