Reviewerly/exHarmony huggingface dataset:
Ideas: Technodiversity as the key to digital decolonization

Dominance has never been achieved solely through the recognition and display of superior technological or military might. Knowledge is power, and it exercises its influence, as Italian philosopher Antonio Gramsci put it, in the field of cultural hegemony, establishing the boundaries of what knowledge is and is not. Information, education and cultural and scientific production form the deep level of geopolitical interaction.
EfficientNet-YOLOv5: Improved YOLOv5 Based on EfficientNet Backbone for Object Detection on Marine Microalgae

Object detection has been a popular task in deep learning. In marine microalgae detection, the dimension of the image in the marine microalgae is too large, but the object is too small compared with the images. Additionally, the number of images in each category differs greatly, which brings a great challenge to object detection. We propose EfficientNet-YOLOv5 to solve the two problems mentioned above. Based on YOLOv5, we improved the Backbone of YOLOv5 with EfficientNet. To further strengthen our proposed EfficientNet-YOLOv5, we offer a variety of useful tricks, such as offline and online data augmentation, multi-scale testing, multi-model ensembled, and LabelSmooling. Extensive experiments on marine microalgae have shown that EfficientNet-YOLOv5 has good performance. It also has very strong interpretability in the marine microalgae scenario. On the marine microalgae detection in microscopy dataset, we used only the EfficientNet-YOLOv5 model and obtained an online score of 44.73 percent. Compared with the baseline model (scored 42.38 percent), EfficientNet-YOLOv5 improved by 2.35 percent. In model ensembled, we received an online score of 50.683 percent using the ensembled model of EfficientNet-YOLOv5 and YOLOv5s for detection. Overall, our model obtained a considerable improvement in detection accuracy. Moreover, it also has excellent performance in inference speed and model size.
Proprietary and Open Source Software Systems in Libraries: A Few Considerations

When considering a major new technological system such as an integrated library system there are many considers including functionality, total cost of ownership, patron privacy, and staffing needed to operate. Another factor that should be considered is if the system is a proprietary and open source software systems in libraries. While the decision to use a commercial system or an open source software system will depend on the specific needs of individual libraries there are some factors that librarians and other decision makers should consider.
Non-fungible token economy in a developing country

Non-fungible tokens (NFTs) are a form of cryptocurrency that is commonly employed in sectors such as collectibles, art, and gaming (Pinto-Gutiérrez et al., 2022). The purpose of this study is to analyse the factors that affect NFT holdings in Thailand. The data was collected from 812 Thai residents who owned digital assets, and it was analysed using binary regression. The results indicated that NFT ownership could be predicted by NFT proficiency, marital status, age, and education, while occupation, monthly income, and savings were not significant. The paper proposes that the Thai government should collaborate with policymakers and regulators to create an extensive plan for the NFT industry, taking into account the significant factors (NFT competence, marital status, age, and education) in analysing investor behaviour, and blockchain companies may use the findings to increase NFT users through marketing.
Interactive Control over Temporal Consistency while Stylizing Video Streams

Abstract Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per‐frame basis. However, per‐frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain‐agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real‐time at full HD resolutions while providing interactive consistency control. We develop a lite optical‐flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state‐of‐the‐art video consistency approaches. maxreimann.github.io/stream‐consistency
Image, recto, verso, context. Approaching Deltiology as a method

The article outlines the proposal for a methodological approach to the systematic study of picture postcards as historical social everyday media.
Spherical search with epsilon constraint and gradient-based repair framework for constrained optimization

In evolutionary computation, search methodologies based on Hyper Cube (HC) are common while those based on Hyper Spherical (HS) methodologies are scarce. Spherical Search (SS), a recently proposed method that is based on HS search methodology has been proven to perform well on bound constraint problems due to its better exploration capability. In this paper, we extend SS to solve Constrained Optimization Problems (COPs) by combining the epsilon constraint handling method with a gradient-based repair framework that comprises of - a) Gradient Repair Method (GRM) which is a combination of Levenberg–Marquardt and Broyden update to reduce the computational complexity and settle numerical instabilities, b) Trigger mechanism that determines when to trigger the GRM, and c) repair ratio that determines the probability of repairing a solution in the population. Ultimately, we verify the performance of the proposed algorithm on IEEE CEC 2017 benchmark COPs along with 11 power system problems from a test suite of real-world COPs. Experimental results show that the proposed algorithm is better than or at least comparable to other advanced algorithms on a wide range of COPs.
Multiple forward-backward strategies for two-stage video denoising

