Reviewerly/exHarmony huggingface dataset:
Secure Storage of Land Records and Implementation of Land Registration using Ethereum Blockchain

Globally, land registration and maintenance process being endured with several issues such as frauds, time consumption and maintenance based cumbersome issues. The traditional approaches and methods of land registration involves many technical gaps. The major goal of this work is to strengthen the land registration procedure in terms of transparency, time, money and reduce instances of fraud. It is feasible to track the hand-to-hand transfers of property using inbuilt featured Blockchain technology. The immutable, transparency, and distributed features of blockchain based transactions make the system as deception less with more feasible to validate the lands as well. This proposed idea helps in transfer of land in more secured way. Moreover, will speed up the registration procedure and be useful in transferring land ownership from the previous owner to the new owner. HTML, CSS, JavaScript, MetaMask, Ganache and Solidity are the technologies used to develop the system.
Wearable Device based Fall Prediction and Alert Mechanism for Aged People using IoT Technology

In actuality, falls increase exponentially with age or with older biological changes, leading in a high rate of falls and eventually fall injuries in ageing populations. Assistive technologies that may aid in the relief of this serious health condition are a societal priority in this environment. The bulk of orthodox medicine alert measurement techniques depend on a pendant or key that the beloved one should press in order to seek help. Such devices are immensely effective and to save life every day. Some elders, however, are at a significantly greater risk than many others. If the beloved person falls, this also will be unable to activate the emergency alert system and request help. This is when Intelligent Fall Prevention comes into play. The benefits of automatic fall detection for elderly people can be tremendous. Additionally, if you or a beloved person suffers from mellitus or some other ailment that raises the chance of falling; this feature may provide you with additional peace of mind. If the loved one is hurt or becomes unconscious as a consequence of a fall and is alone, automatic fall detection equipment significantly improves the chances of obtaining immediate assistance.
Identification of Counterfeit Indian Currency Note using Image Processing and Machine Learning Classifiers

Technology is continuously changing our life. Day by day, it makes our life easy, but some challenges and issues exist. Counterfeit currency is one of them. It happens because of the production and circulation of currency without the permission of an authorized system. Some people use scanning and printing technology to produce such notes and circulate them around us, which is a kind of forgery.It leads to personal loss and degrades the Country's economy. Such notes are very similar to the original, which becomes a problem for ordinary people to identify the authenticity of the currency, especially for visually impaired people. However, most researchers proposed different methods to differentiate real notes from fake ones based on the currency's shape, colors, and size. The notes are easily detected when the condition of the note is good but difficult when it deteriorates over time. Extracting features from such notes is another challenging task. Some systems are available to only specific sectors and not easily available to common people. Therefore, a system that can distinguish between real and fake notes is required. This article classifies Indian Currency notes as real or fake with four supervised Machine Learning algorithms i.e., Support Vector Classifier, K-Nearest Neighbor, Decision Tree, and Logistic Regression followed by Image Processing techniques. This study has implemented these algorithms on the dataset of 1372 currency image samples, out of which 762 are real, and the remaining are fake, available on the UCI machine learning repository. Further, the performance of all the algorithms is measured in terms of accuracy, recall, Precision, and F1-score and after analyzing, it is observed that the K-Nearest Neighbor leverage outstanding results compared to other algorithms.
A Study on Digital Signature in Blockchain Technology

In the recent trends, block chain technology plays a vital role due to its secured methodology. Since many parts of the world severely affected by COVID pandemic situation, people used to perform more of digital transactions day by day. To provide more security for all online transactions, Block chain Technology is one of the solutions that many countries have started to utilize. Every block in a block chain should be validated and verified by the secured hash algorithms and digital signatures. Since Hacking is difficult in Block Chain Technology, due to its secure hash process, many people started using it for various applications. This article discusses about the working of Digital Signatures especially about Elliptic Curve Digital Signature Algorithm - ECDSA. Digital signatures are signatures done digitally for the purpose of security. ECDSA works on the mechanism of Elliptic Curve and Cryptography algorithms of Elliptic curve. The proposed research work has selected the private key as a random point, which is of type integer ranging between 1 and n-1 in Elliptic curve to sign documents. Then Public key is calculated by multiplying a point from elliptic curve with the Private key. Using this Public key, our digital signature is verified. This is much secured because no one can crack/hack private key. This process is used to verify digital transactions in the block chain technology. Our study helps researcher in block chain technologies to proceed ahead.
Voice E-Mail Synced with Gmail for Visually Impaired

