Reviewerly/exHarmony huggingface dataset:
Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing

Digital contact tracing is a valuable tool for containing the spread of infectious diseases. During the COVID-19 pandemic, different systems have been developed that enable decentralized contact tracing on mobile devices. Several of the systems provide strong security and privacy guarantees. However, they also inherit weaknesses of the underlying wireless protocols. In particular, systems using Bluetooth LE beacons are vulnerable to so-called wormhole attacks, in which an attacker tunnels the beacons between different locations and creates false contacts between individuals. While this vulnerability has been widely discussed, the risk of successful attacks in practice is still largely unknown. In this paper, we quantitatively analyze the risk of wormhole attacks for the exposure notification system of Google and Apple, which builds on Bluetooth LE. To this end, we dissect and model the communication process of the system and identify factors contributing to the risk. Through a causal and empirical analysis, we find that the incidence and infectivity of the traced disease drive the risk of wormhole attacks, whereas technical aspects only play a minor role. Given the infectious delta variant of COVID-19, the risk of successful attacks thus increases and may pose a threat to digital contact tracing. As a remedy, we propose countermeasures that can be integrated into existing contact tracing systems and significantly reduce the success of wormhole attacks.
Prosody Transfer from a Small Amount of Voice using Fine Tuning

Text-to-Speech(TTS) is a technology that generates corresponding speech from textual input. A large amount of audio data is needed to build a TTS model generating natural voices. Therefore, if the amount of available data of the target is small, you need to take measures to reduce the amount of data required for training. The purpose of this study is the prosodic transfer of the target speech with a small amount of available data by fine-tuning the base model between the same languages. We investigate how much prosodic information was transferred. As prosodic information, we focus on the speaker&#x2019;s voice quality, and dialect. We evaluate dialect from "pronunciation" in the English TTS model and "intonation" in the Japanese TTS model. At present, it was possible to transfer the voice quality of same-sex speakers from a small amount of voices for about 15 minutes. Regarding the dialect, the difference in pronunciation could not be transferred, but the intonation could be transferred.
Visual Representation Enhancement of Aircraft Recognition Training System in Virtual Reality

This paper presents an enhancement method to improve the immersive degree of an aircraft recognition training project. The project is to train people for recognizing an aircraft and identifying its model, and the project runs on a virtual reality system to train people in a real situation such as aircraft flying (speed, height, distance to an observer) and varying environment (day time, weather). In the results, we present a comparison between the previous version and the enhancement version on (1) the varying aircraft trajectories and (2) the more realistic environments. The varying aircraft trajectories are developed by inserting a mathematical model. The more realistic environments are developed by using a real background of 360-degree camera.
Investigation on continual training of computer-aided diagnosis systems by semi-supervised learning

Medical image analysis systems can help radiologists in reading images accurately and promptly. The systems are expected to be robust to images obtained with any imaging systems by different vendors and at different facilities. However, because of the prevalence of diseases and difficulty of collecting samples with various characteristics, the systems may not apply well to all the images. Artificial intelligence-powered systems are expected to learn from experience and be improved continuously. In this study, we investigated whether continual training with local samples can improve system performance using mammography and lung CT databases. Different training strategies using unlabeled data are compared.
Quantum pulse coupled neural network

Artificial neural network has been fully developed in recent years, but as the size of the network grows, the required computing power also grows rapidly. In order to take advantage of the parallel computing of quantum computing to solve the difficulties of large computation in neural network, quantum neural network was proposed. In this paper, based on the pulse coupled neural network (PCNN), quantum pulse coupled neural network (QPCNN) is proposed. In this model, the basic quantum logic gates are utilized to form quantum operation modules, such as quantum full adder, quantum multiplier, and quantum comparator. A quantum image convolution operation applicable to QPCNN is designed employing quantum full adders and neighborhood preparation module. And these modules are employed to complete the operations required for QPCNN. And based on QPCNN, an quantum image segmentation is designed. Meanwhile, the effectiveness of QPCNN is proved by simulation experiments, and the complexity analysis shows that QPCNN has exponential speedup compared with classical PCNN.
Analysis of Parameterless Particle Swarm Algorithm for Traveling Salesman Problem

Evolutionary Algorithms (EA) are standard search mechanisms that use Natural Selection and Survival of the Best as the fundamental algorithm progressing mechanism. The parameterless portfolio is a special technique designed to resolve various categories of problems without any prior requirement of parameter setting. This technique involves an increase in computational effort that can be considered acceptable. In this work, parameterless swarm algorithm using the method of Particle Swarm Optimization has been defined for the application of Traveling Salesman Problem. The performance of the algorithm through the application of parameterless portfolio has been analysed and it can be deduced that the effort of making the Evolutionary Process parameterless can be justified through the benefits discussed in this work.
Acute coronary syndrome risk prediction by ensemble‐MLPs

