Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification

Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users’ opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embeddings. In this paper, we present a comparative analysis to evaluate both classic and contextualized word embeddings for sentiment analysis. The four most frequently used word embedding techniques were used in their trained and pre-trained versions. The selected embedding represents classical and contextualized techniques. Classical word embedding includes algorithms such as GloVe, Word2vec, and FastText. By contrast, ARBERT is used as a contextualized embedding model. Since word embedding is more typically employed as the input layer in deep networks, we used deep learning architectures BiLSTM and CNN for sentiment classification. To achieve these goals, the experiments were applied to a series of benchmark datasets: HARD, Khooli, AJGT, ArSAS, and ASTD. Finally, a comparative analysis was conducted on the results obtained for the experimented models. Our outcomes indicate that, generally, generated embedding by one technique achieves higher performance than its pretrained version for the same technique by around 0.28 to 1.8% accuracy, 0.33 to 2.17% precision, and 0.44 to 2% recall. Moreover, the contextualized transformer-based embedding model BERT achieved the highest performance in its pretrained and trained versions. Additionally, the results indicate that BiLSTM outperforms CNN by approximately 2% in 3 datasets, HARD, Khooli, and ArSAS, while CNN achieved around 2% higher performance in the smaller datasets, AJGT and ASTD.
A generating model for Finnish nominal inflection using distributional semantics

Abstract Finnish nouns are characterized by rich inflectional variation, with obligatory marking of case and number, with optional possessive suffixes and with the possibility of further cliticization. We present a model for the conceptualization of Finnish inflected nouns, using pre-compiled fasttext embeddings (300-dimensional semantic vectors that approximate words’ meanings). Instead of deriving the semantic vector of an inflected word from another word in its paradigm, we propose that an inflected word is conceptualized by means of summation of latent vectors representing the meanings of its lexeme and its inflectional features. We tested this model on the 2,000 most frequent Finnish nouns and their inflected word forms from a corpus of Finnish (84 million tokens). Visualization of the semantic space of Finnish using t-SNE clarified that a ‘main effects’ additive model does not do justice to the semantics of inflection. In Finnish, how number is realized turns out to vary substantially with case. Further interactions emerged with the possessive suffixes and the clitics. By taking these interactions into account, the accuracy of our model, evaluated with the fasttext embeddings as gold standard, improved from 76% to 89%. Analyses of the errors made by the model clarified that 7.5% of errors are due to overabundance (and hence not true errors), and that 16.5% of the errors involved exchanges of semantically highly similar stems (lexemes). Our results indicate, first, that the semantics of Finnish noun inflection are more intricate than assumed thus far, and second, that these intricacies can be captured with surprisingly high accuracy by a simple generating model based on imputed semantic vectors for lexemes, inflectional features, and interactions of inflectional features.
Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques-such as Magnetic Resonance Imaging (MRI) and biplane radiography-should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.
Impact of anxiety and confidence in virtual reality‐mediated learning transferred to hands‐on tasks

Abstract Background Benefited from advances in technology, virtual reality (VR) has been widely applied to learning content in operational training as well as hands‐on courses. However, most current studies tend to evaluate learning effectiveness in this application, and few were focused on how learners can be benefited from transferring the knowledge learned to real‐world field performance. Moreover, in application‐oriented hands‐on courses such as embedded electronic circuits, to our best knowledge, none were presented in interactive learning materials with the combination of VR and neither were the understanding of the changes in cognition, learning anxiety, and learning confidence during the learning process. Method and Objective We combine VR technology with hands‐on tasks to design a VR‐based embedded electronic circuit learning system. We conduct a quasi‐experiment to investigate differences in knowledge, comprehension, and application levels in different learning environments, and to assess learning performance, learning anxiety, and learning confidence levels when applying the knowledge learned to real‐world hands‐on tasks. Results Groups that receive VR‐mediated training exhibit superior knowledge, comprehension, and application levels. In addition, learners with higher levels of knowledge and application exhibit lower learning anxiety and higher learning confidence during practical application tasks. Conclusions We investigate the learning outcomes of learners on practical hands‐on tasks after VR‐mediated learning. To improve the learning experience and performance for learners in an embedded electronic circuit course, we discuss the learning performance in terms of the knowledge and concepts learned in the VR environment and transferred to the real world. The results show that learners achieve better cognitive levels in the learning process through the VR system described here. Better performance on hands‐on tasks was also obtained as well as low anxiety and high confidence levels.
Global–local–global context-aware network for salient object detection in optical remote sensing images