By combining communication technology with the internet, communication has gotten significantly simpler in the modern day. Additionally, visually impaired users find this technology challenging to utilize since it relies on visual perception to view and comprehend information. Because they need more practice utilizing the available systems than average users do, visually impaired clients cannot utilize this software. The goal of this article is to create an email programme that will make communication services accessible to visually impaired people of all experience levels. The application forbids the person from using the keyboard keys and instead relies solely on mouse movement and speech-to-text conversion. Additionally, any typical person, such as someone who is illiterate, can use this system. The application is entirely reliant on interactive voice response, making it simple to use and effective. Since, it is built entirely on INTERACTIVE VOICE RESPONSE(IVR), the system will be extremely convenient and easy to operate.
Open-Source Software Security Challenges and Policies for Cloud Enterprises

Cloud computing stimulated the development of agile software. The new oil is software. More than 70-90% of the software is open-source, and its usage is inevitable. Open source encourages innovation through collaboration, reduces Time-To-Market, and fuels breakthrough technologies from the past few decades. In a way, open source is eating software or driving the Software world. Open source communities involve more contributors /developers, sometimes posing substantial security challenges. Recently, we have witnessed SolarWinds compromising the entire supply chain, Log4j allowing access to execute code with critical zero-day vulnerability remotely. The digital universe paused because these zero-day vulnerabilities exploded as an outcome. In this work, we will highlight challenges and propose an approach to help organisations protect the code base by safely consuming the Open Source.
Stealing Secrecy from Outside: A Novel Gradient Inversion Attack in Federated Learning

Knowing model parameters has been regarded as a vital factor for recovering sensitive information from the gradients in federated learning. But is it safe to use federated learning when the model parameters are unavailable for adversaries, i.e., external adversaries’ In this paper, we answer this question by proposing a novel gradient inversion attack. Speciffically, we observe a widely ignored fact in federated learning that the participants’ gradient data are usually transmitted via the intermediary node. Based on this fact, we show that an external adversary is able to recover the private input from the gradients, even if it does not have the model parameters. Through extensive experiments based on several real-world datasets, we demonstrate that our proposed new attack can recover the input with pixelwise accuracy and feasible efficiency.
FLForest: Byzantine-robust Federated Learning through Isolated Forest

Federated learning (FL) is a privacy-preserving distributed machine learning technique that allows clients to jointly train a global model under the coordination of cloud server. However, malicious clients can corrupt the global model to predict incorrect labels for testing examples. Currently, existing mainstream Byzantine-robust FL methods are vulnerable to various adaptive attacks, and violates the privacy principle of FL. Moreover, these schemes will be less robust in the face of targeted poisoning attacks with few samples and data distributions that are highly non-independent and identically distributed(non-IID). In this work, to address these issues, we propose a novel Byzantine-robust FL framework based on Isolated Forest. Specifically, before the start of each round, FLForest will calculate the divergences between the model update and the model update of the previous round to decide whether to activate the defense. After that, FLForest trains an isolated forest based on model updates after after dimensionality reduction. Model updates isolated with fewer splits will be considered as malicious model updates and excluded from the global model’s aggregation. Extensive experiments demonstrate that FLForest achieves better performance compared to baseline methods under highly non-IID distribution.
Slovak Dataset for Multilingual Question Answering

SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It consists of more than 91k factual questions and answers from various fields. Each question has an answer marked in the corresponding paragraph. It also contains negative examples in the form of "unanswered questions" and "plausible answers". The dataset is published free of charge for scientific use. We aim to contribute to the creation of Slovak or multilingual systems for generating an answer to a question in a natural language. The paper provides an overview of the existing datasets for question answering. It describes the annotation process and statistically analyzes the created content. The dataset expands the possibilities of training and evaluation of multilingual language models. Experiments show that the dataset achieves state-of-the-art results for Slovak and improves question answering for other languages in zero-shot learning. We compare the effect of machine-translated data with manually annotated. Additional data improve the modeling for low-resourced languages.
Modeling Cross-blockchain Process Using Queueing Theory: The Case of Cosmos

In order to solve the interconnection and intercommunication problem of a large number of co-existing blockchains, such as public chains, private chains, and consortium chains, cross-chain technology has recently become a hot research topic among scholars years. Due to the limited processing speed of cross-chain transactions, too many cross-chain transactions in the short term may cause network congestion and negatively affect cross-chain performance. Therefore, it is necessary to evaluate and optimize the performance of the cross-blockchain transaction process. This paper takes a typical cross-blockchain model Cosmos as an example and proposes a queuing theoretical model based on limited space. The difference equation is established through the three-dimensional continuous-time Markov process, and performance metrics such as average queue length, transaction execution time, and transaction response time are obtained. Finally, we experimentally simulate the analytical solutions of the relevant performance metrics to verify the effectiveness of the proposed model. We believe this analytical approach can be generalized to other cross-blockchain systems.
Application of Mathematical Optimization in Data Visualization and Visual Analytics: A Survey

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.
Dual Clustering Co-Teaching With Consistent Sample Mining for Unsupervised Person Re-Identification

In unsupervised person Re-ID, peer-teaching strategy leveraging two networks to facilitate training has been proven to be an effective method to deal with the pseudo label noise. However, training two networks with a set of noisy pseudo labels reduces the complementarity of the two networks and results in label noise accumulation. To handle this issue, this paper proposes a novel Dual Clustering Co-teaching (DCCT) approach. DCCT mainly exploits the features extracted by two networks to generate two sets of pseudo labels separately by clustering with different parameters. Each network is trained with the pseudo labels generated by its peer network, which can increase the complementarity of the two networks to reduce the impact of noises. Furthermore, we propose dual clustering with dynamic parameters (DCDP) to make the network adaptive and robust to dynamically changing clustering parameters. Moreover, Consistent Sample Mining (CSM) is proposed to find the samples with unchanged pseudo labels during training for potential noisy sample removal. Extensive experiments demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art unsupervised person Re-ID methods by a considerable margin and surpasses most methods utilizing camera information.
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt

Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite many successful interpretations of transformers on sequential data, little effort has been devoted to the interpretation of ViTs, and many questions remain unanswered. For example, among the numerous attention heads, which one is more important? How strong are individual patches attending to their spatial neighbors in different heads? What attention patterns have individual heads learned? In this work, we answer these questions through a visual analytics approach. Specifically, we first identify what heads are more important in ViTs by introducing multiple pruning-based metrics. Then, we profile the spatial distribution of attention strengths between patches inside individual heads, as well as the trend of attention strengths across attention layers. Third, using an autoencoder-based learning solution, we summarize all possible attention patterns that individual heads could learn. Examining the attention strengths and patterns of the important heads, we answer why they are important. Through concrete case studies with experienced deep learning experts on multiple ViTs, we validate the effectiveness of our solution that deepens the understanding of ViTs from head importance, head attention strength, and head attention pattern.
Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

This research aims to optimise performance in a Fuzzy Linear Quadratic Regulator controller by implementing 2 types of algorithm to stabilise the triple-link ‘Robogymnast’ robotic system. The Robogymnast recreates the movements of a human gymnast, with the hand/arm element of the system securely attached to a high bar with ball0bearing mountings, which can rotate freely. This article explores the complex factors involved in swing-up control in the underactuated Robogymnast and presents a linearisation of the mathematical modelling for the system, considering methods which apply Lagrangian equations in defining state space in such systems. Both the Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithm were used for tuning the hybrid Fuzzy Linear Quadratic Regulator controller for later application with the robot and assessment of response stability. In addition, MATLAB Simulink was used to simulate performance and show the effect of altering parameters including time for rising, settling and overshoot. This work aims primarily to explore how a Linear Quadratic Regulator/Fuzzy Logic controller can be applied with acrobatic robots.
Sketch Assisted Face Image Coding for Human and Machine Vision: A Joint Training Approach