Acute coronary syndrome (ACS) is a serious cardiovascular disease. The ACS risk prediction model is of great significance during the hospitalisation of ACS patients. However, traditional machine learning methods are not effective in predicting risk events in the ACS treatment process because of sample imbalance and noise. In this letter, the multilayer perceptron (MLP) and ensemble method are combined and then ensemble-MLPs are proposed, which has made two innovations: 1) increase the diversity of the base MLP classifier at the data level, structure level, and parameter level and 2) improve the ensemble performance by proposing a new ensemble method using f1-score weighted average. Experiments have shown that the proposed method outperforms conventional ensemble MLP method and other traditional machine learning methods on the task of predicting risk events in the ACS treatment process.
Prediction of material properties of propellant waste modified bricks through microstructures by Topographic independent component analysis coupled 3D Convolution neural networks

Deciphering materials properties from the textural information of microstructural images is still a challenge. Proposes a Topographic Independent Component Analysis coupled with 3-D Convolutional Neural Network (TICA- 3DCNN) architecture that processes the features from scanning electron microscope (SEM) images to predict the material properties of high energy propellant (HEP) modified bricks. First, high-energy modified bricks with ten different HEP additives fractions is prepared, and their microstructural information is acquired with SEM. Then, SEM images of each group are pre-processed and subjected to the TICA analysis to decipher the distinct higher-order microstructural dependencies, such as co-activation of components. These microstructural co-activation components are subsequently ordered and passed into 3D CNN architecture to map them with the material properties such as water absorption, compressive strength, density, and porosity. An extreme learning machine is employed as a fully connected classification layer in the 3D-CNN architecture. Finally, the performance of the proposed TICA - 3DCNN approach is compared with the traditional 2D CNN frameworks such as Inception-v4 and Faster RCNN. Proposed TICA-3DCNN architecture has performed with higher sensitivity and specificity than Inception-v4 and Faster RCNN architecture in deciphering microstructural textural properties and mapping them with materials properties. The Proposed model can be used in the construction and building materials to predict material properties through microstructural features.
Neural Bandits for Protein Sequence Optimization

Protein design involves searching over a large combinatorial sequence space. Evaluating the fitness of new protein sequences often requires wet-lab experiments that are costly and time consuming. In this paper we propose a neural bandits algorithm that utilizes a modified upper-confidence bound algorithm for accelerating the search for optimal designs. The algorithm makes adaptive queries as guided by the kernelized neural bandits. The algorithm is tested on two public protein fitness datasets, the GB1 and WW domain. For both datasets, our algorithm consistently identifies top-fitness protein sequences. Notably, this approach finds a diverse and rich class of high fitness proteins using substantially fewer design queries compared to a range of alternative methods.
Classifying Documents based on Formal and Informal Writing Styles using Machine Learning Algorithms

With the advancement of the field of education, sufficient information needed for education and most important things has become available on the internet. Students and scholars need a variety of formal and informal documents for their education purpose but, a large amount of data make it difficult to filter useful information from the internet. Therefore, these documents need labels for students and scholars who are engage in education to use the documents efficiently. As a result, document classification helps to assign a label to formal and informal documents. Text classification according to formal and informal styles is challenging for obtaining good accuracy, as linguistics differences are rich. This article proposed a document classification method based on formal and informal styles. This experiment used 200 text documents that were collected from the web targeting main two categories formal documents and informal documents. After preprocessing the text documents extract the feature vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, and they are converted into numerical representation for adoption to the training model. The classification used Decision Tree (J48), Random Forest, Multilayer Perception (MLP), and Support Vector Machine (SVM) and it is tested with 5 folds cross-validation. Based on the experiment results of four classification algorithms, it indicates that the proposed approach using a Random Forest algorithm can classify the data with 94.97% accuracy with height precision, recall, f-measure values, and lowest error when comparing with the other algorithms.
A Novel Approach for Analytical Comparison of Cryptocurrencies using Time Series

Cryptocurrencies gaining popularity as a digital currency in today’s world. The market is altering dramatically as a result of the rapid increase in investment in digital currency. The ability to accurately forecast and change the behavior of digital currency is becoming a critical aspect in today’s digital environment. This prompted us to investigate bitcoin trading and investor adoption in the market. We began by examining and analyzing the costs of currencies, and after reviewing numerous methods, one of them proved to be the ideal technique for our data representation, taking into account various criteria such as date, time, and year. A time series forecasting technique has been implemented for forecast the future profit or loss in currencies.
Exploring Twitter Messages during the COVID-19 Pandemic in Sri Lanka: Topic Modelling and Emotion Analysis

