Reviewerly/exHarmony huggingface dataset:
Deep Learning in Next-Frame Prediction: A Benchmark Review

As an unsupervised representation problem in deep learning, next-frame prediction is a new, promising direction of research in computer vision, predicting possible future images by presenting historical image information. It provides extensive application value in robot decision making and autonomous driving. In this paper, we introduce recent state-of-the-art next-frame prediction networks and categorize them into two architectures: sequence-to-one architecture and sequence-to-sequence architecture. After comparing these approaches by analyzing the network architecture and loss function design, the pros and cons are analyzed. Based on the off-the-shelf data-sets and the corresponding evaluation metrics, the performance of the aforementioned approaches is quantitatively compared. The future promising research directions are pointed out at last.
Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning

In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l2, 1-norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l2, 1-norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l2, 1-norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results.
Variations in Variational Autoencoders - A Comparative Evaluation

Variational Auto-Encoders (VAEs) are deep latent space generative models which have been immensely successful in many applications such as image generation, image captioning, protein design, mutation prediction, and language models among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data can be generated from the encoded distribution. This concept has led to tremendous research and variations in the design of VAEs in the last few years creating a field of its own, referred to as unsupervised representation learning. This paper provides a much-needed comprehensive evaluation of the variations of the VAEs based on their end goals and resulting architectures. It further provides intuition as well as mathematical formulation and quantitative results of each popular variation, presents a concise comparison of these variations, and concludes with challenges and future opportunities for research in VAEs.
Explainable Automated Essay Scoring: Deep Learning Really Has Pedagogical Value

Automated essay scoring (AES) is a compelling topic in Learning Analytics for the primary reason that recent advances in AI find it as a good testbed to explore artificial supplementation of human creativity. However, a vast swath of research tackles AES only holistically; few have even developed AES models at the rubric level, the very first layer of explanation underlying the prediction of holistic scores. Consequently, the AES black box has remained impenetrable. Although several algorithms from Explainable Artificial Intelligence have recently been published, no research has yet investigated the role that these explanation models can play in a) discovering the decision-making process that drives AES, b) fine-tuning predictive models to improve generalizability and interpretability, and c) providing personalized, formative, and fine-grained feedback to students during the writing process. Building on previous studies where models were trained to predict both the holistic and rubric scores of essays, using the Automated Student Assessment Prize's essay datasets, this study focuses on predicting the quality of the writing style of Grade-7 essays and exposes the decision processes that lead to these predictions. In doing so, it evaluates the impact of deep learning (multi-layer perceptron neural networks) on the performance of AES. It has been found that the effect of deep learning can be best viewed when assessing the trustworthiness of explanation models. As more hidden layers were added to the neural network, the descriptive accuracy increased by about 10%. This study shows that faster (up to three orders of magnitude) SHAP implementations are as accurate as the slower model-agnostic one. It leverages the state-of-the-art in natural language processing, applying feature selection on a pool of 1592 linguistic indices that measure aspects of text cohesion, lexical diversity, lexical sophistication, and syntactic sophistication and complexity. In addition to the list of most globally important features, this study reports a) a list of features that are important for a specific essay (locally), b) a range of values for each feature that contribute to higher or lower rubric scores, and c) a model that allows to quantify the impact of the implementation of formative feedback.
Benchmarking Wearable Robots: Challenges and Recommendations From Functional, User Experience, and Methodological Perspectives

Wearable robots (WRs) are increasingly moving out of the labs towards real-world applications. In order for WRs to be effectively and widely adopted by end-users, a common benchmarking framework needs to be established. In this article, we outline the perspectives that in our opinion are the main determinants of this endeavour, and exemplify the complex landscape into three areas. The first perspective is related to quantifying the technical performance of the device and the physical impact of the device on the user. The second one refers to the understanding of the user's perceptual, emotional, and cognitive experience of (and with) the technology. The third one proposes a strategic path for a global benchmarking methodology, composed by reproducible experimental procedures representing real-life conditions.. We hope that this paper can enable developers, researchers, clinicians and end-users to efficiently identify the most promising directions for validating their technology and drive future research efforts in the short and medium term.
Ontology Integration: Approaches and Challenging Issues

In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey of all ontology integration aspects. We discuss related notions and scrutinize existing techniques and literature approaches. We also detail the role of ontology matching in the ontology integration process. Indeed, the ontology community has adopted the splitting of the ontology integration problem into matching, merging and repairing sub-tasks, where matching is a necessary preceding step for merging, and repairing can be included in the matching process or performed separately. Ontology matching and merging systems have become quite proficient, however the trickiest part lies in the repairing step. We also focus on the case of a holistic integration of multiple heterogeneous ontologies, which needs further exploration. Finally, we investigate challenges, open issues, and future directions of the ontology integration and matching areas.
Word meaning in minds and machines.

Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss promising approaches to grounding NLP systems and argue that they will be more successful, with a more human-like, conceptual basis for word meaning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
The Impact of Information Sharing and Quality Assurance on Customer Service at UAE Banking Sector

In the present competitive market, information sharing and quality assurance are bothneeded for client support. This relationship was set up through a quantitative examinationstudy. The information was gathered utilizing the poll strategy, and the discoveries uncoveredthat, despite options, banks are continually enticed to bundle item contributions to draw inclients. This appeal stems exclusively from the arrangement of excellent administrations. Atlast, ideas are made for additional improvement to remain competitive in the monetary market.
An Evolutionary Pathway for the Quantum Internet Relying on Secure Classical Repeaters

Until quantum repeaters become mature, quantum networks remain restricted either to limited areas of directly connected nodes or to nodes connected to a common node. We circumvent this limitation by conceiving quantum networks using secure classical repeaters combined with the quantum secure direct communication (QSDC) principle, which is a compelling form of quantum communication that directly transmits information over quantum channel. The final component of this promising solution is our classical quantum-resistant algorithm. Explicitly, in these networks, the ciphertext gleaned from a quantum-resistant algorithm is transmitted using QSDC along the nodes, where it is read out and then transmitted to the next node. At the repeaters, the information is protected by our quantum-resistant algorithm, which is secure even in the face of a quantum computer. Hence, our solution offers secure end-to-end communication across the entire network, since it is capable of both eavesdropping detection and prevention in the emerging quantum internet. It is compatible with operational networks, and will enjoy the compelling services of the popular Internet, including authentication. Hence, it smoothens the transition from the classical Internet to the Quantum Internet (Qinternet) by following a gradual evolutionary upgrade. It will act as an alternative network in quantum computing networks in the future. We have presented the first experimental demonstration of a secure classical repeater based hybrid quantum network constructed by a serial concatenation of an optical fiber and free-space communication link. In conclusion, secure repeater networks may indeed be constructed using existing technology and continue to support a seamless evolutionary pathway to the future Qinternet of quantum computers.
YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography.In this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision.The proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well.The proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.
A systematic review on digital literacy

Abstract The purpose of this study is to discover the main themes and categories of the research studies regarding digital literacy. To serve this purpose, the databases of WoS/Clarivate Analytics, Proquest Central, Emerald Management Journals, Jstor Business College Collections and Scopus/Elsevier were searched with four keyword-combinations and final forty-three articles were included in the dataset. The researchers applied a systematic literature review method to the dataset. The preliminary findings demonstrated that there is a growing prevalence of digital literacy articles starting from the year 2013. The dominant research methodology of the reviewed articles is qualitative. The four major themes revealed from the qualitative content analysis are: digital literacy, digital competencies, digital skills and digital thinking. Under each theme, the categories and their frequencies are analysed. Recommendations for further research and for real life implementations are generated.
Digital image steganography: A literature survey

Steganography is the art of concealing information in a cover media in such a way that the presence of the information is unknown. Digital image steganography accomplishes the potential for protected communication that is crucial in most of the applications nowadays. Steganography has several beneficial applications. It has been driven to the frontrunner of present security systems by the amazing development in computational power, the rise in security consciousness. The main challenge in proposing a steganographic technique is to maintain a suitable balance among higher embedding capacity, imperceptibility, and security that separate it from correlated systems like cryptography and watermarking. This article offers an extensive state-of-the-art review and analysis of some recent steganographic techniques. Furthermore, we have discussed popular steganography tools in detail. Challenges in the recent deep learning based steganographic techniques have been addressed. To explore the domain, the article concludes with mentioning some future research directions.
An AHP-GIS based site suitability analysis for integrating large-scale hybrid CSP+PV plants in Morocco: An approach to address the intermittency of solar energy

