Reviewerly/exHarmony huggingface dataset:
Life Centered Design: Unpacking a Post-humanistic Biodesign Process

The current ecological crisis highlights a need for an alternative design approach to counteract the mainstream human-centered design methodologies. This pictorial aims to bridge this gap by introducing a novel design approach, Life Centered Design, and its design process. The findings from a biodesign project on bioluminescent microalgae are presented and further reflected through a panel of experts. The autoethnographical notes taken throughout the design process surfaced five design considerations, concerned with; combining perspectives, collaboration with the non-human, communication (challenges), the benefit ratio and ethical debate of human-non-human relations. These design considerations were iterated with a panel of experts to explore their applicability and generalizability across other biodesign projects. The case, the design considerations, and further discussions are organized as the body of Life Centered Design, providing both a practical guide and a reflection tool for post-human centred biodesign processes.
Optical character recognition guided image super resolution

Recognizing disturbed text in real-life images is a difficult problem, as information that is missing due to low resolution or out-of-focus text has to be recreated. Combining text super-resolution and optical character recognition deep learning models can be a valuable tool to enlarge and enhance text images for better readability, as well as recognize text automatically afterwards. We achieve improved peak signal-to-noise ratio and text recognition accuracy scores over a state-of-the-art text super-resolution model TBSRN on the real-world low-resolution dataset TextZoom while having a smaller theoretical model size due to the usage of quantization techniques. In addition, we show how different training strategies influence the performance of the resulting model.
Setting a rule base for a fuzzy classifier using the grasshopper optimization algorithm and the clustering algorithm

The article presents a description of a hybrid algorithm for generating fuzzy rules for a fuzzy classifier using grasshopper optimization algorithm and the K-means data clustering algorithm. The performance of clustering was evaluated by three fitness functions: total variance, Davis–Bouldin index, and Calinski–Harabasz index. Triangular and Gaussian membership functions have been investigated. The efficiency of the generated fuzzy rule bases has been tested on real datasets. The best combination is to use the total variance as the fitness function and the Gaussian function as the membership function.
On APN Functions Whose Graphs are Maximal Sidon Sets

The graphs $$\mathcal{G}_F=\{(x,F(x)); x\in \mathbb {F}_2^n\}$$ of those (n, n)-functions $$F:\mathbb {F}_2^n\mapsto \mathbb {F}_2^n$$ that are almost perfect nonlinear (in brief, APN; an important notion in symmetric cryptography) are, equivalently to their original definition by K. Nyberg, those Sidon sets (an important notion in combinatorics) S in $$({\mathbb {F}}_2^n\times {\mathbb {F}}_2^n,+)$$ such that, for every $$x\in {\mathbb {F}}_2^n$$ , there exists a unique $$y\in {\mathbb {F}}_2^n$$ such that $$(x,y)\in S$$ . Any subset of a Sidon set being a Sidon set, an important question is to determine which Sidon sets are maximal relatively to the order of inclusion. In this paper, we study whether the graphs of APN functions are maximal (that is, optimal) Sidon sets. We show that this question is related to the problem of the existence/non-existence of pairs of APN functions lying at distance 1 from each others, and to the related problem of the existence of APN functions of algebraic degree n. We revisit the conjectures that have been made on these latter problems.
Sentiment Analysis of Online Movie Reviews using Machine Learning

Many websites encourage their users to write reviews for a wide variety of products and services. In particular, movie reviews may influence the decisions of potential viewers. However, users face the arduous tasks of summarizing the information in multiple reviews and determining the useful and relevant reviews among a very large number of reviews. Therefore, we developed machine learning (ML) models to classify whether an online movie review has positive or negative sentiment. We utilized the Stanford Large Movie Review Dataset to build models using decision trees, random forests, and support vector machines (SVMs). Further, we compiled a new dataset comprising reviews from IMDb posted in 2019 and 2020 to assess whether sentiment changed owing to the coronavirus disease 2019 (COVID-19) pandemic. Our results show that the random forests and SVM models provide the best classification accuracies of 85.27% and 86.18%, respectively. Further, we find that movie reviews became more negative in 2020. However, statistical tests show that this change in sentiment cannot be discerned from our model predictions.
Understanding Users’ Perception of Cute Aesthetics in Mobile Interface Design

