Reviewerly/exHarmony huggingface dataset:
Enhancing Visual Place Inference via Image Ranking Using Fuzzy Similarity and Agreeability

We present a fuzzy approach to visual place recognition. Our approach consists in designing a hierarchical fuzzy system to leverage matching between query and a given image while taking into account agreeability between different feature extractors, and introducing a fuzzy ranking on the basis of matching and agreeability in order to permit re-ranking of top-ranked images. Fuzzy ranking uses fuzzy similarity and agreeability on features determined by three different CNNs. For each of them the cosine similarity between global features representing an examined image and a query image is calculated. The cosine scores are then fed to a similarity FIS. They are also fed to an agreeability FIS, which operates on linguistic variables representing the agreeability between three cosine scores. The outcomes of Mamdani fuzzy inference systems are fed to a ranking Sugeno-type FIS. The mAP scores achieved by the proposed FIS were compared with mAP scores achieved by the similarity FIS. The algorithm has been evaluated on a large dataset for visual place recognition consisting of both images with severe (unknown) blurs and sharp images with 6-DOF viewpoint variations. Experimental results demonstrate that the mAP scores achieved by the proposed algorithm are superior to results achieved by NetVLAD as well as an algorithm combining crisp outcomes of CNNs that were investigated in this work.
A Fall Warning System for the Elderly with Multiple Nodes Based on DTW Algorithm

Aiming at the problem of real-time fall warning for the elderly when walking, this paper improves the traditional dynamic time warping (DTW) algorithm for speech recognition, processes the gait data, and designs a multi node wear system with fall warning function. There are three nodes in the system, which are located in the waist and two ankles. The acceleration data of these three nodes are collected by the motion sensors of the elderly when they walk. Moreover, the system will use DTW algorithm to calculate the difference between the waist acceleration data of two gait cycles as the index of human stability, and calculate the difference between the left and right ankle acceleration data of one gait cycle as the index of human balance, and then use k-nearest neighbor (KNN) algorithm to realize the recognition function. When the elderly walk unsteadily, it will give an alarm, so as to help the elderly avoid walking unsteadily Fall. The experimental results show that the system can effectively identify the elderly gait instability which is the prelude to fall, and the accuracy rate is 95 %.
Domain Adaptative Semantic Segmentation by alleviating Long-tail Problem

The domain adaptive method based on the adversarial network can be effectively applied to unsupervised semantic segmentation tasks. State-of-the-art approaches have proved that domain alignment at the semantic level can improve segmentation networks' performance. Based on data observation between different domains, we find that the long-tail problem exists in these datasets. We propose a two-level class balancing model to alleviate the semantic class imbalance of data to address this problem. Specifically, we count the category frequencies in the datasets and treat this frequency information as mutual information. Then, we feed this mutual information to the cross-entropy method for fitting so that the model can alleviate the long-tail problem globally. Besides, we resample the data in the model's training process by using two classifiers to balance the head class and the tail class locally. Finally, we use self-supervised learning to supervise the target domain's alignment and the source domain, thus achieving further improvement. We conduct experiments on the benchmark of mainstream unsupervised domain adaptive semantic segmentation tasks, and the experimental results show that our proposed method is effective.
Design and Implementation of a SSH Proxy System Based on B/S Architecture

Abstract The SSH protocol is one of the most commonly used protocols in the current operation and maintenance process, but the commonly used SSH clients are all implemented based on the C/S architecture, which causes the operation and maintenance personnel to face cross-platform problems, and when accessing from the external network It is also necessary to develop corresponding VPN according to different platforms. This paper proposes a SSH proxy system based on B/S architecture. The client of the system is based on a browser, which solves the cross-platform problem of different clients and facilitates the use of remote operation and maintenance personnel.
Punch Card Patterns Designed with GAN

Abstract Knitting punch cards codify different stitch patterns into binary patterns, telling the machine when to change color or to generate different stitch types. This research utilizes Neural Networks (NN) and image-based Generative Adversarial Networks (GAN), with an image database of knitting punch cards, to generate new punch card designs. The hypothesis is that artificial intelligence will learn the basic underlying structures of the punch cards and the pattern makeup that is inherent across patterns of different styles and cultures. Different neural networks were utilized throughout the research, such as Neural Style Transfer (NST), AdaIN Style Transfers, and StyleGAN2. The results from these explorations offer different insights into pattern design and various outcomes of the different neural networks. Ultimately physically testing these punch card designs, these patterns were knit on a domestic knitting machine, resulting in novel fabrication and design techniques that are both digital and craft-based.
Byte Shrinking Approach for Lossy Image Compression