Video denoising is a low-level task that uses the information of neighbor frames to denoise polluted frames. Using the information provided by neighboring frames, noise can be effectively removed and the denoising effect is improved. Through continuous development, single-stage and multi-stage denoising methods have been proposed. However, these existing denoising methods simply use the information provided by neighbor frames to denoise the polluted frames, paying insufficient attention to the polluted frames and making insufficient use of the information of the polluted frames. We propose a two-stage video denoising network called multiple forward-backward strategies for two-stage video denoising, which improves the denoising performance and retains more detailed information by enhancing the utilization of information of polluted frames. Extensive experiments have been conducted, and the experimental results show that the proposed method outperforms the state-of-the-art denoising methods in both qualitative and quantitative comparisons.
An Image-Denoising Framework Using ℓq Norm-Based Higher Order Variation and Fractional Variation with Overlapping Group Sparsity

As one of the most significant issues in imaging science, image denoising plays a major role in plenty of image processing applications. Due to the ill-posed nature of image denoising, total variation regularization is widely used in image denoising problems for its capability to suppress noise and preserve image edges. Nevertheless, traditional total variation will inevitably yield undesirable staircase artifacts when applied to recorded images. Inspired by the success of ℓq norm minimization and overlapping group sparsity in image denoising, and the effective staircase artifacts removal by fractional total variation, the hybrid model which combines the fractional order total variation with overlapping group sparsity and higher order total variation with ℓq norm is developed in this paper to restore images corrupted by Gaussian noise. An efficient algorithm based on the parallel linear alternating direction method of multipliers is developed for solving the corresponding model and the numerical experiments demonstrate the effectiveness of the proposed approach against several state-of-the-art methods, in terms of peak signal-to-noise ratio and structure similarity index measure values.
“That’s all it takes to be trans”: counter-strategies to hetero- and transnormative discourse on YouTube

Abstract The discourse surrounding transgender people has for a long time been influenced by certain narrative practices necessary to authenticate people’s trans status to medical professionals. This conventional narrative (master narrative), based on ideals of hetero- and cisnormativity, has led to stereotypical representations of trans identities. These largely continue to exist today. Nevertheless, counter-discourse to these stereotypical representations is becoming more prominent. Particularly YouTube has become an increasingly popular platform for counter-discursive action. The current case study therefore focusses on two transgender YouTubers who challenge the normative ideals by creating their own counter-discourse. The YouTubers address four major topics of stereotypical representation: the ideal of binary gender, heterosexuality, the wish to transition in order to pass as cisgender, and the belief that transgender people have always identified as the other gender. The two creators recognise the discursively reproduced stereotypes and use a combination of five different strategies to refute them: Inversion, Parody, Complexification, Shift , and Personal Experience . Making use of these strategies, the subjects’ positive discourse aims at presenting a multi-faceted representation of transgender identities.
Visual security index of selective encrypted images based on multi-directional structure and content-aware features

Selective encryption algorithms have emerged as a popular technique for protecting the privacy of images during real-time transmission. For selectively encrypted images, it is necessary to evaluate their security and usability with visual security indices, and there have been a series of studies in this area. However, those proposed visual security indices (VSI) are often ineffective. We present a multi-directional structure and content-aware features-based visual security index (MCVSI) to perform an objective assessment on selectively encrypted images. Considering that selectively encrypted images prevent the main contents from being easily identified, stable local features in the images are extracted to indicate the degree of image content leakage. Meanwhile, we extract spatial structure information that closely aligns with human visual perception to indicate the level of variation in the overall image skeleton. Next, these features are subjected to similarity measurements to produce two types of similarity, content perception feature similarity and structure feature similarity. Finally, our visual security index is built by connecting all feature similarities and their corresponding visual security scores using the regression module. The experimental results and analyses indicate that the proposed MCVSI outperforms many existing mainstream VSI in terms of higher performance and stronger robustness, particularly on low and medium quality images.
Short term solar irradiation forecasting using Deep neural network with decomposition methods and optimized by grid search algorithm

