Reviewerly/exHarmony huggingface dataset:
On enhancing the robustness of Vision Transformers: Defensive Diffusion

Privacy and confidentiality of medical data are of utmost importance in healthcare settings. ViTs, the SOTA vision model, rely on large amounts of patient data for training, which raises concerns about data security and the potential for unauthorized access. Adversaries may exploit vulnerabilities in ViTs to extract sensitive patient information and compromising patient privacy. This work address these vulnerabilities to ensure the trustworthiness and reliability of ViTs in medical applications. In this work, we introduced a defensive diffusion technique as an adversarial purifier to eliminate adversarial noise introduced by attackers in the original image. By utilizing the denoising capabilities of the diffusion model, we employ a reverse diffusion process to effectively eliminate the adversarial noise from the attack sample, resulting in a cleaner image that is then fed into the ViT blocks. Our findings demonstrate the effectiveness of the diffusion model in eliminating attack-agnostic adversarial noise from images. Additionally, we propose combining knowledge distillation with our framework to obtain a lightweight student model that is both computationally efficient and robust against gray box attacks. Comparison of our method with a SOTA baseline method, SEViT, shows that our work is able to outperform the baseline. Extensive experiments conducted on a publicly available Tuberculosis X-ray dataset validate the computational efficiency and improved robustness achieved by our proposed architecture.
NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation

With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://github.com/Jeff-sjtu/NIKI
Image Pedestrian Re-identification Based on ShuffleNet-Beyong and Batch-DropBlock

Due to the changes of posture, occlusion and illumination intensity in pedestrian images, the accuracy of pedestrian re- recognition will be reduced. Researchers have used a large number of methods and experiments to improve the accuracy of recognition, but they did not consider that while improving the accuracy, they should reduce the amount of calculation as much as possible. In the current internet era, mobile terminal devices are very popular, so the neural network with large amount of computing is not suitable for these terminal devices. In order to solve this problem, this paper first proposes a ShuffleNet-Beyong unit, which add two 1*1 convolution cores to the left branch of ShuffleNet unit to extract more robust features, and add channel shuffle after the last 1*1 Gconv of the right branch of ShuffleNet unit to solve the problem of channel information non circulation. Then, a ShuffleNet-Beyong network is constructed based on these ShuffleNet units, and an image pedestrian re-recognition network structure based on the ShuffleNet-Beyong network and the Batch-Dropblock network is proposed. In this network structure, the ShuffleNet-Beyong network is used as the backbone network to extract features. After the backbone network, the Batch-Dropblock network is used to extract local features, which are fused with the global features to obtain the final feature expression, so as to obtain more robust features. The comparison of experimental results on Marketing-1501 and DukeMTMC-reID datasets show that the image pedestrian re-recognition network structure proposed in this paper is very robust, which can reduce the amount of calculation and improve the accuracy of recognition, and can achieve the same effect in the process of ablation experiments.
Digital educational technology content production and validity about Japanese bathtub (ofurô) in neonatal unit

To describe the elaboration and content validity stages of a digital educational technology for nursing professionals about Japanese bathtub (ofurô) in newborns in neonatal care units.A methodological study conducted between August 2019 and July 2021, in a public maternity hospital in Manaus, Amazonas, developed in two stages. In the first, the e-book images, text and editing were produced. In the second, material content validity was carried out, through the opinion of 15 expert judges, nurses and neonatal care experts. In data analysis, the percentage of agreement was applied with agreement score estimation. Items with 80% or more agreement were considered valid.The e-book "Japanese bathtub: manual for nursing professionals" was produced, organized into nine sessions, which describe the technique execution. Content was considered valid by expert judges (general score 90%).The e-book was considered suitable for use in training nursing professionals, with potential for dissemination of knowledge regarding humanization of care for premature babies.
Rounding Meets Approximate Model Counting

