Reviewerly/exHarmony huggingface dataset:
Smart Glasses: A Visual Assistant for the Blind

Computer vision has helped systems gain high-level understanding in the field of image and video processing. The Smart Glasses allows partially blind and partially sighted individuals to identify and understand the workplace tools that surround them, which they can see through mini camera. Our research aims to utilize the computer vision to detect objects using MS COCO dataset and trained a CNN (Convolutional Neural Network) model. It recognizes faces using deep learning approach. It recognizes text using EAST (Efficient Accurate Scene Text Detector) and EASYOCR models and gives output using Festival Speech synthesis. The glasses are provided by Ultrasonic sensor which is used to measure the required distance between the users and object to avoid obstacle. The Smart glasses start detecting using the wake word “UP” which is trained using CNN and TensorFlow and Vosk speech recognition module for simple commands. The system is a complete visual assistance for the blind.
The philosophy of logical practice

While we now have an increasingly detailed understanding of the varied goals and methods that constitute the sciences and mathematics, our understanding of logic as a research area lags behind. A significant reason for this deficiency is that, unlike in the philosophies of science and mathematics, philosophers of logic have yet to embrace a practice‐based approach to their field, re‐orientating their attention towards logic as it is actually practised by logicians. This paper makes the case for a new area of research, the philosophy of logical practice , to sit alongside traditional philosophy of logic and systematically investigate logic's aims and methodologies using the practice‐based approach.
Automatic fall detection by using Doppler-radar and LSTM-based recurrent neural network

Falls are a major public health concern among seniors living independently at home. This article presents a new approach for automated real-time fall detection by a single continuous-wave micro-Doppler radar and deep learning. Unlike related methods, we apply Long Short-Term Memory based recurrent neural network (RNN) to time-frequency spectrograms of Doppler-radar returns to identify falls. The approach does not require extra hardware or big data set to classify falls from non-fall actions and has superior accuracy compared to CNN-based fall detection.
XOR-based visual cryptography scheme with essential shadows

Visual cryptography scheme with essential shadows (EVCS) is of great significance since it provides different levels of the importance to shadows. In this paper, we propose a general construction method for (t, s, k, n)-VCS with essential shadows based on XOR operation ((t, s, k, n)-EXVCS), which originates from the partition of access structure. The secret image is encrypted into s essential shadows and n-s non-essential shadows. Any k shadows including at least t essentials can cooperate to decode the secret image and the decoding process is implemented by XOR operation on the involved shadows. Our scheme achieves perfectly reconstruction of secret image in the revealed image and the less size of shadows and revealed images. The experiments are conducted to testify the feasibility and practicability of the proposed scheme.
Strain Measuring of Composite Grid Using Digital Image Correlation

Digital Image Correlation (DIC) is a new advanced technique for measuring the deformation of a specimen using high-resolution images. It has been used by numerous researchers since it can measure the deformation of specimens without interference because it is contactless. Moreover, the DIC technique can be applied to any materials to which normal measuring equipment is difficult to attach such as Fiber-Reinforced Polymer (FRP) grid in this paper, undulated and small surfaces. The DIC technique is easy to set up and provides reliable results compared to conventional equipment like a strain gauge. Although it is good, its associated equipment is too expensive to be readily affordable. Hence, this paper uses an open-source DIC program called Ncorr that works in MATLAB to analyze deformations of six FRP grids from direct tensile tests by comparing their results to the results from a strain gauge. Young’s modulus—calculated from ASTM 3039, ACI 440-3R-04, and regression analysis—of each specimen will be used for comparison. The results show that the difference between Young’s modulus from DIC and strain gauge is &lt;5% if the stress–strain graph of data from the strain gauge is perfectly linear without straying.
A Deep Learning-Based System for Document Layout Analysis

Document image understanding is an essential process in the digital transformation era. Those systems automatically convert a paper document to a digital document for storing and information extracting. In practice, document layout analysis is a critical step for the success of document image modeling. This paper introduces a page segmentation system based on deep neural networks. Our system uses two auto encoder-decoder networks to segment the text-line and non-text components simultaneously. The paragraph segmentation is then realized based on the text line and separator mask. Besides, the non-text elements are also identified. Our algorithm has been tested RDCL2019. Experimental results show that our method is more stable and more comfortable to adapt with a new format layout than the previous commercial and publishing systems.
Memory-aware and context-aware multi-DNN inference on the edge

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted by executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. It is of paramount importance to guarantee low response times of such multi-DNN executions as it affects not only users quality of experience but also safety. The challenge, largely unaddressed by the state of the art, is how to overcome the memory limitation of edge devices without altering the DNN models. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution and scheduling framework, which requires no modification of DNN models. The aim of Masa is to consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. The enabling features of Masa are (i) modeling inter- and intra-network dependency, (ii) leveraging complimentary memory usage of each layer, and (iii) exploring the context dependency of DNNs. We verify the correctness and scheduling optimality via mixed integer programming. We extensively evaluate two versions of Masa, context-oblivious and context-aware, on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.
Decentralized finance (DeFi): The Future of Finance and Defi Application for Ethereum blockchain based Finance Market