Due to the variable nature of solar energy, it is necessary to manage a bilateral contract negotiation between suppliers and customers. Therefore, to fulfil this condition, this paper proposed an ensemble approach to forecast the solar irradiation. The signal processing techniques Variational Mode Decomposition (VMD) and Discrete Wavelet Transform (DWT) used with deep neural network to forecast the solar irradiation. The hyperparameters of deep learning model are optimized using grid search optimization with in a suitable tolerable search range. The data of three years (2012-14) is used; where data of year 2012-2013 is used to train model and testing is done on data of year 2014 for New Delhi location. Among all developed models, Bi-LSTM-VMD-Grid Search performance is better in terms of RMSE (5.456W/m2), MAPE (0.948%) and R2(0.924%), Because Bi-LSTM process the information twice and faster than other algorithms and VMD refine the quality of input data better as comparison to DWT. The result of proposed model is compared with existing techniques that predicted the solar irradiation and the forecasted results are more efficient and reliable.
Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset

Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics.
Three-Dimensional Shape and Deformation Measurements Based on Fringe Projection Profilometry and Fluorescent Digital Image Correlation via a 3 Charge Coupled Device Camera

We propose a novel hybrid FPP-DIC technique to measure an object's shape and deformation in 3D simultaneously by using a single 3CCD color camera, which captures the blue fringe patterns and red fluorescent speckles within the same image. Firstly, red fluorescent speckles were painted on the surface of the specimen. Subsequently, 12 computer-generated blue fringe patterns with a black background were projected onto the surface of the specimen using a DLP projector. Finally, both the reference and deformed images with three different frequencies and four shifted phases were captured using a 3CCD camera. This technique employed a three-chip configuration in which red-green-blue chips were discretely integrated in the 3CCD color camera sensor, rendering independent capture of RGB information possible. Measurement of out-of-plane displacement was carried out through the implementation of Fringe Projection Profilometry (FPP), whereas the in-plane displacement was evaluated using a 2D Digital Image Correlation (DIC) method by leveraging a telecentric-lens-based optical system. In comparison to the traditional FPP-DIC hybrid methodology, the present approach showed a lower incidence of crosstalk between the fringe patterns and speckle patterns while also offering a corrective for the coupling of the in-plane displacement and out-of-plane displacement. Experimental results for the in-plane cantilever beam and out-of-plane disk comparisons with the traditional 3D-DIC method indicated that the maximum discrepancy obtained between FPP-DIC and 3D-DIC was 0.7 μm and 0.034 mm with different magnifications, respectively, validating the effectiveness and precision of the novel proposed FPP-DIC method.
Passive Architectural Design Solution to Enhance Thermal Comfort using Building Information Modelling (BIM) on Cafe building in Bandung

ABSTRAK Saat ini café menjadi tempat yang digemari oleh masyarakat untuk melakukan pertemuan formal maupun informal. Kenyamanan termal merupakan suatu kondisi dimana seseorang merasa nyaman untuk melakukan suatu kegiatan terutama di dalam ruangan. Penelitian ini bertujuan untuk menganalisis kenyamanan termal pada bangunan kafe "Beda Stories" di Bandung dan dilakukan dengan menganalisis temperatur udara, kelembaban udara, kecepatan udara, serta mengeksplorasi kemungkinan alternatif desain pasif untuk mencapai kenyamanan yang diinginkan. Analisis ini menekankan pada proses penelitian dengan teori-teori yang sudah ada dengan menggunakan identifikasi dan alat ukur independen dengan menggunakan analisis statis. Penelitian ini menggunakan pendekatan penelitian kuantitatif dengan menggunakan data numerik. Hasil penelitian menunjukkan bahwa lingkungan dalam ruangan di kafe Beda Cerita Bandung memiliki kondisi kenyamanan termal yang rendah pada siang hari, karena memiliki situasi kepuasan termal yang berbeda, oleh karena itu dari hasil yang diperoleh, penelitian ini telah melakukan alternatif desain pasif dengan menggunakan Building Information Modelling (BIM) untuk mencapai kondisi kenyamanan dalam ruangan. Kata kunci : Cafe, Building Information Modelling (BIM), Kenyamanan Termal ABSTRACT Currently the café is a place favored by the public for both formal and informal meetings. Thermal comfort is a condition where a person feels comfortable to carry out an activity, especially indoors. This study aims to analyze the thermal comfort of "Beda Stories" Cafe Building in Bandung and conducted to analyze air temperature, humidity, air speed.The study also has explored the possibility of designing the passive design alternative to achieved comfort. This analysis emphasizes the research process with already-existing theories using identification and independent measuring tools utilizing static analysis. It applies quantitative research approaches employing numerical data. The results showed that the indoor environment in cafe Beda Cerita Bandung had low thermal comfort conditions during the day, as it also had different thermal satisfactory situations.Therefore, from the results obtained, the study has conducted alternative passive design using Building Information Modeling (BIM) to achieve indoor comfort condition. Cafe, Building Information Modelling (BIM), Kenyamanan Termal Keyword : Cafe, Building Information Modelling (BIM), Thermal Comfort
MICHAEL OHER’S PERSONALITY DEVELOPMENT IN MICHAEL LEWIS’S THE BLIND SIDE