Despite growing interest in interface designs for mobile applications, research on the use of cute aesthetics in mobile interface design has largely been unexplored. Hence, this research aims to fill this gap by studying perceptions of cute aesthetics in mobile interface designs. Here, cute aesthetics refer to the use of cute design elements and bright colors in the interface designs as they are deemed to be attractive and appealing. A total of 166 participants were recruited to participate in a study to evaluate three sets of cute aesthetic designs. Findings revealed that participants responded favorably toward cute aesthetics and suggest that cute aesthetics is a plausible approach to engaging online mobile users. This study contributes to a better understanding of the conceptualization of cute aesthetics to be used in mobile interface design for the masses.
Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering

The classification of emails is one crucial part of the email filtering process, as emails have become one of the key methods of communication. The process for identifying safe or unsafe emails is complex due to the diversified use of the language. Nonetheless, most of the parallel research outcomes have demonstrated significant benchmarks in identifying email spam. However, the standard processes can only identify the emails as spam or ham. Henceforth, a detailed classification of the emails has not been achieved. Thus, this work proposes a novel method for the identification of the emails into various classes using the proposed deep clustering process with the help of the ranking of words into severity. The proposed work demonstrates nearly 99.4% accuracy in detecting and classifying the emails into a total of five classes.
Revisiting Batch Norm Initialization

Batch normalization (BN) is comprised of a normalization component followed by an affine transformation and has become essential for training deep neural networks. Standard initialization of each BN in a network sets the affine transformation scale and shift to 1 and 0, respectively. However, after training we have observed that these parameters do not alter much from their initialization. Furthermore, we have noticed that the normalization process can still yield overly large values, which is undesirable for training. We revisit the BN formulation and present a new initialization method and update approach for BN to address the aforementioned issues. Experiments are designed to emphasize and demonstrate the positive influence of proper BN scale initialization on performance, and use rigorous statistical significance tests for evaluation. The approach can be used with existing implementations at no additional computational cost. Source code is available at https://github.com/osu-cvl/revisiting-bn-init .
Commodities Price Prediction using Various ML Techniques

The progress immensely helped people in rural India with communication technology. People can use the internet or a smart phone to get information. In India, agriculture is the primary job for most people. However, farmers still do not know about the changes in technology in agriculture. Farmers in India have a big problem: they do not get a reasonable crop price. Another big problem is that farmers do not get enough money or a better price for the things they grow. They do not know the market trend or what is happening between markets. Because they do not know what the price will be in the future, they cannot decide when and where to sell their crops. In this article, we proposed a model for estimating commodity prices. By using techniques like Linear Regression, Random Forest, and Decision Trees. The model performed on standard database and achieved an accuracy score of more than 95%.
ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

Sentiment analysis in the Arabic language is challenging because of its linguistic complexity. Arabic is complex in words, paragraphs, and sentence structure. Moreover, most Arabic documents contain multiple dialects, writing alphabets, and styles (e.g., Franco-Arab). Nevertheless, fine-tuned bidirectional encoder representations from transformers (BERT) models can provide a reasonable prediction accuracy for Arabic sentiment classification tasks. This paper presents a fine-tuning approach for BERT models for classifying Arabic sentiments. It uses Arabic BERT pre-trained models and tokenizers and includes three stages. The first stage is text preprocessing and data cleaning. The second stage uses transfer-learning of the pre-trained models’ weights and trains all encoder layers. The third stage uses a fully connected layer and a drop-out layer for classification. We tested our fine-tuned models on five different datasets that contain reviews in Arabic with different dialects and compared the results to 11 state-of-the-art models. The experiment results show that our models provide better prediction accuracy than our competitors. We show that the choice of the pre-trained BERT model and the tokenizer type improves the accuracy of Arabic sentiment classification.
Quantum entanglement of anyon composites