This article examined how to work transactions that are stored in the digital ledgers are recorded in the Blockchain. It stores all the valuable data about the user's transactions. Results show that Blockchain has made the financial sector to great peak and got a good reputation among the people who thought it would not work well. By the use of cryptocurrency in the blockchain technology, the Ethereum market has more rise into the second-largest cryptocurrency platform in 2021. Furthermore, a key aspect of the user's gain on share markets as investments when its value goes peak. Due to a dilemma, in decentralized financial [Defi], Many of the users felt insecure while joining into the network. But after the use of transactions in the blockchain, Through serial investigational factor study, the recentlygrow scale was adapt to involve seven main characteristics of team-development intervention (i.e., Decentralization, immutability, transparency, persistency, auditability, security, and privacy). Many of the organizations came forward to implement blockchain technologies in the Ethereum platform to do the decentralized financial [Defi] exchanges. In this article, the theoretical and practical implications of building an application for financially exchanging the bitcoins (Crypto-coins) by using the compound protocol. This protocol makes our application highly secured and confidential in exchanging the Crypto-coins by following the rules.
Improved segmentation of overlapping red blood cells on malaria blood smear images with <scp>TransUNet</scp> architecture

Abstract Malaria is a serious disease that especially affects developing countries. Resource constraints in developing countries make automated and expert replacement systems particularly important. The machine learning algorithms were studied to provide this automation where malaria parasites are detected and counted. The machine learning pipeline for malaria parasitemia classification includes segmentation and classification steps for end‐to‐end assessment. In the segmentation step, red blood cells (RBCs) are segmented for individual evaluation of RBCs. However, it is a challenging task in case of overlapping RBCs. In the proposed study, the segmentation task was studied. The purpose of this work is to improve the segmentation of overlapping RBCs. To this end, CNN‐transformer hybrid architecture, TransUNet was introduced to improve the segmentation with the help of labeled data that promotes the separation of overlapping red blood. The proposed work achieved a 94.5% Jaccard similarity index. This surpassed the results of previous approaches.
Provable training of a ReLU gate with an iterative non-gradient algorithm

In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can train a ReLU gate in the realizable setting in linear time while using significantly milder conditions on the data distribution than previous such results. Leveraging certain additional moment assumptions, we also show a first-of-its-kind approximate recovery of the true label generating parameters under an (online) data-poisoning attack on the true labels, while training a ReLU gate by the same algorithm. Our guarantee is shown to be nearly optimal in the worst case and its accuracy of recovering the true weight degrades gracefully with increasing probability of attack and its magnitude. For both the realizable and the non-realizable cases as outlined above, our analysis allows for mini-batching and computes how the convergence time scales with the mini-batch size. We corroborate our theorems with simulation results which also bring to light a striking similarity in trajectories between our algorithm and the popular S.G.D. algorithm - for which similar guarantees as here are still unknown.
A Supporting Tool for Enhancing User’s Mental Model Elicitation and Decision-Making in User Experience Research

User Experience (UX) research is intended to find insights and elicit applicable requirements to guide usable designs. Card Sorting is one of the most utilized methods. It is used to uncover the user's mental model and increase the usability of existing products. However, although Card Sorting has been widely utilized, most applications are based on spreadsheets. Furthermore, existing tools are principally intended to obtain qualitative information or customized quantitative outcomes to improve the information architecture. In this paper, a supporting tool based on the Card Sorting method is presented and detailed, including a comprehensive use case showing the main features. The tool implements predictive analysis of results through advanced statistics and machine learning techniques, providing comprehensive reports that enable evaluators and UX researchers to obtain high-level knowledge and important quantitative clues to enhance decision-making. The tool has been evaluated with participants and evaluators, obtaining relevant usability results and feedback.
Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.
Knowledge Base component of Intelligent ALMM System based on the ontology approach

This paper presents the implementation of a knowledge base supporting an intelligent system to solve problems of optimization especially problems of discrete production processes optimization called Intelligent Algebraic-Logical Meta-Model (ALMM) Solver. Using a unified description of selected optimization problems, an ontological knowledge base was designed, which allows for selective selection of Intelligent ALMM Solver components necessary to solve and model problems. Using the definitions of the properties of optimization problems, scalable components describing exemplary optimization jobs were selected. Ontology for this area was developed, with particular emphasis on the requirements of the ALMM Solver. Using the possibility of interactive communication with the ALMM ontology in the form of SQL queries in the experimental part of the work, exemplary queries for the designed Knowledge Base (KB) module were presented, and the response generated by the system is a scenario of intelligent selection of a set of components modeling and solving a given problem. Such an innovative approach allows for dynamic construction of algorithms solving problems of discrete optimization. The use of knowledge about the properties of the considered processes and ALMM technology universalizes the proposed KB system making it an intelligent and efficient tool for solving discrete optimization jobs. The key advantage of the proposed ontological approach is the ability to flexibly expand it and extend its use to other classes of problems which have already been described in the ALMM technology.
An Intelligent IoT enabled Health Care Surveillance using Machine Learning

