Reviewerly/exHarmony huggingface dataset:
Patch-to-Sample Reasoning for Cervical Cancer Screening of Whole Slide Image

Deep learning has been instrumental in improving the accuracy of cervical cancer screening using whole-slide images (WSIs) in recent years. Due to the complexity of the computer-aided screening task, the pipeline typically involves detecting “abnormal” cervical cells, and runs classification at the patch and sample levels, respectively. While the patch-level classification for normal or abnormal cells cannot be perfect, the errors may accumulate across individual patches and make the subsequent sample-level classification even more difficult. To address these issues, we propose a Patch-to-Sample (P2S) reasoning method to screen the cervical abnormality in this paper. We first improve the patch-level classifier by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hard patch mining</i> strategy, such that the classifier is not only more accurate but also more powerful to represent suspicious cells in local patches. Then, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score embedding</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">token pooling</i> to a transformer network, which aggregates multiple patches and derives the diagnosis result at the sample level. Experiments show that our P2S method can more effectively utilize the key patches in individual samples, and thus outperforms existing methods.
Energy Efficient and Fast CNN Inference by Exploring Weight Approximation and Computational Reuse

The challenges of Convolutional neural networks (CNNs) based AI inference on edge devices include computing complexity, large memory requirements, and high power consumption. Researchers have pursued efficient hardware, dataflow optimization, and new algorithms to tackle these obstacles. This paper introduced a novel acceleration methodology designed to significantly enhance the processing speed and power of existing CNN architectures. The proposed approach leverages the inherent static nature of pre-trained CNN weights through the prism of linear approximation. Different weight approximation options are evaluated, including within and across kernels. By using the same weight index from different kernels for approximation, computation reuse becomes possible, as the same index from different kernels is sampled with the same data from the input feature map. The proposed approach is evaluated through the design and analysis of four CNN architectures, considering hardwarere sources, power consumption, and latency. The proposed algorithms are implemented on a gem5-based RISCV simulator, demonstrating a speedup of approximately 2× compared to the baseline. Additionally, the proposed CNN accelerators are implemented on Xilinx Kintex 7 Field Programmable Gate Array (FPGA), resulting in a 50% reduction in FPGA hardware resources. When benchmarked with AlexNet, VGG16, SqueezeNet, and ResNet, the proposed approach achieves a 50% reduction in multiplications compared to previous works while maintaining accuracy with loss of 0.9% for VGG16 and 3.1% for AlexNet. This work can be used as a framework to evaluate the different tradeoffs between accuracy, latency, power and cost of different CNN architectures and applications
Advantage of Gaze-Only Content Browsing in VR using Cumulative Dwell Time Compared to Hand Controller

Head-mounted displays(HMDs) are expected to be used as daily devices. Developing interfaces to control contents projected in a head-mounted display (HMD) is key to leading the spread of HMD usage. With the need for a new interface of the HMD, gaze interface has been researched. One of the main challenges has been detecting the user’s control intention from gaze movements. Considering short periods of gaze movement away, cumulative gaze dwell time is considered suitable for predicting operations from natural gaze movements while browsing contents. In this study, we evaluated a gaze-only contents browsing method using cumulative dwell time by comparing the hand controller method. The results showed that the proposed gaze method can achieve the same level of time and usability with less physical workload than the controller method. It was also found that more people prefer the proposed gaze-based method (16 in total; gaze: 3, rather gaze: 6, neutral: 1, rather controllers: 6). These results indicate the applicability of implicit interaction by gaze for browsing contents on HMDs.
Towards an Interactive Environment for Enhancing Reading Experience in Extended Reality

With the rise of digital media, how we read and share information has undergone significant changes. However, existing XR reading projects still have barriers to promoting the reading experience. Thus, the study proposes using XR technology to enhance the reading experience by projecting a 3D reading UI into 3D space. Instead of simply displaying a 2D UI or media in reality, as seen in existing VR/AR reading samples, this study designs an adaptive UI environment covering four aspects: 3D layout, navigation, interactive data, and sharing. The project aims to facilitate efficient and immersive online reading by providing users with a seamless spatial reading experience. This study seeks to showcase the scalability of XR technology in online reading.
Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory

Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.
The Effectiveness of Using E-learning and Distance Learning Applications in Jordanian Universities in the light of the Coronavirus Pandemic from the Students Point of View

This study aims to determine the effectiveness of the use of e-learning and distance learning (DL) tools in Jordanian universities according to the students perception of the coronavirus. The study followed a descriptive research method using descriptive questionnaires. A sample of (372) male and female undergraduate and graduate students from different Jordanian universities. The results showed that (73.7%) of the respondents used online distance learning every day during the coronavirus pandemic. Most of the students believe that the coronavirus epidemic has changed the use of online education more and more and the use of electronic devices to increase communication with others (teachers, colleagues). There is no significant difference in the level (α≥ 0.05) of the effectiveness of using E-Learning and Distance Learning applications according to gender and residence. Although a significant difference occurred between the variable age and the degree of university. The results of the study can be used to advise the teacher to focus more on activities that show a high level of enjoyment, especially website optimization and instruction. Colleges are now seen as important places of learning in the classroom. Websites and tutorials must be updated regularly.
Generalized global solar radiation forecasting model via cyber-secure deep federated learning