Image coding is one of the most fundamental techniques and is widely used in image/video processing and multimedia communications. Current image coding methods are mainly human-oriented, and the visual quality is always unsatisfactory, especially at low bitrates. Moreover, the recent emergence of machine vision goes beyond the scope of current coding. With these considerations, we proposed a sketch assisted face image coding for human and machine vision by a joint training approach. In the proposed approach, we design a new feature representation: a color sketch, which aims to satisfy both low-frequency features of human vision and high-frequency features of machine analysis. Then, we present a novel end-to-end image codec framework with joint training that consists of three models: an image-to-image translation module, a coding module, and a two-stage reconstruction module. Specifically, the input image is first translated into the edge map with the Canny edge as the auxiliary label to merely preserve the structure information. Afterward, the backpropagation from reconstruction module guides the edge map to increase or decrease the information through joint training, which results in the generation of color sketch. Then, the generated sketch is compressed into the bitstream and decompressed back to a sketch in the coding module. Finally, the decompressed sketch is reconstructed to support the machine and human tasks, respectively. In this way, the color sketch is designed to bridge the gap between human and machine vision, and the joint training strategy helps to adjust the low-frequency information in the sketch. The experimental results on challenge datasets demonstrate that our proposed algorithm offers 40.9%-86.6% bitrate savings on machine vision and is comparable to state-of-the-art image coding methods on human vision.
Long Short-Term Memory Network PV Power Prediction Incorporating Extreme Extreme Gradient Boosting Algorithm

The proportion of photovoltaic (PV) power generation to the total global power generation is increasing. Accurate prediction of PV power generation is crucial to the real-time dispatch of PV power generation. In response to the problems that traditional prediction modeling methods can overfit the situation and also have low prediction accuracy for complex and high-dimensional data, a long short-term memory (LSTM) PV power prediction model incorporating extreme gradient boosting (XGBoost) and attention mechanism is proposed. Firstly, the XGBoost and Pearson correlation coefficient method are used to feature select the data to remove the redundant and unimportant features; secondly, the attention mechanism is set to increase the weight of important features to enhance the model's understanding of features and feature values. The XGBoost-LSTM model is obtained for PV power prediction using trial-and-error method and cyclic selection method, and the experimental results show that the PV power prediction by this method is more efficient and accurate than the traditional LSTM and support vector machine methods.
An improved Bi-LSTM performance using Dt-WE for implicit aspect extraction

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we trained the proposed domain-trained word embeddings (Dt-WE) model using explicit and implicit aspects. Second, interpolate Dt-WE model as a front layer in Bi-LSTM. Finally, extract implicit aspects by testing the trained architecture using the opinionated reviews that comprise multiple implicit aspects. Our model outperforms several of the current methods for implicit aspect extraction (Source Code).
Design of Pedestrian Detection System based on OpenCV

Since the emergence of computer vision, pedestrian detection technology has been widely concerned by scholars at home and abroad. The technology is widely used in real-time monitoring, intelligent transportation and human-computer interaction. Most of the existing pedestrian detection systems have problems such as large program volume, high dependence on operating environment, and inability to guarantee recognition rate in complex situations. In order to improve these problems, a pedestrian detection system based on OpenCV vision library is designed, which uses deep learning framework for reasoning and separates model training from prediction. Firstly, YOlOv5 based on PyTorch framework is rewritten under OpenCV DNN framework, and the core algorithm of target recognition including intersection over union, non-maximum suppression and other modules is written. Then the user interface of pedestrian monitoring system is developed on Windows platform. Finally, multi-scene experiments are carried out to verify that the designed pedestrian monitoring system has high recognition accuracy.