Reviewerly/exHarmony huggingface dataset:
ERA: Enhanced Rational Activations

Activation functions play a central role in deep learning since they form an essential building stone of neural networks. In the last few years, the focus has been shifting towards investigating new types of activations that outperform the classical Rectified Linear Unit (ReLU) in modern neural architectures. Most recently, rational activation functions (RAFs) have awakened interest because they were shown to perform on par with state-of-the-art activations on image classification. Despite their apparent potential, prior formulations are either not safe, not smooth, or not “true” rational functions, and they only work with careful initialisation. Aiming to mitigate these issues, we propose a novel, enhanced rational function, ERA, and investigate how to better accommodate the specific needs of these activations, to both network components and training regime. In addition to being more stable, the proposed function outperforms other standard ones across a range of lightweight network architectures on two different tasks: image classification and 3d human pose and shape reconstruction.
Improving Sociable Conversational Recommender Systems via Topic-related Preference Graph

Conversational recommender systems discover users' preferences through dialog and make proper recommendations. Previous works fall into task-oriented and sociable conversational recommender systems. However, these works are not interpretable and sociable simultaneously. To address this problem, we propose a conversational recommender system with topic-related preference graph (CRTPG), consisting of a topic-related preference graph (TP-Graph) construction module, a key entity prediction module, and a dialog generation module. The TP-Graph recognizes the user's entity-level preference and keeps preference information for the recent topics. The key entity prediction module provides key entities as explicit content guidance for dialog generation based on TP-Graph. The dialog generation module generates appropriate responses based on the TP-Graph and knowledge related to key entities. TP-Graph and key entity help humans understand the precise information the system makes decisions based on, improving the interpretability of the system. We conduct automatic and human evaluations on the DuRecDial dataset. Experimental results show that CRTPG achieves state-of-the-art results on recommendation and dialog generation.
Edge Preservation Gradient based Smoothing to Multiplicative the Image Noise

The employment of the included offered algorithm, the most recent strategy successfully boosts the unique contrast inside digital photos. Gradient-based smoothing will also be employed to retain the edges and borders. Furthermore, the proposed technique has produced some really valuable outcomes, making it even more useful. Python programming is used to build and implement the proposed approach. The recommended solution is created and implemented with the Scikit image processing library. Many different sorts of images have also been studied for purpose of testing. Different quality criteria for evaluating the efficiency of the suggested technique are also given. The suggested technique outperforms the alternatives, as evidenced by a comparison of existing and new methodologies. Some picture performance measures such as root mean square error and edge preservation index will be used to compare new and current median filter algorithms.
Few-shot and Fast Texture Segmentation Based on Non-padding Convolution

To solve the segmentation problem of textures, the accuracy of classical algorithms of machine vision is not satisfactory because easily confused textures are often difficult to distinguish accurately. However, state-of-art semantic segmentation algorithms based on deep learning require expensive equipment, long time and large amounts of data for training, and large human resources for data annotation. This paper is committed to building a simplified convolution model based on non-padding convolution, which requires a very small number of images to train, and it can realize fast texture segmentation on the CPU device. On the dataset used in this paper, FB-IoU can reach 92.8%. On the 2.3GHz CPU, the average processing time of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1920\times 1080$</tex> images is 428ms. The accuracy has obvious advantages over classical segmentation algorithms or few-shot segmentation algorithms based on meta learning, and the processing speed has obvious advantages over lightweight semantic segmentation models based on deep learning.
RESULTS OF THE SCIENTIFIC SEMINAR "THEORY AND PRACTICE OF APPLIED CULTURAL RESEARCH" DEDICATED TO THE 570TH ANNIVERSARY OF THE BIRTH OF LEONARDO DA VINCI (SIBERIAN FEDERAL UNIVERSITY, KRASNOYARSK)