Over the last decade, health care enabled services and accompanying advanced technology have been the subject of much research. Medical equipment, sensors, and healthcare professionals may all be linked through the Internet of Things (IoT) to deliver high-quality medical services everywhere. Patient safety has improved as a consequence, health care costs have decreased, access to health care has grown, and the sector as a whole has become more efficient and productive. According to recent study, Internet of Things (IoT)-based technologies offer enormous promise for enhancing health care. The most effective data analysis methods now use machine learning-assisted techniques. Due to the conductivity of the top three layers, intelligent applications and services such as sickness detection, behavioural recognition, and intelligent support may be supplied. This research examined medical IoT improvements in terms of enabling technology, health care services, and implementations to address a variety of health care challenges. Additionally, potential issues and concerns about the Health Care IoT system are explored. As a consequence, future researchers interested in investigating and discovering new applications for health care IoT will have a credible resource for learning about the technology's many applications.
Super Learner for Malicious URL Detection

Malicious Uniform Resource Locator (URL) detection is one of the prominent research areas in Cyber security. Machine learning and statistical models are mainly used for this task due to their ability to adapt complex patterns. This research study mainly focused on implementing a machine learning classifier model using Super Learner ensemble to classify malicious URLs. Static feature set is extracted using only the URL information with less latency and reduced computational complexity to support offline and real-time detection. Proposed binary classifier model is used to separate malicious URLs from benign ones whereas the proposed multi-class classifier model separates URLs into benign and multiple categories of attacks (phishing, malware, spam and defacement). These classifiers are tested on a dataset comprising around 750,000 URLs. The empirical results show that the proposed model works well in malicious URL detection. The binary classifier provides 95.145% accuracy and 96.844% precision whereas the multi-class classifier provides 94.69% accuracy and 96.234% precision. Also, the comparison results show that the proposed model outperforms leading supervised machine learning algorithms in malicious URL detection.
Predicting Personalized Recommendations using GNN

The suggestion calculation is the main part of building up a fruitful modified learning framework. Graph neural network- based recommender systems have had similar success. This suggested project uses Graph Neural Networks to provide tailored Top-N recommendations (TP-GNN). This has been extensively researched in order to assist users in locating interesting products. It's likewise conceivable to utilize neural shared separating to extricate significant elements of successive examples with the current arrangement of items. Despite the fact that these strategies have achieved good results, they have two fundamental flaws. These models rarely learn a precise representation of item structural relationships. Second, most Top-N customized suggestion models focus on the client's overall inclinations over ongoing things. Investigates a genuine world dataset of film focal points shown that NGCF could further develop results. To adapt to the client's arrangement of neighbors, two unique accumulation strategies and a consideration component are used to assemble the last thing portrayal. NGCF, GNN, GCN, and inserting are a portion of the terms utilized in this paper.
Target localization based on cross-view matching between UAV and satellite

Matching remote sensing images taken by an unmanned aerial vehicle (UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset (University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.
Privacy-Preserving Vertical Federated Logistic Regression without Trusted Third-Party Coordinator

Federated learning is a new distributed learning paradigm, which allows multiple parties to cooperatively train a centralized model without sharing their data. In this paper, a privacy-preserving logistic regression (LR) training algorithm for vertical federated learning (VFL) is proposed. First, this paper analyzes the related works and point out the privacy leakage risks. Then, based on the mini-batch SGD and parameter encryption method, a secure VFL model training scheme for LR without the assistance of a trusted third-party is designed. Next, to protect the privacy of model parameters, a differentially-private algorithm and comprehensive privacy analysis are provided. Finally, experiments show that the algorithm not only guarantees the security and privacy, but also ensures the model utility.
Nonconvex clustering via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math> fusion penalized regression

Cluster analysis has attracted widespread attention in the past several decades. Generally speaking, clustering is considered as an important unsupervised learning method because its goal is to discover unknown subgroups in data without category label information. In this paper, we propose the ℓ0 fusion penalized clustering model (ℓ0-PClust), which is a novel clustering framework founded on the penalized regression method. Theoretically, we first analyze the existence of the optimal solutions of our model and deduce an upper bound of the tuning parameter. Then we define the Karush-Kuhn-Tucker point and P-stationary point of the ℓ0-PClust model, and establish the relationship between them and local optimal solutions. Moreover, based on the P-stationary point of the ℓ0-PClust model, we prove that the distances among different cluster centers are greater than a positive threshold. Computationally, we solve the ℓ0-PClust model via the famous alternating direction method of multipliers, whose limit point is a P-stationary point and local optimal solution of the model. Finally, we conduct extensive experiments on both synthetic and real data sets. Experimental results show outstanding performance of our method in comparison with several state-of-the-art clustering methods.