Studying quantum entanglement in systems of indistinguishable particles, in particular anyons, poses subtle challenges. Here, we investigate a model of one-dimensional anyons defined by a generalized algebra. This algebra has the special property that fermions in this model are composites of anyons. A Hubbard-like Hamiltonian is considered that allows hopping between nearest neighbor sites not just for the fundamental anyons, but for the fermionic anyon composites. Some interesting results regarding the quantum entanglement of these particles are obtained.
Aerial Image Stitching Based on Fusion of Geographic Coordinates and Image Features

With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles have been widely used to obtain ground remote sensing images. Due to the limited field of view of a single remote sensing image, it is necessary to stitch the images to obtain a large-scale scene image to apply to various practical applications. This paper introduces a two-stage stitching algorithm combining geographic coordinate information and image features. The stitching of sequence images is divided into intra-line stitching and inter-line stitching. Firstly, we use geographic coordinates and image features to calculate transformation parameters between a row of images, respectively, and select an appropriate transformation matrix to stitch a single row of images based on the rotation parameters in the homography matrix. Then, we also calculate the transformation matrices between different rows of images based on the geographic coordinate information and image features, and select the appropriate transformation matrix based on the comparison of the rotation parameters in different transformation matrices. Stitching experiments are carried out for various scenarios. Compared with traditional stitching methods, the proposed method integrates information from different sources, has higher reliability, and can adapt to stitching of various types of scenarios.
An image steganography method based on texture perception

Existing image steganography technology can resist image attacks, however, the quality of generated encoded image is poor and the low-frequency region can be perceived. To improve the quality of the encoded image, an image steganography method based on texture analysis is proposed. In this method, the gray-level co-occurrence matrix is used to extract the texture information of the cover image, the information is embedded in the complex position of the image, in and the network structure of the StegaStamp encoder is improved, small convolution layers are added in the down-sampling process to expand the receptive field, and channel attention is used to assign different weights to feature channels to enhance the influence of important features of images. The experimental results show that the proposed method can effectively improve the PSNR and SSIM values of coded image, and the quality is improved without degrading the robustness and embedding capacity.
An Inert and Efficient Fusion Replanning Method for UAV Fast Autonomous Flight

Path planning is one of the key components to achieve fast autonomous flight of unmanned aerial vehicle (UAV) in unknown environments. However, it remains a challenge to generate a high-quality trajectory with less time cost and less replanning number. In this paper, an inert and efficient fusion replanning method is proposed. At first, we design a fast and robust guiding path generation method. Based on the guiding path, we build the safe flight corridor (SFC) and generate the high-quality initial flight trajectory by using the hard-constrained method. And then, to quickly avoid newly discovered obstacles, the gradient-based method is used to generate a local new collision-free trajectory in real-time based on the initial flight trajectory. Finally, we design an inert replanning strategy to reduce the number of replanning on the premise of ensureing the trajectory quality. Benchmark comparisions and real-world experiments are conducted to verify the efficiency. Experimental results show that the proposed method achieved better performance compared with other methods.
Research on the Application of Computer Multimedia Technology in Chinese Cloud Classroom Teaching Practice

The Chinese teaching system is an open system. The function and structure of the Chinese teaching system will directly affect the teaching effect. Only when the teaching structure of the classroom is reasonable, the development direction of the teaching system is correct, and the feedback link of the system is complete, the best results can be achieved. The effect of language teaching. This paper studies a task-based Chinese classroom interactive teaching system based on ZigBee technology. On the basis of formulating the principle of task-based classroom teaching, the system has functions such as user management, file download, and classroom communication. The effect of the system implementation shows that the functional modules of this system have very high reusability, which can not only improve the teaching efficiency and quality, but also meet the diversified needs of Chinese network-assisted teaching to a large extent. It is worth promoting and applying to various disciplines teaching.
[An improved Vision Transformer model for the recognition of blood cells].