This text presents a summary of the work of the educational, scientific and methodological seminar "Theories and Practices of Applied Cultural Research" (May 13, 2022, Krasnoyarsk, Siberian Federal University). The seminar was devoted to the reflection of a team of scientists on the development of the Krasnoyarsk school of art history from the 1990s to the present, and timed to coincide with the 570th anniversary of the birth of the brilliant Italian Renaissance artist Leonardo da Vinci. The seminar held in three areas - 1) Krasnoyarsk School of Art History, 2) Modern trends in the development of conceptual art, 3) Current research in Russian art.
Traffic Signs Recognition using Convolutional Neural Networks

The generated Machine Learning (ML) models are applied in several areas of management and research. One of these fields, automobile technology, uses the commercialized system Advanced Driver Assistance Systems (ADAS), that includes recognition of road signs as a section. Driver assistance with Traffic chevrons recognition is a crucial element that contributes to the safety of drivers, pedestrians, and vehicles. Convolutional Neural Networks (CNN) are now being utilized to perform many article recognizable proof difficulties. Deep learning-based Traffic chevrons recognition (TSR) is fast advancing. TSR, in particular, includes two technologies: categoriesification of Traffic chevrons (TSC) and detection of Traffic chevrons (TSD). Our technology would aid in detecting and identifying Traffic chevrons without causing drivers to lose focus while driving. We create a CNN model to categories images into appropriate groups. For image categorization, CNN is the best option. CNN is implemented using TensorFlow. Without Traffic chevrons, all cars would have no idea what was ahead of them, and highways could become shambles.
Dynamic Network Model for Multi-Domain End-to-end Task-Oriented Dialogue System

Dialogue State Tracking (DST) is an important part in task-oriented dialog system, whose target is to infer the current dialog states and user intentions according to the dialog history information. To this end, we have achieved improvements to the existing work and proposed a dynamic network model suitable for multi-domain dialog, which can explicitly use domain information and better cope with zero-shot tasks. The model is composed of three modules: an encoder, a decoder and a slot classifier. The encoder module introduces a mixed-separate framework so that it can obtain the feature information of each domain on the premise of extracting the shared information between all domains. The experimental results show that the model achieves joint accuracy of 48.38% for the five domains of MultiWOZ, which is superior to existing models. Besides, by simulating the zero-shot scenario, the knowledge transferability of the model has also been well proven. Finally, in order to verify the effectiveness of the robot simulation system, this paper also uses the robot simulation technology to simulate the common tasks of helping users complete the task of taking items in the home environment service.
Fractional Differential-interpolation Combined Image Amplification Algorithm

Image magnification is an image processing technique that obtains high-resolution images from low-resolution images. This paper is proposed a combined image enlargement algorithm of fractional differential and bilinear interpolation, which makes up for the defect that the texture of the image cannot be saved compared with the previous integer order. First, the original image is reduced to one-half, and then the image is magnified by two times by bilinear interpolation, and then the image magnification algorithm is constructed by using the fractional order differential operator to enlarge the image, and the image magnification algorithm of the combination of the fractional partial differential operator and the bilinear interpolation is constructed. Experimental results show that the combined image magnification algorithm can maintain the basic characteristics of the image while better maintaining the texture features of the image detail edges.
Performance Analysis of a Modified Lightweight Authentication Scheme for Quantum Key Distribution

Network applications have become ubiq uitous in today's world, and are accessed through both wired and wireless networks. Therefore, safeguarding the network infrastructure is a necessity and extra precaution has to be taken to ensure that the data is protected from any security breaches, ensuring privacy. The Heisenberg Uncertainty Principle and Quantum Entanglement, two principles of quantum physics that indicate that measuring one thing has an impact on another, are employed as an alternative and novel lightweight authentication methodology for quantum key distribution. The analysis of results for the quantum key generation with and without interception by an eavesdropper is simulated for detection of the eavesdropper and for a sample size of 20, 0.02% chance of being spied upon unknowingly is established which is infinitesimal and ensures the security of the network infrastructure.
DMM: Dual-Modal Model for Person Re-Identification