The problem of model counting, also known as #SAT, is to compute the number of models or satisfying assignments of a given Boolean formula $F$. Model counting is a fundamental problem in computer science with a wide range of applications. In recent years, there has been a growing interest in using hashing-based techniques for approximate model counting that provide $(\varepsilon, \delta)$-guarantees: i.e., the count returned is within a $(1+\varepsilon)$-factor of the exact count with confidence at least $1-\delta$. While hashing-based techniques attain reasonable scalability for large enough values of $\delta$, their scalability is severely impacted for smaller values of $\delta$, thereby preventing their adoption in application domains that require estimates with high confidence. The primary contribution of this paper is to address the Achilles heel of hashing-based techniques: we propose a novel approach based on rounding that allows us to achieve a significant reduction in runtime for smaller values of $\delta$. The resulting counter, called RoundMC, achieves a substantial runtime performance improvement over the current state-of-the-art counter, ApproxMC. In particular, our extensive evaluation over a benchmark suite consisting of 1890 instances shows that RoundMC solves 204 more instances than ApproxMC, and achieves a $4\times$ speedup over ApproxMC.
PELATIHAN REPARASI HANDPHONE BAGI SISWA SMK 5 DI KOTA LHOKSEUMAWE

Currently the use of mobile phones (HP) is not only in the field of communication but extends to various online transactions. Not only that, but during a pandemic, the learning process and seminars are conducted online. Utilization of HP is not only from certain circles or ages, but almost all groups and ages, both adults and children, use HP for business and entertainment. Webinars and online are one of the important strategies to fill school activities that were stopped during the pandemic. Over time and during the pandemic, the use of HP increased sharply. It is common nowadays that we see every opportunity and in various places people spend their time with HP. This of course will result in HP being damaged or error quickly. Seeing this condition, it is important for SMK students to have the expertise to repair cellphones that are damaged or have errors. Repairing a cellphone must first understand the theory of the device both hardware and software. Hardware-wise, the first step is how to open the HP case itself and use a precise toolkit, recognize the electronic components contained inside, map out potential damage that occurs and finally replace damaged components or modules. In software, what is done is to find the cause of the operating system or application program that is corrupt or degraded. Is it necessary to take action to upgrade or update the application software? For that students need basic training on how to repair HP. Sometimes everyone who has a damaged cellphone immediately takes it to a technician, and the price is very expensive even though the level of damage is very simple. If you understand the symptoms of damage and understand the basics of electronics, the HP damage can be repaired.
Some features of intra-group relations influence university learners’ personality

The paper considers the peculiarities of consolidated intercommunication and in-group interrelations of students studying at higher educational institutions. The process of group influence on the individual progress, behavioral features, and advancement of academic and competent activeness of the undergraduates is defined. The socializing and academic impact of the undergraduate students’ group on the competent process and moral stability development of students’ personalities are analyzed. The processes are analyzed by determining the effectiveness of future specialists’ progress and conduct, and the effectivity of education-professional performance. The study resulted in conclusions and proposals for educational institutions to provide optimal intercourse functioning as an improving education efficiency method.
PENERAPAN METODE PEMBELAJARAN TERBIMBING DALAM MENINGKATKAN KEMAMPUAN SISWA KELAS X IPA SMA NEGERI 1 TINOMBO SELATAN MENULIS SURAT PRIBADI

The main problem in this study is the low learning outcomes of students of class X IPA SMA Negeri 1 Tinombo Selatan on the material of writing personal letters. There are several things that cause these problems, including: 1) students do not understand the concepts being taught and 2) the methods used by teachers in carrying out learning make students less active, also of course other factors. This research was conducted with the aim of improving the ability of students of class X IPA SMA Negeri 1 Tinombo to write personal letters through the guided practice method. Benefits Improve student learning outcomes, especially learning to write personal letters and hone the ability to work alone through exercises under the guidance of the teacher, and do not feel neglected by the teacher because they are still monitored in carrying out their duties. The method used in this research is descriptive method. The design of this study refers to the Kemmis and Mc Taggart model which consists of four components, namely 1) planning, 2) action implementation, 3) observation and 4) reflection. The data collected in this study is in the form of teacher and student activity data during the learning process obtained by using observation sheets and student learning outcomes data obtained by giving individual tests to students. In the research that has been carried out, it was found that the students of class X IPA SMA Negeri 1 Tinombo Selatan experienced an increase in classical absorption from the pre-action results of 90%
An Encrypted Traffic Identification Method Based on RepVGG