Leukemia is a common, multiple and dangerous blood disease, whose early diagnosis and treatment are very important. At present, the diagnosis of leukemia heavily relies on morphological examination of blood cell images by pathologists, which is tedious and time-consuming. Meanwhile, the diagnostic results are highly subjective, which may lead to misdiagnosis and missed diagnosis. To address the gap above, we proposed an improved Vision Transformer model for blood cell recognition. First, a faster R-CNN network was used to locate and extract individual blood cell slices from original images. Then, we split the single-cell image into multiple image patches and put them into the encoder layer for feature extraction. Based on the self-attention mechanism of the Transformer, we proposed a sparse attention module which could focus on the discriminative parts of blood cell images and improve the fine-grained feature representation ability of the model. Finally, a contrastive loss function was adopted to further increase the inter-class difference and intra-class consistency of the extracted features. Experimental results showed that the proposed module outperformed the other approaches and significantly improved the accuracy to 91.96% on the Munich single-cell morphological dataset of leukocytes, which is expected to provide a reference for physicians' clinical diagnosis.白血病是一种常见多发且较为凶险的血液疾病，其早期发现与治疗至关重要。目前白血病类型的诊断主要依靠病理医师对血细胞图像进行形态学检查，该过程枯燥、费时，且诊断结果有较强的主观性，易发生误诊与漏诊。针对上述问题，本文提出了一种基于改进Vision Transformer的血细胞图像识别方法。首先，使用快速区域卷积神经网络从图像中定位并裁剪出单个血细胞图像切片。然后，将单细胞图像划分为多个图像块并输入到编码层中进行特征提取。本文基于Transformer的自注意机制提出了稀疏注意力模块，该模块能够筛选出图像中的辨识性区域，进一步提升模型的细粒度特征表达能力。最后，本文采用对比损失函数，进一步增加分类特征的类内一致性与类间差异性。实验结果表明，本文模型在慕尼黑血细胞形态学数据集上的识别准确率为91.96%，有望为医师临床诊断提供参考依据。.
A Light Field Image Quality Assessment Method Based on Stereo Vision

Light field imaging is an important achievement in visual information exploration in recent years, which can capture more abundant visual information from the real world. However, most existing light field image quality assessment (LF-IQA) indicators rely heavily on feature extraction based on high complexity statistics in quality evaluation tasks, which is not comprehensive in future modern applications or power-limited devices. To solve this problem, the research puts forward a light field image quality evaluation method based on stereo vision. By introducing a brand-new light field image coding method and training the neural network, the purpose of image quality evaluation is finally achieved. Some experiments show that our model achieves good results in open source data sets.
Sitting Posture Detection System Based on Stacking Ensemble Learning

Recently, the prevalence of diseases such as myopia, hunchback and lumbar herniation caused by the long-term bad sitting posture of young people is increasing. For a healthy life, we propose a human sitting posture detection model based on the human skeleton key point detection algorithm, AlphaPose, and the integrated learning method Stacking. The model extracts frames at intervals from the video stream obtained by the camera, and uses the AlphaPose algorithm to detect the extracted images to obtain the corresponding human skeleton key point coordinate information in the image. A high-dimensional sitting posture feature vector is constructed. finally, a two-layer Stacking classification model is applied to identify various bad sitting postures. In our experiment, a human sitting pose dataset of scale 6913 is constructed, and the proposed ensemble classification model is trained and tested on this dataset. The experimental results show that the average classification accuracy of the method for 9 sitting postures reaches 98.55%, which is higher than some state-of-art methods, and the implementation cost is low, which has high value in practical application.
EfficientNetB7 and Bi-LSTM with GloVe Vector Based Myanmar Image Captioning