This paper explores how to boost the performance of current person re-identification (ReID) models by incorporating auxiliary information such as contour sketch. Most current ReID methods consider only RGB images as input, with little attention on extra yet important information contained in other modal images. We propose a dual-modal model (DMM), consisting of a main stream that inputs RGB images, and an auxiliary stream that inputs other modal images, to explore how the auxiliary information will help to promote the performance of existing ReID models. To fuse these two streams, a novel dual-modal attention (DMA) mechanism is proposed. Specifically, we apply spatial attention to auxiliary feature maps to take full advantage of the informative spatial locations contained in this stream. Then channel attention is applied to the spatially refined main feature maps, resulting in further refined representations. Moreover, we adopt DMA at multiple scales to exploit different semantics from low to high levels, which finally generates more discriminative feature representations. Comprehensive experiments on publicly available datasets, Market1501, DukeMTMC, MSMT17, and Black ReID, show that our proposal achieves SOTA results.
Unbiased Manifold Augmentation for Coarse Class Subdivision

Coarse Class Subdivision (CCS) is important for many practical applications, where the training set originally annotated for a coarse class (e.g. bird) needs to further support its sub-classes recognition (e.g. swan, crow) with only very few fine-grained labeled samples. From the perspective of causal representation learning, these sub-classes inherit the same determinative factors of the coarse class, and their difference lies only in values. Therefore, to support the challenging CCS task with minimum fine-grained labeling cost, an ideal data augmentation method should generate abundant variants by manipulating these sub-class samples at the granularity of generating factors. For this goal, traditional data augmentation methods are far from sufficient. They often perform in highly-coupled image or feature space, thus can only simulate global geometric or photometric transformations. Leveraging the recent progress of factor-disentangled generators, Unbiased Manifold Augmentation (UMA) is proposed for CCS. With a controllable StyleGAN pre-trained for a coarse class, an approximate unbiased augmentation is conducted on the factor-disentangled manifolds for each sub-class, revealing the unbiased mutual information between the target sub-class and its determinative factors. Extensive experiments have shown that in the case of small data learning (less than 1% fine-grained samples of commonly used), our UMA can achieve 10.37% average improvement compared with existing data augmentation methods. On challenging tasks with severe bias, the accuracy is improved by up to 16.79%. We release our code at https://github.com/leo-gb/UMA .
Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors

Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing.However, traditional TinyML methods can only perform inference, limited to static environments or classes.Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable.For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear.Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters.This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification.Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms.
Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori Algorithm

Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters’ feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB’s commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied Apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB’s timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dissatisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics.
A Bert-based Joint Model of Intent Recognition and Slot Filling Cross-correlation

Intent recognition and slot filling are two key steps in natural language understanding. In the past, the two steps were often completed separately, and a large number of joint modeling methods have recently demonstrated that the two are closely related and can leverage the shared knowledge between tasks to achieve better performance. Previous studies have focused on multi-task implicit joint modeling or slot filling tasks relying on information from intent recognition, ignoring that intent recognition and slot filling are interrelated. In this paper, the joint model Bi-Correlation is improved to form the cross-correlation of intent classification and slot filling. It includes two modules, IR2SF and SF2IR so that the performance of intention recognition and slot filling can be mutually enhanced; and considering the lack of generalization in the case of small datasets, In this paper, the Bert pre-training model is used to improve the generalization of the model, thereby improving the performance of the model. Experiments are carried out on two Chinese datasets, CAIS and SMP-ECDT. The experimental results show that the model in this paper performs better than the existing models, and the accuracy is significantly improved.
A Development of Multi-Platform Based Forestry Wildfire Prevention System Using Incremental Model: Case study: a peatland area in Siak Regency

Forest fires might be caused by human or natural aspects. One of the regions in Riau Province that contains a forest with a large and fertile peatland is Siak regency. However, extreme weather occurrences and resident activity have caused a significant amount of peatlands to dry out since 2019, making them more prone to fire. Our work focuses on the prevention aspect, due to the different characteristics of peatlands when a fire occurs. Furthermore, this study aims to develop an early warning system based on IoT using an incremental model, due to involves several platforms and needs to be developed separately. The information is periodically generated and sent to the cloud server by node sensors and gateways deployed on particular peatland areas for data storing purpose. This data becomes information for the three platforms that users can use to inspect the peatland, namely the web platform, mobile platform, and LED board platform. According to test results, every system component functioned as intended, and the three platforms correctly displayed the data on the peatlands area.
Offense Detection Using BERT and CNN