With the emergence of encrypted traffic, more and more researchers use AI technology to improve the accuracy of traffic identification. However, machine learning needs to rely on human experience to extract features, and the training of deep learning models depends on a large number of labeled samples.To solve these problems, we propose an encrypted traffic identification method based on RepVGG. First, the pre-trained model RepVGG-A0 on the ImageNet dataset is migrated to the encrypted traffic dataset, and a dropout layer is added before the linear classifier in order to avoid overfitting. Then, to reduce the impact of sample imbalance, different weight parameters are assigned to different categories in the training process.Finally, we make a comparison with other traffic identification methods.The experimental results show that the proposed method can achieve 99.98% accuracy in binary classification and 97% accuracy in multi-classification experiments, which proves the effectiveness of the method.
Coordinate Attention-enabled Ship Object Detection with Electro-optical Image

Shipping safety is one of the factors restricting the development of navigation. In particular, the route near the shore is prone to unknown risks due to the existence of multiple types of ships, the density of ships, the shielding between ships, and other reasons. This paper presents a method for detecting medium-range ships, which can improve security for ships. This method is based on the You Only Look Once Version 5 network (YOLOv5). To improve the accuracy, the coordinate attention model is integrated into the detection network. The main research content and experimental work of this paper are as follows. Firstly, the YOLOv5 network and spatial attention mechanism are analyzed. Then, detection experiments were carried out based on YOLOv5 and Singapore Maritime Data Set (SMD). Then, the coordinate attention model was used to improve the network. Finally, by adjusting training parameters and improving attention, the mAP of test results of the object detection network reaches 73%, and the feasibility of object detection of the YOLOv5 algorithm with coordinate attention is confirmed.
MEFormer: Multi-Object Tracking with Multi-Scale Feature Enhanced Transformer

Track-by-attention model, such as TrackFormer, have been one of the most popular deep models for multi-object tracking (MOT). However, one of their core limitations is image recognition accuracy and local feature attention due to the long sequence attention mechanism. To remedy this problem, MEFormer, a multi-scale feature enhanced model is proposed, in which both the channel attention and spatial attention mechanisms are leveraged for feature enhanced. MEFormer can effectively capture discriminative representation for MOT with attention and aggregation. Experiments on the MOT17 dataset indicate that the proposed MEFormer is able to achieve competitive performance on the task of multi-object tracking with 56.0% MOTA and 2400 ID switches.
Determinism of Multirelations

Binary multirelations can model alternating nondeterminism, for instance, in games or nondeterministically evolving systems interacting with an environment. Such systems can show partial or total functional behaviour at both levels of alternation, so that nondeterministic behaviour may occur only at one level or both levels, or not at all. We study classes of inner and outer partial and total functional multirelations in a multirelational language based on relation algebra and power allegories. While it is known that general multirelations do not form a category, we show that the classes of deterministic multirelations mentioned form categories with respect to Peleg composition from concurrent dynamic logic, and sometimes quantaloids. Some of these are isomorphic to the category of binary relations. We also introduce determinisation maps that approximate multirelations either by binary relations or by deterministic multirelations. Such maps are useful for defining modal operators on multirelations.
SSD Object Detection Algorithm Based on Feature Fusion and Channel Attention

Abstract Aiming at the problems of low object detection accuracy due to complex background and insufficient semantic information of shallow features in the object detection SSD algorithm, this paper improves the existing SSD algorithm. First, the original vgg16 network is replaced by the ResNet50 network, and the residual network structure as well as the Batch Normalization layer are added, which are used to improve the accuracy of the feature extraction network; Second, a feature fusion module is designed to fuse adjacent feature maps to improve the detection effect by integrating contextual information; Third, the SE attention mechanism is introduced to give channel weights adaptively and enhance the useful feature channels; Finally, the object detection analysis experiments are conducted on the PASCAL VOC2012 dataset. The experimental results show that the improved SSD algorithm in this paper is able to achieve an mean average precision of 72.7% in the data set, which is 2.1% better than the original SSD-VGG16 and greatly improves the object detection effect.
A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