The tremendous growth of social media empowers the rapid dissemination and amplification of information by becoming the most influenceable communication tool with a significant extent of human lives. People extensively use those platforms during the outbreak of the COVID-19 to cope with social distancing and isolation by expressing their opinions, views, and issues they are facing. The study used Twitter as the social media platform to discover the emotional perspective of Sri Lankans during the outbreak. The emotion analysis was conducted based on 8-scale emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) provided by the NRC Emotion Lexicon. The extraction of noteworthy topics propagated in different domains of COVID-19 helps to identify the perception of society during the pandemic. Latent Dirichlet Allocation was applied to explore relevant and accurate topics. Furthermore, the wordcloud was generated by representing the most insightful data points which help to keep track of social concerns or viewpoints. Our experiment shows that people discuss under three major themes of "COVID-19 Immunization", "Government rules and regulations to prevent the spread of COVID-19" and "COVID-19 related health statistics". Furthermore, the "fear" is dominated among eight emotions followed by "anticipation" and "sadness" when Sri Lankans express their thoughts and feelings related to COVID-19. There is a noticeable decrease in "joy" which is the least expressed emotion followed by "trust". Hence, clearly defined, these findings support in predicting the COVID-19 related outcomes and addressing the concerns, emotions, and reactions of citizens by the government and the policymakers.
Three Phase AC Induction Voltage Controlled Motor Speed Control

A wide range of appliances and industrial controllers rely on three-phase induction motors. Capacitor with a long-term split. The three-phase induction motor is the most common and widely utilized induction motor design. By their very nature, three-phase induction motors are one-way devices. It is possible to alter the rotational motion by either adding more windings and auxiliary switches and switches, or employing gear mechanisms. Gear for the forward and backward engagement and disengagement are engaged and disengaged by mechanical means when gears are utilised. Relays and switches are used to electrically reverse the orientation of the beginning winding depending on the desired direction. A simple ON and OFF control system would be far less expensive if some of these components were removed. An investigation into how to regulate the speed of three-phase induction motors in both directions using a PIC16F877A microcontroller and a 3-Phase Inverter Bridge utilising the PWM approach is the focus of this paper's research. PIC16F877A microcontroller-based control of three phase induction motors is simple to implement and allows for two-way control of the motor. Flexible and efficient control may be achieved by employing a three-phase inverter bridge. You may use a Microchip PIC16F877A to build the control algorithm on a low-cost general purpose microcontroller.
A Fast Method for Protecting Users’ Privacy in Image Hash Retrieval System

Effective search engines based on deep neural networks (DNNs) can be used to search for many images, as is the case with the Google Images search engine. However, the illegal use of search engines can lead to serious compromises of privacy. Affected by various factors such as economic interests and service providers, hackers and other malicious parties can steal and tamper with the image data uploaded by users, causing privacy leakage issues in image hash retrieval. Previous work has exploited the adversarial attack to protect the user’s privacy with an approximation strategy in the white-box setting, although this method leads to slow convergence. In this study, we utilized the penalty norm, which sets a strict constraint to quantify the feature of a query image into binary code via the non-convex optimization process. Moreover, we exploited the forward–backward strategy to solve the vanishing gradient caused by the quantization function. We evaluated our method on two widely used datasets and show an attractive performance with high convergence speed. Moreover, compared with other image privacy protection methods, our method shows the best performance in terms of privacy protection and image quality.
A Complete hands-free electric powered wheelchair for Quadriplegic Individuals and Home automation using IoT

This paper portrays a natively created without hands wheelchair for actually crippled persons. [1] The proposed gadget works dependent on the Head Gesture Recognition method utilizing an Acceleration sensor. Customary electric fuelled wheelchairs are normally constrained by joysticks or hand motion innovation, which can't satisfy the necessities of a nearly totally incapacitated individual who has confined appendage developments also, can scarcely move or turn his head as it were. The speed increase sensor is utilized for the head motion acknowledgment and RF (radio recurrence) module is utilized for the shrewd remote controlling (controlling electrical machines) and furthermore incorporates a call work utilizing receivers and speakers. With the difference in head motion, information is shipped off the micro controller-based engine driving circuit to control the development of the Wheel Chair in five distinct modes, specifically FRONT, BACK, RIGHT, LEFT and an exceptional locking framework to STAND still at someplace[fig2]. with the assistance of facial cheek motions, the information is shipped off the regulator then, at that point, exchanging hand-off modules remotely utilizing RF (radio recurrence) powers (ON/OFF) electrical appliances.[4] The proposed gadget is manufactured utilizing parts gathered from the neighbourhood market and tried in the lab for fruitful working, test results are remembered for this paper.
Dunn Semantics for Contra-Classical Logics