This study is about personality traits and personality development of the main character in The Blind Side by Michael Lewis. The research uses two theories to analyze. The first theory is big five theory by Robert McCrea and Paul Costa to analyze Michael Oher’s personality traits. The second theory is by Elizabeth B. Hurlock to analyze Personality developments which consist of eight determinants. The study uses a psychoanalytical approach and descriptive qualitative method to analyze the novel. The results of the analysis show that the main character in The Blind Side has several personality traits, along with the journey of his life, he experiences personality development, which changed from someone who was always alone, desperate, and hopeless slowly becoming someone who is confident, passion, enthusiasm to start his day and strong ambition for a better future.
IMPOLITENESS STRATEGIES PERFORMED BY THE MAIN CHARACTER IN “THE PROPOSAL” MOVIE

This research aimed to investigate impoliteness strategies that used in The Proposal movie by the main character. This strategies based on Culpeper's theory (1996). This study used descriptive qualitative method to analyze the data and to describe the result of this analysis. The source of the data is The Proposal movie and script of the movie. The researcher does the research by watch the movie, check the reliability between the movie and the script, identify character’s utterance, and identify impoliteness strategies by the main character utterance. The researcher discover five strategies of impoliteness based on Culpeper's theory (1996) that used by the main character in The Proposal movie, there are positive impoliteness, negative impoliteness, sarcasm or mock impoliteness, bald on record, and withholding impoliteness. The result from this study that researcher found five impoliteness strategies through the main character in The Proposal (2009) movie, there are 4 bald on record, 4 positive impoliteness, 5 negative impoliteness, 1 sarcasm or mock impoliteness, and 1 withholding impoliteness.
An integrated differential evolution of multi-population based on contribution degree

Abstract The differential evolution algorithm based on multi-population mainly improves its performance through mutation strategy and grouping mechanism. However, each sub-population plays a different role in different periods of iterative evolution. If each sub-population is assigned the same computing resources, it will waste a lot of computing resources. In order to rationally distribute computational resources, an integrated differential evolution of multi-population based on contribution degree (MDE-ctd) is put forth in this work. In MDE-ctd, the whole population is divided into three sub-populations according to different update strategies: archival, exploratory, and integrated sub-populations. MDE-ctd dynamically adjusts computing resources according to the contribution degree of each sub-population. It can effectively use computing resources and speed up convergence. In the updating process of integrated sub-populations, a mutation strategy pool and two-parameter value pools are used to maintain population diversity. The experimental results of CEC2005 and CEC2014 benchmark functions show that MDE-ctd outperforms other state-of-art differential evolution algorithms based on multi-population, especially when it deals with highly complex optimization problems. Graphical abstract An integrated differential evolution of multi-population based on contribution degree
CMLocate: A cross‐modal automatic visual geo‐localization framework for a natural environment without GNSS information

Abstract In this paper, a new approach to visual geo‐localization for natural environments is proposed. The digital elevation model (DEM) data in virtual space is rendered and construct a panoramic skyline database is constructed. By combining the skyline database with real‐world image data (used as the “queries” to be localized), visual geo‐localization is treated as a cross‐modal image retrieval problem for panoramic skyline images, creating a unique new visual geo‐localization benchmark for the natural environment. Specifically, the semantic segmentation model named LineNet is proposed, for skyline extractions from query images, which has proven to be robust to a variety of complex natural environments. On the aforementioned benchmarks, the fully automatic method is elaborated for large‐scale cross‐modal localization using panoramic skyline images. Finally, the compound index is delicately designed to reduce the storage space of the positioning global descriptors and improve the retrieval efficiency. Moreover, the proposed method is proven to outperform most state‐of‐the‐art methods.