Digital image storage and transmission is a challenging task nowadays. As per statistics, an average of 1.8 billion images are transmitted daily. Hence, image compression is inevitable. Here, we discuss a novel approach, which is a type of lossy image compression. But the quality of reconstructed image is higher. This method makes use of reducing the number of bytes by storing the number of ‘1’s in each bitplane of three adjacent pixels and recording the count of number of ‘1’s. This count value is stored, and later Huffman compression is applied. Reconstruction is done by energy distribution method. This gives better results in terms of quality at lower PSNR values compared to JPEG algorithm. Here we have used another metric to measure the quality of the image which is discussed in detail.
Capturing movement

This chapter focuses on timbre, morphology, kinetics and their progression over time through a creative approach in which movement is extracted from sounds and visuals derived from recordings of natural environments. In this context, movement is extracted from the behaviour of matter as it unfolds in its natural setting. Then, gestures and textures are created by 1) examining the characteristics of the movements inherent in the recorded sources and 2) organising and combining the materials. The purpose of these manipulations is to create meaning that is not only linked to the nature of the sounds and images themselves but to the manner in which they move and are organised in time. On the whole, the reception of music and moving imagery can create powerful connections outside the realm of traditional story-telling.
ON THE QUESTION OF FORMATION OF COGNITIVE ACTIVITY OF SENIOR SCHOOLS THROUGH PROJECT ACTIVITY

The article analyzes the process of organizing project activities among senior schoolchildren in general education institutions as a means of forming cognitive activity and implementing a system-activity approach within the framework of the Federal State Educational Standard. The structural components of the cognitive activity of senior schoolchildren (behavioral, motivational, emotional-volitional, cognitive) are indicated and the principles of the system-activity approach are outlined (the principle of obtaining new knowledge, the principle of changing the role of a teacher, the principle of forming motives for activity). The mutual influence of the stages of project activity implementation (initiating, fundamental, pragmatic/final) and the content of the components of cognitive activity is reflected. In the course of the research, theoretical (literature analysis) and practical methods were used (analysis of official websites of educational institutions, questionnaires of school teachers, the method of observation and conversations with students of senior classes of secondary schools). The problems of forming the cognitive activity of senior schoolchildren through project activities are revealed (the absence of project activity as a mandatory component of the educational process and, as a result, the impossibility of constantly improving the existing skills of teachers; the low level of innovative and pedagogical mobility of teachers within the framework of project activities; the growth of cognitive passivity of senior schoolchildren) and the ways of their solution are proposed (creation of a single regulation for the organization of project activities in educational institutions; providing an inter-level exchange of experience between representatives of pedagogical professions; monitoring the professional skills of a teacher; a separate unit for organizing project activities in an educational institution; implementing project activities taking into account the sphere of interests of senior schoolchildren with the involvement of specialists from the real sector of the economy; using forms and methods that increase the cognitive activity of students in regular and extracurricular activities).
Sequential Recommendation with Context-Aware Collaborative Graph Attention Networks

Recently, sequence features have been extensively studied to improve the performance of recommender systems. However, advanced sequential recommendation methods that rely only on item IDs still face the challenge of modeling fine-grained user preference from interactive data. Furthermore, context-aware sequential recommendations have the hardness of modeling the relationship between items and items, items and users. Both of these two methods ignore the effect of categories on users' next click tendency and the interactive learning between categories and items. In this paper, we propose a method named Contextual Collaborative Graph Attention Network (CCGAT) to model the sequence. Methodologically, user behavior sequences are constructed as graph-structured data, and we apply two similar graph self-attention networks to model the item transitions and the category click probability. CCGAT takes advantage of the fact that users tend to click on the same or similar categories under specific purposes, and provides a simple but effective way to train two networks collaboratively. Extensive experiments on five real-world datasets show that our model outperforms state-of-the-art methods, and demonstrate the validity of modeling both contextual information and graph features.
A Novel Pre-processing Method for Enhancing Classification Over Sensor Data Streams Using Subspace Probability Detection