Image Captioning (IC) is one of the most widely discussed topic in Artificial Intelligence.In this paper, Myanmar image caption is generated using EfficientNetB7 and Bidirectional Long Short-Term Memory (Bi-LSTM) with GloVe embedding and described about the comparative analysis results.For the purpose of achieving better performance, Myanmar image caption corpus is created and annotated over 50k sentences for 10k images, which are based on Flickr8k dataset and 2k images are selected from Flickr30k dataset.Two different types of segmentations such as word and syllable segmentation level are studied in text pre-processing step and then constructed our own GloVe vectors for both segmentations.As far as being aware and up to our knowledge, this is the first attempt of applying syllable and word vector features in neural network-based Myanmar IC system and then compared with one-hot encoding vectors on various different models.According to the evaluations results, EfficientNetB7 with Bi-LSTM using word and syllable GloVe embedding outperforms than EfficientNetB7 and Bi-LSTM with one-hot encoding, other neural networks such as Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM), VGG16 with Bi-LSTM, NASNetLarge with Bi-LSTM models as well as baseline models.EffecientNetB7 with Bi-LSTM using GloVe vectors achieved the highest BLEU-4 score of 35.09%, 49.52% of ROUGE-L, 54.34% of ROUGE-SU4 and 21.3% of METEOR score on word vectors, and the highest BLEU-4 score of 46.2%, 65.62% of ROUGE-L, 68.43% of ROUGE-SU4 and 27.07% of METEOR score on syllable vectors.
A Collaborative method for Code Clone Detection Using Lexical, Syntactic, Semantic and Structural Features

Software code clones (CC) in software programs are degrading the performance of software systems.many code clones detection (CCD) methods proposed in the literature detect only individual cloned types efficiently.This paper proposes a collaborative code clones detection (CCCD) method by utilizing lexical, syntactic, semantic and structural features for effectively identifying all types of clones including type-4.Initially, a large variance mapper (LV-mapper) is utilized to identify clone pairs (CPs).Then CPs are converted into lexical features by directly applying Word2vec.The synonyms of CPs are obtained using the WordNet tool and converted as semantic features by Word2vec.Additionally, code size metrics (CZM) and object oriented metrics (OOM) are additionally measured as structural features of a program's code blocks (CBs).The syntactic features are extracted through the abstract syntax tree (AST) from the source code.Finally, the joint feature vector is generated by combining all the features together.In order to detect CCs in any new software, the joint feature vector of known clone type source codes is generated first (training set) and then the joint feature vector of unknown clone types source codes is generated next.The Euclidean distances between training and testing of joint feature vectors determine the clone type of test features.Finally, the experimental outcome demonstrates that the proposed CCCD technique has an accuracy of 87.8%, 92.3% and 95.5% for the dataset Apache Maven 3.8.3,Appache ant 1.10.12 and Opennlp-master 1.9.1, respectively, compared to the existing LV-CCD, ES-CCD, TBCNN-CCD and CPVDetector methods.
Blood Cell Count and Detection Method Based on YOLO

Blood Cell Count and Detection (BCCD) has always been a popular topic in object detection and many researchers have applied and modified the two basic models: Faster RCNN and Yolo. However, it is still difficult to tell which model or modification would perform better on other BCCD datasets. Thus, this paper mainly focuses on finding a better model and modifications to BCCD example datasets containing 364 images of blood cells. Faster RCNN and Yolo v5 were used as the basic two models for the dataset. Through training and comparisons between the two models, the better model was chosen to make further modifications or adjustments to achieve a better maP result possible. The result shows that in this specific dataset, Yolo v5 performs better. The modified Yolo v5 model also has an improvement of 0.6 percent of map 0.5 and 0.5 percent of map 0.95 comparing to the original model, showing that modification of model configuration, model structures including head and backbone would efficiently improve the time taken for training and maP.