Greater integration of solar energy into the worldwide energy mix is increasingly ineluctable and implies a proper site selection. Photovoltaic (PV) is economically more considerable due to its falling price, but storage issues arise with large-scale integration and might be tackled with Concentrated Solar Power (CSP) considering its thermal energy storage (TES) system. CSP + PV hybridization may constitute an optimal option for better energy dispatchability, higher capacity factors with a reduced Levelized Cost of Electricity (LCOE). Herein, the overarching goal is to help locating and classifying appropriate sites suited for hybrid solar plants by dint of the Analytic Hierarchy Process (AHP) method associated with Geographic Information System (GIS) tools. Given that CSP operates with either dry or wet cooling systems, the current assessment regarded both configurations to generate two potential maps within Morocco. Outcomes reveal that 11.2% and 32.2% represent the field share of excellent sites to host CSP + PV plants with wet and dry cooling correspondingly. Additionally, roughly 26.53% of the total area was left out representing the restriction mask. A sensitivity analysis was subsequently conducted to examine the most and least sensitive criteria. Thereafter, a techno-economic feasibility study in a hotspot was carried out for validation purposes: yield performances and LCOE of the hybrid CSP + PV plant - with 100 MWe capacity - were evaluated. Actually, the plant using wet cooling system can produce 312 GWeh/year with an LCOE of 0.0800 $/kWh. Whereas, with a dry cooling option, the annual production is of 333 GWeh/year and the LCOE is of 0.0816 $/kWh. This paper brings a novelty: the introduction of the “Water risk index (WRI)” amongst the decision factors from a visionary viewpoint. Being the first of its kind, the present research work may provide support for scientists and policy makers to evaluate their region's suitability for CSP + PV plants. • Screening of sites for hybrid CSP + PV plants through AHP-GIS approach in Morocco. • Roughly 26.53% of the overall area was deemed inappropriate. • Assessment criteria incorporated for the first time the Water Risk Index (WRI). • 11.2% and 32.2% represent excellent ratios of wet and dry cooling respectively. • Hotspot reports LCOE of $0.0800/kWh for wet cooling versus $0.0816/kWh for dry.
Do Not Recommend? Reduction as a Form of Content Moderation

Public debate about content moderation has overwhelmingly focused on removal: social media platforms deleting content and suspending users, or opting not to do so. However, removal is not the only available remedy. Reducing the visibility of problematic content is becoming a commonplace element of platform governance. Platforms use machine learning classifiers to identify content they judge misleading enough, risky enough, or offensive enough that, while it does not warrant removal according to the site guidelines, warrants demoting them in algorithmic rankings and recommendations. In this essay, I document this shift and explain how reduction works. I then raise questions about what it means to use recommendation as a means of content moderation.
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images

Currently, the automatic segmentation of breast tumors based on breast ultrasound (BUS) images is still a challenging task. Most lesion segmentation methods are implemented based on a convolutional neural network (CNN), which has limitations in establishing long-range dependencies and obtaining global context information. Recently, transformer-based models have been widely used in computer vision tasks to build long-range contextual information due to their powerful self-attention mechanism, and their effect is better than that of a traditional CNN. In this paper, a CNN and a Swin Transformer are linked as a feature extraction backbone to build a pyramid structure network for feature encoding and decoding. First, we design an interactive channel attention (ICA) module using channel-wise attention to emphasize important feature regions. Second, we develop a supplementary feature fusion (SFF) module based on the gating mechanism. The SFF module can supplement the features during feature fusion and improve the performance of breast lesion segmentation. Finally, we adopt a boundary detection (BD) module to pay additional attention to the boundary information of breast lesions to improve the boundary quality in the segmentation results. Experimental results show that our network outperforms state-of-the-art image segmentation methods on breast ultrasound lesion segmentation.
Higher Education Future in the Era of Digital Transformation

A significant number of educational stakeholders are concerned about the issue of digitalization in higher educational institutions (HEIs). Digital skills are becoming more pertinent throughout every context, particularly in the workplace. As a result, one of the key purposes for universities has shifted to preparing future managers to address issues and look for solutions, including information literacy as a vital set of skills. The research of educational technology advances in higher education is now being discussed and debated, with various laws, projects, and tactics being offered. Digital technology has been a part of the lives of today’s children from the moment they are born. There are still many different types of digital divisions that exist in our society, and they affect the younger generation and their digital futures. Today’s students do not have the same level of preparation for the technology-rich society they will have. Universities and teaching should go through a significant digital transformation to fulfill the demands of today’s generation and the fully digitized world they will be living in. The COVID-19 pandemic has quickly and unexpectedly compelled HEIs and the educational system to engage in such a shift. In this study, we investigate the digital transformation brought about by COVID-19 in the fundamental education of the younger generation. Additionally, the study investigates the various digital divides that have emerged and been reinforced, as well as the potential roadblocks that have been reported along the way. In this paper, the study suggests that research into information management must better address students, their increasingly digitalized everyday lives, and basic education as key focus areas.