In this paper I show, with a rich and systematized diet of examples, that many contra-classical logics can be presented as variants of FDE, obtained by modifying at least one of the truth or falsity conditions of some connective. Then I argue that using Dunn semantics provides a clear understanding of the source of contra-classicality, namely, connectives that have either the classical truth or the classical falsity condition of another connective. This requires a fine-grained analysis of the sorts of modifications that can be made to an evaluation condition, analysis which I offer here as well.
The Construction of Data Intelligent Search Model and Music Database Management and Control System Based on Machine Learning

At present, machine learning can bring new research ideas to researchers on existing problems in some fields and lower the application threshold in this field, such as data mining and database fields. It has become the most cutting-edge research direction of artificial intelligence. For one, this article delves into a data mining model based on machine learning. Data intelligence combines the way of thinking that mimics the human brain with data mining theory, which has a great effect on data enhancement, and applies the theory of data intelligence search model to practical problems. The concept of the number of intelligent objects with the same data is proposed, and the intelligent search model of continuous data in this field is established to provide help to solve the intelligent search problem under the background of big data. This paper also studies the music database management and control system. Nowadays, there are many kinds and numbers of music resources, which has caused confusion in management. In addition, the existing database system is difficult to expand and reuse. In response to the above situation, this paper designs a standard-based universal custom music database system. Based on the comprehensive research and analysis of system requirements and functions, it completes the formulation of the music data element, public data element standard, and the development and realization of the universal music database management and control system. Finally, passed a complete functional test and interface test. Based on the research of machine learning, this paper examines the data intelligent mining model and the music database management and control system, respectively, creates an intelligent management and control system and a search model, and lays the foundation for the development of intelligence.
King Abdullah International Medical Research Center (KAIMRC)’s breast cancer big images data set

The purpose of this project is to prepare image data set to develop AI systems to serve screening and diagnosis of breast cancer research field. Whereas early detection could have a positive impact on decreasing mortality, as this could offer more options for successful intervention and therapies to reduce the chance of malignant and metastatic progression. Six students, one research technologist, and one consultant in radiology collected the images and the patients’ information. The images extracted from three imaging modalities: the Hologic 3D Mammography, Philips and Super Sonic ultrasound Machines, and GE and Philips machines for MRI. The cases were graded by a trained radiologist. A total of 3085 DICOM format images have collected for the period between 2008 – 2020 for 890 females patients ages 18 to 85. The largest portion in the data is dedicated for mammograms (51.3%), and then ultrasound (31.7%), and MRI exams (17%). There were 593 malignant cases while the benign cases were 2492 cases. The diagnosis was confirmed by biopsy technique after mammogram and ultrasound exams. The data will be continually collected in the future to serve the artificial intelligence research field and the public health community. The updated information about the data will be available on: <a href="https://kaimrc.med.sa/?page_id=11767072">https://kaimrc.med.sa/?page_id=11767072</a>
Analysis of Backpropagation Method in Predicting Drug Stock

Health is one of the needs of the society to be able to carry out activities in daily life. The high public need for health makes the provision of drug stocks being an important aspect for every health service, one of which is the Public Health. To find out how many drugs are received, the Public Health conducts a data collection on the drug stock that is carried out. However, the Public Health could not determine the exact amount of drug stock needed so that some drugs expired due to excess drug stock. With these problems, an analysis using the Backpropagation method was carried out to predict how much drug stock was needed. The purpose of this study is to ensure that the stock of drugs that enter the Public Health is in accordance with the needs of the Public Health so that it can reduce the expired of the drugs. For analysis using the Backpropagation Method, there are two processes used, namely: with the training process to find new weights which will later be used in the second process, namely the testing process. testing is only carried out on the forward propagation process. To carry out the training process and testing process, data from 2019-2020 that has been recapitulated will be used. The results of the analysis using the Backpropagation method show that the highest accuracy results are 88.0356% at epoch 900, learning rate is 0.001, and goal is 0.00001 with the lowest MAPE (Mean Absolute Percentage Error) is 11.964% which shows that the ability of forecasting or analysis models good because how much in the range of 10-20%