Abstract Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.
MoViT: Memorizing Vision Transformers for Medical Image Analysis

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.
Agile, User-Centered Design and Quality in Software Processes for Mobile Application Development Teaching

Agile methods in undergraduate courses have been explored in an effort to close the gap between industry and professional profiles. We have structured an Android application development course based on a tailored user-centered Agile process for development of educational digital tools. This process is based on Scrum and Extreme Programming in combination with User Experience (UX) approaches. The course is executed in two phases: the first half of the semester presents theory on Agile and mobile applications development, the latter half is managed as a workshop where students develop for an actual client. The introduction of UX and user-centered design exploiting the close relationship with stakeholders expected from Agile processes allows for different quality features development. Since 2019 two of the projects have been extended and one project has been developed with the described process and course alumni. Students and stakeholders have found value in the generated products and process.
Organizational Conformity of Basic School ‎Principals within Qasabat Irbid Directorate of ‎Education and its Relevance to Teachers' ‎Psychological Stress Psychological Stress ‎

The study aimed to identify the degree to which the principals of government in in Qasabat Irbid Directorate of Education basic ‎schools practice organizational conformity, and its relationship to the psychological pressures of teachers from their point of ‎view. The descriptive correlative approach was used‏.‏‎ The study sample was made up of 367 teachers, which is 10% of the total ‎study population of 3,670 teachers. The (electronic) questionnaire was used as a tool for the study to reach its goals. The findings ‎revealed that the degree to which the principals of government basic schools in Qasabat Irbid Directorate of Education practiced ‎organizational conformity received a "high" rating. Further, the level of psychological stress among Qasabat Irbid Directorate of ‎Education teachers received a "medium" rating. The findings also revealed a strong negative relationship between organizational ‎conformity and psychological stress. In light of the findings, the researchers suggested focusing on organizational conformity due ‎to its efficacy in reducing the psychological stress experienced by teachers.‎ ‎(Keywords: Proposed Indicators, Organizational Conformity, Psychological Stress, Directorate of Education, Qasabat Irbid, ‎Jordan)‎
Intrinsic and Extrinsic Reading Motivation ‎among Tenth-Grade Students in Jordan and ‎their Relationship to some Variables

The study aimed to define the level of intrinsic and extrinsic ‎reading motivation among tenth-grade students in Jordan and ‎their relationship to some variables. The study sample consisted ‎of 839 tenth grade students selected using the stratified cluster ‎sampling method. To achieve the study objectives, a reading ‎motivation scale was developed. The study results showed that ‎the level of reading motivation among tenth-grade students was ‎high, as extrinsic reading motivation to read came in first with a ‎high level, followed by intrinsic reading motivation with a high ‎level. The study found that there is a statistically significant ‎relationship between father’s educational level and reading ‎motivation among tenth-grade students, while there is no ‎statistically significant relationship between mother’s ‎educational level and reading motivation among tenth grade ‎students. The study revealed a statistically significant correlation ‎between academic achievement in Arabic and reading ‎motivation among tenth grade students. The results found ‎statistically significant differences in reading motivation among ‎tenth grade students due to gender, in favor of females. In light ‎of the results, some recommendations were provided.‎ ‎(Keywords: Reading Motivation, Intrinsic Motivation, ‎Extrinsic Motivation, Tenth Grade, Jordan)‎
Practical Pathways for Cultivating Innovative Talents in Higher Education from the Perspective of Innovation Culture

With the rapid development of society and continuous technological progress, innovative capability has become a crucial aspect in higher education. Starting from the concept and connotation of innovation culture, this article explores the significant role of innovation culture in higher education, analyzes the challenges currently faced by innovative talent cultivation in universities, and proposes practical strategies for cultivating innovative talents in higher education from the perspective of innovation culture. Through the implementation of these strategies, it can better facilitate the cultivation and development of innovative talents in universities, contributing positively to social advancement and development.
Evaluation of gaze-to-object mapping algorithms for use in “real-world” translatable neuropsychological paradigms.