For the salient object detection in optical remote sensing images (ORSI-SOD), many existing methods are trapped in a local–global mode, i.e., CNN-based encoder binds with a specific global context-aware module, struggling to deal with the challenging ORSIs with complex background and scale-variant objects. To solve this issue, we explore the synergy of the global-context-aware and local-context-aware modeling and construct a preferable global–local–global context-aware network (GLGCNet). In the GLGCNet, a transformer-based encoder is adopted to extract global representations, combining with local-context-aware features gathered from three saliency-up modules for comprehensive saliency modeling, and an edge assignment module is additionally employed to refine the preliminary detection. Specifically, the saliency-up module involves two components, one for global–local context-aware transfer towards pixel-wise dynamic convolution parameters prediction, the other for dynamically local-context aware modeling. The corresponding position-sensitive filter is aware of its previous global-wise focus, thus enhancing the spatial compactness of salient objects and encouraging the feature upsampling achievement for multi-scale feature combinations. The edge assignment module enhances the robustness of preliminary saliency prediction and assigns the semantic attributes of preliminary saliency cues to the shallow-level edge feature to obtain final complete salient objects in a spatially and semantically global manner. Extensive experiments demonstrate that the proposed GLGCNet surpasses 23 state-of-the-art methods on three popular datasets.
Hepatic vessels segmentation using deep learning and preprocessing enhancement

Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.
Multimodal Graph Contrastive Learning for Multimedia-Based Recommendation

Multimedia-based recommendation is a challenging task that requires not only learning collaborative signals from user-item interaction, but also capturing modality-specific user interest clues from complex multimedia content. Though significant progress on this challenge has been made, we argue that current solutions remain limited by multimodal noise contamination. Specifically, a considerable proportion of multimedia content is irrelevant to the user preference, such as the background, overall layout, and brightness of images; the word order and semantic-free words in titles; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . We take this irrelevant information as noise contamination to discover user preferences. Moreover, most recent research has been conducted by graph learning. This means that noise is diffused into the user and item representations with the message propagation; the contamination influence is further amplified. To tackle this problem, we develop a novel framework named Multimodal Graph Contrastive Learning (MGCL), which captures collaborative signals from interactions and uses visual and textual modalities to respectively extract modality-specific user preference clues. The key idea of MGCL involves two aspects: First, to alleviate noise contamination during graph learning, we construct three parallel graph convolution networks to independently generate three types of user and item representations, containing collaborative signals, visual preference clues, and textual preference clues. Second, to eliminate as much preference-independent noisy information as possible from the generated representations, we incorporate sufficient self-supervised signals into the model optimization with the help of contrastive learning, thus enhancing the expressiveness of the user and item representations. Note that MGCL is not limited to graph learning schema, but also can be applied to most matrix factorization methods. We conduct extensive experiments on three public datasets to validate the effectiveness and scalability of MGCL <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> We release the codes of MGCL at <uri>https://github.com/hfutmars/MGCL</uri>. </fn> .
Autonomous learning and the use of digital technologies in online English classrooms in higher education

This study investigated to what degree students can be autonomous in EFL classroom by deploying different digital technologies, including &lt;i&gt;Google Form, Quizizz, Quizlet, Kahoot!&lt;/i&gt;, and &lt;i&gt;Socrative&lt;/i&gt;, and the efficacy of these tools in online English classrooms. Utilizing a quasi-experimental research design, a sample of 48 first-year railway mechanical technology students from an Indonesian polytechnic was used and assigned to control and experimental groups. Data was gathered through a survey questionnaire and two tests (pre- and post-test), and was analyzed using descriptive statistics, the &lt;i&gt;N&lt;/i&gt;-gain formula, the paired-sample t-test and ANCOVA. The results revealed learner autonomy in listening, structure, and reading skills, encompassing self-reliance, information literacy, linguistic confidence, and learning strategy. Digital classes incorporating &lt;i&gt;Google Form&lt;/i&gt;, &lt;i&gt;Quizizz&lt;/i&gt;, &lt;i&gt;Quizlet&lt;/i&gt;, &lt;i&gt;Kahoot!&lt;/i&gt;, and &lt;i&gt;Socrative &lt;/i&gt;were more effective than traditional classes in terms of learning outcomes. Therefore, this study proposed a learning model utilizing digital technologies and autonomous learning concepts to improve students’ learning outcomes.
Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-Servers

Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (QoE). We allocate transcoding tasks to edge-servers in a multiple-access edge-computing (MEC) architecture, taking into account server capacity, wireless network coverage, and the cost budget of broadcasters, as well as QoE. Our algorithm first chooses candidate transcoding tasks by giving higher priority to the tasks that make the most cost-effective contribution to popularity-weighted video quality (PWQ). It assigns these tasks to edge-servers in a greedy manner, taking network coverage and computational load into account. Subsequently, it meets a cost budget by reassigning some tasks and removing other assignments altogether, while trying to minimize the effect of these alterations on total PWQ. Simulation results show that our scheme achieves 0.06% to 94.62% (average 25.3%) more PWQ than alternative schemes under the same cost budget.
Exercisable Learning-Theory and Evidence-Based Andragogy for Training Effectiveness using XR (ELEVATE-XR): Elevating the ROI of Immersive Technologies