Abstract Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity category, which results in the accumulation of errors. So, named entity recognition model based on multi-task learning and pointer network is proposed, and innovations are made in the task Construction and domain entity information utilization in the model of Named entity recognition. This model is based on Transformer with multi-Head attention mechanism, and decomposes traditional tasks in entity recognition tasks and entity classification tasks, and carries out multi-tasks Task learning to reduce the accumulation of errors between tasks. Model in this paper also uses the similarity calculation based on the Comprehensive description of the entity category in entity classification Task for better pertinence of the domain entity. Experiments are conducted on public datasets and domain datasets to prove the advancement of the Model.
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain. Existing works show that Vision Transformers are more robust than CNNs and show that this is related to the visual grouping property of self-attention. In this work, we propose a novel hierarchical grouping transformer (HGFormer) to explicitly group pixels to form part-level masks and then whole-level masks. The masks at different scales aim to segment out both parts and a whole of classes. HGFormer combines mask classification results at both scales for class label prediction. We assemble multiple interesting cross-domain settings by using seven public semantic segmentation datasets. Experiments show that HGFormer yields more robust semantic segmentation results than per-pixel classification methods and flat grouping transformers, and outperforms previous methods significantly. Code will be available at https://github.com/dingjiansw101/HGFormer.
GFDC: A Granule Fusion Density-Based Clustering with Evidential Reasoning

Currently, density-based clustering algorithms are widely applied because they can detect clusters with arbitrary shapes. However, they perform poorly in measuring global density, determining reasonable cluster centers or structures, assigning samples accurately and handling data with large density differences among clusters. To overcome their drawbacks, this paper proposes a granule fusion density-based clustering with evidential reasoning (GFDC). Both local and global densities of samples are measured by a sparse degree metric first. Then information granules are generated in high-density and low-density regions, assisting in processing clusters with significant density differences. Further, three novel granule fusion strategies are utilized to combine granules into stable cluster structures, helping to detect clusters with arbitrary shapes. Finally, by an assignment method developed from Dempster-Shafer theory, unstable samples are assigned. After using GFDC, a reasonable clustering result and some identified outliers can be obtained. The experimental results on extensive datasets demonstrate the effectiveness of GFDC.
Exploration of Micro Skills for High School English Continuing Writing after Reading

With the advancement of the reform of English curriculum based on core literacy, the idea of proposition and question type structure of college entrance examination are also changing, and the teaching of continuing writing after reading has attracted much attention. The new question type of college entrance examination can organically combine the input and output of language. In practical teaching, it can improve students' reading and writing ability quickly to carry out micro skills teaching after reading and writing. Based on personal teaching practice, this paper expounds the micro-skill teaching of each aspect from the three aspects of clarifying the main idea of the article, expanding the story plot and language expression training, and explores the continuance of micro-skill after reading high school English, in order to improve and develop its teaching.
A Methodology to Generate a Funsize URL Using Django Frame Work

Abstract: A proposed system used for building a web-based system using Django framework that shortens the URL provided by the user. By using this system user can use the services by just visiting the shrinky.in website. There are two types of users Guest users and Authorised users. The Guest users can visit to the website any time and can short their required URLs and the shortened URLs are valid for 24Hrs. The Authenticated users can Register/Login to their account and can use some additional services such as Generating QR code for the URLs, end-to-end access for the URLs, create custom URLs and a fully functioning Dashboard for the Users where the user can keep track the URLs
Evaluating digital corporate identity: Application of AHP method in website quality analysis

Due to the increasing importance of websites as elements of digital corporate identity, website quality has become an area of great importance in practice and academic research. The goal of the research is to evaluate the quality dimensions of the website and determine their importance in choosing the most desirable option among several competing options. Given that the online visibility of companies from the construction industry is unsatisfactory, this sector was selected for the research, and the Web pages of selected companies from this sector were analyzed. By using the Analytical Hierarchy Process (AHP) method, it was examined which dimensions of quality have the greatest influence on the quality of a Web page. It was found that loading speed (0.703) and information quality (0.554) have the greatest influence on the quality of the website. The results of the research can be used by companies and Web designers in practice, in order to make the proper decisions regarding website content, design and functionality and their use in the context of improving the digital corporate identity.