The rapid development of the Internet of Things has led to the widespread use of sensors in everyday life. Large amounts of data through sensing devices are collected. The data quantity is massive, but most of the data are repetitive and noisy. When traditional classification algorithms are used for classifying sensor data, the performance of the model is often poor because the classification granularity is too small. In order to better data mine the knowledge from the Internet of Things data which is a kind of big data, a new classification model based on subspace probability detection is proposed. This model can be well integrated with traditional data mining algorithms, and the performance on sensor data mining is greatly improved.
Articulation During Voice Disguise: A Pilot Study

Speakers can conceal their identity by deliberately changing their speech characteristics, or disguising their voices. During voice disguise, speakers alter their normal movements of the articulators, such as tongue positions, according to a predetermined strategy. Even though technology for accurate articulatory measurements has existed for years, few studies have investigated articulation during voice disguise. In this pilot study, we recorded articulation of four speakers during regular and disguised speech using electromagnetic articulography. We analyzed imitation of foreign accents as a voice disguise strategy and utilized functional t-tests as a novel method for revealing articulatory differences between regular and disguised speech. In addition, we evaluated discovered articulatory differences in the light of the performance of an x-vector-based automatic speaker verification system.
Soft and Hard Filter Pruning via Dimension Reduction

Filter pruning is widely used to reduce the computation of deep learning, enabling the deployment of Deep Neural Networks (DNNs) in resource-limited devices. Conventional Hard Filter Pruning (HFP) method zeroizes pruned filters and stops updating them, thus reducing the search space of the model. On the contrary, Soft Filter Pruning (SFP) simply zeroizes pruned filters, keeping updating them in the following training epochs, thus maintaining the capacity of the network. However, SFP, together with its variants, converges much slower than HFP due to its larger search space. Firstly, we generalize SFP-based methods and HFP to analyze their characteristics. Then we propose a Gradually Hard Filter Pruning (GHFP) method to smoothly switch from SFP-based methods to HFP during training and pruning, thus maintaining a large search space at first, gradually reducing the capacity of the model to ensure a moderate convergence speed. Furthermore, we view filter pruning as dimension reduction and propose a novel dimension reduction block integrated into GHFP to significantly outperform other methods by a moderate margin.
Leveraging GANs via Non-local Features

Recent years, Generative Adversarial Networks (GANs) have achieved tremendous success in image synthesis, which usually employ the convolutional operation to extract image features. However, most existing convolutional GANs only extract features in a local neighborhood at a time, which may often cause a lack of non-local information resulting in generating the wrong semantic object in the wrong position. In this paper, we propose a Graph Convolutional Architecture (GCA) for GANs to tackle this problem. GCA constructs a pixel-level graph structure between image regions through an attention mechanism and leverages Graph Convolutional Networks (GCNs) to extract non-local features. GCA extracts the connections between different regions of the image through GCNs, which is a more effective method of using relationship information than directly adding long-range dependencies to the model. We implement the GCA into Deep Convolutional Generative Adversarial Networks (DCGAN), Self-Attention Generative Adversarial Networks (SAGAN), and Concurrent-Single-Image-GAN (ConSinGAN). Extensive experiments are conducted to verify the performance of GCA. The results demonstrate that the GCA can significantly boost the quality of the generated image with more non-local features.
Eye Movement Detection using Histogram Oriented Gradient and K-Nearest Neighbors

Hand defects will hinder human activities in interacting. In general, people with hand defects will replace the function of the hand with doing an activity in other organs, such as the feet, head, or eyes. Eye movement technologies have been used for different purposes in various industries, from medical, gaming, controlling display menu to assistive technology for people with disabilities. The development of new technologies focused on disability is vital as it improves the quality of life and incorporation into society. Therefore, eye movement detection can later be developed to select menus from the LCD screen. Detection of eye movements is divided into four classes, namely looking up, down, left, and right. The menu on the LCD screen contains options of activities such as calling the nurse, choosing a food menu, and going to the toilet, making it easier for nurses to understand the user's activities. This system is made using Histogram Oriented Gradient combining with Haar Cascade for eye detection and K-Nearest Neighbors as a classifier. Finally, this work shows that the KNN can be used to classify with an average accuracy of 83.33%.
A Sentiment Analysis of a Boycott Movement on Twitter