In today's unconventional world, the amount of user-generated content, such as articles, books, blogs, etc., is increasing exponentially. Some might find them offensive. As is well known, RNN and LSTM take a long time to analyze input since the words are processed one at a time; however, this happens all at once in BERT. In this paper, the suggested model uses BERT with CNN layers. These CNN layers help the model to improve its accuracy further. Hate Speech and Offensive Language is the dataset used. In this dataset there were three categories hate, offensive and neither, the accuracy with these was about 91%, since the paper focused on offense, the two categories were taken into consideration, offensive and not offensive. The accuracy of BERT without CNN was 85%, whereas BERT with CNN 2 categories was almost 95%.
Design and Construction of a Colourblind-Friendly Surabaya City Angkot Route Map Prototype

Angkot is the most often found public transportation in Surabaya City. However, there is no angkot routes map, and the officially published route information is textual, thus hard to get the transit information quickly. Meanwhile, people with colour vision impairment have a different perception of colour compared to people with normal vision. It can affect them in making decisions when reading a map. The purpose of this study is to design a colourblind-friendly Surabaya City angkot route map prototype and to conduct a cartographic evaluation of the map by considering the colour vision impairment factor. The map was created using ArcGIS and CorelDRAW then checked by using several software packages to ensure that the colours are colourblind-friendly then tested on people with normal vision and people with colour vision impairment. Fifteen out of 15 respondents with normal vision and 11 out of 11 respondents with colour vision impairment could distinguish the colours of the route. All respondents mentioned that symbols and some texts were too small. It shows that the colours on the map can accommodate both groups, but they have difficulty reading the route map because the size of the symbols and the text is too small.
SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation

Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks. Due to the long tail bias problem of the object and predicate labels in the available annotated data, the scene graph generated from current methodologies can be biased toward common, non-informative relationship labels. Relationship can sometimes be non-mutually exclusive, which can be described from multiple perspectives like geometrical relationships or semantic relationships, making it even more challenging to predict the most suitable relationship label. In this work, we proposed the SG-Shuffle pipeline for scene graph generation with 3 components: 1) Parallel Transformer Encoder, which learns to predict object relationships in a more exclusive manner by grouping relationship labels into groups of similar purpose; 2) Shuffle Transformer, which learns to select the final relationship labels from the category-specific feature generated in the previous step; and 3) Weighted CE loss, used to alleviate the training bias caused by the imbalanced dataset.
A Cloud-based Free View Solution

We provide a real-time cloud-based free view solution composed of ingestion, processing, distribution, and interaction with optimization. Results show that our solution reduces the cost and complexity of free-viewpoint video (FVV) deployment and improves the performance by adopting the lightweight synthesis algorithm and the CMAF standard. Our demo video is available on https://github.com/Amygu1994/FreeView/raw/main/Demo_A%20Cloud-based%20Free%20View%20Solution.mp4
Introducing Diversity In Feature Scatter Adversarial Training Via Synthesis

In an attempt to understand how deep learning models interpret inputs, it has been found that they change their prediction when a carefully optimized imperceptible noise termed adversarial perturbation is added to the input data. Many researchers are focusing on developing methods to counter such effects, but such methods do not generalize well to adversarial test data. Recently, Feature-Scatter adversarial training has come up to solve such a problem, but this method uses the traditional adversarial training framework as its basis that cannot generate diverse perturbations.In this paper, we propose an approach that combines both the Feature-Scatter adversarial training and the generator-based adversarial training framework to optimally explore the adversarial data manifold achieving better robust generalization. We perform extensive experimentation across a wide variety of datasets such as Cifar10, Cifar100, and SVHN. Our framework significantly outperforms the state-of-the-art methods against both strong white-box attacks and black-box attacks.
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph

Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.