eXtended Reality (XR) promises to provide a powerful capability to disseminate a vast array of instructional content appropriate for all types and levels of learners, ultimately revolutionizing education and training. Yet, while XR presents an opportunity for a powerful paradigm shift in education, a scientifically grounded andragogy for harnessing the didactic power of XR has yet to emerge. Herein, a unified framework of research-based instructional strategies for guiding the design of proficiency-based XR educational and training content is proposed, along with an associated naval maintenance use case. While this work draws on relevant pedagogical and andragogical theories, as well as fundamental learning theories applicable to learners of all ages, the framework specifically targets adult learners and training of complex skills applicable to various professional domains. Within the framework, learning theories have been aligned with learner proficiency level to specify competency-based learning objectives and activities, XR form factors, and learning loops that are anticipated to boost XR training efficacy. The proposed framework—the Exercisable Learning-theory and EVidence-based Andragogy for Training Effectiveness using XR (ELEVATE-XR)—is meant to serve as a guide to drive effective and efficient design of XR applications that enhance adult learning.
Design and assessment of building integrated PV (BIPV) system towards net zero energy building for tropical climate

Recent increment trend of renewable energy generation demand has revolutionised on how modern civilisation harnessing energy from renewable source especially the solar energy. According to IRENA, about 53% increase of solar energy installed capacity had been reported from year 2017 to 2021. This trend is expected to be doubles as revised Energy Efficient Directive (EU) is committed to add renewable share from 17% from 2015 to 34% in 2030. Among the effort to achieve the target highlighted by International Energy Agency (IEA) in their Net Zero by 2050 Roadmap is to develop or improved energy management on existing and new building to meet net zero energy building. One of the emerging and fast-growing solution is implementing Building Integration Photovoltaic (BIPV) as it offered not only clean electricity generation but also seamless integration aspect to the building envelope. However, due to a limitation factor such as insufficient BIPV infrastructure which dominantly base in European countries. This scenario limits the technology progress especially in Asian countries. Although attractive benefit shown by BIPV system, it is still having a crucial limitation on the visual and aesthetic appearance which considered as "unattractive" to the building appearance by viewers. Thus, there will be quite an issue to implement BIPV on architectural sensitive building such as historic and iconic building. In order to solve this issue, a pilot study of coloured BIPV system performance is conducted on an iconic building in tropical climate region which is Daya Bumi Building, Kuala Lumpur, Malaysia. The method adopted is based on cross-platform between 3D modelling simulation in Building Information Modelling (BIM) software and detail solar analysis (PVsyst) to obtain accurate analysis results. Varies of coloured PV modules are assess and compared to obtain the optimum BIPV system configuration. The proposed BIPV system is able to produce energy of 679.72 MW annually with carbon saving of 10367.66 tCO2/year of CO2 emission. The coloured BIPV application are expected to camouflage the PV panel appearance on the building which could preserve the original architectural aesthetic. This research promotes conscious of BIPV as a crucial innovative solution in implementing PV panel on building without sacrificing the architectural aesthetic value. Furthermore, BIPV system design using BIM software can be replicate to provide seamless work transition between building architecture, structural engineering, renewable energy engineering and building operations. This is in line with Malaysian 10-10MySTIE Framework effort to transform into a knowledge-intensive and innovation driven economy which include production, management and distribution of energy from renewable energy sources.
Toward universal texture synthesis by combining texton broadcasting with noise injection in StyleGAN-2

We present a universal texture synthesis approach that incorporates a novel multiscale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of a broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset, NUUR-Texture500, that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space.
A visually secure image encryption scheme based on adaptive block compressed sensing and non-negative matrix factorization

The traditional block compressive sensing theory cannot make full use of the difference of image block information characteristics for sampling, and the measurement matrix cannot fully measure the image according to the image block information characteristics. To solve these problems, we design an adaptive block compressed sensing (ABCS) algorithm, and we propose a visually secure image encryption scheme based on ABCS and non-negative matrix factorization (NMF). First, the plain image is decomposed by Tetrolet transform. Then, we perform matrix shuffling to optimize the sparsity of the sparse matrix. Next, adaptive sampling and measurement are performed according to the information distribution characteristics between the image blocks to compress the image to a quarter of its original size. Last, we design a scrambling–diffusion framework based on tetrominoes to obtain the secret image, which is then embedded into the carrier image by means of NMF to obtain a visually secure cipher image. The experimental results show that the proposed scheme can consider the security of image information transmission and the integrity of reception, and has good resistance to various attacks.
A Computational Framework for Preserving Privacy and Maintaining Utility of Geographically Aggregated Data: A Stochastic Spatial Optimization Approach

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