Sentiment analysis refers to determining emotional content from a textual input. The Internet world is now run by the term “Web 2.0,” and one of the major platforms that made this web to web 2.0 is Twitter, a microblogging social network where one can post short messages known as tweets. The general public uses Twitter social networking platform very often to post opinions on various topics ranging from reviews to current affairs. And one of the trending topics in India in the year 2020 was the boycotting online shopping services such as Amazon and Flipkart due to issues related to political clash between India and China and also issues related to nepotism in Bollywood. The border issue between India and China was a big talking point as it involved clashes between the Chinese and Indian soldiers which resulted in few martyrs. This made a major public anger toward China and its products which then resulted into the boycott movement. The other talking point was the nepotism in Bollywood. The recent suicide of Sushant Singh Rajput has brought out the public sadness and blamed Bollywood nepotism as the result of his suicide. This resulted in public boycotting products that were endorsed by actors/actresses that were having a favoritism which was granted by their relatives in the Bollywood industry. Hence, we decided that it was a great opportunity to extract tweets related to this boycott movement and do sentiment analysis on it to determine various emotions expressed by the general public.
Designing Individual Optimistic and Pessimistic Emotional Tendency Identification System Based on Digital Image Processing

Handwriting analysis, commonly referred to as Graphology, can reflect a person's personality because writing movements are controlled by the brain, which contains memories about various life experiences and stored in the subconscious. Currently, the process of identifying human personality through handwriting or Graphology is still performed manually. This process requires a reference book to analyze every aspect of a person's handwriting. As well as the baseline pattern of handwriting, still performed manually to decide whether it tends to be up, down, or straight. In this paper, the aspects studied were the primary writing lines to identify a person's personality traits and characteristics towards emotional individuals with optimistic and pessimistic characters. The test is carried out using the method classification of the ArcTan geometric formula to determine the angle of slanted of the line basic handwriting. System inputs were using handwriting samples obtained from 42 subjects, ranging from 19–27 years old. The system was designed to identify two classes of emotions, which are optimistic and pessimistic. Then three essential line aspects of handwriting, namely tend up, tend down, and straight, were classified according to the arctan geometric formula. The accuracy of this graphology system is 90.47%; it can be concluded that the system successfully identifies handwriting per 1 line or 1 page of HVS paper.
A Probabilistic Deontic Logic

In this article, we introduce a logic for reasoning about probability of normative statements. We present its syntax and semantics, describe the corresponding class of models, provide an axiomatization for this logic and prove that the axiomatization is sound and complete. We also prove that our logic is decidable.
Texture Mapping of Flags onto Polandball Characters using Convolutional Neural Nets

Polandball comics are hand-drawn satirical content that portray personified countries in a unique style. Although certain parts of these comics, such as ball outlines, are easy to draw, some country flags are complex and require time, effort, and skill to depict correctly. Convolutional Neural Networks have shown success in image synthesis tasks but lack the ability to rescale and rotate images for texture mapping. The domain of Virtual Try-On Networks has made great progress in networks that can handle spatially invariant transforms. We show that similar methods can be used in another domain dependent on texture mapping, namely generating valid, rule-abiding Poland-ball characters given an outline and a country flag. To evaluate our method we make use of the Fréchet Inception Distance where we achieved a score of 34.9. Multiple configurations of the model were evaluated to show that all modules used in the model contribute to the achieved performance. The main contributions in this paper are: a model that can be used by Polandball artists to aid in comic creation and a dataset with over 40,000 labeled Polandball characters for computer vision tasks.
Encoding true second‐order arithmetic in the real‐algebraic structure of models of intuitionistic elementary analysis

Abstract Based on the paper [4] we show that true second‐order arithmetic is interpretable over the real‐algebraic structure of models of intuitionistic analysis built upon a certain class of complete Heyting algebras.
The Standard of Scientific Pedagogical Expertise: a Creative Dimension

Pedagogical expertise is considered as a system-forming scientific technology in education, which has a significant impact on the practice of teaching and the formation of a creative personality, actually becoming a method of scientific cognition in the context of improving the quality of education. It is advisable to design all programs of higher pedagogical education according to the deductive principle-from the general content component presented in the structure of technologies of pedagogical expertise of educational projects and programs, to private methodological practice-oriented recommendations proposed by experts as an indicative basis for teacher actions.&#x0D; The role of an expert and his personal qualities are of particular importance in the composition of pedagogical expertise. Based on the professional analysis of the expert's activity, his personal portrait is formed. In pedagogical expertise, the ratio of the objective (various normative documents regulating the activities of an expert) and the subjective is crucial, when the quality of the examination depends on the competence, experience of an expert specialist, his wisdom, intuition and creative insight. The analytical review of the mistakes of pedagogical experts should be used as the basis of the methodology for training teachers and expert specialists in the field of education.