Eye-tracking technology is commonly used for identifying objects of visual attention. However, applying this technology to virtual reality (VR) applications is challenging. This report analyzes the performance of two gaze-to-object mapping (GTOM) algorithms applied to eye-gaze data acquired during a "real-world" VR cue-reactivity paradigm.Two groups of participants completed a VR paradigm using an HTC Vive Pro Eye. The gazed objects were determined by the reported gaze rays and one of two GTOM algorithms - naïve ray-casting (n=18) or a combination of ray-casting and Tobii's G2OM algorithm (n=18). Percent gaze duration was calculated from 1-second intervals before each object interaction to estimate gaze accuracy. The object volume of maximal divergence between algorithms was determined by maximizing the difference in Hedge's G effect sizes between small and large percent gaze duration distributions. Differences in percent gaze duration based on algorithm and target object size were tested with a mixed ANOVA.The maximum Hedge's G effect sizes differentiating large and small target objects was observed at an 800cm3 threshold. The combination algorithm performed better than the naïve ray-casting algorithm (p=.003, ηp2=.23), and large objects (>800cm3) were associated with a higher gaze duration percentage than small objects (≤800cm3; p<.001, ηp2=.76). No significant interaction between algorithm and size was observed.Results demonstrated that Tobii's G2OM method outperformed naïve ray-casting in this "real-world" paradigm. As both algorithms show a clear decrease in performance for detecting objects with volumes <800cm3, we recommend using gaze-interactable objects >800cm3 for future HTC Vive Pro Eye applications.
Traditional learning and audio-visual-based e-learning post covid-19 in university students

The preference for traditional or e-learning brought with it hybrid learning whose preference is much greater than the previous ones. The general objective is to analyze and describe the experiences of university students on traditional and electronic learning post covid-19, for which the methodology of quantitative descriptive and correlational type was used with a measurement instrument through an anonymous survey and on a voluntary basis with 1240 participants. The results have acceptable statistics and are demonstrated with the confirmatory factor analysis, showing the preference for hybrid learning. In conclusion, students request that universities implement this methodology in order to work properly and have a quality education in the future.
Comprehension of Indirect Speech Act according to Emoticon Expression Types in Instant Messenger Conversation of School-Aged Children

Objectives: This study aims to examine if there is a difference in indirect speech comprehension ability based on grade level and to identify which type of emoticon is most helpful in the students’ understanding of indirect speech. Methods: A total of 60 students were gathered, consisting of 20 children each from the second, fourth, and sixth grades of elementary school, who lived in Seoul and Gyeongi-do. The indirect speech comprehension task divided the emoticons into four types: facial expression clue, motion clue, object clue, and no clue. The experiment was conducted using a slideshow, where students read sentences and answered clues. They were then required to select the meaning of the underlined part in the last sentence of the article among three answer choices: the correct answer, example of surface meaning answer, and example with weak connection answer. The task consisted of a total of 32 questions, with 8 questions for each emoticon type and an additional 16 questions involving direct conversations as filler questions. Results: A statistically significant difference was observed in indirect speech comprehension scores between second and fourth graders, as well as between second and sixth graders. Statistical significance was found in the indirect speech comprehension task with regard to grade level and emoticon types. In addition, the effect of interaction between grade level and emoticon type was statistically significant. There was a decreasing trend in the overall frequency of error types as grade level increased. Conclusion: This study revealed that, as the grade level increases, individuals develop the ability to comprehend indirect speech accurately, even when the meaning is not explicit on the surface, by understanding the social context and utilizing their knowledge of the world. For future research, it is essential to examine the tendencies of children with disabilities or conversational language difficulties compared to non-disabled children.
A Contrastive Study on ‘(不能)VC’ and ‘V得(不)C’ from the Perspective of Modality.

Until now, Chinese grammar scholarship has considered 能(不能)VC and V 得(不)C as grammatical structures representing the meaning of possibility and has tried to identify the distinctive features of the two expressions. In fact, although 能(不能)VC and V得(不)C represent 'possibility', this is a secondary function. The main functions represent modalities, which correspond to dynamic modality and epistemic modality, respectively. Based on this point, this paper examined how 能(不能)VC and V得(不)C came to have different modality functions through the transition process from a diachronic perspective, and re-identified the distinctive features between the two structures. In addition, this paper also discussed how different types of modality between the two structures cause differences in discourse contex from synchronic perspective, focusing on utterance focus and practiculity.
Use of Immersive Augmented Reality Technology in Public Works: Myth or Reality? – A Case Study on Implementation Challenges

The State of Qatar witnessed an unprecedented transformation during the past three decades in various sectors, more specifically in public infrastructure. The rapid transformation coupled with urbanization has increased the density of public infrastructure assets both at the surface and sub-surface. Ashghal, the Public Works Authority (PWA) contributes to achieving the goals of Qatar National Vision 2030 (QNV) by leading the transformation of Qatar's public infrastructure delivery and improving operational efficiency. Augmented Reality (AR) Technology that interfaces digital information with the real-world environment both spatially and temporally engenders tangible impact in the area of construction planning, operations, and maintenance of public works and assets. AR Initiative is identified as part of the Geospatial Strategic Master Plan for PWA. AR Technology has proven successful in various industries like entertainment, health, manufacturing, etc., however, it has serious limitations and lacks adopting changes in large-scale implementation in the context of public works. In this paper, the intent, technology selection, and implementation roadmap are discussed through a case study. This case study presents various use cases considered in the public works domain including the use of AR with Geospatial and BIM Technology. This paper further discusses the strategy for deployment, best use, limitation, and factors to be considered for full-scale deployment. This paper summarizes the study outcomes of the current effort undertaken by PWA in deploying AR solutions and discusses the feasibility of AR solutions in public work is a myth or reality.